International Journal of Sensor Networks (45 papers in press)
Recent Advances in Wireless Sensor Networks with Environmental Energy Harvesting
by Lei Shu, Wanjiun Liao, Jaime Lloret, Lei Wang
Clustering algorithm for wireless sensor networks : the honeybee swarms nest-sites selection process based approach
by Ado Adamou Abba Ari, Nabila Labraoui, Blaise Omer Yenké, Abdelhak Gueroui
Abstract: The design of low-power, scalable and energy efficient clustering protocols in
order to extend the lifetime of wireless sensor networks remain an active area of research. Cluster-based sensor networks with data aggregation on cluster heads is the most popular approach for optimizing the energy consumption of nodes, in order to maximize the overall network lifetime. Clustering is also used for optimizing QoS and scalability in sensor networks. In the case of large scale networks, the management of nodes becomes a challenge. It is therefore necessary that, these sensors act in a self-organized manner to perform tasks for the overall network. A number of novel protocols, architectures, algorithms and applications has been proposed and implemented. Swarm intelligence
provides the most powerful models of social insects that provide a global intelligence
through local and simple actions in a self-organized and distributed manner. In this paper, we proposed a distributed clustering approach based on the nest-sites selection process of a honeybee swarm. In the design of our proposed algorithm, we focused on the distribution of load balancing among each cluster member in order to extend network lifetime. The performance of the proposed approach was evaluated by performing extensive experiments and the results demonstrated that our algorithm delivers better performance in terms of network lifetime, amount of packets received by the base station, end-to-end delay, energy consumption and efficiency.
Keywords: Wireless sensor networks; Clustering; Cluster Head; Swarm Intelligence; Honeybees; NEST.
Gateway Bandwidth Arrangement of Data Transmission in Cyber-Physical System
by Hao Wang, Jianzhong Li, Hong Gao
Abstract: Along with the development of wireless communication technology, different wireless technologies suitable for variety situations have been widely used. Wireless technologies with different characteristics have deeply promoted the development of Cyber-Physical System. Various kinds of devices such as sensors, actuators and data processors in a Cyber-Physical System have been used to complete different tasks and also be equipped with different kinds of wireless communication technologies in order to meet different needs of work. Meanwhile, all these devices have to make communication mutually to keep the whole system effective. Gateways have been used to connect these equipments with different kinds of wireless technologies. Different devoices pack information in data packets, and transmit them to each other through gateways. We propose a dynamic gateway bandwidth allocation algorithm, in order to maximize total packets delivery ratio. Simulation results have shown that this algorithm is efficient.
Keywords: packet delivery; bandwidth allocation; hybrid wireless network; Cyber-Physical System.
Clustering-Based Energy-Efficient Routing Approach for Underwater Wireless Sensor Networks
by Gurkan Tuna
Abstract: As a result of the time-varying and harsh environment conditions, in underwater wireless sensor network (UWSN) scenarios, along with the excessive error rate of acoustic channels and long propagation delays, one of the most critical issues which must be addressed is to minimize energy consumption of sensor nodes. Moreover, it is hard to replace the batteries of the underwater sensor nodes frequently, therefore extending the lifetime of the nodes is one of the most important design objectives. In UWSNs, although energy dissipation in transmission mainly depends on employed routing algorithms and media access control (MAC) schemes, given the nature and challenges of the acoustic communication in water, all the layers of the protocol stack play an important role on achieving a high energy efficiency. Therefore, novel approaches that handle energy inefficiencies and extend the lifetime of UWSNs are required. In this paper, we propose an energy-efficient routing approach called Clustering-Based Energy-Efficient Routing (CBEER) to prolong the lifetime of UWSNs and evaluate its performance through extensive simulations. Based on a novel clustering-based scheme, the proposed approach can find shortest routing paths to carry data packets and is able to realize uniform network energy consumption profile in the entire underwater sensor network. The simulation based evaluation studies reveal that CBEER performs well in terms of packet delivery ratio and overall network energy consumption.
Keywords: Clustering; Energy Efficiency; Routing; Underwater Wireless Sensor Network.
Cooperative Stackelberg Game Based Optimal Allocation And Pricing Mechanism In Crowdsensing
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.
Maximizing Influence in Sensed Heterogenous Social Network with Privacy Preservation
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.
Rotation Based Coverage Control Algorithm and Protocol for Heterogeneous Sensor Networks
by Quanlong Li, Qing Yang, Lu Su
Abstract: This paper studies the problem of coverage control, one fundamental issue in wireless sensor networks (WSN). In many applications of WSN, the target area is required to be covered for a certain degree. To satisfy coverage requirements, redundant sensors are usually deployed, which may cause unnecessary energy consumption on sensor nodes. To address this challenge, some coverage control methods have been proposed to reduce the number of active sensors and prolong the lifetime of sensor networks. However, none of them can be applied to heterogeneous sensor networks that are composed of different types of sensors with different sensing radii. In the previous work, this problem was solved by a novel coverage control mechanism for heterogeneous sensor networks. This mechanism, unfortunately, still suffers from a major limitation. That is, it may exhaust the energy of the active sensors, leading to unbalanced energy consumption among the sensor nodes. To tackle this problem, in this paper, we propose a novel distributed rotation based coverage control strategy for heterogeneous sensor networks. This strategy only needs to analyze a few special points coverage to determine each sensor node's working schedule, and thus can significantly reduce the complexity of coverage computation. Theoretical analysis and simulation results show that the proposed coverage control algorithm and protocol can outperform prior work in terms of not only the energy saving but also the energy balancing.
Keywords: Rotation-based Coverage; Coverage Control; Wireless Sensor Networks.
A Novel Task Recommendation Model for Mobile Crowdsourcing Systems
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.
Minimum Cost Flow Based Approach for Connectivity Restoration in WSN
by Eman Shaaban
Abstract: A WSN consists of resourced-constrained sensor nodes that cooperate to accomplish specific tasks. In many applications, WSNs operate unattended in harsh environments, which make sensor nodes susceptible to failure due to physical damage or depletion of onboard energy supply. Some of the nodes are critical to the network connectivity and their failure may partition the network into multiple disjoint segments. WSN can autonomously recover from this failure through relocation of some nodes. However such a recovery process remains challenging since tolerating the failed nodes individually is not a globally optimal solution and may cause resource conflict. This paper presents a Recovery approach that forms a topology with Increased Robustness against recurrent failure (RIR). RIR tolerates the failure of multiple connectivity-critical nodes through repositioning of non-critical healthy nodes. RIR can handle multiple simultaneous failures of either collocated or scattered nodes. The approach favors substituting a failed node with one with the highest residual energy in order to sustain the network connectivity for the longest time possible. RIR models the recovery as a Minimum Cost Flow (MCF) problem to determine the best set of node relocations for repairing the network topology while minimizing the motion overhead of the recovery process. The performance of RIR is evaluated through extensive simulation experiments. The simulation results have confirmed the effectiveness of the proposed scheme.
Keywords: wireless sensor networks; connectivity restoration; node relocation; minimum cost flow; WSN recovery.
Accuracy-aware Data Collection in Wireless Sensor Networks
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.
A Radio Link Reliability Prediction Model for Wireless Sensor Networks
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.
Cross-layer control of wireless sensor network for smart distribution grid
by Ruju Fang, Jianping Wang, Wei Sun
Abstract: This article presents an integrated modeling method providing cross layer control for Wireless Sensor Networks (WSNs) communication which is suitable for Smart Distribution Grid (SDG). There are multiple protocol layers in WSNs communication. In order to meet the Quality of Service (QoS) of application for WSNs, as well as more resource-efficient for the resource-constrained nodes, a cross-layer control algorithm based on theory of Fuzzy Cognitive Map (FCM) is proposed. The cross layer control model of the whole communication system is established by utilizing different influence coefficients among fuzzy cognitive map concepts which represent the subordinate relationships of different protocol layers, such as application layer, network layer, MAC layer and physical layer. Influence coefficients among fuzzy cognitive map concepts can be obtained self-learning and self-training way, which can timely adjust the physical layer，MAC layer，transport layer parameters and routing strategy based on the network environment and control needs.rnFinally, communication performance of cross layer control algorithm is evaluated by received data of WSNs network，the average delay and effective throughput, and so on. Results show that cross layer control scheme based on fuzzy cognitive map proposed in paper can meet the real-time and reliability requirements.rn
Keywords: Smart distribution grid; Wireless sensor network; Fuzzy cognitive map; Back-off strategy; Cross layer control.
Aggregation Aware Early Event Notification Technique for Delay Sensitive Applications in Wireless Sensor Networks
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.
Delay Minimizing Depth Based Routing for Multi-Sink Underwater Wireless Sensor Networks
by Chaitanya Kumar Karn, C.P. Gupta
Abstract: Underwater acoustic network is the enabling technology for number of applications viz. ocean exploration, pollution monitoring, etc. Each application requires appropriate network resources. Routing in such networks is challenging due to longer delays and mobility caused due to ocean currents and tides. Existing routing algorithms focus only on delivery of packets with very little consideration to minimizing delay in packet delivery. In this paper, we propose a new beacon-based routing algorithm for underwater sensor networks using multiple sink architecture. DMDBR (Delay Minimizing Depth Based Routing) attempts to minimize delay and uses depth, hop count and possible energy information for making routing decisions. Novelty of our scheme is in a node accepting packets for forwarding if it has sufficient residual energy to forward all the packets in the packet queue. A node declines acting as a forwarder once the estimated residual energy is not sufficient to forward the new packets. Simulation results show that DMBDR improves packet delivery ratio and minimize end-to-end delay in comparison to Depth Based Routing.
Keywords: Underwater Wireless Sensor Networks (UWSN); Forwarding Factor (FF); Delay Minimizing Depth Based Routing (DMDBR); Beaconing Interval.
A Representative Node Selection based Data Gathering Method for Compressive Sensing in WSNs
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.
A dynamic advertisement interval strategy in bluetooth low energy networks
by Jihun Seo, Changsu Jung, Bhagya Silva, Kijun Han
Abstract: Nowadays, many investigators have examined the effects of Bluetooth Low Energy (BLE) for a wide range of applications due to its low-cost and low-power characteristics. Hence, Performance improvement during the discovery process has become one of major concerns. In BLE networks, advertisement interval of advertiser directly influences the performance factors, such as discovery latency and energy consumption, since the advertiser discovers scanners by transmitting advertising Packet Data Units (PDUs). Frequent transmissions of advertising PDUs may result in collisions and serious energy consumption issues. On the contrary, larger advertisement interval may lead to delayed device discovery. In this paper we propose an improved algorithm to enable advertisers to adjust its advertisement interval with respect to the network conditions based on a Carrier Sensing (CS) scheme. The proposed algorithm acquires the network conditions by listening advertising channels before transmission of advertising PDUs, while allowing the advertisers to adjust their parameters accordingly. Simulation results show that the proposed algorithm has outstanding performance compared with basic CS scheme and BLE standard in crowded BLE networks.
Keywords: Bluetooth Low Energy (BLE); Device Discovery; Internet of Things (IoT); Dynamic Advertisement Interval.
Joint Spectrum Access and Transmission Power Management for Energy Harvesting Cognitive Radio Sensor Networks
by Fan Zhang, Tao Jing, Yan Huo, Kaiwei Jiang
Abstract: In this paper, we investigate the joint optimization of spectrum access and the transmission power for an energy harvesting cognitive sensor node, which operates in time-slotted fashion with causal knowledge of channel conditions along with the energy harvesting states. Allowing for the stochastic behavior of the primary channel status as well as the sensing imperfection, we formulate this joint optimization problem as an infinite-horizon discrete-time Markov decision process, in which the cognitive sensor node aims at maximizing the long-term expected throughput. Through using the value iteration approach, an optimal policy which specifies the spectrum access decision as well as the power level to be used upon the transmission is proposed. A theoretical analysis is conducted for the optimal policy, it is indicated that the optimal long-term expected throughput is non-decreasing with the battery available energy. Moreover, in order to reduce the computational complexity, we introduce a low-complexity policy and prove that the optimal low-complexity policy has a threshold structure with respect to the battery available energy. Based on this threshold structure, an efficient algorithm for deriving the optimal low-complexity policy is introduced. Finally, numerical results are presented to confirm the superiority of our proposed policies to other existing policies, and it demonstrates that the optimal low-complexity policy achieves a comparable performance with the optimal policy.
Keywords: Energy Harvesting; Cognitive Radio; Markov Decision Process; Sensor Networks.
Object Localization through Clustering Unreliable Ultrasonic Range Sensors
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.
A Distributed Anomaly Detection Model for Wireless Sensor Networks Based on the One-Class Principal Component Classifier
by Murad Rassam, Mohd Aizaini Maarof, Anazida Zainal
Abstract: The use of Wireless Sensor Networks (WSN) is increasing in various applications with the emergence of the Internet of Things concept which facilitates human life by equipping a sensor for each object/thing to build a network of things. Nonetheless, the sensed data quality and reliability are sometimes affected by factors such as sensors faults, intrusions and unusual events among others. Consequently, the real time and effective detection mechanisms of such anomalous data are necessary to guarantee making reliable decisions. In this paper, we propose a One-Class Principal Component Classifier (OCPCC) based distributed anomaly detection model for WSN. The proposed model utilizes the spatial correlations among sensed data in a closed neighbourhood by distributing the detection process over the nodes in a cluster-based WSN. The feasibility of the proposed model was validated using datasets collected from real world sensor network deployments and compared with local detection and some existing detection approaches from literature. The results show that the proposed distributed model helps to improve the detection rate of anomalous data compared to local model. A comparison with existing distributed models reveals the advantages of the proposed model especially for efficiency metrics while achieving better or comparable detection effectiveness.
Keywords: Wireless Sensor Networks; Distributed Anomaly Detection; Sensor Data Analysis; Principal Component Analysis.
Detecting occupancy and social interaction via energy and environmental monitoring
by Antonino Crivello, Fabio Mavilia, Paolo Barsocchi, Erina Ferro, Filippo Palumbo
Abstract: The demand for human oriented services in indoor environment has received steady interest and it is represent a big challenge for increasing the human well-being. In this work, we present a system able to perform room occupancy detection and social interactions identification, using data coming from both energy consumption information and environmental data. We also study the application of supervised and unsupervised learning techniques to the reference scenario, in order to: i) infer context information related to socialization aspects, by recognizing in real-time social interactions; ii) identify when a room is really occupied by workers or not, for emergencies management. The system has been tested in a real workplace scenario, inside three rooms of the CNR research area in Pisa occupied by different numbers of workers, representing the main core technology for future Active and Assisted Living services.
Keywords: Occupancy Detection; Social Interactions; Wireless Sensor Network.
Passive and Greedy Beaconless Geographic Routing for Real-time Data Dissemination in Wireless Networks
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.
A qualitative study of medium access control protocols supporting mobility in wireless sensor networks
by Zakaria Hamidi-Alaoui, Abdelbaki El Belrhiti El Alaoui
Abstract: In many wireless sensor network (WSN) applications, mobility support has been steadily becoming an important factor in achieving specific tasks. Nevertheless, mobile WSNs are facing several challenges and constraints regarding the wireless medium access which have been already addressed by existing Medium Access Control (MAC) protocols. In fact, these protocols were designed to handle node mobility by considering energy-efficiency and ensure medium access for all network nodes. In our work, we propose a decision-support tool enabling to choose adequate MAC protocol(s) that meet the requirements of a specific target mobile WSN application. This tool will be based on a deep analytical and comparative study of MAC protocols in terms of energy consumption and latency in dynamic contexts. Finally, we apply this approach on an application of wild animals monitoring as an example in order to retain, among the studied MAC protocols, the well-suited ones.
Keywords: wireless sensor network; WSN; mobile WSN; medium access control; MAC; MAC protocol; mobility; mobile node; handover; decision support; latency; energy efficiency.
Creating a secure index for distributed data on the sensor network
by Vandana Bhasin, P.C. Saxena, C.P. Katti
Abstract: Wireless sensor networks that are deployed in public, untrusted or hostile environments, are prone to various security vulnerabilities. In this paper, a method has been developed to reduce the security risk of a node from tampering. The sensed data is distributed using the roots of the polynomial equation, thereby reducing the security risk (A & Kak S, 2009). The number of partitions are fixed as specified by the roots of polynomial equation. We propose a security mechanism on a clustered heterogeneous network: Secure Index on distributed data. The index is created for every partition before encryption. The index allows for efficient retrieval of data as it is located on cluster head.
In this paper, we provide end-to-end confidentiality is not decrypted at the cluster head. The encrypted data can be decrypted only at the base station which has the key for decryption. It offers single value granularity as index is created for each unique data item in the partition. It provides data confidentiality, authentication and authorization. We have analysed the performance of the algorithm in terms of energy consumption. The energy consumption with the addition of security features has increased. We have found that even though the energy consumption has increased, the network can fulfil the purpose of data collection and can be deployed on large scale networks.
Keywords: sensor networks; data partitioning; indexing; implicit security.
Distance Vector hop localization algorithm based on the limitation by the probability to hops
by Xin Shi, Yanping Li, Sen Zhang
Abstract: Distance Vector-Hop localization algorithm, using flooding routing broadcast anchor node information, is confronted with the problem of communication consumption. Using the product of jump distance correction value and the minimum number of hops to represent the distance between the nodes, can lead to cumulative error. Using maximum likelihood estimation method to determine the unknown nodes, it suffers from the excessive computational consumption. This paper presents a new algorithm. Based on the probability property of random distribution of wireless sensor network, our algorithm limits hops between nodes to reduce the network communication consumption and minimize the cumulative error in distance calculation. Moreover, trilateral positioning method or the extended MIN-MAX algorithm, which is employed to calculate the unknown node coordinates, can decrease the computational consumption. Performance analysis and simulation results show that, the proposed algorithm can achieve multi-objective optimization with higher precision and lower cost.
Keywords: DV-HOP; probability distribution; limited number of hops; angle threshold; extended MIN-MAX.
Energy-Efficient Transmission Approach Using Regionalization Compressed Sensing
by Hao Yang, Xiwei Wang
Abstract: In the process of transmission in wireless sensor network, a vital problem is that a centre region closed to the sink will form in which sensors have to cost vast energy. To energy-efficient communication, Compressive Sensing(CS) has been employed gradually. However, the performance of plain CS deeply depends on the specific data gathering strategy in practice. In this paper, we propose an energy-efficient data gathering scheme based on regionalization CS. Firstly, the network is divided into several blocks randomly with no need for relationships or features among sensors. Furthermore, centre sensors for each region are elected with randomization. Subsequently, CS is implemented in each individual region respectively with divisional measurement matrix to generate parts of samplings. Finally, regional samplings in all regions are transported directly to the sink for reconstruction. Subsequently, two issues for practical applications are considered. Experiments reveal that our scheme outperforms distributed CS, straight forward and the mixed scheme by comparing different parameters of the data package and the considered issues further guarantee its feasibility.
Keywords: Regionalization Compressed Sensing; Transmission; Wireless Sensor Network.
Three Dimensional Power Efficient Distributed Node Localization in Wireless Sensor Networks
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
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
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.
An Intelligent Agent System for Managing Heterogeneous Sensors in Dispersed and Disparate Wireless Sensor Network Systems
by Yong Jin Lee, Jarrod Trevathan, Ian Atkinson, Wayne Read
Abstract: Wireless Sensor Network (WSN) systems typically employ middleware solutions to coordinate their various sensors and process their data. Middleware is also used to provide web services for storing/querying sensor network data. Any change in a WSN such as adding a new sensor or configuring existing sensors requires modification to its middleware and web service. This is problematic in a large-scale sensor observation system especially when multiple WSNs are deployed in different locations with heterogeneous sensors, and various middleware solutions and web service interfaces. To integrate hundreds of sensors with different capabilities through various middleware and web service specifications, a system needs to support all the encodings, models and services for registering, tasking and querying sensors. These characteristics are required to be pre-configured in order to use an appropriate function for a particular sensor. This paper presents an intelligent agent, which provides automatic semantic-based registration/configuration in a large-scale sensor observation system. The agent is developed using open source freely available technologies (e.g., Jena), and can react to any changes internally or externally made in WSNs (i.e., adding a new sensor or sending a task request to sensors/support systems). The agent makes a smart decision on which system function to use by integrating a semantic representation of sensor network data including middleware and web service specifications, and applying logical rules to the knowledge-base. We demonstrate how the agent operates using two real-world WSN systems. Each WSN is represented in RDF using a domain ontology, which extends the W3C SSN-XG ontology.
Keywords: Wireless sensor networks; sensor web enablement; semantic technologies; SEMAT.
APRS : Adaptive Real-Time Payload Data Reduction Scheme for IoT/ WSN Sensor board with Multivariate Sensors
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.
Modified Greedy Perimeter Stateless Routing for Vehicular Ad hoc Networking Algorithm
by Tong Wang
Abstract: Vehicular Ad Hoc Networking (VANET) can connect vehicles and Road Side Units (RSUs) as self-organized networks. Thus, many routing protocols have been discussed to improve road security and collision avoidance. The purpose of VANET is to improve road safety and find a path in a well-mannered fashion, which is a priority in this field because driving is dangerous and casualties can occur. Many protocols have been proposed in the last several years for VANET. This paper proposes a modified Greedy Perimeter Stateless Routing (GPSR) protocol to improve the possibility of finding a path and compares it with other routing protocols such as Optimized Link State Routing (OLSR) and Ad Hoc On Demand Vector (AODV).
Keywords: AODV; GPSR; OLSR; RSU; Road Security; VANET.
Lightweight and Practical Node Clustering Authentication Protocol for Hierarchical Wireless Sensor Networks
by Dengzhi Liu, Jian Shen, Anxi Wang, Chen Wang
Abstract: Hierarchical Wireless Sensor Networks(HWSNs) are used to provide real-time data and analysis of the monitoring area by the cooperation of low-cost nodes. The authentication towards multiple nodes and node cluster have become the research hots-pot considering of the requirement of application scenarios. There are many concerns about the sensitivity and privacy of data because the low-cost nodes are neither tamper-proof nor capable of performing public key cryptography efficiently. However, many researchers only focus on the existence of single node in the network, while the arrangement that multiple nodes attached to one cluster is out of consideration. In this paper, we propose a lightweight and practical node clustering authentication protocol for HWSNs. As we assume that one cluster to be authenticated is attached with a group of sensor nodes. The cluster head (CH) node takes full control of the entire authentication process in its cluster. In addition, the protocol is proved to offer enough security assurances and have resistance to various attacks under the security analysis. The regular operation of the network will not be affected or damaged by the incidents occurred during the authentication process.
Keywords: Hierarchical Wireless Sensor Networks; Node Authentication; Lightweight; Practical.
STCS: A Practical Solar Radiation based Temperature Correction Scheme in Meteorological WSN
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
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
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
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.
A Energy Balanced Routing Strategy Based on Non-uniform Layered Clustering
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
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
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
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
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
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
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
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 network’s 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
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