Forthcoming articles

International Journal of Ad Hoc and Ubiquitous Computing

International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC)

These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Ad Hoc and Ubiquitous Computing (39 papers in press)

Regular Issues

  •   Free full-text access Open AccessBilateral multi-issue negotiation of execution contexts by proactive document agents
    ( Free Full-text Access ) CC-BY-NC-ND
    by Jerzy Kaczorek, Bogdan Wiszniewski 
    Abstract: A proactive document can react to its actual environment by autonomously selecting and performing actions integrated into its body and interact with its user. When migrating over a network of execution devices it may encounter diverse execution contexts, each one set up according to temporal characteristics of a receiving device and preferences of its owner. A concept to augment proactive documents with negotiation capability is proposed - to make them responsive to such dynamically changing contexts, and implemented in a system, where they can migrate as attachments to e-mail messages, owing to a dedicated e-mail client capable of handling them. Negotiation is based on a simple game-theoretic mechanism to minimise computation load on execution devices. Four negotiation algorithms are proposed and two of them evaluated in more detail in a series of experiments, when respectively, negotiating parties do not or do have knowledge on past encounters and negotiated contracts.
    Keywords: proactive documents; dynamic execution contexts; mobile agents; ad hoc collaborative processes.
    DOI: 10.1504/IJAHUC.2019.10018357
     
  • A new approach for the recognition of human activities   Order a copy of this article
    by SALIMA SABRI, AlOUI Abdelouhab 
    Abstract: The evaluation of a patient\'s functional ability to perform daily living activities is an essential part of nursing and a powerful predictor of a patient\'s morbidity, especially for the elderly. In this article, we describe the use of a machine learning approach to address the task of recognizing activity in a smart home.We evaluate our approach by comparing it to aMarkov statistical approach and using several performance measures over three datasets. We show how our model achieves signi cantly better recognition performance on certain data sets and with different representations and discretisation methods with an accuracy measurement that exceeds 92%and accuracy of 68%. The experiments also showa signi cant improvement in the learning time which does not exceed one second in the totality of the experiments reducing the complexity of the approach.
    Keywords: Ubiquitous applications; automatic learning; Katz ADL; activity recognition; probabilistic models; wireless sensor network.

  • Data gathering maximization for wireless sensor networks with a mobile sink   Order a copy of this article
    by Zongyuan Han, Tianyun Shi, Xiaojun Lv, Xinchun Jia, Zhongying Wang, Dong Zhou 
    Abstract: Recent studies have demonstrated that the significant benefit can be achieved by using mobile sinks for data gathering in wireless sensor networks (WSNs). However, most of them employed a typical scheme that the mobile collector pauses at the anchor points on its moving tour for a period time to collect the data from nearby sensors. In this paper, the data gathering process is divided into two stages named parking communication (PC) and moving communication (MC). We focus on maximizing the total amount of data gathering by the mobile sink, and formulate the problem as two different optimization models under several constraints for these two different communication stages. Accordingly, dual decomposition and simplex methods are dexterously exploited to derive the optimal communication time and flow rates allocation schemes. Computational results demonstrate the efficiency of the proposed algorithms.
    Keywords: Wireless sensor networks; Mobile data gathering; dual decomposition; nonlinear programming.

  • A Multi-channel Distributed Routing Scheme for Smart Grid Real-time Critical Event Monitoring Applications in the Perspective of Industry 4.0   Order a copy of this article
    by Muhammad Faheem, Rizwan Aslam But, Basit Raza, Md Asri Bin Ngadi, Cagri Gungor 
    Abstract: Recently, the 4th industrial revolution known as Industry 4.0 has paved way for a systematical deployment of the modernized power grid to fulfill the continuously growing energy demand of the 21st century. This industrial revolution in the power sector is known as the smart grid industry (SGI) 4.0. In SGI 4.0, the industrial wireless sensor network (IWSN) and the internet of things (IoT) are envisioned as key promising communication technologies for monitoring various SG applications. In addition, the ubiquitous computing using these communication technologies connects various smart grid devices to carry out monitoring and control logic intelligently from any remote location, worldwide. However, the highly dynamic nature of the SG environments brings several unique challenges to their reliable communication in the smart grid. As a result, the quality of service (QoS) communication is hampering for cognitive radio sensor networks (CRSNs)-based SG applications. Thus, a sophisticated, reliable and QoS-aware multi-hop communications network architecture enabling a real-time exchange of data for various CRSNs-based applications is essential in the SGI 4.0. Hence, this paper proposes a novel channel-aware distributed routing protocol named CARP for CRSNs-based SG applications. In CARP, the proposed cooperative channel assignment mechanism significantly improves the detection reliability and mitigates the noise and congested spectrum bands resulting in reliable and high capacity links for CRSNs-based SG applications. Moreover, to support higher capacity data requirements and to maximize the spectrum utilization, the proposed multi-hop routing mechanism selects a secondary user relay node rich in spectrum information with longer ideal probability at low interference in the network. The extensive simulation results conducted through EstiNet9.0 reveal that the proposed scheme achieves its defined goals compared to existing routing schemes designed for CRSNs-based applications.
    Keywords: Industry 4.0; Smart grid; Cognitive radio; Internet of thing; Wireless sensor networks.

  • Embedded Systems Codesign under Artificial Intelligence perspective: a review   Order a copy of this article
    by Fateh Boutekkouk 
    Abstract: Intelligent Embedded Systems (IES) and their distributed versions, represent a novel and promising generation of embedded systems. IES have the capacity of reasoning about their external environments and adapt their behavior accordingly. Such systems are situated in the intersection of two different branches that are the embedded computing and the intelligent computing. On the other hand, intelligent embedded software (IESo) is becoming a large part of the engineering cost of intelligent embedded systems. IESo can include some Artificial Intelligence-based systems such as expert systems, neural networks and other sophisticated Artificial Intelligence (AI) models to guarantee some important characteristics such as self-learning, self-optimizing and self-adaptation. Despite, the wide spread of such systems, some design challenging issues are arising. Designing a resource constrained software and at the same time intelligent is not a trivial task especially in a real time context. To deal with this dilemma, embedded systems researchers have profited from the progress in Semiconductor technology to develop specific hardware to support well AI models and render the integration of AI with the embedded world a reality.
    Keywords: embedded systems; Codesign; Artificial Intelligence; Intelligent Embedded Systems.

  • Enhancing the Dependability of Wireless Sensor Networks under Flooding Attack: A Machine Learning Perspective   Order a copy of this article
    by Jasminder Kaur Sandhu, Anil Kumar Verma, Prashant Singh Rana 
    Abstract: Wireless Sensor Networks (WSNs) are gaining paramount importance due to its application in real-time monitoring of geographical regions. Its deployment paradigm encompasses myriad of applications in terrestrial, underground and underwater WSNs. Nowadays the paradigm shift is taking place from mobile computing to Internet of Things. Bridging the two technologies result in the development of new applications with security as an essential feature. The discussion of applications without incorporating dependability and security features would be incompetent in a hostile environment. Extensive features such as traffic monitoring, proper resource utilization, security must be added to networks to assure quality transmission of data and enhance dependability. Security deals with detection and mitigation of various attacks. In flooding attack, an attacker repeatedly sends packets at higher rates, thereby causing packet drop and exhaustion of the link capacity. Thus, any further legitimate communication is ignored. To protect the network against the flooding attack, an Intrusion Detection System based on novel randomized and normalized deployment approach is proposed. We have found that Machine Learning models play a significant role in the prediction of Data Flow in WSNs. Network simulator is used to generate the dataset and Machine Learning techniques are used to enhance the dependability against flooding attack.
    Keywords: Wireless Sensor Networks; Dependability; Machine Learning; Data Flow; Flooding.

  • Secure and remote firmware update of cellular IoT micro devices with limited resources   Order a copy of this article
    by Ahmet Cezayirli 
    Abstract: Typically, cellular IoT (Internet of Things) devices are spread over very large areas, usually countrywide or even worldwide. This makes the firmware update operation a troublesome and costly task, and it is usually avoided as long as possible. However, due to some bugs detected in the core software and/or some significant performance enhancements developed after the release of the device, the need for a firmware update becomes inevitable. We propose a new methodology in order to fulfil the firmware update remotely in an easy and secure way for especially the devices with quite limited resources, such as very small RAM and low processing power. We utilize MMS (Multimedia Messaging Service) functions, and send the firmware to the remote cellular IoT devices as encrypted attachment. The preparation steps of the attachment are explained in detail, a handshaking mechanism is established and all are tested on a particular hardware. Some important remarks are also provided, so that major practical aspects of the methodology would be readily available in implementing it on hardware/software architectures of new IoT devices.
    Keywords: Firmware update; cellular IoT; remote device; MMS attachment; code encryption.

  • Using ubiquitous data to improve smartwatches context awareness: A case study applied to develop wearable products   Order a copy of this article
    by Qing-Xing Qu, Yuankun Song 
    Abstract: Most recently, a large amount of data is being generated by various applications on smart devices, which could contain abundant information related to the users daily lives. This study aims to investigate whether the use of ubiquitous data could effectively improve smartwatches context awareness. A prototype of a context-aware system consisting of an Android application and a web application was developed, and an experiment in which 20 participants were recruited to complete several tasks with a smartwatch was conducted. The results showed that the smartwatch with the prototype application successfully decreased the effort cost (operations), task completion time, and error rate by making use of ubiquitous data to capture users real-time contexts and automatically execute corresponding operations. Moreover, users with a proposed context-aware system could have better user experience and more positive affective responses than the non context-aware system. Furthermore, findings in this study could give a better understanding and suggestions to designers when they intend to design a new smartwatch or improve an existing application on a smartwatch with the use of ubiquitous data.
    Keywords: Ubiquitous data; Context awareness; Smartwatch; Location-based service; Context-aware recommendation system; Ubiquitous computing.

  • Incentive Mechanism based Influential Maximization Scheme for Social Cloud Service Networks   Order a copy of this article
    by Sungwook Kim 
    Abstract: An effective interaction between the Social Network Services (SNS) and Cloud Computing (CC) enables the connection of people to the ubiquitous computing universe. The integration of SNS and CC is a technological revolution that presents the future of connectivity and reachability. This paper explores the novel paradigm for future Internet of Things (IoT). Although there have been some studies in social-driven IoT, they merely consider the CC technique to improve service qualities and influences. In this study, we develop a novel social cloud control scheme based on the Incentive Mechanism (IM) approach. To maximize the total SNS system performance, SNSs have been adaptively executed in the CC system while taking into account the social welfare. In addition, we protect each users privacy according to the Differential Privacy (DP) method. Therefore, our study can capture the properties of SNS and CC, and provides an effective solution for the social cloud system while ensuring privacy. Finally, we have conducted extensive simulations. The experimental results demonstrate the efficiency and effectiveness of the proposed scheme for the real world social cloud services. The main contribution of our work lies in the fact that we shed some new light on the relationship among SNS, CC and privacy for the future social cloud system. To the best of our knowledge, there is few previous work about this issue.
    Keywords: Social network services; Social cloud computing; Influential maximization; Incentive mechanism; Differential privacy.

  • Base Station Assisted Relay Selection in Device-to-Device Communications   Order a copy of this article
    by Ushik Shrestha Khwakhali, Prapun Suksompong, Steven Gordon 
    Abstract: The performance of cooperative cellular networks can be enhanced by using social information of users in the network. This paper presents a social-aware midpoint relay selection scheme that increases average throughput of D2D communication between users in the network leveraging social trust among nodes while selecting a relay node. The selection of a relay node is such that it has strong social link with the source and also located near to the midpoint of the distance between the source and the destination. Calculation of average throughput via simulation shows that our proposed scheme outperforms existing hybrid relay selection scheme by Pan and Wang by upto 14%. In addition, our proposed introduction of probe limit and wise selection of social threshold value can further enhance the throughput when social trust among nodes are high.
    Keywords: Cooperative networks; device-to-device (D2D) communications; social networks; social-aware relay selection; data rate; throughput.

  • Cost Efficient Hybrid Techniques for DSM in Smart Homes   Order a copy of this article
    by Rasool Bukhsh, Nadeem Javaid, Majid Iqbal Khan, Zahoor Ali Khan, Imran Usman 
    Abstract: In smart grid, the minimum cost for power consumption is attained by scheduling the appliances load. Shifting the appliances load from on-peak to off-peak time reduces the cost in users bill without compromising the load demand. In this paper, four scheduling algorithms are proposed by hybridizing Elephant Herding Optimization (EHO) with genetic, firefly, bacterial foraging and binary particle swarm optimization algorithms. Extensive simulations are performed to schedule the home appliances using proposed algorithms with three pricing tariffs: day ahead real time pricing, inclined block rates and critical peak pricing. The cost efficiency of optimized power consumption is analyzed. Results show that more cost is reduced with proposed hybrid algorithms as compared to the unscheduled and state-of-the-art algorithms.
    Keywords: Smart Meter; Smart Home; Operation Time Interval; Cost Efficiency; Appliances Schedule.

  • Prefetching and Caching Schemes for IoT Data in Hierarchical Edge Computing Architecture   Order a copy of this article
    by Tzu-Jung Chen, Jang Ping Sheu, Yung Ching Kuo 
    Abstract: With the growing number of the Internet of Things (IoTs) devices and mobile devices, the data traffic produced by these devices causes high network load, and these devices endure long access latencies to communicate with the cloud. The emergence of Edge Computing, caching and prefetching at the edge of the networks are promising solutions to these problems. Therefore, we propose caching and prefetching schemes for the Internet of Things data based on a four-tier hierarchical Edge Computing architecture, and our goal is to reduce data access latency. In our architecture, caches are deployed at 1st- and 2nd-tier edge nodes and the caching scheme is designed especially for the IoTs data. We analyze the relations of all users access requests and prefetch popular data among all users to the cache based on the predictions of user preferences. The experimental results show that the proposed schemes can effectively reduce user access latencies. Comparing with the average latencies under Cloud Computing architecture, the rate of improvement is up to 95%.
    Keywords: cache; edge computing; association rule mining; Internet of Things; prefetching.

  • A Note on Cloud Computing Security   Order a copy of this article
    by Deepak Garg, Jagpreet Sidhu 
    Abstract: Cloud computing has become an emerging technology which has huge impact. Existing security algorithms cannot meet the needs of cloud computing users as security issues have become increasingly prominent, becoming an important factor restricting the development of cloud computing. Reports reveals that organizations have experienced various attacks in past year. Research community has immensely focused on developing and optimizing security techniques to overcome this hurdle in cloud. Due to huge literature it becomes difficult to comprehend overall structure and advancements. This domain needs an analytic approach to completely understand the problem domain that is why this article represents same in specific context of security. Further a notion on security issues in cloud computing with respect to vulnerabilities, threats and attacks is discussed. Paper also proposed two research evaluation parameters that are effective citation and effective impact factor for better appreciation. Article also analyses patterns, trends and other factors for directing research activities. Finally, a global research trends on security domain are evaluated and analysed.
    Keywords: Cloud Computing; Security; Survey; Effective Citation (EC); Effective Impact Factor (EIF); Vulnerabilities; Threats; Attacks.

  • Activity Recognition Approach based on Spatial-Temporal constraints for Aged-care in Smart Home   Order a copy of this article
    by Haibao Chen, Shenghui Zhao, Cuijuan Shang, Guilin Chen, Chih-Yung Chang 
    Abstract: Activity recognition plays an important role in smart homes for aged-care. In this paper, we formulate the problem of activity recognition and propose a new method based on spatial-temporal constraints to carry out activity recognition, which consists of five phases: Initialization, Segmentation, Sensor Data Representation, Activity Exploration as well as Activity Identification. Besides, we analyze the time complexity and space complexity of our approach in theory.To evaluate our approach, we carried out experiments on real dataset from Wireless & Mobile Network Laboratory, Tamkang University. The experimental results demonstrate an improvement of 5.6% in the accuracy on average of recognized activities in comparison to the method of support vector machine (SVM).
    Keywords: Activity recognition; smart home; wireless sensor network;Aged-care.

  • A Novel Reliability based High Performance Decoding Algorithm for Short Block Length Turbo Codes   Order a copy of this article
    by Salija P, B. Yamuna, T.R. Padmanabhan, Deepak Mishra 
    Abstract: Satellite communication applications use Turbo codes as the standard error correcting code due to its Shannons capacity approaching performance. However short block length Turbo codes exhibit significant performance degradation. This is a limiting factor in applications like transmissions over the telecommand links for satellite communications that involve the use of short length Turbo codes. A novel reliability based Turbo decoding algorithm that addresses the performance improvement of short block length Turbo codes, is being proposed in this paper. Simulation results show a coding gain of 2.45 dB at BER of 10-3 for short length codewords. The proposed decoding algorithm has low computational complexity compared to the conventional iterative decoding algorithm. The relatively lower computational complexity and the conspicuous improvement in BER performance make the method quite attractive.
    Keywords: Reliability; Turbo codes; Bit Error Rate; Decoding.

  • An adaptive data rate algorithm for improving energy eciency for multi-gateway LoRaWANs   Order a copy of this article
    by LIN-HENG CHANG, Yi Chang, Chih-Kae Guan, Tong-Ying Juang, Wen-Chang Fang 
    Abstract: The need of the low power, long range, and low cost connectivity for satisfyingrnthe requirement of IoT (Internet of Thing) applications for smart city is leading the emergence of Low Power Wide Area (LPWA) networking technologies. The resource efficiency plays an important role in realistic application, LoRaWAN allows the static end devices to individually adapt and optimize the data rates and the transmission power, which is referred to as Adaptive Data Rate (ADR) problem. In this paper, we develop a new ADR algorithm for multi-gateway LoRaWAN environment to quickly choose the appropriate LoRa transmission parameter for independent LoRa end device based on LoRaWAN specification. To successfully execute the ADR algorithm, we specially rewrite the firmware of Semtech's SX1276 transceivers to implement our proposed ADR algorithm over multi-gateway LoRaWAN system, including end device, multi-gateways and networkrnserver. Our ADR algorithm utilizes the radio link quality, including RSSI, SNR, and packet reception ratio (PRR), from multi-gateways, and the network server determines the appropriate LoRa transmission parameter. Finally, the experimental results illustrate that the proposed algorithm improves the energy efficiency, the effective bit rate, and the battery life of the end-device.
    Keywords: LPWA; LoRaWAN; adaptive data rate (ADR); radio link quality; multi-gateways.

  • A Novel RPL-based Multicast Routing Mechanism for Wireless Sensor Networks   Order a copy of this article
    by Ren-Hung Hwang, Min-Chun Peng, Cheng-Yu Wu, Satheesh Abimannan 
    Abstract: The IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) provides the multipoint-to-point, point-to-multipoint, and point-to-point routing mechanisms for wireless sensor networks. However, for point-to-multipoint, i.e., multicast from the sink node, the RPL poses only the principles of routing operation, and the practical mechanism is still an open issue. Two practical routing mechanisms have been proposed in the literature, namely, Multicast Protocol for Low-Power and Lossy Networks (MPL), and Enhanced Stateless Multicast Forwarding with RPL (ESMRF). The MPL aims to be a highly reliable multicast mechanism, but it also incurs serious delays. In contrast, ESMRF establishes simple rules for transmitting multicast packets, however, it lacks reliability. In addition, both mechanisms do not take energy efficiency into consideration. In this paper, we propose a Wireless Shortest Path Heuristic (W-SPH) mechanism for constructing a multicast tree which builds effective routing topology for multicast traffic, and as a consequence, yields higher energy efficiency. We also introduce the Proactive Multicast Forwarding with RPL (PMFR), a multicast mechanism with proactive retransmission and early relay, to increase the reliability of the multicast. Our simulation results demonstrate that the PMFR can achieve a significantly higher delivery ratio and a 50% lower delay than both the MPL and ESMRF. The PMFR also reduces about 40% energy consumption as compared to the MPL. While considering the energy efficiency, that is the energy consumed per successful packet delivery, the PMFR also yields higher energy efficiency than ESMRF in most cases.
    Keywords: RPL; Multicast; Wireless Sensor Networks.

  • Performance Evaluation of Main Approaches for Determining Optimal Number of Clusters in Wireless Sensor Networks   Order a copy of this article
    by Meryem Bochra BENMAHDI, Mohamed LEHSAINI 
    Abstract: Among important issues in the current energy-efficient routing protocols for wireless sensor networks (WSNs) based on clustering approaches are how to determine the optimal number of clusters, how to generate clusters and how select cluster-heads to improve WSNs performance. These approaches aim to reduce energy consumption of the WSN nodes and to extend the network lifetime. This paper reviews and compares the performance of three clustering techniques for determining number of clusters: Rule of Thumb, Elbow, and Silhouette. Each of these techniques involves a distributed K-means approach to generate clusters. Moreover, we compare these three clustering methods with LEACH (Low Energy Adaptive Clustering Hierarchy), Imp_LEACH and MODLEACH in terms of network lifetime, energy consumption and the number of packets sent to the base station (BS). The results obtained indicate that Rule of Thumb method provides better performance compared to other clustering techniques in terms of energy consumption and network lifetime.
    Keywords: Clustering; K-means; LEACH; Silhouette; Elbow; Rule of Thumb; WSNs.

  • Towards Effective Fault Detection in Heterogeneous Wireless Sensor Networks   Order a copy of this article
    by Mohammad Masdari 
    Abstract: Accurate detection of the faulty nodes and transmitting true status of monitoring environment is one of the crucial challenges in Wireless Sensor Networks (WSNs). This paper puts forward a novel unsupervised faulty node detection algorithm to improve the detection accuracy in the 2D and 3D heterogeneous WSNs. In this approach, the current status of each node is predicted by using the ARIMA methods and factors such as distance, the amount of coverage ratio. Moreover, the nodes status in the previous rounds is utilized to weight the measured values of the neighboring nodes. By using the proposed distributed algorithm, each sensor node can more correctly recognize its status in the presence of the events such as fire and transient faults. Extensive simulations indicate the effectiveness of this algorithm in reducing the false positive problem and improving the detection accuracy in different scenarios considered for a 2D and a 3D heterogeneous WSN.
    Keywords: WSN; Heterogeneous; Fault Detection; Voting; False Positives; True Negatives; ARIMA.

  • Multimedia Quality Evaluation Model in Streaming Service Environments   Order a copy of this article
    by Ja-Ok Koo, Choi Young-June, Zhetao Li, Tingrui Pei, Zelalem Jembre Yalew 
    Abstract: Recently, mobile broadcast services such as digital media broadcasting (DMB), multimedia streaming services, and IPTV, have generated a rapid increase in multimedia services via the Internet compared to traditional cable or satellite delivery. Multimedia via the Internet is consumed under different network conditions and through various devices. The focus is now on clearly defining, measuring, and evaluating elements related to the service quality over the Internet. In this study, we propose a multimedia quality measurement model that evaluates the quality of multimedia from a consumer point of view. First, we select the parameters that affect the quality of multimedia at both the application and network levels, and then use these parameters to produce multiple altered versions of an original video. All videos are then uploaded to the popular streaming site YouTube and are viewed by study participants on multiple devices. For each video and device, participants provide a mean opinion score (MOS). For analysis, we first determine the effect of parameters on videos and devices. After removing those parameters that do not affect the video, we apply multiple regression on the MOS dataset collected from the subjects to devise a model for multimedia quality assessment, which we call MOS multimedia streaming (MOS$_{MS}$). Finally, we use half of the collected data as a training set and the other half as a test set to validate the new model. The proposed model can help content providers to understand how their multimedia is perceived by consumers under different network conditions and through various devices.
    Keywords: MOS;multimedia;quality assessment;mobile devices;streaming services.

  • A Novel Deep Learning Model for Detection of Denial of Service Attacks in HTTP Traffic over Internet   Order a copy of this article
    by V. Punitha, C. Mala, Narendran Rajagopalan 
    Abstract: The technological advancements in Internet and mobile communications bring new dimension to the usage of internet applications and services. The accessibility to the enhanced services is intentionally blocked by the denial of service attacks. This paper proposes a novel deep learning classification model to detect the denial of service attacks in application layer for different network environments, such as wired network, adhoc network and mobile adhoc network. The simulation results illustrate that the performance of the proposed deep learning model is proficiently improved compared to existing bio-inspired and machine learning models in terms of detection accuracy and classification metrics.
    Keywords: Network traffic classification; Denial of Service attack; Application layer DoS attack; Slow rate DoS attacks; Deep learning technique.

  • Securely Solving Privacy Preserving Minimum Spanning Tree Algorithms in Semi-honest Model   Order a copy of this article
    by KOTESWARA R.A.O. CH, Kunwar Singh 
    Abstract: In 1982, Andrew Yao introduced secure two-party computation for the so-called millionaire's problem. The problem is about two millionaires Alice and Bob, interested to determine who is wealthier without revealing their actual private property values. Goldreich generalized the secure two-party computation and formalized the secure multi-party computation. Suppose two telephone companies wish to merge to provide better services to end users. Each company has a cost function for connecting any pair of houses. They want to connect every house with minimum cost in merged company. Mathematically, given two graphs G1, G2 they want to compute MST(min(G1, G2)). Before merging both companies want to know whether merging will bene fit them or not without revealing cost function for any pair of houses. Based on the secure multi-party computation paradigm, we propose new algorithms for privacy-preserving computation of minimum spanning tree. We also investigate how to solve the classical minimum spanning tree problem with the help of Arithmetic Black-Box operations. We propose two more algorithms for solving MST in semi-honest model with secure Arithmetic Block-Box in the secretly shared data environment. To the best our knowledge this is the fi rst time the MST problems are addressed with the help of secure ABB. Our protocols offer perfect security against semi-honest adversaries in secretly shared data environment of multi-party computation.
    Keywords: Secure multiparty computation; privacy; arithmetic block-box; and minimum spanning tree.

  • Refining Channel and Power Allocation for Green Device-to-Device Communications   Order a copy of this article
    by Chih-Shun Hsu 
    Abstract: The Device-to-Device (D2D) communication is considered as one of the possible communication mode in 5G communication systems. Most of the existing researches for D2D communications try to achieve the minimum total transmission power of D2D user devices (DUEs) by performing channel assignment first, and then performing power control according to the QoS requirement of each DUE. However, after power control, the transmission powers and interferences of DUEs have dropped and the qualities of channels have changed, hence there is room to further reduce the transmission power of DUEs. To improve the existing works, we propose three novel channel and power allocation schemes to further reduce the total transmission power of all DUEs. Since the total transmission power drops after each round of power control, the first approach is to perform the power control scheme for several rounds until the dropping transmission power is less than a threshold. The second approach is to perform channel and power reallocation for several rounds until the dropping transmission power is less than a threshold. The channel of each DUE is reallocated after the first round of power allocation to find if there is any channel reallocation can further reduce the transmission power of DUEs and still satisfy the QoS requirement. To reduce the computation cost, the greedy strategy is adopted, which reallocates the channel and adjusts the transmission power of the DUE whose transmission power is the greatest first so as to further reduce the total transmission power of all DUEs. The third approach is a hybrid of the first and the second approaches. Note that, the channel and power are not really allocated to the DUEs until the final refining allocation results are calculated by the base station. The trade-off among transmission power, throughput, and computation cost are discussed through extensive simulations. Simulation results justify the energy efficiency of the proposed refining schemes.
    Keywords: D2D communication; Channel allocation; Power allocation.

  • ECG Patch Monitor : A telemedicine system for remote monitoring and assisting patients during a heart attack   Order a copy of this article
    by Tesnim Charrad, Kaouther Nouira, Ahmed Ferchichi 
    Abstract: This paper deals with real time ubiquitous healthcare monitoring systems. The goal is not just to supply a medical service in hospitals and medical offices, but also to provide a reliable service during the normal daily life. In this purpose, a monitoring system for patients suffering from heart disease is designed. This system is called ECG Patch Monitor which enables cardiac data collection and analysis in real-world environments such as at work or at home. It is an adhesive device attached directly to the skin. ECG Patch Monitor aims to prevent heart attacks or any other form of heart failure. It uses a real time anomaly detection algorithm. Once an anomaly is detected, the device alerts the healthcare center. ECG Patch Monitor is remotely controlled by the healthcare center through a platform. The ECG Patch Monitor allows doctors to call the patient in emergency to verify his health condition. In the extreme case, the doctor can remotely perform an electroshock or a drug injection. The doctor can locate the patient and send an ambulance.
    Keywords: ECG Patch Monitor; Ubiquitous Systems; Healthcare Monitoring; Systems Architecture; Health 4.0.

  • SA-RPL: A Scheduling-Aware Forwarding Mechanism in RPL/TSCH-operated Networks   Order a copy of this article
    by Saeid Afshari, Mohammad Nassiri, Reza Mohammadi 
    Abstract: Wireless sensor networks have shown to be a promising technology for industrial automation in which continuous monitoring is a critical requirement. Deploying an energy-aware sensor network permits increasing the network lifetime and prolonging the monitoring operation. IEEE 802.15.4e and RPL have been used as de-facto protocols at the access and network layer in low power and low-rate wireless networks. Specifically, the Time Synchronized Channel Hopping (TSCH) of 802.15.4e has been designed to provide a reliable access method in low power and lossy networks. More importantly, the combination of TSCH and RPL facilitates providing load-balancing together with energy-saving in such networks. This paper proposes Schedule Aware RPL (SA-RPL) which aims at prolonging the network lifetime while improving load balancing. It periodically collects scheduling matrix information form TSCH to compute a new measure for selecting the next hop at the network layer. More precisely, the parent with minimum number of occupied cells is more likely to be chosen as the preferred parent. To evaluate the performance of SA-RPL, we modified a distributed management scheme already developed in NS2 simulator. Simulation results show that SA-RPL, compared with other methods, prolongs the network lifetime up to two times and achieves a more uniform energy consumption distribution without decreasing other performance metrics.
    Keywords: Routing ; Scheduling; RPL; TSCH; Industrial Automation; Energy Efficiency; Load Balancing.

  • A secure three factor based fully anonymous user authentication protocol for multi-server environment   Order a copy of this article
    by Vinod Mahor, Padmavathi R, Santanu Chatterjee, Sanshray Kumar Dewangan, Manish Kumar 
    Abstract: A single sign-on authentication scheme is required protocol in multi server environment. Recently an authentication protocolrnbased on Lagrange interpolation polynomial to satisfy multi server environment with low computational and communication costrnis proposed. In this paper we have analyzed the above scheme and show that their scheme is vulnerable to various attacks likernInsider attack, server impersonation attack, user impersonation attack and stolen smart card attack. We also show that their scheme fails to provide server anonymity, user revocation in case smart card is lost/stolen or users authentication parameters are revealed. We have also proposed enhanced multi-server authentication protocol using biometric based smart card and Lagrange interpolation which is more secure. The proposed protocol is analysed using BAN logic to show that the proposed protocol provides secure authentication. In addition, we have simulated our scheme using widely accepted and used AVISPA tool to prove that our scheme is secure against passive and active attacks. The proposed protocol provides high security and anonymity along with low communication and computational cost and various security functions.
    Keywords: Authentication; Multi-server authentication; security; smart card; lagrange interpolation; single sign-on; AVISPA; BAN logic.

  • A Swarm Intelligence based Quality of Service aware Resource Allocation for Clouds   Order a copy of this article
    by Ashok Kumar 
    Abstract: The growing popularity of Cloud computing results in very large data centers around the world with vast amount of energy requirements and CO2 emissions. These large sized data centers demand efficient management of resources to conserve energy while satisfying Quality of Service (QoS) requirements of the end users. In this paper, a QoS-aware resource allocation approach using ant colony optimization is proposed. The proposed approach is implemented in CloudSim and comprehensive performance analysis shows upto 12% energy saving. rn
    Keywords: Energy Efficiency;Resource Utilization;Resource Allocation;Swarm Intelligence; Quality of Service.

  • QoS-aware Flow Scheduling for Energy-Efficient Cloud Data Center Network   Order a copy of this article
    by Songyun Wang, Xiaoda Zhang, Jiabin Yuan, Zhuzhong Qian, Xin Li, Ilsun You 
    Abstract: It is highly valuable to achieve energy-efficient cloud data centers, which always act as the basic infrastructures. This paper thus aims at reducing the energy consumption of network devices in cloud data centers by flexible flow scheduling. For such an aim, it is necessary to guarantee the flow-level performance, which is a critical requirement for QoS (Quality of Service) in production data centers. Hence, this paper takes both energy reduction and QoS into account for flow scheduling, and then proposes a three-phase framework to control the energy consumption for cloud data center network (DCN) while guaranteeing flow-level performance. The proposed framework consists of three parts: flow rate estimation, flow path selection and flow rate allocation. The first part is done by exploiting TCP properties, which aims to guarantee flow-level performance. The flow path selection, based on the flow rate estimation, determines the switch states (on or off) to satisfy the rate requirements while accomplishing traffic proportional DCN energy consumption. Finally, we allocate the flow rates the links on the selected paths. From extensive simulations, it is shown that our solution could not only reduce about 20% of energy on average than the case with all switches on, but also maintain good flow-level performance, stability and fault tolerance simultaneously.
    Keywords: Cloud Computing; Data Center Network; Energy Efficient; Flow Scheduling; Flow-level Performance.

  • Room measurement tool combining ultrasonic and inertial sensors in smartphones   Order a copy of this article
    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; ultrasonic sensor.
    DOI: 10.1504/IJAHUC.2019.10024705
     
  • Throughput of cooperative HARQ protocols for underlay cognitive radio networks using adaptive and fixed transmit power   Order a copy of this article
    by Nadhir Ben Halima, Hatem Boujemâa 
    Abstract: In this paper, we study theoretically and by simulations the throughput of cooperative hybrid automatic repeat request (HARQ) protocols for underlay cognitive radio networks (CRN). Both fixed and adaptive power transmission are studied. Different relay selection techniques with amplify and forward (AF) and decode and forward (DF) relaying are investigated. For fixed transmit power, some relays are not available due to interference constraints. The best relay is selected among the available ones. For adaptive transmit power, all relays adapt their power so that interference to primary receiver is always lower than a predefined threshold T. In this case, all relays will be available for retransmitting the secondary source packet. Both average interference power (AIP) and peak interference power constraints (PIP) are studied. We also analyse the effect of primary interference on secondary throughput.
    Keywords: CRN; cognitive radio networks; relay selection; HARQ; hybrid automatic repeat request.
    DOI: 10.1504/IJAHUC.2018.10016902
     
  • GOOSE: goal oriented orchestration for smart environments   Order a copy of this article
    by Vincenzo Catania, Gaetano Carmelo La Delfa, Salvatore Monteleone, Davide Patti, Daniela Ventura, Giuseppe La Torre 
    Abstract: Smart environment scenarios are characterised by the presence of users, with different needs and preferences, and everyday life objects exploited to meet the expectations of users themselves. Connecting objects to the internet and making them accessible from remote is not sufficient to make an environment 'smart' since such ecosystems should also be able to enable context-sensitive actions along with a management of the interaction between objects and users. In this work, we propose GOOSE, a platform which aimed at interpreting users goals expressed in natural language in order to generate, select, and safely enforce a set of plans to be executed to fulfill target goals. After highlighting the main challenges affecting typical machine-to-machine (M2M) communication scenarios, we show how the use of a semantic reasoner can be used to allow the composition of plans consisting of sequences of services to be called on the smart environment objects. Finally, we address the issue of secure communications between platform and objects, and the management of potentially inconsistent goals.
    Keywords: machine-to-machine; M2M; RESTful services; goal-oriented architecture; user-object interactions; semantic descriptions; indoor localisation.
    DOI: 10.1504/IJAHUC.2019.10024678
     
  • H-LPS: a hybrid approach for user's location privacy in location-based services   Order a copy of this article
    by Sonia Sabir, Inayat Ali, Eraj Khan 
    Abstract: Applications providing location-based services (LBS) have gained much attention and importance with the notion of the internet of things (IoT). Users are utilising LBS by providing their location information to third-party service providers. However, location data is very sensitive that can reveal user's private life to adversaries. The passive and pervasive data collection in IoT upsurges serious issues of location privacy. Privacy-preserving location-based services is a hot research topic. Many anonymisation and obfuscation techniques have proposed to overcome location privacy issue. In this paper, we have proposed a hybrid location privacy scheme (H-LPS), a hybrid scheme mainly based on obfuscation and collaboration for protecting user's location privacy while using location-based services. Obfuscation naturally degrades the quality of service, but provides more privacy as compared to anonymisation. Our proposed scheme, H-LPS, provides a very high level privacy yet providing a good accuracy for most of the users. Privacy level and service accuracy of H-LPS is compared with state of the art location privacy schemes and it is showed that H-LPS could be a candidate solution for preserving user's location privacy in location-based services.
    Keywords: location privacy; location-based services; LBS; obfuscation; anonymisation; mobile location privacy.
    DOI: 10.1504/IJAHUC.2019.10024680
     
  • Type-2 fuzzy logic based energy-efficient transceiver resource schedule in multi-radio WSNs   Order a copy of this article
    by Wei Peng, Dongyan Chen, Wenhui Sun, Chengdong Li, Guiqing Zhang 
    Abstract: Inspired by the type-2 fuzzy logic (T2FL), the energy-efficient radio resource schedule mechanism is proposed to deal with the multi-radio resources management problem in MRWSNs. Firstly, three important models, including channel quality estimation (CQE) model, radio energy dissipation model and residual energy model, are established and analysed. Then using the three important factors as the input variables, the selection probability of each transceiver as output variable, the T2FL based energy-efficient radio resource schedule scheme is designed. Moreover, both experimental and simulation results indicate that the proposed schedule mechanism can effectively improve the network performance, such as throughput, energy efficiency, the success rate of data transmission, etc.
    Keywords: multi radio resource; WSN; wireless sensor network; type-2 fuzzy; energy-efficient; transceiver schedule.
    DOI: 10.1504/IJAHUC.2019.10024679
     

Special Issue on: ICSSE 2018 Innovative Services and Emerging Technologies of Smart Cities

  • Sensor-Based Detection of Abnormal Events for Elderly People Using Deep Belief Networks   Order a copy of this article
    by Yo-Ping Huang, Haobijam Basanta, Hung-Chou Kuo, Hsin-Ta Chiao 
    Abstract: Various technological developments in home-care systems have allowed elderly people to live independently without compromising their safety. A pilot study employing deep learning algorithm was conducted to study the daily routines of elderly people. We monitored unsupervised, diverse daily activities of elderly people such as household chores, sleeping, cooking, cleaning, using the bathroom, watching television, and meditating. The activities were monitored to track humanenvironment interactions by using motion sensors, actuators, and surveillance systems that were mounted inside living rooms, bedrooms, and kitchens and on bathroom doorways to detect safety hazards in the environment for elderly people. Such collected data were used in deep belief networks to ascertain and identify activities that are related to various health and self-care problems. Simulation results show that the proposed system outperforms the support vector machines in terms of F1 score and accuracy in identifying daily activities.
    Keywords: sensors; deep belief network; daily activities; abnormal events.

  • Random Forest, Gradient Boosted Machines and Deep Neural Network for Stock Price Forecasting: A Comparative Analysis on South Korean Companies   Order a copy of this article
    by Sanjiban Sekhar Roy, Rohan Chopra, Kun Chang Lee, Concetto Spampinato, Behnam Mohammadi-ivatloo 
    Abstract: Predicting the final closing price of a stock is a challenging task and even modest improvements in predictive outcome can be very profitable. Many computer-aided techniques based on either machine learning or statistical models have been adopted to estimate price changes in the stock market. One of the major challenges with traditional machine learning models is the feature extraction process. Indeed, extracting relevant features from data and identifying hidden nonlinear relationships without relying on econometric assumptions and human expertise is extremely complex and makes deep learning particularly attractive. In this paper, we propose a deep neural network-based approach to predict if the stock price will increase by 25% for the following year, same quarter or not. We also compare our deep learning method against shallow approaches, random forest and gradient boosted machines. To test the proposed methods, KIS-VALUE database consisting of the Korea Composite Stock Price Index (KOSPI) of companies for the period 2007 to 2015 was considered. All the methods yielded satisfactory performance, namely, deep neural network achieved an AUC of 0.806, random forest, 0.850 and gradient boosted machine, 0.853. Interestingly, deep learning achieved the lowest performance, which although marginal can be explained by the lack of enough training data.
    Keywords: Deep neural network; random forest; gradient boosted machine; KOSPI; financial markets.

  • Task Allocation for Crowd Sensing Based on Submodular Optimization   Order a copy of this article
    by Zhiyong Yu, Weiping Zhu, Longkun Guo, Wenzhong Guo, Zhiwen Yu 
    Abstract: Crowd sensing is becoming a hot topic because of its advantages in the field of smart city. In crowd sensing, task allocation is a primary issue which determines the data quality and the cost of sensing tasks. In this paper, on the basis of the sweep covering theory, a novel coverage metric called t-sweep k-coverage is defined, and two symmetric problems are formulated: Minimize user set under fixed coverage rate constraint (MinP) (MinP) and Maximize coverage rate under user set constraint (MaxC). Then based on their submodular property, two task allocation methods are proposed, namely double greedy (dGreedy) and submodular optimization (SMO). The two methods are compared with the baseline method linear programming (LP) in experiments. The results show that, regardless of the size of the problems, both two methods can obtain the appropriate participant set, and overcoming the shortcomings of linear programming.
    Keywords: crowd sensing; task allocation; participant selection; submodular optimization.

Special Issue on: PAAP'18 Architectures, Algorithms, Securities and Programming for Big Data Processing and Deep Learning

  • An Optimal Scheduling Algorithm for DASH Video Streaming over Variable-Bit-Rate Networks   Order a copy of this article
    by Shin-Hung Chang, Kuan-Jen Wang, Jan-Ming Ho 
    Abstract: With the rapid increase of network bandwidth, it becomes popular for people to watch video over the Internet. To cope with dynamic and heterogeneous network condition, video-service providers, e.g., YouTube and Netflix, use DASH streaming technology (Dynamic Adaptive Streaming over HTTP) to serve contents to their users. A DASH server dynamically adjusts rate of streaming to a client according to the clients available bandwidth. In order to evaluate users watching video quality, the QoE (Quality of Experience) is an index of users subjective opinions. Under the limited bandwidth constraint, previous scheduling algorithms usually arrange as many as possible highest-resolution segments in a DASH streaming service for improving users QoE. However, scheduling the most highest-resolution segments in a DASH streaming service will lead to arrange many lowest-resolution segments in this streaming service simultaneously. In this paper, we address that improving the whole video streaming quality and users QoE should start from the idea of scheduling minimum number of low-resolution segments iteratively. Furthermore, we define an M-Low optimization problem and propose a novel M-Low (Minimization of Low-resolution) scheduling algorithm, which adjusts the video resolution and optimizes the QoE indices in a DASH streaming service. We refer to the following QoE measures: minimizing the number of video playback freeze count, minimizing low-resolution segments, minimizing the resolution-switching count, and smoothing the video during resolution changes. We definitely prove that the playback schedule generated by the M-Low algorithm is M-Low optimal. Moreover, we show through simulations that our proposed M-Low scheduling achieves a higher QoE measures than those of previously published algorithms.
    Keywords: Dynamic adaptive streaming over HTTP (DASH); quality of experience (QoE); Internet TV (IPTV); Variable bit rate (VBR).

  • Maintaining Data Integrity in Cloud Systems through Version Management   Order a copy of this article
    by Tsozen Yeh, Yipin Wang, Yiming Tu 
    Abstract: As the era of the Big Data arrives, the enormous amount of data collected has far exceeded what traditional computer systems can appropriately handle and process. Accordingly, cloud computing has been largely used to facilitate the processing of Big Data. Often individual data files contain data inserted at different time, which means they have chronological versions of contents since their creation. The integrity of data stored on cloud systems could be compromised by hardware failure or human mistakes. It will be desirable if the cloud system could efficiently maintain versions of data files, in particular for those important ones, to help users examine and process their prior versions when errors occur. Hadoop is one of the most popular cloud systems used nowadays. Unfortunately, it does not support efficient schemes to conduct version management for files. Previously, we improved Hadoop by realizing autonomous snapshot and extra duplication for files covered in snapshots. In this paper, we report our efforts to design and implement version management for files in snapshots. With the help of autonomous snapshot and extra file duplication, version management can further maintain data integrity for important files contained in snapshots.
    Keywords: big data; data integrity; cloud computing; Hadoop; HDFS;.

  • Personalized Gesture Recognition based on Tri-axis Accelerometer Using Gabor Filters   Order a copy of this article
    by Zhenyu He 
    Abstract: Human gesture is one of the most important ingredients of context information. In this paper, a novel gesture recognition framework based on tri-axis accelerometer mounted on a cell phone is proposed. Since the length of acceleration signals is variable according to different gestures and different subjects input speed, most of recognition algorithms cannot be used. To solve this problem, we propose a novel resampling method by combining decimation and interpolation. After that, we propose 1D Gabor coefficients of acceleration signals as features. However, the dimensionality of Gabor feature space used in gesture recognition is very high. We adopt Boosting and a two-stage method PCA plus LDA to select and compress the Gabor feature. The Classifier we used is fast Multi-class Support Vector Machine. The average recognition results of seventeen complex gestures using the proposed Gabor feature are effective. The performance of experimental results shows that gesture-based interaction can be used as a novel human computer interaction for consumer electronics and mobile devices.
    Keywords: gesture recognition; tri-axis accelerometer; Gabor filter;Boosting; PCA; LDA.