International Journal of Ad Hoc and Ubiquitous Computing (52 papers in press)
Room measurement tool combining ultrasonic and inertial sensors in smartphones
by Yukitoshi Kashimoto, Yutaka Arakawa, Keiichi Yasumoto
Abstract: Obtaining accurate floor plans of buildings is critical for optimising indoor geographic information system (GIS) applications. In this paper, we present a room measurement tool that utilises a smartphone equipped with an ultrasonic sensor. To take measurements, users complete a lap along thewalls of all of the rooms. Then the tool accurately estimates the shape and size of them by tracking the walking paths of users and measuring the distance from the path to the walls with ultrasonic sensors. To track walking paths, we utilise inertial sensors embedded in the smartphone to estimate walking steps and turns, and the ultrasonic sensors to estimate the stride length when walking toward the wall. To account for such adjacent objects as bookshelves that decrease the accuracy of room size estimation, we used a mixed Gaussian filter. Our experimental results show that our tool considerably improved the estimation accuracy of the room shape and size.
Keywords: Room measurement tool; pedestrian dead reckoning; inertial sensor; smartphone;
Outage Probability Analysis for Relay based Cognitive Radio Network
by Perarasi Sambantham, Ganesan Nagarajan
Abstract: This paper is delineating with relay based subjective radio system (CRN) with different optional source hubs and numerous auxiliary goal nodes. The quality of service is mainly ability can be enhanced with proper resource allocation. The link value is preserved by keeping SNR above a threshold called minimum protection ratio. Uplink resource allocation based on hybrid cooperative CR network (HCCRN) is considered in which licensed radio and cognitive radio are combined. Cognitive relay is utilized for range extension to attain greater capacity. CRN stands for Cognitive Radio Network. Medium access layer network coding (MALNC) is connected with two sorts of full duplex transfer to be specific amplify and forward (AF), decode and forward (DF) for the fruitful transmission of data. Identification of data at the collector is anticipated by multiple range groups. Utilization of MALNC lessens to the quantity of vacancies required for correspondence. Log typical conveyances are utilized for divert parameter investigation within the sight of shadowing of sign because of huge hindrances in versatile radio correspondence. Blackout likelihood is portrayed for blurring channel. A variety of the estimation of mean, fluctuation and transmission rate is done to accomplish an effective framework.
Keywords: Cognitive radio network; Relay system; Amplify and forward; Decode and forward; MALNC; Log Normal Distribution.
Energy-efficient fault-tolerant scheduling in a Fog-based smart monitoring application
by Ahmad Sharif, Mohsen Nickray, Ali Shahidinejad
Abstract: Fog environment is a distributed system model that can utilise the processing abilities of Fog Devices (FDs) and is growing as an essential platform for IoT. Network failures become inevitable, with the growing scale of IoT. To achieve high performance, should attend to communication reliability. Fault tolerance becomes a necessary matter to enhance the reliability of the Fog. Notably, fault tolerance studies have been performed only on the Cloud system. To counter this issue, we propose a novel fault-tolerant scheduling algorithm for hybrid modules in Fog. One of the main innovations of this approach is a classification method for different modules by the side of computing the energy consumption of all Fog nodes and finding minimal Fog nodes energy consumption. Proposing ECRBC model, which composes the profits of Extended Checkpoint-Restart and Primary Back up model with Classification, is leading to superior energy-efficiency and reliability. We evaluate the performance of the proposed method by comparing it with four methods in terms of delay, energy consumption, execution cost, network usage, and total executed modules. Analysis and simulation results show, our method augments high reliability and efficiency to the system.
Keywords: Fog Computing; Fault-tolerant scheduling; Checkpointing; Energy efficiency.
Approximation Algorithms for Profit Maximization in Multicast D2D Networks
by Jagadeesha R. Bhat, Jang-Ping Sheu, Wing-Kai Hon, Cian You Yang
Abstract: As the demand for mobile data services increases, telecom companies need to develop wise strategies to retain existing customers. For instance, in a multicast scenario, satisfying individual users quality of service (QoS), data demand at varying rates, etc. are complicated tasks. Earlier works on device-to-device (D2D) multicast have majorly discussed the cases of throughput maximization without considering the individual users data request rates. In this paper, we propose two algorithms to maximize the telecom operators profit collected from the users in a two-hop D2D multicast network, when users have different channel qualities and data request rates while receiving multicast data through a single transmission session. First, we model our multicast scheme for the proposed scenario as a budgeted maximum coverage problem. Later, we propose two approximation algorithms that guarantee approximation ratios of 1-1/?e and 1-1/e, respectively, where e denotes the base of the natural logarithm. Numerical results show that the proposed algorithms perform better than the other candidate algorithms and nearly approximates the optimal solution.
Keywords: Mobile data; multicast; approximation algorithm; device-to-device.
An intrusion detection model using improved convolutional deep belief networks for wireless sensor networks
by Weimin Wen, Cuijuan Shang, Zaixiu Dong, Huan-Chao Keh, Diptendu Sinha Roy
Abstract: Intrusion detection is a critical issue in the wireless sensor networks (WSNs), especially for security application. In literature, many classification algorithms have been applied to address the intrusion detection problems. However, their efficiency and scalability still need to be improved. One challenge is that the prior samples cannot be timely obtained when applying the previous supervision classification algorithms, due to the indefinite features of attack behaviors. Another challenge is that the redundant nodes and data consume the limited energy of sensor nodes, reducing the network lifetime of the WSN. This paper proposes an Improved Convolutional Deep Belief Network-based Intrusion Detection Model (ICDBN_IDM). The model consists of a redundancy detection algorithm, an improved intruder detection algorithm based on convolutional deep belief network as well as a performance evaluation strategy. The redundancy detection can remove non-effective nodes connections and data, reduce the number of data transmissions and hence save the energy consumption of the whole network. The improved CDBN algorithm extracts features from normal and abnormal behavior samples by using unsupervised learning and overcomes the problem of self-learning and automatic detection of abnormal behaviors under the condition of unknown or less prior samples. The performance evaluation is an indispensability part of the ICDBN_IDM, which verifies the correctness, accuracy, and efficiency of the mentioned algorithms. Compared with the commonly used machine learning mechanisms, the ICDBN_IDM achieves high intrusion detection accuracy, reduces the ratio of false alarm while saving the energy consumption of sensor nodes. In comparison, the average intrusion detection ratio of the ICDBN_IDM is 96.82% for five types of attack in a WSN.
Keywords: Intrusion detection; improved convolutional deep belief network; redundancy detection; deeply compressed algorithm; wireless sensor network.
Botnet Detection Used Fast-Flux Technique, Based on Adaptive Dynamic Evolving Spiking Neural Network Algorithm
by Ammar Almomani, Ahmad Al-Nawasrah, Mohammad Alauthman, Mohammed Azmi Al-Betar, Farid Mezian
Abstract: A botnet refers to a group of machines. These machines are controlled distantly
by a speci?c attacker. It represents a threat facing the web and data security. Command and Control C&C service is deemed as the pillar of botnet communications at which the botmasters and bots send a report and orders to one another. It should be noted that botnets are usually categorized based on C&C protocols. Fast-Flux Service Network(FFSN) has been engaged by bot herders for cover malicious botnet activities. Its been engaged by bot herders for increasing the lifetime of malicious servers through changing the IP addresses of the domain name quickly. There are several methods for detecting FFSNs. However, these methods show a low level of detection accuracy. In the present research, the researchers aimed to propose a new system. This system is named Fast Flux Botnet catcher system (FFBCS). This system can detect FF-Domains in an online mode using an Adaptive dynamic evolving spiking neural network algorithm. Comparing with two other related approaches the proposed system shows a high level of detection accuracy,a low false positive and negative rates, respectively. It shows a high performance. The algorithms proposed adaptation increased the accuracy of the detection. For instance, this accuracy reached (98.76%) approximately.
Keywords: Dynamic evolving spiking neural network; Fast-flux techniques detection;Network security; Botnet.
Detection of Phishing Attacks using Probabilistic Neural Network with a Novel Training Algorithm for Reduced Gaussian Kernels and Optimal Smoothing Parameter Adaptation for Mobile Web Services
by Priya Saravanan, Selvakumar Subramanian
Abstract: The rapid escalation of smartphones and online transactions increases the rate of semantic attacks such as phishing that exploit the user credentials for fraudulent financial gains in online social networks. The existing detection methods suffer from low detection accuracy and high False Positive Rate (FPR) due to the lack of generalized algorithms for accurate prediction. The neural network classifiers have shown promising results for detecting such kinds of semantic attacks. Hence, this study focused on the use of Probabilistic Neural Network (PNN) for proposing an effective solution for detecting the phishing attacks. In contrast to conventional PNN, in this study, the perception of PNN is enhanced with a novel training process. A novel Fuzzy Dense K-Modes (FDKM) clustering algorithm is proposed to achieve the objectives. The number of Gaussian kernels used in the pattern layer is reduced by the cluster centers or K-Modes obtained from the FDKM clustering algorithm. In FDKM clustering, a novel dissimilarity measure is introduced to improve the intracluster similarity. Moreover, the performance of PNN is further enhanced by the proposed optimization procedure called Modified Harmony Search with Generation Regrouping (MHS_GR) which finds the optimal smoothing parameter for training the network. In MHS_GR, the diverse population is maintained by generating the initial seeds using the Generalized Opposition Based Learning (GOBL) method. Also, the problem of stagnation in which the searching of global optimum stagnates before finding a globally optimal solution is overcome by the proposed entropy-based generation regrouping strategy. The efficiency of the proposed approach was evaluated on two benchmark phishing datasets obtained from the University of California Irvine (UCI) machine learning repository and on our Phish_Net dataset collected from PhishTank and starting point directory. The experimental results reveal that the proposed PNN with MHS_GR technique (PNN_HS3) achieves higher detection accuracy with lesser FPR when compared to the existing phishing detection techniques. Specifically, PNN_HS3 obtained 98.53\%, 96.92\%, and 97.12\% of detection accuracy and 2.02\%, 3.39\%, and 3.12\% of FPR for UCI_1, UCI_2, and Phish_Net dataset respectively.
Keywords: Mobile web services; Phishing attacks; Online security; Gaussian kernels reduction; Probabilistic Neural Network; Personal privacy.
Mobility Modeling for Urban Traffic Surveillance by a Team of Unmanned Aerial Vehicles
by Farooq Ahmed, Haroon Mahmood, Yasir Niaz
Abstract: Use of Unmanned Aerial Vehicles (UAVs) for road traffic surveillance, within the context of Intelligent Transportation Systems and Smart Cities, is an exciting idea for improving surveillance quality, with several interesting usecases. To study its applicability in large urban environments, calibrated mobility models have a significant role to play as they help analyze several mobility related issues, before as well as after deployment. Although mobility models for general surveillance scenarios, using UAVs, have been studied earlier; a model optimized for urban traffic surveillance has been missing. This paper addresses this problem with the discussion of an energy-aware and scalable mobility model for a team of cooperative Unmanned Aerial Vehicles (UAVs), for monitoring road traffic in an urban area. It also accompanies an extensible framework for territory distribution, to optimize the number of UAVs required for maximizing coverage, without compromising operational performance. Since the problem of territory distribution in general is NP-hard, a Genetic Algorithm based strategy is recommended, using the idea of edge-disjoint paths. Simulation results show that proposed model performs considerably better in terms of area coverage when compared with other mobility models proposed in literature. Scalability of the model to realistic urban territories is also ascertained, using a published dataset simulating vehicular traffic for a mid-size urban city.
Keywords: Mobility modeling; Unmanned Aerial Vehicles; Flying Ad hoc Networks; Intelligent Transportation System.
A robust Guaranteed Time Slots allotment scheme for real-time and reliable communication in WBANs
by Gulshan Soni, Kandasamy Selvaradjou
Abstract: The ever increasing aging population and expenditure related to healthcare, consequently demand the requirement for a state-of-the-art healthcare system, which should be capable enough to handle the existing and near future challenges in health-care systems. Wireless Body Area Networks (WBANs) are the potential contender for this requirement. In the recent past, a substantial amount of research interest has been diverted towards the development of WBANs by the various research communities from academia and industries. The IEEE 802.15.4 standard is an appropriate communication protocol for the requirement of WBANs because of its low-power and low-cost features. In beacon-enabled mode, IEEE 802.15.4 standard supports real-time traffic through Guaranteed Time Slots (GTSs) feature. Nevertheless, it suffers from the reliability and timeliness guarantees. To address this issue, this work advocates a Robust Guaranteed time slots Allotment Scheme (RGAS) that consists two phases, viz. (i) determine the severity of sensor data and (ii) dynamic allocation of available GTS slots to the contending sensor nodes. A Fuzzy Inference System (FIS) is devised in the first phase to evaluate the merit of the sensor nodes which in turn decides the GTS slots to be demanded based on their severity level. On the other hand, the second phase ensures that the high severity sensor nodes always get GTS slots allocated with top most priority in order to enable them to transmit real-time data without any undue delay. Through simulation results we demonstrate that the proposed RGAS significantly outperforms the existing GTS mechanism specified in the IEEE 802.15.4 in terms of various performance metrics such as packets transmission, packet reception rate, packet loss rate, and effective utilization of Contention Free Period (CFP).
Keywords: Wireless Body Area Networks; Guaranteed Time Slot allocation; Contention Free Period; Fuzzy Inference System.
Enhancing Data Privacy through Decentralized Predictive Model with Blockchain-based Revenue
by Sandi Rahmadika, Kyung-Hyune Rhee
Abstract: Federated learning (FL) permits a vast number of connected to construct deep learning models while keeping their private training data on the device. Rather than uploading the training data and model to the server, FL only sends the local gradients gradually. Hence, FL preserves data privacy by design. FL leverages a decentralized approach where the training data is no longer concentrated. Similarly, blockchain uses the same approach by providing a digital ledger that can cover the flaws in the centralized system. Motivated by the merits of a decentralized approach, we construct a collaborative model of simultaneous distributed learning by employing multiple computing devices over shared memory with blockchain smart contracts as a secure incentive mechanism. The collaborative model preserves a value-driven of distributed learning in enhancing users' privacy. It is supported by blockchain with a secure decentralized incentive technique without having a single point of failure. Furthermore, potential vulnerabilities and plausible defenses are also outlined. The experimental results positively recommend that the collaborative model satisfies the design goals.
Keywords: Blockchain; Decentralized Revenue; Decentralized Training; Federated Learning; Predictive Model; User Privacy.
Provably secure authenticated content key distribution framework for IoT enabled enterprise digital rights management systems
by Dheerendra Mishra, Saurabh Rana
Abstract: The Internet of Things (IoT) is one of the fastest-growing technology, which is helping the enterprise industry.
Keywords: Multimedia; Enterprise digital rights management systems; Key agreement; Security; anonymity.
Monitoring in Fog Computing: State-of-the-Art and Research Challenges
by Haftay Gebreslasie Abreha, Carlos J. Bernardos, Antonio De La Oliva, Luca Cominardi, Arturo Azcorra
Abstract: Fog computing has rapidly become a widely accepted computing paradigm to mitigate cloud computing-based infrastructure limitations such as scarcity of bandwidth, large latency, security, and privacy issues. Fog computing resources and applications dynamically vary at run-time, and they are highly distributed, mobile, and appear-disappear rapidly at anytime over the internet. Therefore, to ensure the quality of service and experience for end-users, it is necessary to comply with a comprehensive monitoring approach. However, the volatility and dynamism characteristics of fog resources make the monitoring design complex and cumbersome. The aim of this article is therefore threefold: (i) to analyze fog computing-based infrastructures and existing monitoring solutions, (ii) to highlight the main requirements, and challenges based on a taxonomy, and, (iii) to identify open issues and potential future research directions.
Keywords: fog computing; edge; virtualization; monitoring.
Propagation models and their effect on the congestion control in Vehicular Ad Hoc Network
by Swati Sharma
Abstract: Vehicular networks form a growing field of wireless technology that pays attention to a wide range of applications, from infotainment to safety applications. One of the most vital challenge faced by Vehicular Ad Hoc Network (VANET) to choose an appropriate propagation model providing accurate and realistic propagation details. In VANET, the realistic propagation model requires mobility detail of vehicles, network topology, and information of message dissemination. Although all the vehicles are sharing the same limited bandwidth, an increase in vehicle density results in congestion in the network, leading to packet loss and the degrades network performance. Therefore, it is necessary to control the congestion for the high performance of the vehicular network. This paper illustrates various propagation mechanisms and factors affecting the propagation. Further, the classification of different propagation models is discussed, along with their mathematical expressions. Finally, we provide an overview and taxonomy of several VANET-specific propagation models with their impact on the Decentralized Congestion Control (DCC) algorithm through simulation. The objective of this paper is to provide researchers with the guidelines to understand the effect of propagation models on congestion control and choose the best one according to the scenario.
Keywords: VANET; Propagation model; Congestion control; Simulation.
An e-Healthcare Authentication Protocol Employing Cloud Computing
by Prerna Mohit, Ruhul Amin, G.P. Biswas
Abstract: Telecare Medical Information System (TMIS) provides a wide array of medical information to the respective participants (doctor/patient) over insecure networks. The patient medical data is extremely sensitive and confidential along with authentication. This paper presents an architecture for e-healthcare using TMIS system employing a cloud server to store the medical information of registered patients. In addition, the proposed secure authentication protocol comprises a mobile device to ensure secure communication between the participants and contributes a platform for proper communication. To proof authenticity, both formal and informal security analyses have been successfully executed to resist attacks. Moreover, the computation and communication costs of the reported scheme offered better results in comparison to existing literature.
Keywords: TMIS; Cloud server; BAN logic; Anonymity; Security Attacks.
Energy Efficient Power Allocation with Antenna Selection for Interference Alignment based MIMO-OFDM Cognitive Radio Networks
by Reba P
Abstract: Due to tremendous increase in wireless data traffic, efforts have been taken in the past decades to improve the Spectral Efficiency (SE) of wireless communication system. However, increase in SE leads to high power consumption. Energy efficient communications in Cognitive Radio (CR) networks has been of great interest due to the limited power supply of battery terminals and the increasing CO2 emissions. To address the power consumption problem and to maximize the energy efficiency (EE) in MIMO-OFDM underlay CR systems, an energy efficient Power Allocation (PA) and Antenna Selection (AS) algorithm is proposed. Interference Alignment (IA), which helps in mitigating the interference at primary receivers is considered, as a base to establish the underlay type CR system. The canonical correlation based transmit AS is employed for the SUs on a per-subcarrier basis to improve their QoS. Further, to improve the QoS of both PU and SUs, the AS is followed by optimal energy efficient PA. The problem statement for PA is formulated based on constrained optimization framework to maximize the EE subject to PU threshold rate constraint and total transmit power constraint. Due to fractional nature of EE, the original problem is transformed into equivalent parametric optimization problem based on Dinkelbach method and an iterative solution to solve this optimization problem is proposed. Simulation results confirm that the proposed EE aware algorithms can improve the EE performance of both PU and SUs than the SE aware algorithms.
Keywords: Energy Efficiency; Interference Alignment; MIMO; Power Allocation; Antenna Selection; Underlay Cognitive Radio.
On Fast Fourier Transform Based Decoding of Reed-Solomon Codes
by Yunghsiang Sam Han, Chao Chen, Sian-Jheng Lin, Baoming Bai
Abstract: Reed-Solomon (RS) codes are a popular class of codes that have been implemented in many practical systems. Recently, a fast approach to the error decoding of RS codes based on fast Fourier transform (FFT) was invented. In this work, we derive the key equation based on the Lagrange polynomial and then present erasure-and-error decoding of an $(n,k)$ RS code. This decoding algorithm can simultaneously correct up to $v$ errors and $f$ erasures when $2v+f
Keywords: Coding; Decoding; Reed-Solomon codes; Fast Fourier transform.
Optimal Solar Panel Placement in Mobile Edge Computing
by Seyed Yahya Vaezpour
Abstract: Mobile Edge Computing (MEC) enhances smart phone computational and storage capabilities by offloading tasks from mobile phones to the edge of the network where servers are placed near base stations. Since data centers consume electricity more and more each year, we consider locating renewable energy sources such as solar panels near each MEC. We tackle these challenges by strategically placing solar panels near MECs to minimize the energy costs. We formulate and solve the optimization problem and present two algorithms to solve the optimization problem. Moreover, we analyze the workload of each MEC during past years, and we present an algorithm to predict future workload of different MECs. We evaluate our solution using real Google traces collected over a 29-day period from a Google cluster. Simulation results show that with the help of our optimization algorithms, we can effectively reduce the electricity costs and increase the green energy consumption.
Keywords: Green Energy; Mobile Edge Computing; Solar Panel Placement.
An Overview of Continuous Device-to-device Authentication Techniques for the Internet of Things
by Arwa Badib, Asma Cherif, Suhair Alshehri
Abstract: Internet of Things (IoT) has become an integral part of our daily life. However, this technology exposes many security concerns. With the increasing focus on security and privacy, IoT device authentication becomes a crucial requirement to protect devices against any unauthorized access. Although static authentication ensures the legitimacy of the user or device at the beginning of each session, it does not account for session hijacking that could occur after the initial authentication. Continuous authentication is a more robust authentication technique to ensure security throughout the session. Also, it ensures fast authentication for frequent messages sent during the session. In this research, we analyze the security threats in the context of device-to-device communication. We elaborate on the main authentication requirements for IoT.
Additionally, we survey major contributions in authentication and compare them based on the predefined requirements. We believe that the findings of this paper provide valuable information to researchers by presenting new directions for future research.
Keywords: Continuous authentication; IoT; Security; Device-to-device; Privacy.
Multi-Cluster Flying Ad-Hoc Network for Disaster Monitoring Applications
by Abhishek Joshi, Sarang Dhongdi, Narayan Manjarekar, K.R. Anupama
Abstract: Multiple mini-UAVs can be used in various disaster monitoring applications. These UAVs can be controlled from ground based control station, or they can form Flying Ad-Hoc Network (FANET) with fully distributed nature of operations. To achieve distributed operations, set of network protocols need to be developed as a part of protocol stack. Various networking protocols for FANET have been developed in this paper namely clustering, time division based MAC protocol and routing protocol for a particular networking topology. This FANET network can be used for exploring a larger and disjoint terrain with the help of multiple cluster formation. To maintain isolation between clusters and to optimize energy of the network, UAVs make use of adaptive power communication technique. Extensive simulation experiments have been performed using a platform developed by linking NS-3, ROS and Gazebo tools. Results of network performance are shown with respect to scalability and mobility of UAVs.
Keywords: FANET; UAVs; Clustering; Routing; TDMA MAC; Network Simulator; Robotic Simulator.
Coordinated Sensing Coverage Optimization In Sensor Networks Using RSSI
by Qingdong Huang, Yun Zhou, Sen Hao, Xueqian Yao, Miao Zhang
Abstract: We present a coordinated algorithm for sensors effective deployment in a monitoring area, which aims to optimize coverage and surveillance in distributed sensor networks. In this paper, the easy accessed Received Signal Strength Indication (RSSI) is adopted to form virtual force among neighboring nodes, which maintains the networks connected and the coverage maxmized in sensing field. The virtual force shows some fascinating merits in the process of coverage optimization, but it will encounter oscillation problem when it is nearly reached optimized position. We propose a new method which combines virtual force and the Whale Swarm Algorithm (WSA) according to the RSSI value. When the number of continuous oscillations in using virtual force is greater than the threshold we set in advance, we optimize the cooperative position of adjacent multinodes with the whale swarm algorithm. The regular signal transmission model and Radio Irregularity Model (RIM) are used to simulate the ideal environment and the actual environment. Simulation results demonstrate that the coverage ratio is above 95% . The method achieves the high convergence speed and the low average moving distance of nodes, which verifies the effectiveness and feasibility of this algorithm
Keywords: Wireless sensor networks; Coordinated sensing coverage; Virtual force; Received Signal Strength Indicator.
Optimized Routing Algorithm in Low Earth Orbit Satellite Networks
by Mohammed Hussein, Izzat Ghaith
Abstract: To meet the massive growth in demand for multi-media services on mobile devices and to support connectivityanywhere on the planet, broadband systems everywhere haveattracted much attention from academia and industry alike. Itis expected that satellite networks in general and the Low-EarthOrbit (LEO) satellite, in particular, will play a fundamental rolein the deployment of these systems. However, LEO satellite groupssuch as Iridium and Iridium-NEXT are too expensive to deployand maintain. As a result, extending their service lifetime hasemerged as a significant challenge for researchers and engineers.The main note in this paper is that one can significantly increasethe service life of the satellites by managing the Depth of Discharge(DoD) of their batteries, which is the main factor determining theage of the satellite. Satellites in low earth orbit groups can spendabout 30% of their time under the shadow area, which is whenthey powered from the batteries.In this paper, we present a new routing metric called NewOptimization Routing Metric abbreviated as (NROM), and wederived three versions of this metric, D-NROM, B-NROM, BL-NROM. Where they represent different aspect approaches. Usingrealistic LEO topology and traffic requests, we show that BL-NROM and B-NROM can increase battery life by up to 75% and100%, respectively
Keywords: Energy Efficiency; Routing; LEO Satellite.
A TDMA Protocol with Reinforcement Learning Slot Selection for MANETs
by Chih-Yu Lin, Chih-Hsiang Wang, Yu-Chee Tseng
Abstract: With the rapid development of wireless communication and the advantage of infrastructure-less technologies, mobile ad hoc networks (MANETs) have attracted great attention on military and rescue applications. Medium Access Control is an important issue in MANETs. Contention-based MAC protocols (e.g., CSMA) do not ensure a reliable transmission due to the possibility of collisions. On the contrary, schedule-based MAC protocols (e.g., TDMA) can solve the collision problem with a scheduled transmission plan. However, under an infrastructure-less environment, it is non-trivial for each node to determine its own transmission plan. This work investigates how to use Reinforcement Learning (RL) to help nodes determine their transmission plans in a TDMA protocol. More precisely, we design a cross-layer TDMA protocol with a RL-based slot selection algorithm. We have validated the proposed protocol by the ns-3 network simulator.
Keywords: medium access control; mobile ad hoc networks; ns-3; reinforcement learning; slot assignment; TDMA.
Time and Energy-Efficient Hybrid Job Scheduling Scheme for Mobile Cloud Computing Empowered Wireless Sensor Networks
by Mahfuzulhoq Chowdhury
Abstract: Today, the rapid growth of the emerging applications based on real-time ubiquitous sensory data collection, processing, and delivery poses a significant challenge to researchers due to the WSNs resource limitations. To minimize the WSNs job processing time, cloud computing technology has become a potential solution due to its high processing capability and storage resources. To minimize the WSNs battery power usage and speed up the overall job execution process, the development of a proper job scheduling scheme has emerged as a potential research challenge for cloud empowered WSNs by taking into account different job requirements, multiple job arrival, and resource characteristics. In this paper, we propose a time and energy-efficient hybrid job scheduling scheme for WSNs job execution that not only assigns sensor-cloud resources for each job processing but also allocates network timeslot resources for transferring sensory data over cloud empowered WSNs. We present an analytical model to evaluate the performance of our proposed hybrid job scheduling scheme and compare its performance with both traditional only contention and reservation-based scheme. The experimental results clearly show that the proposed hybrid job scheduling scheme outperforms the existing only contention and reservation-based scheduling schemes in terms of schedule length, mean job execution time, energy cost, financial cost, and communication cost-to computation cost ratio. Our experimental results demonstrate that the proposed hybrid job scheduling scheme supersedes the state of the art up to approximately 28.05% in the metric of schedule length.
Keywords: Cloud Computing; Energy Consumption; Job Scheduling; Job ExecutionrnTime; Schedule Length; Wireless Sensor Networks.
Black Hole Attack Prevent Scheme using Blockchain-Block Approach in SDN-Enabled WSN
by Emre Karakoç, Celal ÇEKEN
Abstract: The success of Software-Defined Networking (SDN) technology in the wired environment has accelerated the research on the deployment of SDN in Wireless Sensor Networks (WSN). Since the wireless environment has a shared nature, it is more vulnerable to routing attacks. Most of the existing security protocols cannot be implemented for WSN, as the wireless environment has physical restrictions and the nodes in the network have limited energy, processing capacity, and memory. This paper investigates the possible threats in SDN-enabled WSN and analyzes the Black Hole attack in detail. In the paper, we have also proposed a novel lightweight security model that exploits the Blockchain-block approach to be able to protect the Flow Table in each node, which is the main target of possible routing attacks. We have generated an unchangeable fingerprint called the signature token for the flow entries with a secret key belonging to each node. Thanks to this token, not only have the flow entries been secured against tampering but also data packets have been transmitted securely along the path and secured against replay attacks and man-in-the-middle attacks. The results show that the proposed security model can be a promising solution for securing SDN-enabled WSN structures.
Keywords: IoT; SDN; WSN; Blockchain; Flow Table Security; Black Hole Attack.
Tree Reconstruction using Energy Aware Sink Mobility to Prolong Network Lifetime in WSN
by Hemanth Kumar, Nagarjun E, Dilip Kumar S M
Abstract: Activities sensed through out the sensor field are forwarded to the sink by the sensor nodes. This leads to faster dissipation of node energy that are positioned around the sink when compared to the energy of other nodes that are deployed away from the sink. This leads to energy hole problem. The main objective lies in collecting the sensed data from all over the sensor filed, aggregate and forward the same to the mobile sink to extend the network lifetime. The sensor network is logically divided into subgrids and each subgrid is uniquely identified and the sink is aware of the count of sensors under each subgrid. During data collection process, the sink node having mobility move towards the potential subgrid so that the energy of all the sensor nodes is equally utilised. The implementation is done through reconstructing the network and generating new routing paths in the network. The sink moves to a new subgrid where sensor nodes have more residual energy, gets associated with new neighbor nodes and continues to operate, which prolongs the network lifetime. This paper is an extended work of our proposed Energy-Aware Sink Mobility (EASM). Parameters like average life time of nodes, packet delivery ratio and throughput of the network are considered for performance analysis. Residual energy obtained from the simulation shows that the lifetime of the network is improved.
Keywords: Wireless Sensor Networks; Residual Energy; Lifetime; Tree Reconstruction;
Mobile Sink; subgrid.
Security Analysis of Image CAPTCHA using Mask R-CNN Based Attack Model
by Vijaypal Rathor, Bharat Garg, Mandar Patil, G.K. Sharma
Abstract: CAPTCHAs are designed to differentiate humans from automated programs to allow only the former to gain access to most of the websites and Internet services. These CAPTCHAs are attacked by developing algorithms which break their underlying design principle. Therefore, the analysis of the robustness of CAPTCHA against the current state-of-the-art attack is the critical requirement. Recently, a neural style transfer based image CAPTCHA called Style Area CAPTCHA (SACAPTCHA) has been reported that provides effective security among the existing image-based CAPTCHAs. The robustness of SACAPTCHA is tested against the Faster R-CNN and FCN architectures while using accuracy as a metric. However, the use of ineffective attack models and metrics is not enough to test the robustness of the SACAPTCHA. In this work, we propose a Mask R-CNN based attack model to critically analyze the robustness of SACAPTCHA. The proposed model performs a shape-wise analysis to test the usability of different shapes and quantifies the model performance on regular as well as irregular shapes in terms of F1 score. The prediction results on two different datasets show that our model prediction does not depend on the regularities of the shape. Further, the model can perform well if the shapes have a better distinction of boundaries, even if the inner contents are similar. The simulation results show the highest F1 score of 0.962 for star shape in one dataset and 0.828 for circle shape in another dataset. The observations prove that SACAPTCHA is vulnerable to object detection attack even after using irregular shapes.
Keywords: Image CAPTCHA; Object Detection; Convolution Neural Network; Security; Deep Learning;.
Detecting LDoS in NB-IoTs by using Metaheuristic-based CNN
by Chi-Yuan Chen, Hsin-Hung Cho, Min-Yan Tsai, Augustine Ho Hann Sii, Han-Chieh Chao
Abstract: Narrow Band Internet of Things (NB-IoT) is a Low Power Wide Area Network (LPWAN) technology and has been included in the 13th edition of the 3rd Generation Partnership Project (3GPP) standard. It also proves that NB-IoT has great potential in the application of the Internet of Things. Therefore, the number of IoT devices will grow explosively, and the IoT network environment will become a hotbed of Botnets. Various IoT devices and platforms will be threatened by Denial-of-Service (DoS) attacks. However, NB-IoT has the characteristics of lower speed (60kpps uplink) so it is not an ideal environment for heavy traffic DoS attacks. Low-Rate Denial-of-Service (LDoS) is the main attack method in the NB-IoT environment. In this environment, the attacker can hide the attack packet for avoiding detection in a data stream with a sufficiently low rate has greatly increased the difficulty of detection. There are already many Convolutional Neural Networks that use artificial intelligence to identify the characteristics of this attack. However, these traditional methods will cause the amount of data to be insufficiently diverse. In order to improve the phenomenon of overfitting, this article uses Simulated Annealing to adjust the weight of the convolutional neural network to achieve better global search. The experimental results show that the method proposed in this article can detect LDoS attacks more effectively.
Keywords: Narrow Band Internet of Things; Intrusion Detection System; Deep Learning; Heuristic Algorithm.
Building a Smart Rehabilitation Delivery Platform with Hybrid Matching Algorithm Based on Long-term Care Plan in Taiwan
by Lun-Ping Hung, Chien-Liang Chen, Ruay-Shiung Chang, Yu-Chen Hsu
Abstract: A smart city uses a new generation of information technology fully deployed in every business to optimize the functionality of the city with innovative ideas and services to improve the citizens quality of life. Smart medical care is a part of urban services. Long-term care is currently an important item urgently needed in the development of smart medical care and after-discharge convalescent medical care is an even more important research agenda. Most of the hospital resources are put into the care of acute patients. To solve the deficiency in medical care budgets and labor forces, the current medical care development trend for most smart cities is going toward dispersive care rather than the concentrated model. This research employs a hybrid matching algorithm to construct a community and home rehabilitation service mechanism so the therapist and the individual case will find the most suitable counterpart by setting the matching parameters. Combining recording of the rehabilitation activities, the feedback from the users, and the therapists recommendations, we make decision suggestions to related medical care agencies. It achieves the goal of optimizing medical care to the patients.
Keywords: Gale-Shapley Algorithm; Home Rehabilitation; Long Term Care; Matching Algorithm; Decentralized Health Services.
Special Issue on: Artificial Intelligence for Edge Computing in the Internet of Things
Application of Artificial Intelligence Technology in CNC System
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
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
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.
Special Issue on: Distributed Secure Computing for Smart Mobile IoT Networks
Privacy-Preserving smart contracts for Fuzzy WordNet Based Document Representation and Clustering using Regularized K-Means Method
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.
A DYNAMIC PRIVACY PRESERVING BASED WSN FRAMEWORK FOR MEDICAL DISEASE PREDICTION
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
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
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.
Design and Implementation of a Mobility Support Adaptive Trickle (MSAT) Algorithm for RPL in Vehicular IoT Networks
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.
Special Issue on: Machine Learning and Deep Learning Methods for the Applications in Ad Hoc and Ubiquitous Computing
An Adaptive Stochastic Central Force Optimization Algorithm for Node Localization in Wireless Sensor Networks
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
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
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
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
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.
Learning Stereo Disparity with Feature Consistency and Condence
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. http://www.cvlibs.net/datasets/kitti/.
Keywords: depth estimation; stereo matching ,confidence measure; feature consistency; multi-distance metrics.
Behavior-based Grey Wolf Optimizer for Wireless Sensor Network Deployment Problem
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.
Special Issue on: Convergence of Soft Computing for Smart Cities
Evaluation of Functional Maturity for a Network Information Service Design & Case Analysis
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
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
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
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
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.
AN UNCERTAINTIES EVALUATION AND ANALYSIS USING QUANTITATIVE TECHNIQUE FOR A SOFTWARE PROJECT
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
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
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
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.