Forthcoming articles


International Journal of Sensor Networks


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International Journal of Sensor Networks (34 papers in press)


Regular Issues


  • Recent Advances in Wireless Sensor Networks with Environmental Energy Harvesting
    by Lei Shu, Wanjiun Liao, Jaime Lloret, Lei Wang 
    Keywords: .

  • Cooperative Stackelberg Game Based Optimal Allocation And Pricing Mechanism In Crowdsensing   Order a copy of this article
    by Chunchi Liu, Rong Du, Shengling Wang, Rongfang Bie 
    Abstract: Crowdsensing has been earning increasing credits for effectively integrating the mass sensors to achieve significant tasks that one single sensor cannot imagine. However in many existing works in this field, some key information of the participants is incomplete to each other, hence causing the non-optimality result. Noticing that a potential cooperation between the players, we propose the cooperative Stackelberg game based optimal task allocation and pricing mechanism in a crowdsensing scenario. Aiming at different optimizing criteria, we propose two optimal Stackelberg games that are either with no budget constraint (No-Budget OpSt Game) or with budget constraint (Budget- Feasible OpSt Game). Analysis of their corresponding Stackelberg Equilibrium is then presented. Lastly, we perform extensive simulations to test the impact of the parameters on our model. Results of our two proposed games are progressively compared to show their optimizations in their respective criteria.
    Keywords: Crowdsensing; Stackelberg game; Optimal Mechanism; Budget Feasible; KKT conditions.
    DOI: 10.1504/IJSNET.2017.10007410
  • Maximizing Influence in Sensed Heterogenous Social Network with Privacy Preservation   Order a copy of this article
    by Meng Han, Qilong Han, Lijie Li, Ji Li, Yingshu Li 
    Abstract: Maximizing influence in a specific scenario to improve the performance of marketing has a significant impact on targeted advertisements and viral product promotion, which has became one of the most fundamental problems in social data analysis. Many attentions have been paid to extensively analyze the influence in social data. However, most of the existing works, unfortunately, neglect the fact that the sensed location data in the cyber physical world could also play an important role in the influence prorogation process. Furthermore, even though a few works consider a little sensed information to enhance the influence maximization, the privacy protection issue of the sensed data (location, social relationship) was directly exposed to the public. This paper considers the problem of maximizing influence in both sensed location Data and Online Social Data with privacy preserving. We firstly merge both the sensed location data from cyber-physical network and relationship data from online social network into a unified framework with one novel model, then an efficient algorithm to solve the influence maximization problem are provided within our resolution. Besides, our model could not only support our influence maximization problem, but also support other applications related to both sensed location data and online social data. Furthermore, our privacy-preserving mechanism could protect the sensitive location and link information during the whole process of data analysis. Real life datasets are empirically tested with our framework and demonstrate the power of sensed and online data combination to influence maximization.rnThe experiment result also suggests that our framework is outperforming most of the existing alternative resolutions and succeeds in preserving privacy while other resolutions not.
    Keywords: Sensed Data; Location; Social Network; Data Privacy; Influence Maximization.
    DOI: 10.1504/IJSNET.2017.10007412
  • A Novel Task Recommendation Model for Mobile Crowdsourcing Systems   Order a copy of this article
    by Yingjie Wang, Xiangrong Tong, Kai Wang, Baode Fan, Zaobo He, Guisheng Yin 
    Abstract: With the developments of sensors in mobile devices, mobile crowdsourcing systems are attracting more and more attention. How to recommend user-preferred and trustful tasks for users is an important issue to improve efficiency of mobile crowdsourcing systems. This paper proposes a novel task recommendation model for mobile crowdsourcing systems. Considering both user similarity and task similarity, the recommendation probabilities of tasks are derived.\r\nBased on dwell-time, the latent recommendation probability of tasks can be predicted. In addition, trust of tasks is obtained based on their reputations and participation frequencies. Finally, we perform comprehensive experiments towards the Amazon metadata and YOOCHOOSE data sets to verify the effectiveness of the proposed recommendation model.
    Keywords: Mobile crowdsourcing systems; Recommendation model; Similarity; Dwell-time; Trust.
    DOI: 10.1504/IJSNET.2017.10007414
  • Accuracy-aware Data Collection in Wireless Sensor Networks   Order a copy of this article
    by Ran Bi, Xu Zheng, Guozhen Tan 
    Abstract: Data collection is a fundamental task in Wireless Sensor Networks. In many practical applications, approximate results with error bound guarantee can satisfy user requirements. To approximate sensed data, the filter based approach is to maintain the filters of each node at both sensor node and base station. The filter of each node is represented by an interval. For a given filter, the sensor node sends the update to the base station if the sensed data is beyond the range of the filter. In this paper, we investigate the accuracy-aware approach for approximate data collection. The filter assignment for optimizing the average of valid filter time is formalized as an integer optimization problem and the hardness of this problem is proven to be NP-Complete. A greedy heuristic based algorithm with low computation overhead is proposed. Moreover, to balance the valid filter time, the filter assignment for optimizing the minimum valid time is formalized as a general max-min problem. We analyze the hardness of the problem and propose an approximation algorithm. The experimental results show that our algorithms achieve better results in terms of communication cost and expected time of valid filters.\r\n
    Keywords: Data Collection; Sensed Data Model; Approximate Algorithms; Wireless Sensor Networks.
    DOI: 10.1504/IJSNET.2017.10007416
  • A Radio Link Reliability Prediction Model for Wireless Sensor Networks   Order a copy of this article
    by Wei SUN, Qiyue Li, Jianping Wang, Liangfeng Chen, Daoming Mu 
    Abstract: The Wireless Sensor Network (WSN) has great prospects in monitoring and control of industrial plants and devices. One of the main challenges of developing WSNs for industrial applications is to satisfy their requirements for reliability with strict bounds. Accurate prediction of a radio links reliability is helpful for upper layer protocols to optimize network level Quality of Service performance. In this paper, to predict the bounds of the confidence interval of the Packet Reception Ratio (PRR), a Radio Link Reliability Prediction (RLRP) model is proposed for describing the relation between the reliability metrics bounds and factors that affect it. Based on the RLRP model, the adaptive extended Kalman filter algorithm is adopted to predict the reliability metric. Real world experiments were performed to demonstrate the proposed model. The results indicate that our RLRP model is accurate in predicting the bounds of link reliability, can better reflect the random characteristic of the radio link, and is more sensitive to dynamic changes of the radio link.
    Keywords: Link Reliability Model; Reliability Prediction; Wireless Sensor Network; Kalman Filter.
    DOI: 10.1504/IJSNET.2017.10007422
  • Aggregation Aware Early Event Notification Technique for Delay Sensitive Applications in Wireless Sensor Networks   Order a copy of this article
    by Sukhwinder Singh Sran, Jagpreet Singh, Lakhwinder Kaur 
    Abstract: In this paper, an aggregation aware early event notification technique (AAEENT) is proposed for WSNs characterized by unpredictable events. The novelties of the proposed technique are two folds. First, reliable routing policy is proposed to deliver events data over high throughput paths to final destination as early as possible. This reliable routing policy minimizes the number of retransmissions, which in turn reduces network congestion. Second, temporal data aggregation has been used to minimize redundant data at each intermediate node. The proposed technique has been implemented by incorporating proposed policy in Collect protocol of Contiki operating system and is evaluated over Cooja network simulator. For performance analysis, the results are compared with the Constant Delay based aggregation, Random Waiting based aggregation policy and the existing Collect Protocol. From the results, it has been observed that due to reduced network bottlenecks in the proposed technique, the event notification time declined upto 35% and overall power consumption is reduced upto 37% in contrast to existing solutions.
    Keywords: Data Aggregation; Energy Consumption; Energy Efficient Routing; Event Notification Time; Wireless Sensor Networks.
    DOI: 10.1504/IJSNET.2017.10007423
  • A Representative Node Selection based Data Gathering Method for Compressive Sensing in WSNs   Order a copy of this article
    by Chengyang Xie, Jiancong Zhao, Yugang Niu 
    Abstract: In traditional compressive data gathering (CDG) , many rounds of data gathering are required to make sure the accuracy of reconstruction signal such that most of nodes are required to participate in data gathering. By using the spatial and temporal correlation character of sensor readings, we propose a compressive data gathering method based on representative node selection (RNS-CDG) to collect measurement vector via just one routing tree. By means of principal component analysis (PCA) and frame potential (FP), the proposed method can select fewer representative nodes from all nodes, by which a data gathering tree will be constructed. And then, via this routing tree, a measurement vector composed of M projections will be received by Sink for recovering signal. It is shown via simulation results that the proposed method can ensure the accuracy of data reconstruction and reduce the transmissions of network.
    Keywords: Compressive data gathering; spatiotemporal correlation; representative node selection; wireless sensor networks.

  • Object Localization through Clustering Unreliable Ultrasonic Range Sensors   Order a copy of this article
    by Lei Pan, Xi Zheng, Philip Kolar, Shaun Bangay 
    Abstract: The increasing popularity and availability of inexpensive ultrasonic sensors facilitates opportunities for tracking moving objects by using a cluster of these sensors. In this paper, we use a cluster of ultrasonic sensors connected to Raspberry Pis. We conduct field tests and discover that their accuracy and reliability of individual sensors depends on the relative position of the tracked object. Hence, we employ data fusion and synchronization techniques for trilateration to improve accuracy using a cluster of sensor nodes. We successfully conduct multiple runs tracking a moving object and report these field test results in this paper. Our average error is in the order of tens of centimeters, and some of our best results match published results for larger clusters.
    Keywords: Ultrasonic Sensor; Sensor Network; Object Tracking; Trilateration; Data Fusion.

  • Passive and Greedy Beaconless Geographic Routing for Real-time Data Dissemination in Wireless Networks   Order a copy of this article
    by Yongbin Yim, Jeongcheol Lee, Euisin Lee, Sang-Ha Kim 
    Abstract: Real-time geographic routing is one of the most popular examples relying on a greedy algorithm to deliver real-time data in wireless networks. In this routing, each sender node decides a next-hop node as a local optimum among one-hop neighbors for achieving the global real-time requirement. However, this sender-side decision paradigm suffers from periodic and network-wide beacon exchange overhead to figure out neighbor nodes. To overcome the limitation, this paper newly suggests a passive and greedy routing algorithm, called PGBR for low-cost real-time data dissemination in beaconless wireless networks. To do this, PGBR forwards real-time data by a receiver-side competition without gathering information of neighbor nodes. To achieve the passive and greedy real-time routing, PGBR focuses on two major challenging issues: a delay estimation procedure and a contention function design. The delay estimation procedure estimates both competition delay and packet transmission delay used as inputs for the contention function design. PGBR also redesigns receiver-side contention function with deliberating the estimated delay and discuss combinations with important performance metrics: residual energy and forward progress. The experimental results show that PGBR could improve the energy-efficiency as well as keeps high delivery deadline success ratio.
    Keywords: Beaconless; Real-time; Delay estimation; Contention function; Routing protocol.

  • Three Dimensional Power Efficient Distributed Node Localization in Wireless Sensor Networks   Order a copy of this article
    by Reza Shahbazian, Seyed Ali Ghorashi 
    Abstract: Wireless sensor networks are usually used in applications in which information is only applicable when the location of informative node is known. The localization algorithms used to determine node's location are performed in a centralized or distributed manner. Although the high accuracy is the main advantage of centralized algorithms, the distributed ones are more efficient in power consumption, robustness and reliability. In this paper, we first propose a three dimensional (3D) distributed node localization algorithm for wireless sensor networks in which sensor nodes cooperate to estimate the unknown location in a distribute manner. Then, we propose a node selection algorithm by switching off some sensor nodes causing a great reduction in the power consumption and extending the life time of the network. We further propose a weighting function to improve the performance of proposed algorithm by improving the localization accuracy. Simulation results confirm that proposed algorithms are applicable in 3D environments while achieving acceptable performance in case of localization error, improving the robustness and scalability of the network and reducing the power consumption at least by 20 percent.
    Keywords: Localization; Distributed; Wireless Sensor Network; Three Dimensional; Power Efficient; Node Selection; Weighing Function.

  • Distributed Constrained Optimization Over Cloud-Based Multi-Agent Networks   Order a copy of this article
    by Qing Ling 
    Abstract: We consider a distributed constrained optimization problem where a group of distributed agents are interconnected via a cloud center, and collaboratively minimize a network-wide objective function subject to local and global constraints. This paper devotes to developing efficient distributed algorithms that fully utilize the computation abilities of the cloud center and the agents, as well as avoid extensive communications between the cloud center and the agents. We address these issues by introducing two divide-and-conquer techniques, both of which assign the local objective functions and constraints to the agents while the global ones to the cloud center. The first algorithm is based on the celebrated alternating direction method of multipliers (ADMM) and the second one is a primal-dual first-order (PDFO) method. Both algorithms naturally yield two layers, the agent layer and therncloud center layer, which exchange intermediate variables so as to collaboratively obtain a network-wide optimal solution. However, the ADMM-based algorithm requires the cloud center and the agents to solve optimization subproblems at every iteration, and hence brings high computation cost. The PDFO method significantly reduces the iteration-wise computation cost by replacing the optimization subproblems with simple first-order gradient descent and projection operations. Both algorithms are proved to be convergent to the primal-dual optimal solution. Numerical experiments demonstrate the effectiveness of the proposed distributed constrained optimization algorithms.
    Keywords: Cloud computing; distributed optimization; alternating direction method of multipliers (ADMM); primal-dual first-order (PDFO) method.

  • An acquisition scheme for communications in multi-antenna sensor networks with low signal to noise ratio   Order a copy of this article
    by Joaquin Aldunate, Christian Oberli 
    Abstract: Recent results show that multiple antenna communications can improve the energy efficiency of wireless communications. These techniques are thus attractive for use in wireless sensor networks. In particular, diversity techniques can be used to improve the acquisition of wireless links, specially for low signal-to-noise ratio (SNR) situations. This paper presents an acquisition scheme well suited for wireless sensor networks such as low-power wide-area networks (LPWAN) with multiple antenna sensor nodes. The scheme exploits a differentially encoded preamble and receive diversity in order to detect signals at very low SNR without requiring channel state information. We show that it achieves a better trade-off between the probability of acquisition false alarm and failed acquisition than single-antenna communications. Therefore, it enables networks with larger node separation at given transmission power, such as in LPWANs. The scheme's performance is thoroughly analysed theoretically and verified by simulations.
    Keywords: multi-antenna; wireless sensor networks; acquisition scheme; preamble; low-power wide-area networks; low signal to noise ratio; packet-based wireless communications; preamble sequence; diversity; differential coding; channel state information; coherent combining; detector; receiver operating characteristic; probability of false alarm; signal detection.

  • APRS : Adaptive Real-Time Payload Data Reduction Scheme for IoT/ WSN Sensor board with Multivariate Sensors   Order a copy of this article
    by NAYEF ALDUAIS, Jiwa Abdullah, Ansar Jamil 
    Abstract: In the applications of the Internet of Things (IoT) based on wireless sensor network (WSN), sensor board depends on battery that having a limited lifetime to function. Multivariate sensing boards pose additional challenges over the battery life time by additional data transmissions, thus draining the battery. In this paper, a new simple mechanism called as Adaptive Real-Time Payload Data Reduction Scheme (APRS) for energy-efficiency purpose is proposed. APRS aims to reduce the transmitted packet size for each sensed payload, moreover it prevents any transmissions if no significant change is reported by the payload sensing block. In the experiment, the APRS used a single variable to represent the row of the measured data (n-variables). It was based on the current relative difference and compared to the last measured data that had been transmitted to the fusion center. The APRS was able to reconstruct the original real-time sensed data (n-variables) from the representing variable at the fusion center. The performance of the APRS was evaluated through simulation by utilising various real-time environmental datasets. In addition, the APRS was successfully implemented in Libelium-Waspmote Gas sensor board for real-time IoT application. In conclusion, the APRS has managed to show its simplicity and flexibility for the real-time IoT/WSN application when it is compared with the other algorithms and its reduction ratio during a transmission is within acceptable range of 81 94 %. The average of the total percentage of energy saved by applied APRS in all nodes is 95%. Overall, the APRS has high performance in the reduction ratio of data and efficiency in energy consumption when it is compared with other recent multivariate data reduction methods.
    Keywords: WSN; IoT; multivariate data; Accuracy; Energy Consumption; data reduction.

  • STCS: A Practical Solar Radiation based Temperature Correction Scheme in Meteorological WSN   Order a copy of this article
    by Baowei Wang, Xiaodu Gu, Shuangshuang Yan 
    Abstract: In order to improve the timeliness, accuracy and availability of realtime meteorological data observation and promote the development of meteorological modernization, we use wireless sensor network to construct intelligent meteorological observation network to achieve fine observation data. But the accuracy of low-cost meteorological sensor is not high. In order to improve the accuracy of meteorological sensor network monitoring, some pioneer methods have been proposed. But the precision always cannot reach the ideal effect. In this article, we propose STCS (a solar radiation based air temperature error correction system). We did some pretreatments to the raw data set, including interpolation, time transformation. Then the relationship between solar radiation and temperature error is established by training GA-BP (Back propagation neural network optimized by genetic algorithm). At last, the trained neural network was applied to the temperature correction. Finally, we did smoothing process to the corrected data. Our results show that STCS achieves an average error of smaller than 0.34◦C. Consider to maximal absolute error, mean absolute error and standard deviation these three indicators, our results were improved by 14%, 6% and 12% respectively.
    Keywords: wireless sensor network; data correction; artificial neural network; solar radiation; meteorological observation.

  • K-Barrier Coverage in Wireless Sensor Networks Based on Immune Particle Swarm Optimization   Order a copy of this article
    by Yanhua Zhang, Xingming Sun, Zhanke Yu 
    Abstract: Barrier coverage of wireless sensor networks (WSNs) has been an interesting research issue for security applications. In order to increase the robustness of barriers coverage, k-barrier coverage is proposed to address this issue. In this paper, the k-barrier coverage problem is formulated as a global optimization problem solved by particle swarm optimization (PSO). However, the performance of PSO greatly depends on its parameters and it often suffers from being trapped in local optima. A novel particle swarm optimization program named AI-PSO (artificial immune-particle swarm optimization) is designed and the model of k-barrier coverage problem is proposed to solve this prob-lem. AI-PSO integrates the ability to exploit in PSO with the ability diversity maintenance mechanism of AI (artificial immune) to synthesize both algorithms strength. Simulation results show that the proposed algorithm is effective for the k-barrier coverage problems.
    Keywords: k-barrier coverage; particle swarm optimization; artificial immune; wireless sensor networks.

  • Home Appliances classification based on multi-feature using ELM   Order a copy of this article
    by Qi Liu, Fangpeng Chen, Zhengyang Wu, Fenghua Chen, Xiaodong Liu, Nigel Linge 
    Abstract: With the development of science and technology, the application in artificial intelligence has been more and more popular, as well as smart home has become a hot topic. And pattern recognition adapting to smart home attracts more attention, while the improvement of the accuracy of recognition is an important and difficult issue of smart home. In this paper, the characteristics of electrical appliances are extracted from the load curve of household appliances, and a fast and efficient home appliance recognition algorithm is proposed based on the advantage of classification of ELM (Extreme Learning Machine). At the same time, the sampling frequency with low rate is mentioned in this paper, which can obtain the required data through intelligent hardware directly, as well as reduce the cost of investment. And the intelligent hardware is designed by our team, which is wireless sensor network (WSN) composed by a lot of wireless sensors. Experiments in this paper show that the proposed method can accurately determine the using electrical appliances. And greatly improve the accuracy of identification, which can further improve the popularity of smart home.
    Keywords: Feature Extraction; Smart Home; Data Collection; WSN; ELM.; Smart Socket; Data analysis.

  • A Novel Meteorological Sensor Data Acquisition Approach Based on Unmanned Aerial Vehicle   Order a copy of this article
    by Chuanlong Li, Xingming Sun 
    Abstract: Meteorological sensor data acquisition is critical for various applications and researches. Currently, meteorological sensor data monitoring and acquisition mainly relies on the automatic weather station, wireless sensor network, satellites, and airborne remote sensing, etc. However, these conventional methods have some insuperable deficiencies. Recent advances in unmanned aerial vehicle for scientific use make drones easily overcome some of the paucity generated by these means. UAV (Unmanned Aerial Vehicle) as a sensor bearing platform can be used to collect sensor data in a more responsive, timely, three dimensional, and cost-effective manners. By implementing sensor network alike methods, drones can simulate the mobile ad-hoc networks in a mission and reduce the communication energy cost between each sensor node. This paper first demonstrates the feasibility of this approach, then proposes a sensor bearing framework aiming to bridge UAV remote sensing technique and customized meteorological sensor. A system prototype is developed and some real-world field tests indicate the application of the proposed framework is practical.
    Keywords: UAV; meteorological sensor; UAS; sensor data acquisition; remote sensing.
    DOI: 10.1504/IJSNET.2017.10013468
  • A Energy Balanced Routing Strategy Based on Non-uniform Layered Clustering   Order a copy of this article
    by Tong Wang 
    Abstract: To tackle the unbalanced distribution of clusters problem and the hot spot problem for routing in Wireless Sensor Networks (WSNs), a Balanced non-uniform Layered Energy Efficient routing scheme (BLEE) is proposed in this paper. Here, two major contributions are involved in the design of BLEE. Firstly, driven by the intelligence (via the knowledge of nodal energy consumption) for non-uniform network layer construction, a novel numerical model is proposed to calculate the number of layers and scale of each cluster. Secondly, upon the guidance of above analysis, a combined intra/inter-cluster communication pattern is proposed to prolong the network lifetime. Both numerical and simulation results demonstrate that BLEE outperforms well known literature works, in terms of reduced energy consumption and prolonged network lifetime.
    Keywords: Wireless Sensor Networks; Balanced Non-Uniform Layered; Energy Consumption; Clustering; Routing Protocol.

  • A Lightweight Multicast Approach based on Bloom Filters for Actuator-Sensor-Actuator Communication in WSANs   Order a copy of this article
    by Marcelo Guimarães, Valério Rosset 
    Abstract: Wireless Sensor and Actuator Networks (WSANs) are a subclass of Wireless Sensor Networks (WSNs) where the presence of actuator devices allows the interaction within a controlled environment. Some applications require WSANs to operate in large-scale remote environments with natural barriers (relief and vegetation) for the direct communication between the devices. Natural disaster monitoring and relief are examples of such applications. In this context, to ensure the communication between multiple actuators, through the network of sensors, is essential. In the literature one may find few approaches regarding this issue. However, most of them rely on a costly unicast routing approach. In line with this, we propose a novel routing protocol, named Multicast Border Oriented Forward Routing Protocol (M-BOFP), whose communication between actuators is performed by multicast implemented on top of the network of sensor nodes. The M-BOFP takes advantage of the Bloom filters, a probabilistic data structure with low memory cost, to perform the multicast communication requiring low and fixed message overhead. We also present a comparative analysis between protocols of the literature and the M-BOFP. Additionally, we carry out a performance evaluation of the proposed protocol considering different scenarios representing small, moderate and large-scale WSANs. To sum up, we show the primary results that support the improvements on data delivery rate and energy conservation achieved by the proposed M-BOFP.
    Keywords: WSAN; Routing; Delivery Efficiency; Multicast; Bloom Filters.

  • Optimal Time and Channel Assignment for Data Collection in Wireless Sensor Networks   Order a copy of this article
    by Yanhong Yang, Huan Yang, Liang Cheng, Xiaotong Zhang 
    Abstract: This paper studies the joint assignment of time slots and frequency channels in tree-based wireless sensor networks (WSNs) for data collection applications. In order to approximate the optimal solution, we propose a series of algorithms that exploit the network topology and maximize concurrent communications within each time slot through dynamic programming. Unlike peer approaches established upon idealized link-layer models, our algorithms are designed to be resilient to link errors and they are evaluated with the presence of unreliable links and in various deployment scenarios. Evaluation results show that our new algorithms outperform the state of the art in terms of data collection delay performance under unreliable conditions with moderate node deployment. Finally, the impacts of assorted implementation-oriented network parameters are investigated and summarized as design guidelines.
    Keywords: Data collection; TDMA; convergecast; wireless sensor network.

  • Energy Efficient Data Collection in Periodic Sensor Networks Using Spatio-Temporal Node Correlation   Order a copy of this article
    by Hassan Harb, Abdallah Makhoul, Ali Jaber, Samar Tawbi 
    Abstract: In wireless sensor networks (WSNs), the densely deployment and the dynamic phenomenon provide strong correlation between sensor nodes. This correlation is typically spatio-temporal. This paper proposes an efficient data collection technique, based on spatio-temporal correlation between sensor data, aiming to extend the network lifetime in periodic WSN applications. In the first step, our technique proposes a new model based on an adapted version of Euclidean distance which searches, in addition to the spatial correlation, the temporal correlation between neighboring nodes. Based on this correlation and in a second step, a subset of sensors are selected for collecting and transmitting data based on a sleep/active algorithm. Our proposed technique is validated via experiments on real sensor data readings. Compared to other existing techniques, the results show the effectiveness of our technique in terms of reducing energy consumption and extending network lifetime while maintaining the coverage of the monitored area.
    Keywords: Periodic Sensor Networks (PSNs); Spatio-Temporal Data Correlation; Sleep/Active Sensors; Real Data Readings.

  • Improving Smart Home Security; Integrating Behaviour Prediction into Smart Home   Order a copy of this article
    by Arun Cyril Jose, Reza Malekian 
    Abstract: The paper highlights various security issues in existing smart home technology and its inhabitant behaviour prediction techniques and proposes a novel behaviour prediction algorithm to improve home security. The algorithm proposed in this work identifies legitimate user behaviour and distinguishes it from attack behaviour. The work also identifies the parameters necessary to predict user behaviour during the seven week learning period. The paper identified three factors namely time parameter, light parameter, users key placement behaviour to successfully predict user behaviour. The algorithm learned normal and suspicious user behaviours during the seven week training period and na
    Keywords: Home automation; Smart homes; Wireless sensor networks; Access control; ZigBee.

  • Distributed Data Aggregation Algorithm Based on Lifting Wavelet Compression in Wireless Sensor Networks   Order a copy of this article
    by Ledan Cheng, Songtao Guo, Ying Wang 
    Abstract: The redundancy of sensing data in wireless sensor networks (WSNs) gives rise to longer transmission delays and more energy consumption. Compressive sensing (CS) is one of the most promising recoverable data aggregation schemes, which can considerably reduce the amount of data transmitted. However, CS technique brings heavy aggregation burden on sensor nodes, which challenges their restricted available energy and computation capacity. In this paper, we focus on the energy-efficient data compression with the objective of recovering the original data set. We first propose a dynamic clustering algorithm based on data spatial correlation (CDSC) to balance aggregation load. Furthermore, we propose a faster data compression approach based on eliminable lifting wavelet, which can eliminate spatial and temporal data redundancy. Also, it offers high fidelity recovery for the raw data. Extensive experiments demonstrate that our CDSC algorithm outperforms other methods on prolonging network lifetime and reducing the amount of data transmitted. Moreover, our data compression algorithm can achieve 98.4% recovery accuracy when the compression ratio equals to 1.3333.
    Keywords: Data aggregation; Spatial data correlation clustering; Compressive sensing; Wavelet compression; Wireless sensor networks.

  • Node Placement Approaches for Pipelines Monitoring: Simulation and Experimental Analysis   Order a copy of this article
    by Abdullatif Albaseer, Uthman Baroudi 
    Abstract: Monitoring oil, gas and water pipeline networks is a critical problem; its impact has serious consequences on the ecosystem. This problem has attracted, and for long time, the attention of industry and academia. One of the challenges in the pipeline monitoring process is how to optimally place the sensors that monitor the pipeline, detect and report any anomaly. Naturally, the deployed sensors are placed in a linear topology. This linear topology requires careful attention in placing sensors to ensure robustness against anomaly, minimize the energy consumption and maximize the network lifetime. In this work, we have studied two existing greedy node placement approaches via simulation and experimental analysis. First, we have validated experimentally the 31 power levels of CC2420 TelosB chipon and their corresponding transmission ranges. Having more power-level resolution provides more flexible power assignment, which yields less energy consumption and longer lifetime compared to traditional 8 power levels. Second, Extensive simulation and real experiments have been conducted. The results demonstrate a considerable drop in power consumption which can reach 73% and 23% extension in the network lifetime when all 31 power levels are adopted.
    Keywords: Leak detection; Wireless Sensor Network; Pipeline Monitoring; Equal-Power Placement; TelosB; Linear Node placement; Reliability; On-line monitoring.

  • On the coverage effects in wireless sensor networks based prognostic and health management   Order a copy of this article
    by Ahmad Farhat, Christophe Guyeux, Abdallah Makhoul, Ali Jaber, Rami Tawil 
    Abstract: In this work, we consider the use of wireless sensor networks (WSN) to monitor an area of interest, in order to real time diagnose its state. This type of network is different from the traditional computer ones as it is composed of a large number of sensor nodes with very limited and almost non renewable energy. Thus, saving the quality of service (QoS) of a wireless sensor network for a long period is very important in order to ensure accurate data. Then, the area diagnosing will be more accurate. One of the most important indexes of the QoS in WSN is its coverage which is usually interpreted as how well the network can observe a given area. Many studies have been conducted to study the problem of detecting and eliminating redundant sensors in order to improve energy efficiency, while preserving the networks coverage. However, in this article, we discuss the coverage problem in wireless sensor networks and its relation with prognostic and health management. The aim of this article is to study the impact of coverage issues in wireless sensor network on these processes. For that, we have used four diagnostic algorithms, to evaluate both prognostic and health management in the presence of coverage issues in such networks. In details, we have studied the impact of scheduling mechanisms usually deployed on such networks regarding the diagnostics quality and the network density. We conclude that these issues are very important, and have a great impact on the coverage, the data accuracy, and therefore the diagnostics quality.
    Keywords: Wireless Sensor Networks; Coverage; Density; Scheduling Mechanisms; Prognostic and Health Management; Diagnostics.

  • RECrowd: A reliable participant selection framework with truthful willingness in mobile crowdsensing   Order a copy of this article
    by Xiaohui Wei, Yao Tang, Xingwang Wang, Yuanyuan Liu, Bingyi Sun 
    Abstract: In crowdsensing systems, participant selection as one of main problems attracts a lot of attention. Most studies focus on how to select reliable participants for task allocation to assure task quality and minimize incentive cost. Conventional methods are mainly based on historical reputation, which is from the statistical result of participant behaviours during a past period. However, these methods could cause unreliable assignment that reduces the task quality , since historical reputation cannot exactly reflect the current state of participants. In this paper, we advocate RECrowd, a reliable participant selection framework that considers both historical records and current truthful willingness. In RECrowd, we formulate an optimization problem with the objective of minimizing incentive cost while ensuring task quality, and design a two-stage online greedy algorithm with the pre-assignment step. During the participant selection, we also consider participants position privacy. Experiment results with real datasets demonstrate our algorithm outperforms other methods.
    Keywords: Mobile Crowdsensing; participant selection; truthful willingness; incentive cost; task quality; privacy.

  • A Profile Based Data Segmentation for In-Home Activity Recognition   Order a copy of this article
    by Mohammed AL Zamil, Rania AL Nadi 
    Abstract: A major problem in smart-home activity recognition is the ambiguity of interpreting the actions that formulate activities in smart home environments. Such ambiguity resulted from the redundancy of irrelevant actions and the concurrent interleaving among activities themselves. In this paper, we present a framework to minimize the effect of such ambiguity using profile based data segmentation and actions refinement. The proposed methodology relies on defining a profile for each sensor in the environment for the purpose of enriching existing features with semantic ones. Furthermore, according to these profiles, irrelevant actions within data segments are removed. Moreover, the proposed methodology addresses the connectivity among actions and their designated activities for the purpose of resolving interleaving among them. Experiments have been conducted to measure the performance of the proposed framework on a well-known datasets in this domain. We evaluated our methodology using three different classifiers: J48 (decision tree), Na
    Keywords: Internet of Things (IoT); Smart Home; Activity Recognition; Data Segmentation; Data Mining; Sensor Profile.

  • Entropy Correlation based Clustering Method for Representative Data Aggregation in Wireless Sensor Networks   Order a copy of this article
    by Nga Nguyen Thi Thanh, Khanh Nguyen Kim, Son Hong Ngo 
    Abstract: One of the popular data aggregation method in wireless sensor network (WSN) is collecting only local representative data based on correlation of sample data. To recognize the local representative nodes, it is necessary to determine the correlation regions. However, recent correlation models are distance based that is not general and need to be determined beforehand or complicated with high computing cost. Thus, in this paper, a novel entropy correlation model is proposed based on joint entropy approximation. Using the proposed model, an entropy correlation-based clustering method is presented and the selection of representative data that satisfying the desired distortion is proposed. The algorithm is validated with practical data.
    Keywords: WSN; correlation; entropy; clustering; representative nodes.

  • Distributed Recursive Least-Squares Fusion Method for Gas Leakage Source Localization   Order a copy of this article
    by Zhang Yong, Zhang Liyi, Han Jianfeng, Yang Yi, Ban Zhe 
    Abstract: Gas leakage source localization has received considerable attention in the field of environmental monitoring and protection. In this study, an adaptive distributed recursive least-squares fusion method is presented with sensor networks, in which the estimator of gas source parameters and the corresponding error are updated with observations and results from the neighboring nodes. The method could be implemented with two sensor node scheduling schemes: the global and local methods. This study aimed to propose an information fusion objective function for optimizing estimation accuracy and energy consumption to balance the performance and resource utilization of sensor nodes. The performance of the two different methods was analyzed. Compared with the global method, the local method was found to achieve the desired performance with a significant reduction of the required sensor nodes, along with a decrease in congestion, energy consumption, and time latency in communication.
    Keywords: Parameter estimation; sensor scheduling; source localization.

  • An Improved MDS Localization Algorithm for a WSN in a Sub-Surface Mine   Order a copy of this article
    by Heng Xu, Qiyue Li, Jianping Wang, Keqiong Chen, Wei Sun 
    Abstract: Wireless sensor networks (WSNs) have been successfully applied in a wide range of application domains. However, because of the properties of wireless signals, WSN applications in underground environments have been limited. In this paper, we present a Kalman-filter-based localization algorithm for use in a WSN deployed in a sub-surface mine for environmental monitoring to identify the positions of a large number of miners, each carrying a wireless mobile node. To improve the positioning accuracy even when current data are not available, we enhance the estimates of the Received Signal Strength Indication signal intensity and range obtained from the Kalman filter by adjusting them using the elastic particle model. Then, we obtain the distance matrix of the WSN based on AoA and the cosine theorem. Finally, we determine the final positions of all mobile nodes using a multidimensional scaling (MDS) algorithm.
    Keywords: Wireless Sensor Networks; Mobile Nodes; Localization; Sub-surface Mine; Kalman Filter; Elastic Particle Model; Angle of Arrival; Multidimensional Scaling Algorithm; No Line of Sight; Clustering.

  • Sensor Management Based on Collaborative Information Fusion Algorithm for Gas Source Localization   Order a copy of this article
    by Zhang Yong, Zhang Liyi, Han Jianfeng, Yang Yi, Ma Xinyuan 
    Abstract: Gas source localization based on sensor networks is of great importance in many fields such as environmental monitoring, security protection and pollution control. Considering the sensor node scheduling and path planning problem in the gas source localization process, an improved collaborative sensor management method based on genetic and ant colony fusion algorithm was proposed in this paper. Simulation results show that, under different certain environmental assumptions, the mobile sensor nodes could achieve accurate gas source prediction positioning based on the proposed fusion method and it is superior to the genetic algorithm and ant colony algorithm with higher localization accuracy and faster convergence speed.
    Keywords: mobile sensor networks; sensor management; gas source localization.

  • An IoT Prototype System for Environmental Sustainability   Order a copy of this article
    by Cynthia Fu, David Cruz, Bo Sun, Guang Sun 
    Abstract: Applications based on Internet of Things (IoT) have opened great potential opportunities to provide seamless and pervasive data collection capabilities for many research disciplines. Unfortunately, the ability of IoT to enable relevant research disciplines is still hampered by the poor integration of IoT into various scientific and engineering fields. In this paper, we present an Intel Edison and Raspberry Pi 3 based proof-of-concept IoT prototype to collect environmental data in a low-cost manner, to advance understanding of water quantity and quality research. By utilizing the Intel Edison board and Raspberry Pi 3 as the central computing platform, we create an IoT prototype to remotely monitor important environment variables, including air temperature, light intensity, and water temperature, pH, and conductivity. Specifically, for Intel Edison, by leveraging existing off-the-shelf sensor probes (Water Temperature Sensor Probe H377, low-voltage precision centigrade sensor probe TMP36, and light sensor SEN-9088) and open source embedded Linux system Yocto, we have built a prototype IoT to collect water temperature, air temperature, and ambient light. For Raspberry Pi 3, we leverage EZO class pH and conductivity sensor probes from AtlasScientific and Raspbian Jessie, to collect water pH and conductivity data. The collected readings are then calibrated and transmitted to a public channel from, a cloud-based data storage platform, which will facilitate data sharing with researchers. Our IoT prototype provides a web service to facilitate online data access and data visualization. Our presented architecture is general. Therefore, other sensor probes could be integrated to collect more types of data for different research purposes. We illustrate our detailed design for the prototype, present preliminary data results, and further point out important future work to extend our proposed IoT full-fledged.
    Keywords: Internet of things; sensor networks; security; prototype; IoT.

  • SmartData: an IoT-Ready API for Sensor Networks   Order a copy of this article
    by Antonio Frohlich 
    Abstract: Despite intense research on Wireless Sensor Networks (WSN) in the last two decades, programmers still do not have a cohesive, highly expressive API to model their sensing applications and to connect them to the Internet of Things (IoT). In this paper, we introduced emph{SmartData}, a high-level API for WSN aiming at leveraging the myriad of features available on such networks while delivering a common abstraction for sensed data that facilitates application development without incurring any significant overhead. SmartData are enriched with enough metadata to become self-contained in terms of semantics, spatial location, timing, and trustfulness. It is meant to be the only application-visible construct in the sensing platform and therefore implicitly mediates all system-level services, including communication, synchronization, and the interaction with transducers and actuators. Reading a SmartData implies in sensing, while writing to it defines a new setpoint for actuators. Both, local and remote sensors utilize the same interface. We demonstrate the concept through the automation of a solar building, using an embedded OS and the Trustful Space-Time Protocol to implement a set of SmartData in C++.
    Keywords: Wireless Sensor Network; Internet of Things; Sensing API; Sensing Data Management.