Reducing data collection latency in Wireless sensor networks with mobile elements

He, Liang; Pan, Jianping; Xu, Jingdong

doi:10.1109/infcomw.2011.5928878

Cited by 16 publications

(5 citation statements)

References 24 publications

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“…According to different application scenarios, the existing studies can be classified into three categories: (1) route patrol for collecting data from fixed targets [10]- [12], (2) detection of mobile targets [4], [5], [13], and (3) target coverage in dynamic environments [14], [15]. In these studies, mobile sensors move actively to improve the surveillance quality, but the optimization of sensor movement is not explicitly considered.…”

Section: Related Workmentioning

confidence: 99%

Minimizing Movement for Target Coverage and Network Connectivity in Mobile Sensor Networks

Liao

Wang

Zhang

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

175

114

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Abstract-Coverage of interest points and network connectivity are two main challenging and practically important issues of Wireless Sensor Networks (WSNs). Although many studies have exploited the mobility of sensors to improve the quality of coverage and connectivity, little attention has been paid to the minimization of sensors' movement, which often consumes the majority of the limited energy of sensors and thus shortens the network lifetime significantly. To fill in this gap, this paper addresses the challenges of the Mobile Sensor Deployment (MSD) problem and investigates how to deploy mobile sensors with minimum movement to form a WSN that provides both target coverage and network connectivity. To this end, the MSD problem is decomposed into two sub-problems: the Target COVerage (TCOV) problem and the Network CONnectivity (NCON) problem. We then solve TCOV and NCON one by one and combine their solutions to address the MSD problem. The NP-hardness of TCOV is proved. For a special case of TCOV where targets disperse from each other farther than double of the coverage radius, an exact algorithm based on the Hungarian method is proposed to find the optimal solution. For general cases of TCOV, two heuristic algorithms, i.e., the Basic algorithm based on clique partition and the TV-Greedy algorithm based on Voronoi partition of the deployment region, are proposed to reduce the total movement distance of sensors. For NCON, an efficient solution based on the Steiner minimum tree with constrained edge length is proposed. The combination of the solutions to TCOV and NCON, as demonstrated by extensive simulation experiments, offers a promising solution to the original MSD problem that balances the load of different sensors and prolongs the network lifetime consequently.

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Section: Related Workmentioning

confidence: 99%

Minimizing Movement for Target Coverage and Network Connectivity in Mobile Sensor Networks

Liao

Wang

Zhang

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

175

114

View full text Add to dashboard Cite

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“…A popular feature to optimize is the energy efficiency of the mobile agent deployments [4], [5], [9], [10], [14]. Another feature to optimize for sparse networks is data latency reduction [3], [8], [11], [15]. Both data latency reduction and energy efficiency are optimized in [28], [29], where a constant-in-time Stochastic Orienteering Problem is solved.…”

Section: Related Workmentioning

confidence: 99%

Exploitation by Informed Exploration between Isolated Operatives for information-theoretic data harvesting

Axelrod¹,

Chowdhary²,

Karaman³

2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-We consider the problem of ferrying data between nodes of a sparsely distributed sensing network of Unattended Ground Sensors (UGS) with endurance-constrained Unmanned Aerial Systems (UAS). The sensing domain wherein the sparsely distributed UGS network is deployed is assumed to be highly nonstationary (time-varying) and noisy. This makes the dataferrying problem very complicated as the expected valueof-information at a sensing location can rapidly change. To address this issue, we present a new data ferrying algorithm termed Exploitation by Informed Exploration between Isolated Operatives (EIEIO), and show that with several reasonable assumptions and a model on the predicted accumulation of value-of-information, the problem can be simplified to a mathematical linear program. To solve the linear program, the UAS learns to anticipate regions in the sensing domain that have the highest degree of change. The degree of change, is learned using a novel implementation of a Cox Process called the CoxGaussian Process (CGP). Our approach does not require a priori knowledge of the sensing domain model to arrive at an optimal UAS allocation strategy.

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“…Finding an optimal strategy of query processing with value to energy efficiency was a vital issue in COSE, which failed to formulate the multiple pipelines for query processing in heterogeneous sensor networks. Dynamic Approximate Caching Scheme (DACS) used approximate cache coherence policy as expressed in [5]. Moreover, DACS required fewer resources to caching data but similarity aware query processing model failed to minimize the processing time on injected query.…”

Section: Related Workmentioning

confidence: 99%

Stochastic Heuristic Optimization based Multi-Query Processing in Wireless Sensor Network using Genetic Algorithm

AntonyAliceJeyaBharathi¹,

Alagarsamy²

2014

IJCA

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Wireless Sensor Network is an infrastructure comprising of sensing, and computing. The communication elements in sensor network give capability to instrument, watch, and respond to events and phenomenon in a particular situation. Query processing in sensor network first transfers the query generated position to the node where the result obtained is similar to this query. Collaborative Query-Centric Framework (COSE) heterogeneous sensor networks are effectual and well-organized for processing of queries. Query processing with respect to energy efficiency attain single pipeline of query processing but fails to address the issues related to multiple pipeline of query processing. COSE major drawback is that it is unable to attain an optimal solution for multiple pipelines. Next, Pocket Driven Trajectories (PDT) algorithm monitors the query processing based on spatial layout of the selected nodes. PDT algorithm efficiently adapts to different types of data collection paths but not effective for multicast query phase. To develop a multiple query processing strategy in wireless sensor network, Stochastic Heuristic Optimization using Genetic Algorithm (SHO-GA) is introduced. The SHO-GA framework process the multiple query plans based on the closeness of nodes required to answer the user query. The multi-query processing using Genetic Algorithm (GA) takes the associations with FROM clause and other multiple query operators. The SHO-GA framework carries the probability of crossover, mutation and the pre-specified number of generations as input, and produces the top 'n' multi-query processing as output. The stochastic heuristic optimization fitness value provides the best fit chromosome to find the improvement in its query processing parameters. The stochastic heuristic for multi-query called base station optimization (BSO) eliminates the redundancy from the original set. The multi-query plans processed involve minimum processing time for answering the user query leading to efficient query processing sensor network system. Experimental evaluation is performed on factors such as average processing cost, cumulative distribution, multi-query processing time, query answer transmitted speed, query processing delay and user accessibility level.

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Reducing data collection latency in Wireless sensor networks with mobile elements

Cited by 16 publications

References 24 publications

Minimizing Movement for Target Coverage and Network Connectivity in Mobile Sensor Networks

Minimizing Movement for Target Coverage and Network Connectivity in Mobile Sensor Networks

Exploitation by Informed Exploration between Isolated Operatives for information-theoretic data harvesting

Stochastic Heuristic Optimization based Multi-Query Processing in Wireless Sensor Network using Genetic Algorithm

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