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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Coverage is a central issue in the design of wireless sensor networks. There are many measures for coverage, based on what aspect of surveillance quality we wish to address. Designing a network that achieves desired standards in terms of the measure chosen is a non-trivial problem. In this paper we take the Maximal Breach Path measure and formulate the sensor-network design problem as a geometric optimisation problem. We present improved polynomial time algorithms for computing the aforesaid measure for a given sensor network. Also, as a first step toward solving the optimisation problem posed in this paper, we present a geometric transformation on a given configuration of sensors that brings the maximal breach to a “local optimal” – in the sense that the resulting breach is the best we can get keeping the topology of the starting configuration intact. This paper addresses the coverage breach problem in wireless sensor networks with limited bandwidths. In wireless sensor networks, sensor nodes are powered by batteries. To make efficient use of battery energy is critical to sensor network lifetimes. When targets are redundantly covered by multiple sensors, especially in stochastically deployed sensor networks, it is possible to save battery energy by organizing sensors into mutually exclusive subsets and alternatively activating only one subset at any time. Active nodes are responsible for sensing, computing and communicating. While the coverage of each subset is an important metric for sensor organization, the size of each subset also plays an important role in sensor network performance because when active sensors periodically send data to base stations, contention for channel access must be considered. The number of available channels imposes a limit on the cardinality of each subset. Coverage breach happens when a subset of sensors cannot completely cover all the targets. To make efficient use of both energy and bandwidth with a minimum coverage breach is the goal of sensor network design. This paper presents the minimum breach problem using a mathematical model, studies the computational complexity of the problem, and provides two approximate heuristics. Effects of increasing the number of channels and increasing the number of sensors on sensor network coverage are studied through numerical simulations. Overall, the simulation results reveal that when the number of sensors increases, network lifetimes can be improved without loss of network coverage if there is no bandwidth constraint; with bandwidth constraints, network lifetimes may be improved further at the cost of coverage breach. One of the fundamental issues in sensor networks is the coverage problem, which reflects howwell a sensor network is monitored or tracked by sensors. In this paper, we formulate this problem as a decision problem, whose goal is to determine whether every point in the service area of the sensor network is covered by at least k sensors, where k is a given parameter. The sensing ranges of sensors can be unit disks or non-unit disks. We present polynomial-time algorithms, in terms of the number of sensors, that can be easily translated to distributed protocols. The result is a generalization of some earlier results where only k =1 is assumed. Applications of the result include determining insufficiently covered areas in a sensor network, enhancing fault-tolerant capability in hostile regions, and conserving energies of redundant sensors in a randomly deployed network. Our solutions can be easily translated to distributed protocols to solve the coverage problem. Sensing coverage is an essential functionality of wireless sensor networks (WSNs). However, it is also well known that coverage alone in WSNs is not sufficient, and hence network connectivity should also be considered for the correct operation of WSNs. In this paper, we address the problem of k-coverage in WSNs such that in each scheduling round, every location in a monitored field (or simply field) is covered by at least k active sensors while all active sensors are being connected. Precisely, we study sensors duty-cycling strategies for generating k-coverage configurations in WSNs. First, we model the k-coverage problem in WSNs. Second, we derive a sufficient condition of the sensor spatial density for complete k-coverage of a field. We also provide a relationship between the communication and sensing ranges of sensors to maintain both k-coverage of a field and connectivity among all active sensors. Third, we propose four configuration protocols to solve the problem of k-coverage in WSNs. We prove that our protocols select a minimum number of sensors to achieve full k-coverage of a field while guaranteeing connectivity between them. Then, we relax some widely used assumptions for coverage configuration in WSNs, to promote the use of our proposed protocols in real-world sensing applications. Our simulation results show that our protocols outperform an existing distributed k-coverage configuration protocol.