Scheduling Sensors for Guaranteed Sparse Coverage

Sensor networks are particularly applicable to the tracking of objects in motion. For such applications, it may not necessary that the whole region be covered by sensors as long as the uncovered region is not too large. This notion has been formalized by Balasubramanian et.al. as the problem of $\kappa$-weak coverage. This model of coverage provides guarantees about the regions in which the objects may move undetected. In this paper, we analyse the theoretical aspects of the problem and provide guarantees about the lifetime achievable. We introduce a number of practical algorithms and analyse their significance. The main contribution is a novel linear programming based algorithm which provides near-optimal lifetime. Through extensive experimentation, we analyse the performance of these algorithms based on several parameters defined

Achieving Minimum Coverage Breach under Bandwidth Constraints in Wireless Sensor Networks

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

An Energy-Efficient Distributed Algorithm for k-Coverage Problem in Wireless Sensor Networks

Wireless sensor networks (WSNs) have recently achieved a great deal of attention due to its numerous attractive applications in many different fields. Sensors and WSNs possesses a number of special characteristics that make them very promising in many applications, but also put on them lots of constraints that make issues in sensor network particularly difficult. These issues may include topology control, routing, coverage, security, and data management. In this thesis, we focus our attention on the coverage problem. Firstly, we define the Sensor Energy-efficient Scheduling for k-coverage (SESK) problem. We then solve it by proposing a novel, completely localized and distributed scheduling approach, naming Distributed Energy-efficient Scheduling for k-coverage (DESK) such that the energy consumption among all the sensors is balanced, and the network lifetime is maximized while still satisfying the k-coverage requirement. Finally, in related work section we conduct an extensive survey of the existing work in literature that focuses on with the coverage problem

Sensor network coverage and data aggregation problem: solutions toward the maximum lifetime

In the coverage problem, an optimal solution is proposed for the maximum lifetime sensor scheduling problem, which could find the upper bound of a sensor network\u27s lifetime. This research reveals the relationship between the degree of redundancy in sensor deployment and achievable extension on network lifetime, which can be a useful guide for practical sensor network design --Introduction, page 4

Review of Optimization Problems in Wireless Sensor Networks

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Some Target Coverage Issues of Wireless Sensor Network

Wireless Sensor Network is an emerging field that is achieving much importance due to its immense contribution in varieties of target specific applications. One of the active issues is Target Coverage that deals with the coverage of a specific set of targets. Static sensor nodes are being deployed in a random manner to monitor the required targets and collect as much information as possible. In this Paper we have presented an overview of WSN and some of the strategies of the Target Coverage Problem

Coverage Issues in Wireless Ad-Hoc Sensor Networks

Wireless Ad-Hoc sensor networks have a broad range of applications in the military,vigilance, environment monitoring, and healthcare fields. Coverage of the sensor networks describes how well an area is monitored. The coverage problem has been studied extensively, especially when combined with connectivity and well-organized. Coverage is a typical problem in the wireless sensor networks to fulfil issued sensing tasks. In general, sensing analysis represents how well an area is monitored by sensors. The quality of the sensor network can be reflected by levels of coverage and connectivity that it offers. The coverage issues have been studied extensively, especially when combined with connectivity and energy efficiency. Constructing a connected fully covered, and energy efficient sensor network is valuable for real world applications due to limited resources of sensor nodes. The survey recent contributions addressing energy efficient coverage problems in the context of static WASNs, networks in which sensor nodes do not move once they are deployed and present in some detail of the algorithms, assumptions, and results. A comprehensive comparison among these approaches is given from perspective of design objectives, assumptions, algorithm attributes and related results