A Cellular Learning Automata-based Algorithm for Solving the Coverage and Connectivity Problem in Wireless Sensor Networks

Presence of redundant nodes is common in wireless sensor networks because of various reasons such as high probability of failures and necessity of long lifetime. When such redundancy exists, some distributed algorithms are needed for selecting minimal subset of nodes as active nodes in a manner that network area is covered entirely with the selected active nodes. In this paper, a distributed algorithm is proposed which attempts to minimize the number of active nodes in the network using cellular learning automata in such a way that the following two conditions are met: 1. network area is covered entirely, and 2. network of selected active nodes is connected. In the proposed algorithm, each node is equipped with a learning automaton which locally decides for the node to be active or not based on the remaining energy of the node and its neighbors’ situations. To ensure the network connectivity, we analytically determine the radio transmission range of sensor nodes according to their sensing range so that complete coverage of the network area guarantees the connectivity of active nodes. The time and space costs of the proposed algorithm are analytically determined and compared with those of similar existing algorithms such as PEAS and PECAS. Simulation results in J-Sim simulator environment specify the efficiency of the proposed algorithm over existing algorithms such as PEAS and PECAS—especially against high ratio of unexpected failures and nodes’ energy depletion.