In this paper, we propose a distributed algorithm based on a generalization of the Cellular Automata concept called Graph Cellular Automata (GCA) to solve the Maximum Lifetime Coverage Problem (MLCP) in wireless sensor networks (WSNs). In GCA, we adapt life-like state transition functions inspired by Conway’s Game of Life in order to solve the problem. The goal of this paper is to study the quality of state transition functions for an objective provided by the MLCP in WSNs. The proposed algorithm possesses all the advantages of a localized algorithm, i.e., using only some knowledge about neighbors, a WSN is able to self-organize in such a way as to prolong its lifetime, at the same time preserving the required coverage ratio of the target field. Our experimental results show that certain rules are better solvers of the given problem than others. The paper also presents the results of an experimental study of the proposed algorithm and comparison with a centralized Genetic Algorithm.
Energy optimization problem in Wireless Sensor Networks (WSN) is a backbone of efficient performance of sensor network consisting of small devices with limited and non-recovering battery. WSN lifetime maximization problem under assumption of that the coverage is main task of the network is known as Maximal lifetime coverage problem (MLCP). This problem belongs to a class of NP-hard problems. In this paper we propose a novel simulated annealing (SA) algorithm to solve MLCP. The proposed algorithm is studied for high dense WSN instances under different parameter setup.
In this paper we consider Maximal Lifetime Coverage Problem in Wireless Sensor Networks which is formulated as a scheduling problem related to activity of sensors equipped at battery units and monitoring a two-dimensional space in time. The problem is known as an NP-hard and to solve it we propose two heuristics which use specific knowledge about the problem. The first one is proposed by us stochastic greedy algorithm and the second one is metaheuristic known as Simulated Annealing. The performance of both algorithms is verified by a number of numerical experiments. Comparison of the results show that while both algorithms provide results of similar quality, but greedy algorithm is slightly better in the sense of computational time complexity.
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