Forthcoming and Online First Articles

International Journal of Ad Hoc and Ubiquitous Computing

International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC)

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International Journal of Ad Hoc and Ubiquitous Computing (67 papers in press)

Regular Issues

  • Optimal Harvesting Duration for CDMA Systems   Order a copy of this article
    by Sami Touati, Rachid Sammouda, Musaed A. Alhussein 
    Abstract: This paper studies the throughput of Code Division Multiple Access (CDMA) systems with energy harvesting. A CDMA transmitter $T$ harvest energy using the received signals from node $N$. There are $P$ paths between $N$ and $T$. Then, the harvested energy is used to transmit data to a CDMA receiver $R$. There are $L$ paths between $T$ and $R$. We also suggest to optimize harvesting duration to maximize the throughput. CDMA systems with energy harvesting don\'t require to recharge or change the battery of transmitter $T$. Our results are valid for any number of paths between $N$ and $T$ and any number of paths between $T$ and $R$.
    Keywords: Code Division Multiple Access (CDMA); energy harvesting; multipath channels; throughput analysis.

  • Machine learning soft computing fuzzy set to identify ubiquitous integrated network state at different AM operations   Order a copy of this article
    by Kadapa Harinadha Reddy 
    Abstract: Issues arising during the operation like interruption, disorder and uncertainty of network to be resolve. One of the situations is island state, and require to secure human beings and converter devices those are working in integrated energy sources environment. Fuzzy sets are to be identifying this state and these are updating with device control parameters of network like voltage, frequency. This paper presents a k-nearest neighbor Machine Learning (knn-ML) to obtain Upper Limit (UL) Lower Limit (LL) of parameters for continuous fuzzy based state estimation. Also proposed knn-ML helps to overcome existing challenges and motivate to better recognition of integrated network state. Outcomes of the paper, to update device control at every Abnormal Mode (AM) of operation and as when system parameter changes at different conditional operating modes. Effective outcome has been obtained with knn-ML soft computing algorithm.
    Keywords: Fuzzy Set; Fuzzy control; Island state; Integrated network; Machine learning; Abnormal mode.

  • A Trust-Based Secure AODV Routing Scheme for MANET   Order a copy of this article
    by Charu Wahi, Shampa Chakraverty, Vandana Bhattacherjee 
    Abstract: Secure routing in mobile ad hoc networks remains a challenging research problem due to their dynamic and distributed nature of the operation. Cryptographic approaches to establish secured routing are not quite suitable for ad hoc networks because of the inevitable presence of a third party and due to high computational overheads involved. A viable alternative approach stems from the trust-based communication protocol embedded in the network layer. We introduce a trust-based solution named Trust-based Secure AODV (TSAODV), to establish a secure and reliable inter-node communication with minimal memory and communication overhead. Each nodes trustworthiness is assessed based on its current performance as well as its past behavior. Simulation results demonstrate that the proposed trust driven model improves packet delivery ratio and throughput as compared with conventional ad-hoc routing schemes while minimizing the penalty in terms of end-to-end delay.
    Keywords: Mobile ad-hoc networks; Trust-based routing; Adhoc On-demand Distance Vector (AODV) protocol; secured routing.

  • A General and Efficient Distance Bounding Protocol with multi-Objective Optimization for RFID Applications over a Noisy Channel   Order a copy of this article
    by Ebrahim Shafiee, Abolfazl Falahati 
    Abstract: It is common to employ the Distance Bounding (DB) protocols to prevent relay attacks over RFID communication systems. It is impossible to utilize only one specific version of a DB protocol as a general resolution for secure identification and optimal performance due to the diversity and development of the RFID platforms such as ad hoc and IoT. Therefore, a general DB (GDB) protocol employing a multi-objective optimization algorithm is proposed to find the appropriate GDB protocol's parameter values, increase security, and reduce costs. On the other hand, reducing the false-accept probability against possible attacks increases security, and decreasing the protocol false-reject probability, runtime, and memory reduces costs. The final desired optimal protocol is obtained from Pareto optimal solutions using the linear non-dimensionalization scaling method and the LINMAP decision-making process. Ultimately, thanks to the sensitivity analysis and the comparisons with other appropriate protocols, a novel general relation is extracted from the obtained optimal protocols. Introducing the Figure of Merit (FOM) criterion, analytical analysis, and simulation results indicate significant improvements in the Protocol efficiency.
    Keywords: RFID; Distance Bounding Protocol; Relay attacks; Multi-objective ?optimization; Fraud attacks; Communication security; Noisy wireless channel.

  • Real-time road object segmentation using improved light-weight convolutional neural network based on 3D LiDAR point cloud   Order a copy of this article
    by Guoqiang Chen, Bingxin Bai, Zhuangzhuang Mao, Jun Dai 
    Abstract: It is critical that autonomous navigation systems can segment objects captured by their sensors (cameras or LiDAR scanners) in real time. A convolutional neural networks (CNN) is proposed for real-time semantic segmentation of road objects (pedestrians, cars, cyclists) in this paper. The proposed network structure is based on the light-weight network SqueezeNet, which is small enough to be stored directly in the embedded deployment of an autonomous vehicle. The input of the proposed CNN is the transformed 3D LiDAR point cloud, and the domain transform (DT) makes the segmentation object precisely align its boundary, which results in the preferable point-wise label map as the output. In addition to comparing our segmentation results with the pipelines based on deep learning, the visual comparison with the traditional 3D point cloud segmentation pipelines is also made. Experiments show that the proposed CNN can achieve faster running time (6.2ms per frame) and realize real-time semantic segmentation for objects in autonomous driving scenes while ensuring the comparable segmentation accuracy.
    Keywords: Road object segmentation; convolutional neural network; 3D LiDAR point cloud; domain transform.

  • Identifying and Providing Museum Services for Ubiquitous Visitors   Order a copy of this article
    by Preeti Khanwalkar, Pallapa Venkataram 
    Abstract: Recent technological advancements have enabled personalized services for the museum ubiquitous visitors. Museum ubiquitous visitors are the visitors who are free to access required services anywhere, any time through any possible devices without making the explicit requests. In this work, we propose the system of identifying and providing museum services to cater to the individual requirements of the ubiquitous visitors. The system is designed to provide the exhibit information museum services for the ubiquitous visitors. The proposed system employs the Ubiquitous Visitor Model and automatically identifies the required exhibit information URL for the ubiquitous visitors. Further, from the identified URL, based upon the dynamic requirements of the ubiquitous visitors, the system provides tailored exhibit information to the visitors. The simulation results show that the proposed system, using the Ubiquitous Visitor Model, significantly reduces the time to identify and provide required exhibit information museum services for the ubiquitous visitors.
    Keywords: Museum Ubiquitous Visitors; Museum Ubiquitous Visitor Model; Museum Services; Exhibit Information URL Identification; Exhibit Information Providing.

  • A method to constructing connected dominating set for consensus in ad hoc wireless network   Order a copy of this article
    by Qingdong Huang, Yun Zhou, Qing Liu 
    Abstract: This paper proposes a concise-connected dominating set (C-CDS) algorithm of constructing connected dominating set (CDS) for ad hoc wireless networks. By introducing the eigenvector centrality value related to topology-information as unique id number of node, this method can reduce the omitted nodes caused by randomness of node numbering, retain the dominant node with high influence and remove more dominating nodes with small influence. In addition, adding and perfecting reduction rules further simplify the CDS after the reduction of original two rules, which can significantly reduce the size of connected dominating set compared with the existing methods at a small computational cost. Finally, we also propose a fast method for consensus which can reach consensus in one round based on CDS, the non-dominated node of the entire network is allocated to each dominating set node without duplication as the independent neighbor node of the dominating set node, and each node calculates its state value making use of the CDS, then the final consensus result is shared to the entire network. Simulation results verify that there is better performance in generating C-CDS and consensus than the existing algorithm.
    Keywords: Ad hoc wireless network; Connected dominating set; Eigenvector centrality; Consensus.

  • Reliability Improvement of Frame Based Equipment for Ultra-Reliable and Low Latency Communication in Unlicensed Spectrum   Order a copy of this article
    by Hsueh-Yi Chen, Sheng-Shih Wang, Shiann-Tsong Sheu 
    Abstract: Based on frame based equipment (FBE), the 3GPP Release-17 has considered user equipment-initiated channel occupancy time to enhance ultra-reliable and low latency communication (URLLC) under the controlled environment, where all devices operating on the unlicensed spectrum and unexpected coexisting interference only sporadically happens. However, as the presence of interference sources (e.g., Wi-Fi) increases accidentally, the URLLC reliability rapidly decreases. This paper proposes a transmit/receive switching scheme which considers the busy tone transmission to reduce the coexisting interference. We also introduce four policies based on channel quality to balance the URLLC reliability and the impact on Wi-Fi. Moreover, this paper proposes a fixed frame period offset adjustment scheme to increase the spectrum efficiency and further enhance the FBE reliability. Simulation results showed that, compared to the method only using the legacy FBE, the technique using the proposed schemes significantly achieves a higher reliability for URLLC transmissions, especially in the high interference environment.
    Keywords: Busy tone; Frame based equipment (FBE); Reliability; Ultra-reliable and low latency communication (URLLC); Unlicensed spectrum.

  • Collaborative Editing over Opportunistic Networks   Order a copy of this article
    by Noha Alsulami, Asma Cherif, Abdessamd Imine 
    Abstract: Fostered by the high availability of mobile devices and the maturity of wireless communications, Opportunistic Networks (OppNets) raise a challenging issue on how to share data efficiently without relying on fixed network infrastructure. Most of the current research works focus only on message routing and data dissemination. However, few research works consider the deployment of collaborative applications over opportunistic networks specifically for sharing immutable data (e.g., photos/video files). Thus, sharing mutable data in collaborative editing works with opportunistic communication remains an open issue. Collaborative Editors rely on protocols that allow many users to concurrently edit replicated shared documents (e.g., text and multimedia documents) while enforcing the convergence of all replicas. In this paper, we investigate the adaptation of an existing operational transformation-based synchronization protocol to be deployed over OppNets. For the message delivery between communicating users/nodes, we compare the efficiency of the most known routing protocols in OppNets, PRoPHET, and Epidemic, through simulations in terms of data convergence preservation. We show how the behavior of each routing protocol impacts the convergence of shared data. From our experimental evaluation, it turns out that PRoPHET outperforms Epidemic in achieving convergence.
    Keywords: Collaborative editors; Convergence; Operational transformation; Opportunistic networks.

  • Fuzzy Independent Circular Zones Protocol for Heterogeneous Wireless Sensor Networks   Order a copy of this article
    by Djamal Djabour, Wided Abidi, Tahar Ezzedine 
    Abstract: Wireless Sensor Networks (WSNs) are a set of sensor nodes. They are used in various area monitoring applications; the nodes communicate to obtain data collected by the base station or a receiving node. Nodes have two roles. The first is to detect the data, and the second is to act as relay nodes. The latter function is considered for intermediate nodes, where this role is more costly for nodes that are closed to the base station due to high traffic load. In this paper, we proposed a Fuzzy Independent Circular Zones Protocol (FICZP). In our protocol, cluster heads election is based on two different measures, one representing by nodes number of neighbours and the second representing by nodes remaining energy. These two metrics are merged by the fuzzy logic approach for defining nodes lifetime cost-values. Fuzzy Independent Circular Zones Protocol compared to Stable Election Protocol (SEP), Zonal-Stable Election Protocol (Z-SEP), and other recent proposed protocols. Simulation results prove that the proposed protocol performs better than its predecessors in network lifetime extension, first and last dead node and network stability.
    Keywords: Wireless Sensor Networks; Fuzzy Independent Circular Zones Protocol; cluster heads’ election; lifetime; number of neighbours; remaining energy; Fuzzy logic.

  • Resource provisioning optimization for cloud computing systems serving multi-class requests   Order a copy of this article
    by Rohit Sharma, Prateek Gupta, Raghuraj Singh 
    Abstract: With a large number of organizations switching to cloud-based systems, the variety of domains using cloud services is increasing steadily. Often, companies own private cloud computing systems to cater to their own needs and reduce dependency on third-party services. These systems serve a multitude of requests of different classes that often require different types of resources. We present a model to optimize the cost of such a cloud computing system serving multi-class requests. The system has been modeled as a queuing network. A cost model has been employed to quantify the economics of the system, and an optimization problem has been set up. The assumption of having only linear and constant terms for the cost function has been forgone, and higher-order terms have been used. These terms account for the diminishing marginal productivity. The law of diminishing marginal productivity is used in microeconomics to study the behavior of cost of individual service-producing entities i.e. firms. This model provides a realistic scenario where the cost of the system needs to be minimized to maintain profitability. In this study the cost of the system has been optimized by calculating the optimal quantity of resources provisioned to the cloud system and the behavior of the cost of maintaining a profitable cloud computing system has been discussed.
    Keywords: cloud computing; request phase; queuing theory; multi-class.

  • RCAPChA: RSU Controlled AHP-based Prioritized Channel Allocation Protocol for Hybrid VANETs   Order a copy of this article
    by Bidisha Bhabani, Judhistir Mahapatro 
    Abstract: Restricted geographical scope and bounded time relevance are two main attributes of emergency and non-emergency messages in Vehicular Ad Hoc Networks (VANETs). Therefore, a suitable Medium Access Control (MAC) protocol is required explicitly for VANETs which can deliver timely and reliably these sort of messages over a wireless collaborative environment. However, channel allocation remains as a challenging problem in VANETs, in fact, in any broadcast networks. The channel allocation among the vehicles in hybrid VANETs is normally done by a RSU (Road Side Unit). In this paper, we propose a RSU controlled prioritized channel allocation strategy named as RCAPChA to minimize waiting time of the most deserving vehicle wanting to disseminate a message in the network. It is also realized that the prioritization of the vehicles is necessary in this process of channel allocation. Though there are some existing works which really acknowledge the necessity of a single-criterion based priority scheme, very few uses multi-criteria decision making approaches. In this context, our proposed algorithm RCAPChA uses a multi-criteria based decision making model named as Analytic Hierarchy Process (AHP). RCAPChA enables the RSU to compute the priority values for all the vehicles requesting for channels, and ranks them accordingly based on the calculated priority values. We present a case study of a network of vehicles involving three criteria including severity of the message to be disseminated, vehicle speed, and channel occupancy time to compare our proposed scheme with the existing. Our simulation results show that RCAPChA performs better in minimizing the overall delay for 60% of the deserving vehicles in the network. It also defeats the existing schemes regarding delay, PSR, PDR and Throughput at the expense of more energy in saturated data traffic condition.
    Keywords: VANET; channel assignment; AHP; prioritization; RSU.

  • An Adaptive Algorithm for Performance Enhancement of Long-Haul MMW/RoF System for Next Generation Mobile Communications   Order a copy of this article
    by Tuan Nguyen Van, Dien Nguyen Van, Nhat Nguyen Dong, Son Tran The, Duy-Tuan Dao, Toan Nguyen Khanh, Hung Nguyen Tan 
    Abstract: The performance of high-speed and long-haul Millimeter Wave/Radio-over-Fiber (MMW/RoF) system is investigated both analytically and numerically for use in next generation mobile communications (5G and beyond 5G). By applying the proposed adaptive algorithm, corresponding to any given transmission distance in advance from 200 km to 300 km, we can accurately determine optimal values of 5 essential system parameters including laser power (PLaser), local oscillator power (PLO) at coherent receiver, optical amplifier (OA) gains (G1, G2) and OA's position on optical fiber link (L1) so that BER at the receiver is maintained in the given range of (10-11-10-9). For example, numerical results show that with the adaptive algorithm, BER of 10 Gb/s QPSK signal corresponding to 300 km-transmission distance satisfies properly in the range of (10-11-10-9) with PLaser, PLO, G1, G2, and L1 of 1 dBm, 5 dBm, 20 dB, 33 dB, and 18 km, respectively.
    Keywords: Radio access networks; Radio over Fiber; Millimeter Wave; Advanced modulation formats.

  • Co-operative Evolution of SVM-Based Resource Allocation for 5G Cloud-Radio Resource Network System with D2D Communication   Order a copy of this article
    by NAVEEN KUMAR, Anwar Ahmad 
    Abstract: In Fifth Generation (5G) communication networks, the Cloud-Radio Access Network (CRAN) is a new technique, where baseband processing units are decoupled from remote radio heads and a remote cloud-based centralized pool of baseband processing units is established. However, as the systems capacity grows, managing interference between Cellular Users (CUs) and Device-to-Device (D2D) users becomes a critical issue. This paper proposes a multi-class classification resources allocation scheme based on the co-operative evolution of Support Vector Machine (SVM) to assign Macrocellular Users (MUs) spectrum resources to Remote-head Users (RUs) and D2D pairs, allowing sub channels to be reused without compromising Quality of Service (QoS). First, the key resource allocation sets are determined, and a C-RAN resource allocation model is created. Because CUs and D2D nodes are allowed to access the same sub-channel, the ultimate challenge is described as a many-to-one matching sub-game. The 5G C-RAN system allocates resources via a co-operative evolution of an SVM-based multi-class classification algorithm based on user position estimates, with intercell and intracell interference utilized to build the training dataset. The training data set is prepared based on inter-cell and intra-cell interference. Finally, the results show that the proposed cooperative evolution
    Keywords: 5G; C-RAN; machine learning; resource allocation; support vector machine.

  • Learning Automata and Lexical Composition Method for Optimal and Load Balanced RPL Routing in IoT   Order a copy of this article
    Abstract: Low power and lossy network, Internet of Things (IoT) motivates energy-efficient and load-balanced routing in the network layer to extend network lifetime. IoT application scenarios exploit the Routing protocol for low-power and lossy networks (RPL) due to the significant potentials. The core components of RPL are the trickle algorithm and Objective Functions (OF) for creating Destination Oriented Directed Acyclic Graph (DODAG) and data forwarding. The RPL needs more attention to avoid hotspot problems and unnecessary energy depletion. Most of the existing routing protocols take a single either hop count or ETX, or multiple routing decision metrics. However, the RPL cannot select appropriate link metrics efficiently against the dynamic and lossy environment without considering the relationship between those metrics. Thus, the proposed methodology takes important routing metrics, such as hop count, expected transmission count, and traffic-related metric, and composites the metrics using learning automata and lexical composition method. The special attention on network energy balancing through Expected Transmission Energy (ETT) avoids a hotspot issue and inefficient routing energy. The proposed work supports multiple metrics-based OF with considerable routing overhead by tuning the Trickle parameter. Moreover, the proposed work is evaluated to show its advantages over the dynamic and lossy network, IoT.
    Keywords: IoT; Energy Efficient Routing; Hotspot Problem; Learning Automata; and Lexical Composition technique.

  • A Simulation Study on the Necessity of Working Breakdown in a State Dependent Bulk Arrival Queue with Disaster and Optional Re-service   Order a copy of this article
    by Deena Merit C K 
    Abstract: Sudden increase in service requests immediately after an occurrence of a disaster due to the scarcity of resources creates congestion in service. Effective queue management should ensure that the services are available for every request with an acceptable delay. Unfortunately, the unexpected breakdowns causing huge delays were observed many times in real-life scenarios. Server utilization and waiting time of a customer are the primary factors in analyzing the breakdown of the queueing system. The working break-down is introduced in this paper to address this issue in an M^X/G/1 disaster queueing system wherein an occurrence of a disaster at the main server is handled immediately by replacing a substitute server with a different or same service rate. State-dependent arrival and optional re-service are also included to make the model more realistic and robust. Various performance indices of the model such as the probability of server indifferent states, expected number of customers in the system, effects of repair time, optional re-service, etc., are established using the supplementary variable technique. Numerical illustration is carried out for the analytical approach. Further, this concept was simulated and the results are interpreted after validation and verification. It has been proved in a real-time scenario that the working breakdown is effective when the arrival rate is tremendously larger immediately after the disaster.
    Keywords: bulk arrival; state dependent; disaster; working breakdown; re-service,rnsubstitute server; simulation.

  • Identity based secure data aggregation in big data wireless sensor networks   Order a copy of this article
    by Radhakrishnan Maivizhi, Palanichamy Yogesh 
    Abstract: Secure data aggregation (SDA) is an inherent paradigm in big data wireless sensor networks (WSNs) to reduce data transmissions, maximize the network lifetime and provide security. However, SDA protocols that use elliptic curve suffer from mapping and reverse mapping functions. Currently, there is no known mapping function which is both homomorphic and effective in reverse mapping. In addition, SDA protocols that use public key infrastructure incurs high computation and communication cost. To overcome these challenges, this paper proposes a novel identity based secure data aggregation (ISDA) protocol for big data wireless sensor networks. This protocol is based on bilinear pairing and combines identity based homomorphic encryption scheme with identity based signature to achieve end-to-end security. In WSNs, this is the first SDA protocol that employs both identity based encryption and identity based signature scheme. ISDA shares the same public/private keys during encryption and signature generation, which significantly reduces the complexity of the protocol. Security analysis reveals that ISDA is secure against various internal and external attacks and proves the correctness of the proposed protocol. Performance evaluation shows that ISDA incurs less overhead than state-of-the-art bilinear pairing based SDA schemes, thereby it minimizes the energy consumption and increases the lifetime of wireless sensor networks.
    Keywords: Big Data; Secure Data Aggregation; Wireless Sensor Networks; Confidentiality; Integrity.

Special Issue on: Artificial Intelligence for Edge Computing in the Internet of Things

  • Application of Artificial Intelligence Technology in CNC System   Order a copy of this article
    by Chunhui Dong, Cheng Zhong 
    Abstract: Since the development of computer numerical control technology from hardware numerical control to software numerical control, computer numerical control technology is still in a period of continuous improvement of functions. Although some novel numerical control technologies have been proposed, on the whole, it has not yet broken through the traditional framework. And because of the reliability research process of CNC machine tools, it is difficult to collect reliability data, which makes the reliability distribution model not unique. Based on the above background, the purpose of this article is to study the application of artificial intelligence technology in numerical control systems. The main method is to use the ANN model to expand the small amount of reliability data collected, and then use the Since the development of computer numerical control technology from hardware numerical control to software numerical control, computer numerical control technology is still in a period of continuous improvement of functions. Although some novel numerical control technologies have been proposed, on the whole, it has not yet broken through the traditional framework. And because of the reliability research process of CNC machine tools, it is difficult to collect reliability data, which makes the reliability distribution model not unique. Based on the above background, the purpose of this article is to study the application of artificial intelligence technology in numerical control systems. The main method is to use the ANN model to expand the small amount of reliability data collected, and then use the KS test to analyze the expanded data to determine the reliability data model. At the same time, in the process of determining the parameters of the reliability distribution model, the mixed Particle Swarm Optimization (HPSO) algorithm is introduced into the maximum likelihood estimation to solve the problems of easy to fall into the local optimal solution and low efficiency when solving some complex distribution models with small sample data. The experimental results show that the reliability distribution model of CNC machine tools is not unique in the case of a small sample. The reliability model of CNC machine tools can be uniquely determined after analyzing the data using the ANN model and the KS test method. Achieve a good balance between solution efficiency and convergence performance. Comparing the results of all the solving models, the relative mean square error of the 2-fold 3-parameter Weibull distribution after the ANN model expansion is the smallest, with a value of 0.0428, which shows that using this method to solve the reliability distribution model of CNC machine tools is feasible and can obtain More accurate results.
    Keywords: CNC Machine Tools; Artificial Intelligence; ANN Model; HPSO Algorithm.

  • Functional feature-aware APP recommendation with personalized PageRank   Order a copy of this article
    by Yueyue Xia, Xiangliang Zhong, Yiwen Zhang, Yuanting Yan 
    Abstract: With the explosive growth of mobile Apps and the widespread deployment of Internet of Things (IoT) services, how to recommend applications that users are interested in has become an urgent problem to be solved. And most of the current mobile APP recommendation methods are based on the user\'s behaviour data or context-aware information, to some extent, ignoring the user\'s preference for APP functional features. Therefore, a functional feature-aware mobile APP recommendation method, named S-AppRank, is proposed in this paper. S-AppRank first extracts the functional features of mobile applications and their internal association through weight calculations, then constructs a directed graph of user functional features with the association rules, and then adds user ratings to the traditional PageRank algorithm, incorporating explicit feedback into recommendation personally. Finally, the user\'s interests in the overall APP is predicted, and a recommendation list is generated. The experiments on the real dataset of Huawei application market show that the S-AppRank proposed in this paper is better than other comparison methods.
    Keywords: functional feature-aware; APP recommendation; PageRank; user preference.

  • Development of Internet Finance Industry with the Core of E-commerce Platform Services Optimized by the Edge Computing of the Internet of Things Based on Artificial Intelligence   Order a copy of this article
    by Baojun Yu, Anni Zhao 
    Abstract: E-commerce is one of the important modes of modern commerce. This paper discusses how the logistics service of the Internet of things optimized by the edge computing of the Internet of things based on artificial intelligence affects the satisfaction of online shoppers. The purpose of this paper is to determine the importance of the Internet of things logistics service based on the edge computing optimization of the Internet of things based on artificial intelligence to affect the satisfaction of online shoppers. In this paper, a total of 178 respondents with online shopping experience were interviewed face-to-face using structured questionnaire. Pearson correlation and multiple regression were used to analyze the data. The results show that service recovery, delivery service and customer service are the positive factors that affect the satisfaction of e-commerce shoppers. The significant level of service recovery (P = 0.000) and delivery service (P = 0.001) was 1% of the importance. The significance level of customer service (P = 0.024) was 5%. Service recovery (? = 0.399) has the largest weight in influencing the satisfaction of e-commerce shoppers, followed by delivery service (? = 0.343) and customer service (? = 0.244). The results of this study show that the Internet of things based on the edge computing optimization of artificial intelligence is important to the service-oriented optimization of e-commerce platform. The Internet of things technology supported by edge computing can promote the service-oriented optimization of e-commerce platform.
    Keywords: E-Commerce Platform; Internet of Things; Edge Computing; Logistics Services.

  • Artificial Intelligence Technology in Internet Finance and Analysis of Security Risk   Order a copy of this article
    by Qi Sun, Hong Wu, Binglong Zhao 
    Abstract: Due to the characteristics of Internet finance, borderlessness, technical specialization, and the lack of relevant laws and regulations, its characteristics have exacerbated the complexity of risk management and also put forward new requirements for Internet financial security. This research focuses on the application of artificial intelligence technology in Internet finance and the analysis of security risks. This research regards the impact and specific application of artificial intelligence in Internet finance as a system, and analyzes the relationship between various elements within the system to find out the countermeasures that can solve or mitigate the negative effects it brings. The study found that the external technical risk score was 0.118, the industry-level risk score was 0.258, and the financial business risk score was 0.407. According to the \"maximum affiliation\" principle of the fuzzy comprehensive evaluation method, the maximum value is 0.407, that is, the overall score of China\'s Internet financial risk level is at a relatively high level. The results show that the risks and response strategies based on artificial intelligence technology in Internet finance can not only supplement and improve the existing theoretical knowledge of Internet financial risk prevention and control, but also provide full disclosure of Internet financial risks, improve laws and regulations and regulatory policies, and achieve regulatory innovation. A more intuitive reference basis and realistic basis.
    Keywords: Artificial Intelligence; Internet Finance; Financial Risk; Logit Regression Model; Coping Strategies.

  • Reducing the Internet traffic in the IoT based monitoring and control system through a combination of WSN and LoRaWAN networks   Order a copy of this article
    by S. Bhavatharangini 
    Abstract: Lora is a wireless communication technology with a long transmission distance, low power consumption, low transmission speed, low complexity, and low cost. Through the comparison and analysis of several wireless communication technologies, a temperature monitoring platform based on LoRa spread spectrum and wireless sensors are proposed. Thus a low-cost and battery-supplied wireless sensor network (WSN) for fine-grained, flexible, and data-centric temperature monitoring system is proposed. The proposed system uses localized LoRa based communication among the nodes of a Wireless Sensor Network and IoT based monitoring and control through a single LoRaWAN Gateway. Thus, the proposed system offers several advantages over a conventional IoT based monitoring and control system, namely; (i) Reduced Internet Traffic (ii) Low-cost end devices with simple LoRa transceivers and a single coordinator per cluster with LoRaWAN gateway (iii) Remote monitoring and control of end devices through the Gateway.
    Keywords: LoRa; LoRaWAN; Wireless sensor network(WSN); Internet of Things (IoT); Remote control; Energy Efficient Protocol; Internet Traffic.

Special Issue on: Distributed Secure Computing for Smart Mobile IoT Networks

  • Design and Implementation of a Mobility Support Adaptive Trickle (MSAT) Algorithm for RPL in Vehicular IoT Networks   Order a copy of this article
    by N. V. R. SWARUP KUMAR JAVVADI, Suresh D 
    Abstract: With the development of the "Internet of Things," the scope and research for stable mobile assistance in Low-Power Wireless Networks (LPWNs) are growing. The components like single radio transceivers have been included in the low-power and low-cost products, interacting at very low TX / RX power to make basic electronics and antennas. These features result in insecure, asymmetric, and poor wireless communications, which affects the LPWN's quality of operation. In specific application to risk-benefit mobility nodes, vehicular networks, and preventive/corrective maintenance in manufacturing settings, this operation level is often harder to obtain. In this context, the proposed paper ensures efficient and timely contact in LPWNs under node mobility. RPL as a routing protocol is used in wireless low-power networks (LPWNs). This protocol is intended for the static wireless sensor network, so RPL needs to be changed to suit with Vehicular Networks' extremely dynamic topology. In the RPL protocol, the Trickle algorithm establishes routes between nodes in the network at different intervals. The Trickle algorithm is designed to reduce RPL's disadvantages. This paper proposes the vehicle's location as the RPL metric (MSAT-RPL), enabling RPL to be respond timely and propose RPL tuning trickle algorithm strategies in vehicular networks. A simulation was set up, "Cooja 3.0," is used to show the output of MSAT-RPL and compare it with existing models. MSAT-RPL has a high Packet Delivery Ratio (PDR), fair OverHead (OH), low latency (EED), and less power consumption (PC) as a result of the simulation.
    Keywords: IoT; Vehicular Networks; RPL; DODAG; Trickle; Cooja 3.0; Listen Only Period; Adaptive DIO Period; Mobility Support Adaptive Trickle.
    DOI: 10.1504/IJAHUC.2022.10039477
  • An optimized machine learning algorithm for classification of Epileptic Seizures using EMD based dynamic features of EEG   Order a copy of this article
    by Sateesh Kumar Reddy Ch, Suchetha M 
    Abstract: The electrical activity in the brain will establish the seizure due to unexpected change in neurons, which leads to the second most common disease of the brain called epilepsy. An automatic seizure detection technique is essential for primary diagnosis and treatment because the traditional methods of seizure detection are time-consuming and inaccurate. In this regards, this proposal shows a novel seizure detection technique centred on two unique features of time-frequency analysis. The proposed two novel features based on empirical mode decomposition (EMD) such as relaxation time (RT) and dynamic bandwidth (DB) to distinguish seizure movements in EEG. The study was carried out on two different data sets. Then the two novel features are used to classify healthy and seizure subjects by Support Vector Machine (SVM) with Marginal Sampling approach. The efficiency of the proposed method has compared with different classification methods such as K-Nearest Neighbours (KNN), Decision Tree (DT), andrnNaive Bayes (NB) respectively. The MATLAB platform is used to carry the process of the proposed method, and the performance results are calculated in terms of accuracy, sensitivity, specificity, precision, and F-measure. We observed that the proposed two novel features withrnSVM marginal sampling combined with k-fold cross validation achieve the best average performance with an accuracy of 99.23% with less computational time. The result analysis shows that the proposed method is an efficient and suitable method for the classification of seizure data than existing techniques in electroencephalogram signal processing
    Keywords: Empirical mode decomposition;Seizure detection; Electroencephalogram (EEG) Support vector machine (SVM); Epilepsy.

  • Secured Personal Health Records using Pattern Based Verification and 2-Way Polynomial Protocol in Cloud Infrastructure   Order a copy of this article
    by DNVSLS Indira, R. Abinaya, Ch Suresh Babu, Ramesh Vatambeti 
    Abstract: This present research proposes the digitalized healthcare systems enable patients to generate, aggregate and store in the form of Personal Health Records (PHR). This requires more attention on cost effectiveness and less response time on public cloud platform. The emerging need of PHR monitoring and data collection on dynamic data sets, the companies need to adapt the open framework analyzing and effective storage tuples. Unfortunately, the third-party companies are failed to implement the systemic approach for immediate verification and correction models on increasing data sets. The storage and computation are two prime factors. Moreover, cloud systems need more attention on security and privacy breaches. In this proposed model the publisher-observer pattern-based healthcare systems allow the patients to verify and correct the PHR before any type of computations. The cloud system act as backend framework that offers openness and easy accessibility. The experimental segment ensures the computational cost and response time for multiple polynomial PHR variations. The details evaluation also ensures the security and privacy preservation on sensitive healthcare data sets.
    Keywords: Privacy; Security; Patient Health Records; Correctness; Healthcare; 2-way polynomial.

  • An Energy and Delay Aware Routing Protocol for Wireless Sensor Network assisted IoT to maximize network lifetime   Order a copy of this article
    by Vinmathi M S, Josephine M.S, Jeyabalaraja V. 
    Abstract: IoT is a technology where different devices are connected and integrated to provide solutions. This is otherwise in digital world called the Internet of everything which comprises of components from web which is the ultimate source of gathering and processing a large data obtained from the respective ecosystems by making use of the sensors and various other communication devices. In this research article, A protocol for routing is proposed which is aware of the delay and energy is a WSN (EDAR- WIoT) for the maximization of the lifetime of a network. The proposed EDAR-WIoT protocol enhances and compromises the energy and delay without affecting network lifetime. The IoT network is composed by serving nodes and end users. Here, the optimal clustering is performed by the chaotic spiral dynamic (CSD) algorithm, which reduces the chaotic in nature of energy utilization. Then, a queue based swarm optimization (QSO) algorithm is utilized to next optimal node for inter cluster routing. The proposed EDAR-WIoT protocol preserves the energy efficiency and network lifetime in high density sensor networks. The proposed EDAR-WIoT protocol is experimented using the NS-2 simulator tool. It is observed from the experiments that the proposed protocol outperformed the other existing protocols in terms of Energy Consumption, lifetime of the network , throughput , rotational frequency of the head and end-end delay.
    Keywords: Routing Protocol; internet of things; dynamic algorithm; optimization algorithm; network security.

  • Architecture and Routing Protocols for Internet of Vehicles: A Review   Order a copy of this article
    by Farhana Ajaz, Mohd. Naseem, Sparsh Sharma, Gaurav Dhiman, Mohammad Shabaz, S. Vimal 
    Abstract: Modern vehicles should be able to commute a tremendous amount of data and information within their neighborhood. To incorporate the requirements of modern vehicles, the conventional Vehicular Ad-hoc Network (VANETs) are emerging to the Internet of Vehicles. IoV keeps all the smart vehicles connected with the help of Sensors, GPS, Entertainment System, Brakes and throttles. These devices send and store their data with the help of cloud. This paper intends to contribute to the review of IoV, its challenges, characteristics and application. A detailed discussion on architectures and routing protocols along with its classification is also discussed. This paper ought to guide and motivate researchers working in the area of IoV to develop scalable and efficient routing protocols.
    Keywords: Internet of Vehicles; Internet of Things; Routing protocols; Architecture; VANETs; MANETs; Cloud Computing; FOG Computing.
    DOI: 10.1504/IJAHUC.2022.10040496
    by THOTAKURA VEERANNA, Kiran Kumar R 
    Abstract: Due to the widespread utilization of intent, the computer systems are more prone to several kinds of security threats that have led to the invention of Intrusion Detection Systems (IDSs). However, the major issue in IDSs is the presence of huge redundant and duplicate features which cause a larger processing time. These features not only slow down the process and also make the classifier to take inaccurate decision and consequences to an insertion of serious attacks into the system. To solve these problems, we propose a Sliding Windowing Assisted Mutual Redundancy Based Feature Selection (MRFS) algorithm that finds the duplicate features analytically and selects optimal feature for detection. This MRFS evaluates the mutual redundancy between as well as within network traffic connections and then selects an optimal feature subset to represent each connection attribute. After feature selection, they are fed to Multi-Class Support Vector Machine (MC-SVM) based IDS for classification. The performance of MRFS-IDS is evaluated using a standard intrusion dataset, i.e., NSL-KDD and performance is measured in terms of accuracy and false alarm rate. The simulation results demonstrate that the proposed MRFS-IDS model has gained an accuracy of 95% approximately and false alarm rate of 0.70% which is much better than the counterpart methods.
    Keywords: Intrusion Detection; Data redundancy; Mutual Redundancy; Sliding Windowing; Mutual Information; MC-SVM.

  • An Approach Using Heuristic Pheromones-Based ACO Modeling for Green Vehicle Routing Optimization   Order a copy of this article
    by Ravi Prakash, Shashank Pushkar 
    Abstract: A mathematical heuristic-based method was introduced for addressing the issue in Green Vehicle Routing Optimization (GVRO). It analyzes a large number of vehicles along with a limited refueling network. A standard solution to this problem is given in this paper. GVRO seeks to minimize travel time renewable fuel sources while ensuring fewer emissions from greenhouse gases. An effective algorithm relies on a branch/slice optimization algorithm that combines a variety of valid inequalities in exams to increase lower limits. Implementation of an optimization algorithm based on heuristic Ant Colony Optimization (ACO) to obtain the best routes. In addition, the GVRO is better able to handle an accident and eliminates pollution by using the best alternatives.
    Keywords: Green vehicle routing; Greenhouse gases; Heuristic approach; Environmental pollution; Pheromones; Ant colony optimization.

  • Secure Exchange and Effectual Verification of Educational Academic Records Using Hyperledger Fabric Block chain System   Order a copy of this article
    by Suresh Babu Erukala, Srinu Mekala, Naganjaneyulu Satuluri, Srinivasa Sesha Sai M., RAJENDRA KUMAR G 
    Abstract: Education plays a vital role in the countrys economic development. It improves the quality of people lives and leads to broad social benefits to individuals and society. Education raises peoples productivity and creativity, and promotes entrepreneurship and technological advances. Academic certificates in education are the documents that serve as proof of an individuals achievements or skill sets. These certificates are the symbol of excellence and vouch for professional skill sets of an individual in various numerous fields of the education sector. However, traditional academic certificates disconnect lifelong learning records, an increase of frauds, and the fake certificates that pose a serious problem in todays world. Existing centralized certificate verification process creates difficulty in verifying the authenticity, and lack of transparency in issuance of the certificates. Blockchain is a promising technology that claims to provide distributed, transparent, secure and reliable solutions to various business use-cases, which are having multiple participants with transparency and enhance trust among them. In this paper, we provide a solution to the educational certification problem by employing the blockchain network. The proposed network is a permissioned blockchain infrastructure, which is implemented in hyperledger fabric. The proposed system provides various services to issuing institutions, verifying organization, identification and authentication of the issuer, verifier and securely share academic records to the recipients, stores the academic records in the blockchain in a distributed manner, ensuring the privacy of stored records of the recipient. When compared to Ethereum,hyperledger fabric provides additional functionalities like efficient parallelism, concurrency, multiple transaction executions, and efficient commitments of the transaction into the ledger. The proposed system is a secure and trusted system that can be applied to any education sector, which provides the learners and education institutions to safeguard their brands and reputations, transparent sharing of certificates, and easyverification of academic achievements. The experimental analysis of the proposed system has been executed to test the performance of invoking and query transactions (certificates) using Hyperledger Caliper. We analyze the throughput and transaction latency of the proposed work as well. The experimental results exhibit that the proposed system achieves better transaction processing power and security compared to existing systems.
    Keywords: Permissioned Blockchain; Academic Certificates; Hyperledger Fabric; Distributed Ledger.

  • A Novel Bio-Inspired Approach for VM Load Balancing and Efficient Resource Management in Cloud   Order a copy of this article
    by Purshottam J. Assudani, Balakrishanan P 
    Abstract: In cloud, balancing the load on Virtual machines and Efficient utilization of resources is very crucial and challenging, which becomes complex due to heterogeneous nature of virtual machines and users tasks in distributed environment. To maintain the service quality, it is mandatory that users tasks should be scheduled efficiently with immediate response, while satisfying QoS needs mentioned in SLA (Service Level Agreement). Concerning these issues, researchers have designed bio-inspired algorithms to solve optimization problems for resource scheduling. In this paper, we have proposed a bio-inspired method namely Inventive Particle Swarm Optimization (IPSO), which not only schedules users task efficiently, but also uniformly distributes the load among different VMs. Additionally, we have also designed another algorithm named as Merge Sort with Divide and Conquer (MSDC) approach to allocate the resources in cloud dynamically in an efficient manner. The experimentation is done on CloudSim simulator, which shows that proposed algorithms give better response time, VM utilization and execution time.
    Keywords: Bio-Inspired Algorithms;Cloud Manager; VM Load Balancing; Scheduling of Resources; Resource Utilization; Dynamic Resource Allocation.

  • Efficiency Evaluation of HRF mechanism on EDoS attacks in Cloud Computing Services   Order a copy of this article
    by Rajendra Kumar G, Veeraiah Duggineni, Suneetha Bulla, Nageswara Rao Jarapala, J. Sunny Deol . G. 
    Abstract: Cloud computing is one of the most notable innovations in the IT Industry. It gives computing resources, software, web benefits for all cloud users on a rent basis. Security on the cloud is a blend of virtual assets, specialized and informational security issues. Denial of Service is one of the renowned compared to other known assaults and it produces from many sources contradicted to one single victim, these are called Distributed Denial of Service (DDoS) assaults. The conventional DDoS assaults can be transformed into EDoS (Economic Denial of Sustainability) assaults because of cloud elasticity. This EDoS assault uses the cloud assets for creating administration inaccessibility to the clients. There is a mandate to diminish EDoS assaults. HRF is the most suitable and effective mechanism to identify and diminish such assaults, in which assailant requests are recognized and dropped preceding arriving at the webserver. This paper assesses and examines the cost and performance sway using queuing theory and assesses the experimental model in terms of key performance metrics which incorporate QoS and cost metrics. Different scenarios appropriate to the HRF mechanism are taken into consideration and examined. Performance is compared with existing approaches using the game-theoretical methodology. To get the systematic solution and calculation of game value, various probabilities of defending techniques and assaulting strategies through numerical outlines are done lastly conclusions are drawn.
    Keywords: HRF mechanism; Queuing theory; Game theory; Web Application Firewalls; EDoS attacks.

  • Privacy-Preserving smart contracts for Fuzzy WordNet Based Document Representation and Clustering using Regularized K-Means Method   Order a copy of this article
    by Venkata Nagaraju Thata, Sudhir Babu A, Haritha D 
    Abstract: Key technology for unsupervised intelligent classification of any textual content is the clustering of documents. Prior document knowledge is not required for document clustering, which is an unsupervised method of learning as compare with document classification. For clustering rather than classification, little prior knowledge of the data is needed. The crucial challenges of document clustering are the high dimensionality, measurability, preciseness, extraction of semantic relationships from texts, and meaningful cluster labels. Fuzzy wordnet based document representation and clustering using the regularized k-means method as an efficient framework is introduced in the present paper with the purpose of improving the quality of document clustering.To estimate the performance of this framework we carried out experiments on different datasets. Experimental results show that this framework improves the quality of document clustering when compared to other existing methods. Furthermore, this system gives generalized and concrete labels for documents and improves the speed of clustering by reducing their size.
    Keywords: Document Clustering; Regularized K-Means; WordNet; Fuzzy Weighting Score,TF-IDF.

    by B. Madhuravani, Murthy DSR, ViswanadhaRaju S 
    Abstract: Wireless sensor networks (WSNs) play a vital role in the real-time data communication process. Cloud based WSNs is used to improve the processing speed and the storage capacity in the real-time applications. Most of the conventional approaches are based on sensor resources with limited cloud services due to high computational cost. Also, these models are not efficient in processing large volumes of data in real-time applications due to computational memory and time. In order to improve these limitations, a hybrid machine learning based sensor network is developed to predict the medical disease patterns using the cloud servers. In this work, a hybrid PSO, support vector machine and sensor security algorithms are implemented on the real-time medical sensor devices. Experimental results show that the machine learning based sensor framework has better efficiency in terms of sensor processing time, accuracy and error rate than the conventional approaches.
    Keywords: Machine learning; medical disease prediction; sensor network.

  • A Secure New HRF Mechanism for Mitigate EDoS Attacks   Order a copy of this article
    by Suneetha Bulla, Basaveswararao B, Gangadhara Rao K, Chandan K 
    Abstract: This paper proposes HTTP Request Filtering (HRF) mechanism to detect and mitigate EDoS attacks and compare the performance with existing mechanism through game theoretical approach. The HRF mechanism was implemented with three stages and hosted on Web Application Firewalls (WAF). The performance of these mechanisms with cost analysis is done using finite queuing model. The efficiencies are compared with the formation of two player non cooperative zero-sum game and gains are calculated based on loss probability as a QoS metric. To obtain the analytical solution and computation of game value, different probabilities of defending strategies and attacking strategies through numerical illustrations are carried out. The results are discussed and finally conclusions are drawn.
    Keywords: HTTP Request Filtering; Cloud Security; Web Application Firewalls; Honeypot; Game Theory.

  • Improving QOS in flow controlled CR-Adhoc Network with Multi criteria Routing assisted with cooperative Caching and information redundancy   Order a copy of this article
    by Subaskar Reddy C V, Subramanyam M V, Ramana Reddy P 
    Abstract: Cognitive Radio Adhoc Network with intrinsic capabilities of Cognitive Radio solves the problem in wireless network caused due to limited available spectrum and inefficiency in spectrum usage. Due to spectrum availability, the network topology changes frequently and routing is disrupted. Packet delivery becomes a big problem due to frequent routing disruptions in Cognitive Radio Networks. In this work, a flow controlled multi criteria routing assisted with cooperative caching and packet reconstruction with information redundancy in packet is proposed to improve the QOS in Cognitive Radio adhoc network. The multi path routing path selection is guided with four parameters of current buffer occupancy in nodes, predicted link availability time, spectrum availability and cumulative path delay. Different from conventional multipath path routing, in this work propagation is controlled in proportion to the prediction based on observed channel quality thereby packet delivery ratio is increased without causing additional network overhead and at lower delay. The performance of the solution is tested against different speed and compared with state of art existing solutions.
    Keywords: CR-Adhoc Network; QOS in flow control; Secure computing; IoT.

  • Analytical Review on Secure Communication Protocols for 5G and IoT Networks   Order a copy of this article
    by Premalatha J, Iwin Thanakumar Joseph S, Harshavardhanan Pon, Anandaraj S.P, JeyaKrishanan V 
    Abstract: Internet of Things (IoT) provides an exciting future for the interaction of devices utilizing computing and sensorial capabilities. Among the existing technologies, fifth generation (5G) schemes is expected to be the motivating force for the realization of IoT concept. This research paper reviews existing state of art on the secure communication for 5G and IoT networks. We have created taxonomy and categorized the research works based on the protocols such as Light weight authentication protocol, trust-aware routing protocol, service-oriented authentication protocol, and application-oriented protocol. The research gaps and the challenges faced for secure communication in 5G and IoT networks are listed for further enhancement in the security protocols. We further analyzed the research work based on the classification methods, performance metrics, and the year of publication. Our analysis reveals that the most commonly used classification technique is Trust-aware routing protocols, and the most frequently used performance metrics is execution time, the execution time of most of the research work lies between 10ms to 300ms, and the communication overhead ranges from bits to bits.
    Keywords: Authentication Protocol; Internet of Things; Privacy Preservation; and Secure Communication.

    by Swaminathan Amudha, Murali M 
    Abstract: With an Internet of Things, remote health monitoring has significantly increased and playing an effective role in human disease diagnosis. Patients clinical data are collected from variety of tiny sensors and are transmitted to the medical physician, Care givers and medical center in remotely. Different solutions have been proposed to monitor health condition in wireless Body Area Network. To prevent security issues, malicious users involvement, timely delivery and also to ensure data integrity, a high-end security algorithms are needed. Internet of things is now integrated with secured Fog Gateways to provide high end security solution with less latency. Many traditional algorithms were incorporated in the IoT network, due to its light weight, low power and low memory requirements. But these algorithms can be easily broken and affected by several attacks due to the poor mathematical operations. This research work propose a new hybrid chaotic maps FoG based Chaotic Henon Integrated Logistic-Tents Schemes which uses the 3D-Chaotic maps for key generation and used as symmetric key for crypto graphical operations. These 3D hybrid chaotic maps are hard and have been evaluated by extensive experimentations which show the impact of the proposed 3D chaotic maps has significantly increased the security of the clinical data when compared with the other conventional algorithms.
    Keywords: AES; ECC; F-CHIL Map;3D-Chaotic Maps; Logistics Tent algorithms; Lorenz algorithms; Bifurcation; Diffusion and Permutation.

    by Nagageetha M 
    Abstract: Multi-class leaf disease prediction is one of the challenging tasks in large image databases due to uncertainty and high dimensional feature space. Most of the traditional deep learning framework are used to classify a single class disease prediction with limited feature space. However, these frameworks have high false positive rate and error rate due to various background, noisy appearance and semantic high- and low-level features for classification problem. Also, feature extraction and classification are the major problems in traditional convolution neural network (CNN) on multi-class leaf datasets. In this work, a novel image feature extraction based deep learning classifier is designed and implemented on large multi-class leaf datasets. In this framework, a hybrid statistical leaf shape extraction method is used to find the essential features in the conditional probability based principal component analysis (BPCA) approach. A novel deep learning classifier is proposed to improve the leaf disease prediction rate with high true positivity and accuracy on the multi-class leaf disease datasets. Experimental results show that the present framework has high computational performance than the traditional deep learning frameworks for multi-class classification.
    Keywords: Leaf disease classification;extreme learning; bayesian estimators; principal component analysis.

  • A Filter based Machine learning classification framework for cloud based medical databases   Order a copy of this article
    by Devi Satya Sri Velivela, Srikanth Vemuru 
    Abstract: Machine learning tools and techniques play a vital role in the medical field and cloud computing applications. Most of the traditional machine learning models use static metrics, limited data size and limited feature space due to high computational processing time. In this work, a hybrid outlier detection and data transformation approaches are implemented on the cloud based medical databases. Proposed data filtering module is applicable to high dimensional data size and feature space for classification problem. In the classification problem, an advanced boosting classifier is implemented on the filtered data in order to improve the true positive and error rate. Experimental results are simulated on different medical datasets such as tonsil and trauma databases with different feature space size and data size. Simulation results proved that the proposed boosting classifier has better error rate and statistical accuracy than the conventional approaches.
    Keywords: Cloud computing Medical databases; Machine learning.

    by Mohana Sundaram K, Nageswari D, Prakash J 
    Abstract: Abstract: Purpose This paper aims to provide the secured communication between the networks of WSN and this security is provided by creating the secrete keys between the neighboring keys. In this paper, the location based key (LBK) management is utilized. This paper also addresses the performance of the network more efficiently by cuckoo search assisted Fuzzy logic algorithm. Design/Methodology/approach In this paper, each node is analyzed to find the occurred threats and to find the security levels. This paper uses the cuckoo search assisted fuzzy logic algorithm to improve the network performance and the security level is enhanced by location based key (LBK) management. By this method, the load at each node is balanced with the improved security level and also the lifespan of the network is improved. Findings This proposed method demonstrates the level of security in communication between the nodes. By using this algorithm the delay, performance, error and the security level of the network are identified. Originality The Cuckoo search assisted fuzzy logic algorithm is the novel method, which is used to get better performance of the network and to improve the communication security. Key words: WSN, Fuzzy logic, Cuckoo search algorithm, LBK.
    Keywords: WSN; Fuzzy logic; Cuckoo search algorithm; LBK.

    Abstract: Artificial Intelligence is one of the most energizing innovations applied in many fields like text processing, image processing. Every case, which is present in the courtroom, is inspired to get justice. Since every individual has their own view on a particular topic, the irregularities in the views of the people lead to the conflict and make them seek justice. In this paper, we propose a decision forecasting model of cases in the Supreme Court of India. The model interprets the legal cases in a similar fashion as a lawyer and forecasts a decision based on the information given. The model aims to predict whether the filed case in the Supreme Court of India will win or not by considering the past similar cases from the years 2000-2019. The proposed model does not only considers the cases filed in the Supreme Court but also the cases with an unsatisfied decision from the lower court. This is to be able to better predict if the current case will win if an appeal is chosen. In this paper, two algorithms have been proposed (i) Bi-SVM, it is used to classify the nature of the cases as civil or criminal. (ii) C-XGB is used to predict the chances of whether the case will win or not. When an out-of-sample case, for which a decision is to be made is given as input, the model yields 96% of accuracy which is higher than the accuracy of the existing models.
    Keywords: Neural Networks; Machine Learning; Feature Engineering; chi2 (?2); CNN; error metrics.

Special Issue on: Machine Learning and Deep Learning Methods for the Applications in Ad Hoc and Ubiquitous Computing

  • A reliable transmission scheme for 3D point cloud based on partial decode-and-forward relay over burst error fading channel   Order a copy of this article
    by Jianjun Hao, Luyao Liu, Wei Chen 
    Abstract: A reliable and efficient transmitting scheme based on partial decode-and-forward (PDF) relaying for UAV-Ground wireless communication system is proposed in this article. In a multi-UAVs to ground case, we implement a 3D point cloud transmission system based on: (1) Polar-RS concatenated channel coding; (2) a relay adopt partial decode-and-forward strategy. Hence simulations for point cloud transmitting over a burst jamming channel and a burst deep fading channel are performed respectively. The simulation results show that Polar-RS code achieves higher reliability against burst error channel than RS-Polar scheme does under the same codeword length and code rate, also the proposed PDF relay reduces the time delay and implementation complexity compared to the fully decode-and-forward (FDF) scheme.
    Keywords: PDF relay; Polar-RS coding; burst error channel; 3D point cloud.

  • An Adaptive Stochastic Central Force Optimization Algorithm for Node Localization in Wireless Sensor Networks   Order a copy of this article
    by Pei-Cheng Song, Shu-Chuan Chu, Jeng-Shyang Pan, Tsu-Yang Wu 
    Abstract: Node localization in wireless sensor networks is a common and important practical application problem. Among the many localization algorithms, the MDS-MAP algorithm is a more effective one. However, the positioning effect of the MDS-MAP algorithm is not accurate in some cases, so the metaheuristic algorithm is implemented to further optimize the estimation results of the MDS-MAP algorithm in this paper. The improved central force optimization algorithm uses adaptive parameters to achieve randomness, while adding the restart strategy and accelerate strategy so as to avoid getting stuck in a local optimum. The CEC2013 and CEC2014 benchmark test suites used to verify the proposed algorithm are more competitive than some other existing algorithms. The improved central force optimization algorithm is applied to the MDS-MAP localization algorithm. The experimental results show that the improved central force optimization algorithm has a further optimization effect on the position estimation results of MDS-MAP.
    Keywords: Central Force Optimization; Restart strategy; Adaptive; Stochastic; MDS-MAP; WSN.

  • A Compact GBMO Applied to modify DV-Hop based on layers in wireless sensor network   Order a copy of this article
    by Jeng-Shyang Pan, Min Gao, Jian-po Li, Shu-Chuan Chu 
    Abstract: Gases Brownian Motion Optimization (GBMO) has been shown a useful optimization method. The compact concept is implemented to the GMBO named Compact Gases Brownian Motion Optimization (CGMBO) so as to improve the efficiency and effectiveness of the GMBO. Simulation results based on the 23 test functions consisting of the unimodal, multimodal, fixed-dimensional functions and composite multimodal functions demonstrate the superior of the proposed CGMBO. The idea of layer concept is also proposed to implement the Distance Vector-Hop (DV-Hop) by modifying the original average distance of each hop called Layer DV-Hop (LDV-Hop), experimental results also show the proposed LDV-Hop really improve the average positioning accuracy of each node for wireless sensor network. Finally, the proposed CGMBO is combined with the proposed LDV-Hop so as to greatly reduce the position error compared with the DV-Hop. The actual error per-hop distance between nodes is large. When the calculated average hop distance of the nodes does not reach the ideal value, the actual distance between the nodes and the calculated distance will have a large deviation.
    Keywords: Gases Brownian Motion Optimization; Wireless Sensor Network; Compact Gases Brownian Motion Optimization; LDV-Hop.

  • Auto Insurance Fraud Identification Based on CNN-LSTM Fusion Deep Learning Model   Order a copy of this article
    by Huosong Xia, Yanjun Zhou, Zuopeng (Justin) Zhang 
    Abstract: The traditional auto insurance fraud identification method relies heavily on feature engineering and domain knowledge, making it difficult to accurately and efficiently identify fraud when the amount of claim data is large and the data dimension is high. Deep learning models have strong generalization abilities and can automatically complete feature extraction. This paper proposes a deep learning model for auto insurance fraud identification by combining Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and Deep Neural Network (DNN). Our proposed method can extract more abstract features and help avoid the complex feature extraction process that is highly dependent on domain experts in traditional machine learning algorithms. Experiments demonstrate that our method can effectively improve the accuracy of auto risk fraud identification.
    Keywords: auto insurance fraud; deep learning; CNN-LSTM.

  • Optimal Dense Convolutional Network Model for Image Classification in Unmanned Aerial Vehicles based Adhoc Networks   Order a copy of this article
    by Hephzi Punithavathi, Dhanasekaran S, Duraipandy P, Laxmi Lydia E, Sivaram M, K. Shankar 
    Abstract: Unmanned aerial vehicles (UAVs) has the potential of generating an ad hoc communication network on the fly. They are presently employed in different application areas like security, surveillance, rescue operations, and so on. Aerial image classification gains more importance in the remote sensing community and several studies have been carried out in recent days. On the other hand, deep learning (DL) is currently exhibiting excellent performance in several processes namely object detection, tracking, image classification, and so on. In this view, this paper presents an optimal Dense Convolutional Network (DenseNet) with bidirectional long short term memory (Bi-LSTM) based image classification model called optimal DenseNet (ODN)-BiLSTM for UAV based adhoc networks. The proposed model involves two major processes namely feature extraction and classification. Firstly, DenseNet model is applied as a feature extractor, where the hyperparameters of DenseNet are tuned by the use of Adagrad optimizer. Secondly, the Bi-LSTM model is applied as a classifier, which classifies the aerial images captured by UAV. Detailed performance analysis of the proposed model takes place using UCM aerial dataset and the results are investigated under several dimensions. The experimental outcome ensured the goodness of the presented ODN-BiLSTM model on the applied UCM aerial dataset over the compared methods. The ODN-BiLSTM model has provided effective image classification results over the other methods with the maximum accuracy of 98.14% and minimum execution time of 80s.
    Keywords: Adhoc networks; Deep learning; Image classification; Unmanned aerial vehicle;.

  • Two-Stage Adaptive Weight Vector Design Method for Decomposition Based Many-Objective Evolutionary Algorithm   Order a copy of this article
    by Xiaofang Guo, Yuping Wang, Xiaozhi Gao 
    Abstract: For many-objective problems, in which the Pareto Front (PF) is complex, e.g., incomplete and degenerate, it is challenging to use the conventional weight vector method to obtain a set of uniformly distributed weight vectors on the target PF. In order to acquire the searching information of the irregular shape of the PF accurately, this paper proposes a two stage adaptive weight vector design approach for decomposition based many objective evolutionary algorithms. Firstly, a preset initial weight vector generation strategy based on the crowding information is proposed to capture the effective area of the distribution of population. Next, the Self-Organized Mapping (SOM) weight vector design method using the feedback of weight vectors in the first stage as the initial weight neurons is adopted to generate weight vectors to capture the topological structure of the distribution of PF. An external archive is further employed to store the newly generated cumulative offspring and update the weight vectors of the SOM, which can help to adaptively adjust the weight vectors. In addition, a new composite aggregation function combined metric distance with angle-distance is proposed to improve both convergence and diversity in dealing with the many objective optimization problems. The performance of our algorithm is examined using a total of 10 test problems with both regular and irregular PF. The experimental results show that the proposed method can significantly improve the convergence and diversity performance with irregular shape of PF.
    Keywords: Many-objective evolutionary algorithm; two stage; crowding; self-organized mapping; Lp-metric.

  • Behavior-based Grey Wolf Optimizer for Wireless Sensor Network Deployment Problem   Order a copy of this article
    by Yu Qiao, Hung-Yao Hsu, Jeng-Shyang Pan 
    Abstract: The existing Grey wolf optimizer doesnt perform well in convergence and diversity of population. This paper investigates the grey wolf optimizer and proposes a behavior-based grey wolf optimizer (BGWO) based on the real behaviors of the wolf pack. In BGWO, it mainly consists of two strategies: Lost wolf strategy and mating strategy. Lost wolf strategy benefits from the phenomenon that wolves with weak adaptability will get lost during the migration of wolves. The mating strategy comes from the competition in the wolf pack for mating. In the BGWO strategy, abandoning the low-adapted individuals in the wolf pack and competing with the wolf pack for mating behavior not only increases the population diversity in wolf pack, but also reduce the possibility of getting stuck in a local solution during optimization. Eighteen benchmark functions in CEC2017 are used to test the performance of BGWO and the result shows that the performance of BGWO is better than existing algorithms in the literature such as GWO, PSO, FA and PSOGWO. Moreover, In the WSN problem, a combination of coverage rate, connectivity rate and total network energy consumption is proposed as the objective function and optimized by BGWO. The experimental results show that BGWO perform well than other algorithms.
    Keywords: BGWO; Lost Wolf Strategy; Mating Strategy; WSN Deployment.

  • Learning Stereo Disparity with Feature Consistency and Con dence   Order a copy of this article
    by Liaoying Zhao, Jiaming Li, Jianjun Li, Yong Wu, Shichao Cheng, Zheng Tang, Guobao Hui, Chin-chen Chang 
    Abstract: Most of the existing stereo matching methods have been formulated into four regular parts: feature extraction (FE), cost calculation (CC), cost aggregation (CA), and disparity refinement (DF). They can obtain high precision results in most regions through modifying parts of the four methods, but still have problems in some ill-posed regions. This paper focuses on feature consistency and confidence (FCC), discovers the new attributes of the feature, and proposes a novel neural network structure for stereo matching by measuring the consistency and confidence of features. Base on this method, the paper fuses the cost volume and calculates the pixel confidence map for cost calculation and cost aggregation. The experimental results show the proposed method outperforms most of the state-of-the-art methods on both SceneFlow and Kitti benchmarks and lowers the estimation error of stereo matching down to 1.82\% ranking at the 7th position in the Kitti 2015 scoreboard six months ago.
    Keywords: depth estimation; stereo matching ,confidence measure; feature consistency; multi-distance metrics.

  • Research on Multi-feature Fusion Entity Relation Extraction Based on Deep Learning   Order a copy of this article
    by Shiao Xu, Shuihua Sun, Zhiyuan Zhang, Fan Xu 
    Abstract: Entity relation extraction aims to identify the semantic relation category between the target entity pairs in the original text and is one of the core technologies of tasks such as automatic document summarization, automatic question answering system, and machine translation. Aiming at the problems in the existing relation extraction model that the local feature extraction of the text is insufficient and the semantic interaction information between the entities is easily ignored, this paper proposes a novel entity relationship extraction model. The model utilizes a multi-window convolutional neural network (CNN) to capture multiple local features on the shortest dependency path (SDP) between entities, applies segmented bidirectional long short-term memory (BiLSTM) attention mechanism extracts the global features in the original input sequence, and merges the local features with the global features to extract entity relations. The experimental results on the SemEval-2010 Task 8 dataset show that the model's entity relation extraction performance is further improved than existing methods.
    Keywords: deep learning; multi-feature fusion; entity relation extraction; shortest dependency path; attention mechanism.

Special Issue on: Convergence of Soft Computing for Smart Cities

  • An SDN-IoT based Secure Simulation system for Smart Cities   Order a copy of this article
    by Seema Begum, Yao Nianmin, Syed Bilal Hussain Shah, Thompson Stephan, Xingwang Li 
    Abstract: The massive spike in security threats is one of the greatest security problems facing the Internet of Things (IoT). Many IoT gadgets do not have any protection features, and even the ones they have are mostly primitive and can thus easily be subject to attacks. Compromising one or more infrastructure components will theoretically lead to the vulnerability of the IoT network's entire infrastructure. The Software Defi ned Networking (SDN) poses many possibilities in this sense and offers the ability to address those difficulties. In this paper, the proposed solution may not resolve all the challenges; however, it overcomes a particular challenge concerning Software Defi ned Networking (SDN) without any modi fication to the present network architecture. The proposed secure solution allows SDN to work as an integrated and programmable controller for smart cities. According to various studies, IoT and SDN's integration allows unlocking several routes that provide security and making smart cities a reality with SDN. The SDN-based IoT network is studied in this paper, and it further proposes an effective IoT-based security system with the help of SDN IoT design.
    Keywords: Software Defined Networks; Internet of things; Security; OpenFlow; Smart city.
    DOI: 10.1504/IJAHUC.2021.10039528
  • Evaluation of Functional Maturity for a Network Information Service Design & Case Analysis   Order a copy of this article
    by Mohammad Al Rawajbeh, Vladimir Sayenko, Issam AlHadid, Fadi Al-Turjman, Lakshmana Kumar Ramasamy 
    Abstract: The current study discusses methodological issues for monitoring process to control the state of developing information system. The main goal of this evolutionary process is to achieve the optimal value for some criteria. The proposed approach is considered as a conception for the evolution of properties for system (object) at the planning phase. The study has proposed the evolution conception of system properties for planning (development) phase, concept of improving the system properties, the indicators of the functional maturity, and the technique of evaluation of functional maturity. the planning phase; the quality indicators named functional maturity; a concept of the evaluation of functional maturity; estimates of the functional maturity indicators and a technique for functional maturity evaluation monitoring conception. The implementation of this technique has developed a tier-based procedure. The results have demonstrated that network planning should follow integrated work practices along with the implementations of solid system design.
    Keywords: COBIT; Development; Functional Maturity; Information System,; IT System; Methodological Issues; Monitoring Process; Network Information Service,; Network Structure; Planning Phase.

  • Lion plus Firefly Algorithm for Ternary based Anomaly Detection in Semantic Graphs in Smart Cities   Order a copy of this article
    by M. Sravan Kumar Reddy, Dharmendra Singh Rajput 
    Abstract: The detection of suspicious or abnormal entities in massive data sets is really a complex task in homeland security in smart cities. Even though there were several technologies for data mining and social network analysis; detecting the abnormalities in huge semantic graphs is often the complex aspect as the nodes are deeply linked with countless links. A node is termed to be the abnormal node when it carries unique or abnormal semantics in network. This paper aims to introduce a new idea of finding the abnormal or suspicious nodes, and this is done by modeling the graph structure using diverse nodes and links associated to the node in a particular distance through edges. Once the graph structure is framed, the ternary list formation is done, and the list of all nodes is based on the nearby list of neighborhood nodes. Subsequently, the logic is induced in this work to detect the abnormal nodes via optimizing the node pairs. For this, a new hybrid algorithm termed as Threshold-based LA Update (T-LAU) that hybridizes the concepts of Lion Algorithm (LA) and Firefly (FF), respectively. The similarity among mined features in terms of rules recognizes the abnormal behavior of nodes in network. Finally, the analysis is carried out for validating the enhancement of the presented model.
    Keywords: Semantic graph; Anomaly detection; Ternary List; Lion Algorithm; Firefly Algorithm; Smart Cities.

  • User Participation Behavior in Crowdsourcing Initiatives: Influencing Factors, Related Theories and Incentive Strategies   Order a copy of this article
    by Xu Zhang, Zhanglin Peng, Qiang Zhang, Xiaonong Lu, Hao Song 
    Abstract: Crowdsourcing is a powerful paradigm that leverages collective intelligence to solve problems. Good performance in crowdsourcing initiatives depends on energetic user participation. This paper reviews and analyzes the current existing research works that are related to user behavior in crowdsourcing initiatives. Particular attention is paid to the following aspects. First, we summarize the influencing factors of user participation behavior in crowdsourcing initiatives. Second, we review the related behavior theories to further understand the relationship between these factors and user behavior. Third, we generalize about incentive strategies from the perspective of requesters and crowdsourcing platforms. Finally, the research directions of user behavior in crowdsourcing initiatives are discussed in this study.
    Keywords: Crowdsourcing; user participation; user behavior; motivation; incentive strategies.

  • GSFI_SMOTE: A Hybrid Multiclass Classifier for Minority Attack Detection in Internet of Things Network   Order a copy of this article
    by Geeta Singh, Neelu Khare 
    Abstract: The Internet of Things (IoT) network is prone to several cyber-attacks, due to many obligations of IoT devices. Minority attack detection in the IoT network is a challenge. This paper proposed three multiclassifier models to address this challenge at different IoT layer. Random Forest (RF) classifier is the main component in proposed models. RF hyperparameters are tuned with Grid-Search Cross-Validation (GSCV) to build an initial GS model that achieves a 100% normal traffic detection rate. It can efficiently separate normal and anomalous traffic at the IoT network layer. GS is extended with Feature-Importance (FI) based feature reduction technique and the Synthetic Minority Oversampling Technique (SMOTE) successively to realize GSFI and GSFI_SMOTE models that achieve better minority attack detection rates and applicable to the resource-limited fog/edge infrastructure, and the critical IoT infrastructure respectively. GSFI_SMOTE outperforms the existing methods. The UNSW-NB15 is used as a use case for experimenting with proposed models.
    Keywords: Security; Internet of Things; Network Monitoring; Attack Detection; Anomaly; Machine Learning; Random Forest; Feature Importance; Oversampling; Parameter Tuning.

  • Optimization of K-means algorithm based on sample density Canopy   Order a copy of this article
    by Guoxin Shen, Zhongyun Jiang 
    Abstract: Since the random selection of the initial centroid and the artificial definition of the number of clusters affect the experimental results of K-means, therefore, this article uses sample density and Canopy to optimize the K-means algorithm. This algorithm first calculates the sample density of each data, and selects the data point with the smallest density as the first cluster centroid; then combines the Canopy algorithm to cluster the original sample data to obtain the number of clusters and each cluster center. As initial parameters of the K-means; finally combines the K-means algorithm to assemble the original samples. UCI dataset and self-built dataset were used to compare simulation experiments. The results show that the algorithm can make clustering results more accurate, run faster, and improve the stability of the algorithm.
    Keywords: Clustering; K-means algorithm; Density; Neighborhood; Initial centroid.

    by Harvinder Singh, Adarsh Kumar, Kiran Kumar Ravulakollu, Manoj Kumar, Thompson Stephan 
    Abstract: Opportunities cannot be converted into achievements without assessing risk. Software developers and project managers presume that activities would proceed smoothly and according to plan in software project development. However, in reality, this is not always true due to the existence of unseen risks that inrnuence the development process and thereby performance. Hence, there is a need for more realistic risk evaluation criteria that can address the project managers need. This paper proposes a Project Risk Evaluation Technique (PRET) to determine the attractiveness of a software project. The proposed technique used normal distribution (DN) approach for risk understanding and thereby use the activity network to assess them. Using the graph theory method, with nodes as phases of the development process and edges as the duration of phases and the whole network as a software project, the algorithm systematically represented to extend various influential aspects of risk analysis. Hence, using quantitative analysis, the proposed algorithm is evaluated against negative exponential distribution (DNE) and is found to have a slight degree of uncertainty associated with risk and attractiveness. This signifies that there is still a scope of improvement for stabilizing the proposed technique with minimal improvements in the project factors.
    Keywords: Risk Analysis; Software Risk Management; PRET Algorithm; Normal Distribution; Negative Exponential Distribution.

  • Recent advances in Blockchain Technology: Asurvey on Applications and Challenges   Order a copy of this article
    by Saqib Hakak, Wazir Zada Khan, Gulshan Amin , Mamoun Alazab, Sweta Bhattacharya, G. Thippa Reddy, Basem Assari 
    Abstract: The rise of blockchain technology within a few years has attracted researchers across the world. The prime reason for worldwide attention is undoubtedly due to its feature of immutability along with the decentralized approach of data protection. As this technology is progressing, lots of developments in terms of identifying new applications, blockchain-based platforms, consensus mechanisms, etc are taking place. Hence, in this article, an attempt has been made to review the recent advancements in blockchain technology. Furthermore, we have also explored the available blockchain platforms, highlighted and explored future research directions and challenges.
    Keywords: Blockchain technology; Blockchain platforms; Healthcare; Cybersecurity; Finance; Smart city; Smart Grids; Logistics; Supply Chain; Ownership services; Hyperledger.

  • Device and Method for Dynamic Image Display of Financial Transaction Operation Data   Order a copy of this article
    by Jingdong Yan, Wuwei Liu 
    Abstract: With the booming economy, financial transactions have also become very active, but there are often situations where operational data is not obvious in the process of financial transactions. This paper aims to improve the visibility of data in the process of financial transactions, provide data support for transaction decision-making, and ensure the security of the operation process. This research mainly discusses the devices and methods for dynamic images to display financial transaction operation data. The device is divided into an early warning module, a user interaction module, an intelligent management module and a monitoring module. The early warning module is mainly based on a large amount of financial transaction data to warn customers against credit risks. The user interaction module is mainly to provide users with the display of dynamic images during financial transactions. The intelligent management system provides an interface for model meta data management to the model manager, and provides an interface for obtaining requirements to the requirements reporting staff. The monitoring module mainly realizes the monitoring of transactions, and promptly warns illegal transactions that occur during the transaction. At the same time, the user's transaction data displays the calculation results of the calculation engine through the result display page, which includes the chart of the sub-sequence to be returned and related information of the sequence. After referencing the results, the user can return to the home page to perform a new search. Based on the brightness perception function of HDR images, combined with the conversion chain reaction in the form of Open EXR, appropriate decimal floating point data is embedded in each pixel to hide the user's transaction information. During the test, the average transmission efficiency of archive logs was 77%, and the average transmission efficiency of active logs was 79%. The research results show that the dynamic image display system device of financial transaction operation data has excellent performance, which can provide real-time transaction information to users.
    Keywords: Dynamic Images;Financial Transactions;Operating Data;Intelligent Management.

  • A Machine Learning Approach for Celebrity Profiling   Order a copy of this article
    by Durga Prasad Kavadi, Fadi Al-Turjman, Adi Narayana Reddy K, Rizwan Patan 
    Abstract: The celebrity profiling is a type of authorship profiling, which is used to predict the sub profiles like gender, fame, birthyear and occupation of a celebrity for a given textual content. Knowing the details of the celebrities by analysing their written text became one interesting area of research to the researchers. The task of Celebrity Profiling is introduced in PAN competition 2019. The researchers from different countries participated in the competition and proposed different types of solutions to the Celebrity Profiling. Most of the researchers showing interest on the stylistic features like most frequent terms, most frequent word n-grams, character n-grams, part of speech tags n-grams to differentiate the writing styles of the celebrities. In this work, the experiment started with a set of stylistic features for predicting the accuracies of sub profiles like gender, fame and occupation. The experiment continued with the most frequent terms used in the corpus for sub profiles prediction. In both experiments, we used frequency measure in the representation of a vector value in the document vector representation. It was observed that the accuracies of two approaches are not satisfactory. A new approach named as sub profile based weighted approach is proposed to improve the accuracy of celebrity profiling. In this approach, most frequent terms are used to compute the document weight. The document weights were used to represent the document vectors instead of weights of the features. Two different term weight measures are used to calculate the weight of the terms and a document weight measure is proposed to compute the document weight. The document vectors are given to machine learning algorithms as input to build the training model. Three algorithms such as random forest, support vector machine and naive bayes multinomial are used in the experiment to generate the model. This model is used to predict the sub profile of a celebritys text. The accuracies of proposed approach for sub profiles prediction are improved when compared with existing approaches for celebrity profiling.
    Keywords: Celebrity Profiling; Author Profiling; Gender Prediction; Fame Prediction; Occupation Prediction; Machine Learning Algorithms; Accuracy.

  • Computational Trust Evaluation Algorithm (C-FTEA) for Cloud Models Using Fuzzy Logic Approach   Order a copy of this article
    Abstract: Cloud computing era has brought us many services on the pay-as-you-go business paradigm. And offers several advantages over traditional computing models in industries, so applications which are developed for smart cities requires large amount of storage. Hence, smart cities applications are using cloud computing technologies to manage their applications with great flexibility and availability. But, cloud computing has many challenges to be addressed and one of the most trending challenges is security and trust. After several research by various researchers, they have designed trust evaluation algorithms and trust evaluation models that can be used in a cloud computing environment. But a satisfactory level of trust is not achieved yet and hence we propose a computational trust evaluation algorithm using a fuzzy logic approach. Fuzzy theory is used by researchers in the area of resource optimization, scheduling, and service dependability in cloud computing security. But data storage, security, and trust using fuzzy logic have been ignored. Moreover, there is no special trust evaluation algorithm for cloud service users. In this paper, C-FTEM is designed to strengthen the security at data storage for smart cities a computational trust evaluation algorithm is designed by using a fuzzy logic approach and evaluated to prove its effectiveness.
    Keywords: Cloud computing; cloud service provider; cloud service user; cloud security; computational trust models; fuzzy logic; smart cities.

  • Development of Image-guided Puncture Robot used in Trigeminal Neuralgia Treatment   Order a copy of this article
    by Bin LIU, Hongbo YANG, Kai GUO, Caijun LUO, Chang LIU, Senhao ZHANG, Yingying ZHANG, Zhenlan LI 
    Abstract: Recently, invasive surgery had developed rapidly and was widely used in the fields of tumor treatment, spine, and joint disease treatment. At present, most of the analgesic puncture operations were performed manually by doctors in the CT environment. High skills are required during such complex surgery, it also increases the doctors risk due to exposure to the radiation environment. This paper studies image-guided puncture robots. In this work, through the use of binocular vision cameras, real-time medical image data was collected during the surgery using CT. Also, we set up the model for the spatial solution of the manipulator and integrated with the image data, to make a set of graphical operation interface. Under the guidance of the doctors, the posture of the manipulator was controlled to achieve the specific position required by the doctors. Through the development of software, hardware, and algorithm, we designed and developed an image-guided, minimally invasive surgical real-time robotic system to improve the safety and accuracy of the surgeon during the puncture procedure.
    Keywords: Image-guided; CT-compatible; Puncture; Surgical robot.

  • A Recommendation Algorithm Based on Modified Similarity and Text Content to Optimize Aggregate Diversity   Order a copy of this article
    by Shuhao Jiang, Hongyun Zhao, Zhenzhen Li 
    Abstract: With the popularity of smart phones, many people use mobile phones to provide personalized recommendations in a smart city. Aggregate diversity is defined as recommending different categories of items to different users. This paper proposes a personalized recommendation method based on modified similarity and text content. The algorithm optimizes the similarity value through modified similarity algorithm, solves the problem of unclear item category by extracing the text features of user browsing. And it clusters according to user category preference, and research and practice personalized recommendation algorithm based on aggregate diversity optimization. Experimental results show that the proposed algorithm can improve the aggregate diversity of recommendation results while ensuring the accuracy of recommendation.
    Keywords: Personalized Recommendation; Aggregate Diversity; Similarity Calculation; Text Features.

  • An ICT-Based Solid Waste Management System for Smart Cities: A case of Municipality in India   Order a copy of this article
    by Rutvij Jhaveri, Prerak Shah, Neel Patel, Dhrumil Patel, Shashank Thanki, Akash Kumar Bhoi, Jitendra Bhatia 
    Abstract: Smart cities are technology enabled and data driven intelligent cities with prominent use of ICT based technologies integrated with sensor devices. This paper addresses this issue by proposing a smart solid waste management system for municipalities in urban localities. This low-cost, dynamic, energy-efficient and easily deployable embedded system is proposed to effectively manage solid waste. A kit comprising of a micro-controller, a pair of ultrasonic sensors and global system for mobile communication (GSM) module is installed into the garbage container which can sense the level of garbage in the container and automatically informs the municipality authority about the same whenever the container gets full or empty. At the same time, the kit stores and updates the status of the container on a cloud storage for further analysis. The data analysis on the cloud dataset through diverse parameters provides a dashboard view via web application to infer the waste collection process.
    Keywords: Smart cities; Solid waste management; Information and communications technologies; Linear regression.

  • SHA-AMD: Sample-Efficient Hyper-Tuned Approach for Detection and Identification of Android Malware, Family and Category   Order a copy of this article
    by Aamir Rasool, Abdul Rehman Javed, Zunera Jalil 
    Abstract: Smart cities provide smart security solutions against cyberattacks to the communities. Lately, Android-based smartphones emerge as the best selling product in the market. Android technology is advancing in both usage and popularity as more people are using it to connect with every passing day. Due to this popularity and extensive usage, the android OS in mobile technology has become a lucrative target for attackers as they are coming up with new types of viruses, malware, and threats. The malware of Android devices is emerging in different forms, and each time with a different signature. Conventional approaches for detecting/classification malware need continuous improvement to perform well against these changing malicious intentions. Specific malware features can be picked for detection, and performance can be improved using deep ensemble techniques. In this paper, we propose a solution for the detection and identification of novel malware. We have utilized deep learning models for malware detection, identification, and classification with feature reduction to optimize model performance and use ensemble techniques for better results. Experimental results demonstrate better performance than state-of-the-art techniques.
    Keywords: Smart Cities; Smart Security; Malware; Family; Category; Deep learning; Machine learning; Cyberattack.

  • A CA-GRU Based Model for Air Quality Prediction   Order a copy of this article
    by Jingyang Wang, Xiaolei Li, Tingting Wang, Jiazheng Li, Qiuhong Sun 
    Abstract: Smart cities aim to maximize the optimization of urban functions, promote economic growth, and use smart technology and data analysis to improve the life quality of urban residents. The air quality index (AQI) is an important evaluation index of air pollution, describing the degree of air pollution and its impact on the health of human beings. Therefore, it is particularly important for smart cities to accurately predict AQI. The accuracy of AQI prediction remains a challenge for current methods. In this paper, an AQI prediction model based on CA-GRU is proposed, which includes convolutional neural networks (CNN), attention mechanism, and gated recurrent unit (GRU). In this model, the feature extraction of input data is realized through CNN, weights are assigned according to the states of old data with attention mechanism, and the AQI is predicted with GRU. To prove the validity and accuracy of the CA-GRU prediction model, we take experiments using air quality data and weather data from 1st January 2017 at 00:00 to 30th September 2020 at 23:00 in Shijiazhuang city of China and compare this model with other models. Finally, mean absolute error (MAE), mean square error (MSE), explained variance score (EVS), and R2 are used to evaluate the performance of the model in this experiment. The results shows that this model has the highest performance than the others, with the MAE of 6.099281, MSE of 90.781522, EVS of 0.972560, and R2 of 0.972495.
    Keywords: CNN; Attention; GRU; AQI Prediction.