Lifetime Maximization for Connected Target Coverage in Wireless Sensor Networks (original) (raw)
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Target Coverage in Wireless Sensor Networks
Coverage problems are fundamental and crucial in designing a wireless sensor networks. The target coverage problem is finding an optimal scheduling for sensors such that the time (called lifetime) to monitor every target can be as long as possible. Unfortunately, the target coverage problem has been proved to be NP-complete. Most of previous work only considers one or two factors exclusively and thus fails to prolong the lifetime to near the optimum. The main objective of this work is to design efficient scheduling algorithms to maximize the lifetime of a given whole wireless sensor network by considering adjusting sensing range, locations of target and sensors, residue battery power of sensor nodes, and assignment between sensors and targets simultaneously. A maximum weighted matching algorithm is devised by considering full coverage and the maximum total monitored duration for each target-sensor assignments. We also conduct simulations to demonstrate that the proposed algorithms can achieve very high network lifetime closed to the optimum.
Greedy Algorithms for Target Coverage Lifetime Management Problem in Wireless Sensor Networks
When several low power sensors are randomly deployed in a field for monitoring targets located at fixed positions, managing the network lifetime is useful as long as replacing battery of dead sensors is not often feasible. The most commonly investigated mechanism for coverage preserving while maximizing the network lifetime is to design efficient sleep scheduling protocols, so that sensors can alternate their state between being active or not. Maximizing lifetime of a sensor network while satisfying a predefined coverage requirement is an optimization problem, which most of times cannot be optimally solved in polynomial time. In this paper, we address this problem by using set cover approach. We propose a greedy algorithm that distributes sensors among disjoints and non-disjoints set covers with the requirement that each set cover satisfies full targets coverage. The algorithm is an improvement of the classical greedy set cover algorithm, and its approximation ratio is verified to be not worse than log(m). Simulation results show good performance over some other solutions found in the literature. We provide also a comparison of several greedy techniques found in the literature addressed in the context of different design choices linked to the target coverage problem.
Target Coverage Management in Wireless Sensor Networks
As an important issue reflecting the QoS of the sensing task, coverage problem impacts widely on the performance of wireless sensor networks. The target coverage lifetime maximization problem is yet a challenging problem, which tries to settle a compromise between managing the coverage of a set of targets and maximizing the lifetime of the network. This problem becomes more accurate when targets detection is distance dependent. In this paper, we address the target coverage lifetime maximization problem by considering a probabilistic coverage model, which takes into account the distance parameter. We propose an algorithm based on a modified version of the classical well-known weighed set cover which organizes sensors in disjoint and non-disjoint set covers. Performance evaluation of our solution indicated good performance in managing coverage of targets while extending the network lifetime.
Solving coverage problems in wireless sensor networks using cover sets
Ad Hoc Networks, 2010
To achieve power efficient monitoring of targets by sensor networks, various coverage algorithms have been proposed. These algorithms divide the sensor nodes into cover sets, where each cover set is capable of monitoring all targets. Generating the maximum number of cover sets has been proven to be an NP-complete problem and, thus, algorithms producing sub-optimal solutions have been proposed. In this paper we present a novel and efficient coverage algorithm, that can produce both disjoint cover sets, i.e. cover sets with no common sensor nodes, as well as non-disjoint cover sets. While searching for the best sensor to include in a cover set, our algorithm uses a cost function that takes into account the monitoring capabilities of a sensor, its association with poorly monitored targets, but also the sensor's remaining battery life. Through simulations, we show that the proposed algorithm outperforms similar heuristic algorithms found in the literature, producing collections of cover sets of optimal (or near-optimal) size. The increased availability offered by these cover sets along with the short execution time of the proposed algorithm make it desirable for a wide range of node deployment environments.
Lifetime and Coverage Maximization in Wireless Sensor Networks
1st IFAC Workshop on Estimation and Control of Networked Systems, 2009
In the considered scenario, a wireless sensor network (WSN) operates in a difficult to reach (or even hostile) environment, and is therefore required to autonomously configure itself and tune its parameters. We investigate techniques that guarantee a good compromise between conflicting requirements: 1) a good coverage of the area, 2) a long lifetime, 3) a good temporal accuracy in classifying
2008
A major challenge in Wireless Sensor Networks is that of maximizing the lifetime while maintaining coverage of a set of targets, a known NP-complete problem. In this paper, we present theoretically-grounded, energy-efficient, distributed algorithms that enable sensors to schedule themselves into sleep-sense cycles. We had earlier introduced a lifetime dependency (LD) graph model that captures the interdependencies between these cover sets by modeling each cover as a node and having the edges represent shared sensors. The key motivation behind our approach in this paper has been to start with the question of what an optimal schedule would do with the lifetime dependency graph. We prove some basic properties of the optimal schedule that relate to the LD graph. Based on these properties, we have designed algorithms which choose the covers that exhibit these optimal schedule like properties. We present three new sophisticated algorithms to prioritize covers in the dependency graph and simulate their performance against state-of-art algorithms. The net effect of the 1-hop version of these three algorithms is a lifetime improvement of more than 25-30% over the competing algorithms of other groups, and 10-15% over our own; the 2-hop versions have additional improvements, 30-35% and 20-25%, respectively.
M-Connected Coverage Problem in Wireless Sensor Networks
ISRN Sensor Networks, 2012
Solving coverage problem alone is not adequate in a wireless sensor network, since data has to be transmitted to the base station. This leads to the lookout for an energy efficient method to solve connected coverage problem. This paper addresses M-connected (each sensor node will have at least M other sensor nodes within its communication range) target coverage problem in wireless sensor networks, where the required level of connectivity and coverage may be high or low as required. We propose a heuristic for M-connected target coverage problem, where initially a cover is decided and later on it is checked for M-connectivity. M-connectivity for simple coverage, k-coverage, and Q-coverage is focussed on in this paper. We use a Low-Energy Adaptive Clustering Hierarchy (LEACH) inspired model, where a cluster is considered as a set of sensor nodes satisfying M-connectivity and required level of coverage. It is enough if one among these nodes transmits the monitored information to the bas...
Maximize the Coverage Lifetime of Sensor Networks
Lecture Notes in Computer Science, 2006
When deploying sensors in the field in order to collect useful information, one of the most important issues is how to prolong the lifetime of the network because of energy constraint of the sensors while guaranteeing that every point in the network is covered. In this paper, we propose the formulation of integer linear programming (ILP) model to find the optimal network flow in the sensor fields in order to maximize the network lifetime while maintaining the coverage and connectivity. By dividing the network into grid structure, the problem can become manageable in size and complexity thus can be applied to large network with high number of nodes. The experimental results show that our proposed scheme outperforms previous protocols in terms of coverage lifetime.
Coverage problems in wireless sensor networks: designs and analysis
International Journal of Sensor Networks, 2008
Recently, a concept of wireless sensor networks has attracted much attention due to its widerange of potential applications. Wireless sensor networks also pose a number of challenging optimization problems. One of the fundamental problems in sensor networks is the coverage problem, which reflects the quality of service that can be provided by a particular sensor network. The coverage concept is defined from several points of view due to a variety of sensors and a wide-range of their applications. Several different designs and formulations of coverage problems have been proposed. They are subject to various topics such as types of interest regions (areas vs. targets) and different objectives (maximum network lifetime, minimum coverage breach) with other constraints. In this paper, we survey the state-of-the-art coverage formulations, present an overview and analysis of the solutions proposed in recent research literature.
The Optimal Deployment, Coverage, and Connectivity Problems in Wireless Sensor Networks: Revisited
IEEE Access
Finding an optimal node deployment strategy in Wireless Sensor networks (WSNs) that would reduce cost, be robust to node failures, reduce computation and communication overhead, and guarantee a high level of coverage along with network connectivity is a difficult problem. In fact, Sensing coverage and network connectivity are two of the most fundamental problems in WSNs as they can directly impact the network lifetime and operation. In this paper, we consider deriving optimal conditions for connectivity with coverage in WSNs. Most versions of this problem are (NP-complete) while approximation algorithms cannot be developed for some versions of polynomial time, unless P = NP. Hence, we also develop a heuristic for some versions of the problem and the efficacy of the heuristic will be evaluated through extensive simulations. We are also interested in determining the probability of finding a path between a given pair of nodes over a given topology of WSNs. This will serve as a measure of connectivity with coverage of the network. Hence, we derive necessary and sufficient conditions for connectivity with coverage over a clustered structure in WSNs. Then, employing queuing networks modeling techniques, we present a dynamic programming study of the connectivity with coverage of clustered structure and its effect on routing in generalized WSNs. The performance evaluation of the proposed schemes shows that availability of nodes, sensor node coverage, and the connectivity were sufficiently enhanced to maximize network lifetime.