An Adaptive Scheduling Algorithm for Set Cover Problem in Wireless Sensor Networks: A Cellular Learning Automata Approach

Abstract

Redundant node deployment is a common strategy in wireless sensor networks. This redundancy can be due to various reasons such as high probability of failures, long lifetime expectation, etc. One major problem in wireless sensor networks is to use this redundancy in order to extend the network lifetime while keeping the entire area under the coverage of the network. In this problem, which is known as set cover problem, the main objective is to select a subset of sensor nodes as active nodes so that the set of active nodes covers the entire area of the network. In this paper, an scheduling algorithm is presented for solving the set cover problem using cellular learning automata. In this algorithm, each node is equipped with a learning automaton which locally decides for the node to be active or not based on the situations of its neighbors. Simulation results in J-sim simulator environment specify the efficiency of the proposed scheduling algorithm over existing algorithms such as PEAS and PECAS. In this paper, a distributed, adaptive scheduling algorithm based on cellular learning automata for wireless sensor networks is proposed. The main purpose of this algorithm is to maintain coverage in the network with minimum number of active nodes, so that the total consumed energy of nodes is minimized. In the proposed algorithm, each node is equipped with a learning automaton which decides for the node to be active or not based on the states (either active or not) of its neighboring sensor nodes. We used J-sim simulator to evaluate the performance of the proposed algorithm. Simulation results specify the efficiency of the proposed algorithm over existing algorithms such as PEAS (2) and PECAS (3) -especially against high ratio of unexpected failures. The rest of this paper is organized as follows. In Section II, a literature overview is presented. In Section III, learning automata and cellular learning automata are briefly reviewed. The problem statement is given in Section IV. The proposed scheduling algorithm is described in Section V. Simulation results are given in Section VI. Section VII is the conclusion.