An Evidential Approach for Area Coverage in Mobile Wireless Sensor Networks

2021 ◽  
Vol 10 (3) ◽  
pp. 30-54
Author(s):  
Adda Boualem ◽  
Marwane Ayaida ◽  
Cyril De Runz ◽  
Youcef Dahmani
Keyword(s):  
Physical World ◽  
Area Coverage ◽  
Wireless Sensor ◽  
Passive State ◽  
Coverage Problem ◽  
Single Node ◽  
Dempster Shafer Theory ◽  
Area Of Interest ◽  
Mobile Wireless Sensor ◽  
Shafer Theory

The study of coverage problem in uncertain WSN environment requires the consideration of this uncertainty by taking the best possible decisions, since it is impossible to explicitly represent all the combinatorics to produce a conditional active/passive state nodes' planning in the area of interest, and allow reasoning on various environmental states of the partially known physical world. This paper addresses the problem of area coverage based on the Dempster-Shafer theory. The authors aim to ensure the full area coverage while using a subset of connected nodes, with minimal costs using a minimal number of dominant nodes regardless of the type of used deployment (random or deterministic). This is ensured by activating a single node in each subset of each geographic sub-area, thus extending the lifetime of the wireless sensor network to its maximum. The comparison of the proposed model denoted evidential approach for area coverage (EAAC) with two well-known protocols and with a recent one showed a better performance and a slight improvement in the covered area.

Application of Computational Intelligence Techniques in Wireless Sensor Networks the State of the Art

2020 ◽  
pp. 1580-1600
Author(s):  
Subhendu Kumar Pani
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Computational Intelligence ◽  
State Of The Art ◽  
Area Coverage ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Sensor Devices ◽  
Huge Variety

A wireless sensor network may contain hundreds or even tens of thousands of inexpensive sensor devices that can communicate with their neighbors within a limited radio range. By relaying information on each other, they transmit signals to a command post anywhere within the network. Worldwide market for wireless sensor networks is rapidly growing due to a huge variety of applications it offers. In this chapter, we discuss application of computational intelligence techniques in wireless sensor networks on the coverage problem in general and area coverage in particular. After providing different types of coverage encountered in WSN, we present a possible classification of coverage algorithms. Then we dwell on area coverage which is widely studied due to its importance. We provide a survey of literature on area coverage and give an account of its state-of-the art and research directions.

Application of Computational Intelligence Techniques in Wireless Sensor Networks the State of the Art

2016 ◽  
pp. 441-461
Author(s):  
Subhendu Kumar Pani
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Computational Intelligence ◽  
State Of The Art ◽  
Area Coverage ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Sensor Devices ◽  
Huge Variety

A wireless sensor network may contain hundreds or even tens of thousands of inexpensive sensor devices that can communicate with their neighbors within a limited radio range. By relaying information on each other, they transmit signals to a command post anywhere within the network. Worldwide market for wireless sensor networks is rapidly growing due to a huge variety of applications it offers. In this chapter, we discuss application of computational intelligence techniques in wireless sensor networks on the coverage problem in general and area coverage in particular. After providing different types of coverage encountered in WSN, we present a possible classification of coverage algorithms. Then we dwell on area coverage which is widely studied due to its importance. We provide a survey of literature on area coverage and give an account of its state-of-the art and research directions.

DISTRIBUTED ENERGY-EFFICIENT ALGORITHMS FOR COVERAGE PROBLEM IN ADJUSTABLE SENSING RANGES WIRELESS SENSOR NETWORKS

2009 ◽  
Vol 01 (03) ◽  
pp. 299-317 ◽  
Author(s):  
CHINH VU ◽  
ZHIPENG CAI ◽  
YINGSHU LI
Keyword(s):  
Network Lifetime ◽  
Complexity Analysis ◽  
Boundary Effect ◽  
Area Coverage ◽  
Distributed Scheduling ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Continuous Space ◽  
Algorithm Efficiency ◽  
Wide Range

Due to wide range of applications of Wireless Sensor Network (WSN), lots of effort has been dedicated to solve its various issues. Among those issues, coverage is one of the most fundamental ones of which a WSN has to watch over the environment such as a forest (area coverage) or set of subjects such as collection of precious renaissance paintings (target of point coverage) and collect environment parameters and maybe, further monitor the environment. With variable sensing range, the difficulties to cover a continuous space (where number of points is infinity) in the area coverage problem becomes somewhat harder than covering limited number of discrete points in the target coverage problem. Very few papers have paid effort for the former problem. In this paper, we consider the area coverage problem for WSN where sensors can arbitrarily change their sensing ranges under some upper bound. We first improve the work in [1] so that the boundary effect is ruled out and the monitored area can be completely covered at all cases. Next, we extend that improved algorithm by introducing two distributed scheduling algorithms which are trade-off in term of network lifetime and algorithms efficiency. The major objective of each of our 3 proposed algorithms in this paper is to balance energy consumption and to maximize network lifetime. Our proposed algorithm efficiency is shown by algorithms complexity analysis and extensive simulation. In compared with the work in [1], our proposed algorithms are not only better in providing coverage quality, they could also greatly lengthen network lifetime and greatly reduce the unnecessary coverage redundancy.

scholarly journals An area coverage algorithm for wireless sensor networks based on differential evolution

2018 ◽  
Vol 14 (8) ◽  
pp. 155014771879673 ◽  
Author(s):  
Ning-ning Qin ◽  
Jia-le Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Differential Evolution ◽  
High Energy ◽  
Area Coverage ◽  
Wireless Sensor ◽  
Compensation Strategy ◽  
Coverage Ratio ◽  
Area Of Interest ◽  
Binary Differential Evolution

Lifetime requirements and coverage demands are emphasized in wireless sensor networks. An area coverage algorithm based on differential evolution is developed in this study to obtain a given coverage ratio [Formula: see text]. The proposed algorithm maximizes the lifetime of wireless sensor networks to monitor the area of interest. To this end, we translate continuous area coverage into classical discrete point coverage, so that the optimization process can be realized by wireless sensor networks. Based on maintaining the ε-coverage performance, area coverage algorithm based on differential evolution takes the minimal energy as optimization objective. In area coverage algorithm based on differential evolution, binary differential evolution is redeveloped to search for an improved node subset and thus meet the coverage demand. Taking into account that the results of binary differential evolution are depended on the initial value, the resulting individual is not an absolutely perfect node subset. A compensation strategy is provided to avoid unbalanced energy consumption for the obtained node subset by introducing the positive and negative utility ratios. Under the helps of those ratios and compensation strategy, the resulting node subset can be added additional nodes to remedy insufficient coverage, and redundancy active nodes can be pushed into sleep state. Furthermore, balance and residual energy are considered in area coverage algorithm based on differential evolution, which can expand the scope of population exploration and accelerate convergence. Experimental results show that area coverage algorithm based on differential evolution possesses high energy and computation efficiencies and provides 90% network coverage.

scholarly journals Transforming Area Coverage to Target Coverage to Maintain Coverage and Connectivity for Wireless Sensor Networks

2012 ◽  
Vol 8 (10) ◽  
pp. 254318 ◽  
Author(s):  
Xiu Deng ◽  
Jiguo Yu ◽  
Dongxiao Yu ◽  
Congcong Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Area Coverage ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Target Coverage ◽  
Coverage Problem ◽  
Minimum Number ◽  
New Perspective ◽  
Target Coverage Problem

Area coverage is one of the key issues for wireless sensor networks. It aims at selecting a minimum number of sensor nodes to cover the whole sensing region and maximizing the lifetime of the network. In this paper, we discuss the energy-efficient area coverage problem considering boundary effects in a new perspective, that is, transforming the area coverage problem to the target coverage problem and then achieving full area coverage by covering all the targets in the converted target coverage problem. Thus, the coverage of every point in the sensing region is transformed to the coverage of a fraction of targets. Two schemes for the converted target coverage are proposed, which can generate cover sets covering all the targets. The network constructed by sensor nodes in the cover set is proved to be connected. Compared with the previous algorithms, simulation results show that the proposed algorithm can prolong the lifetime of the network.

scholarly journals Maximum Target Coverage Problem in Mobile Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 184
Author(s):  
Dieyan Liang ◽  
Hong Shen ◽  
Lin Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Approximation Algorithm ◽  
Optimization Problem ◽  
Optimal Algorithm ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Mobile Wireless ◽  
Limited Mobility ◽  
Mobile Wireless Sensor

We formulate and analyze a generic coverage optimization problem arising in wireless sensor networks with sensors of limited mobility. Given a set of targets to be covered and a set of mobile sensors, we seek a sensor dispatch algorithm maximizing the covered targets under the constraint that the maximal moving distance for each sensor is upper-bounded by a given threshold. We prove that the problem is NP-hard. Given its hardness, we devise four algorithms to solve it heuristically or approximately. Among the approximate algorithms, we first develop randomized (1−1/e)-optimal algorithm. We then employ a derandomization technique to devise a deterministic (1−1/e)-approximation algorithm. We also design a deterministic approximation algorithm with nearly ▵−1 approximation ratio by using a colouring technique, where ▵ denotes the maximal number of subsets covering the same target. Experiments are also conducted to validate the effectiveness of the algorithms in a variety of parameter settings.

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