In mobile wireless sensor networks (MWSNs), each sensor has the ability not only to sense and transmit data but also to move to some specific location. Because the movement of sensors consumes much more power than that in sensing and communication, the problem of scheduling mobile sensors to cover all targets and maintain network connectivity such that the total movement distance of mobile sensors is minimized has received a great deal of attention. However, in reality, due to a limited budget or numerous targets, mobile sensors may be not enough to cover all targets or form a connected network. Therefore, targets must be weighted by their importance. The more important a target, the higher the weight of the target. A more general problem for target coverage and network connectivity, termed the Maximum Weighted Target Coverage and Sensor Connectivity with Limited Mobile Sensors (MWTCSCLMS) problem, is studied. In this paper, an approximation algorithm, termed the weighted-maximum-coverage-based algorithm (WMCBA), is proposed for the subproblem of the MWTCSCLMS problem. Based on the WMCBA, the Steiner-tree-based algorithm (STBA) is proposed for the MWTCSCLMS problem. Simulation results demonstrate that the STBA provides better performance than the other methods.
The coverage problem is an important issue in a wireless sensor network, in which each sensor has its own mission to monitor a region through the sensor's sensing range. Different applications have various coverage requirements @cite_13 @cite_21 @cite_24 @cite_1 . In @cite_13 , the area coverage problem is discussed in the way to deploy sensors to form a wireless sensor network such that a particular area will be fully covered and ensure the network connectivity. In @cite_21 , the area coverage problem is studied to deploy sensors to form a connected wireless sensor network even if unpredicted obstacles exist in the sensing field. In @cite_24 , the problem of constructing a minimum size connected wireless sensor network such that the critical grids in a sensing field are all covered by sensors is addressed. In @cite_1 , the barrier coverage problem, the problem of deploying sensors to construct a barrier such that invaders will be detected by at least one sensor, is studied.
@cite_24 @cite_1 @cite_21 @cite_13
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Wireless sensor networks are formed by connected sensors that each have the ability to collect, process, and store environmental information as well as communicate with others via inter-sensor wireless communication. These characteristics allow wireless sensor networks to be used in a wide range of applications. In many applications, such as environmental monitoring, battlefield surveillance, nuclear, biological, and chemical (NBC) attack detection, and so on, critical areas and common areas must be distinguished adequately, and it is more practical and efficient to monitor critical areas rather than common areas if the sensor field is large, or the available budget cannot provide enough sensors to fully cover the entire sensor field. This provides the motivation for the problem of deploying the minimum sensors on grid points to construct a connected wireless sensor network able to fully cover critical square grids, termed CRITICAL-SQUARE-GRID COVERAGE. In this paper, we propose an approximation algorithm for CRITICAL-SQUARE-GRID COVERAGE. Simulations show that the proposed algorithm provides a good solution for CRITICAL-SQUARE-GRID COVERAGE. Proposing a new approach to barrier coverage in wireless sensor network.Modeling barrier coverage with stochastic edge-weighted graph.Finding an optimal solution for the network stochastic edge-weighted coverage graph.Comparing the performance of the proposed method with the greedy and optimal methods. Barrier coverage is one of the most important applications of wireless sensor networks. It is used to detect mobile objects are entering into the boundary of a sensor network field. Energy efficiency is one of the main concerns in barrier coverage for wireless sensor networks and its solution can be widely used in sensor barrier applications, such as intrusion detectors and border security. In this work, we take the energy efficiency as objectives of the study on barrier coverage. The cost in the present paper can be any performance measurement and normally is defined as any resource which is consumed by sensor barrier. In this paper, barrier coverage problem is modeled based on stochastic coverage graph first. Then, a distributed learning automata-based method is proposed to find a near optimal solution to the stochastic barrier coverage problem. The stochastic barrier coverage problem seeks to find minimum required number of sensor nodes to construct sensor barrier path. To study the performance of the proposed method, computer simulations are conducted. The simulation results show that the proposed algorithm significantly outperforms the greedy based algorithm and optimal method in terms of number of network barrier paths. This paper introduces an optimal deployment algorithm of sensors in a given region to provide desired coverage and connectivity for a wireless sensor network. Our paper utilizes two separate procedures for covering different regions of a symmetrical rectangular area. The proposed method divides the given area of interest into two distinct sub-regions termed as the central and edge regions. In each region, a unique scheme is used to determine the number and location of sensors required to monitor and completely cover the region keeping the connectivity and coverage ranges of the sensors, their hardware specification and the dimensions of the region concerned as the constraints. Our scheme reduces the overhead in determining the position of sensors for deployment by following a coverage and connectivity algorithm of lesser complexity rather than those present in related schemes. Finally, we compare our deployment scheme with the interpolation scheme of [2] in regions of different dimensions and different coverage and connectivity levels with different sensing ranges of the sensors to show our cost efficiency over the latter.