Optimization deployment of wireless sensor networks based on culture–ant colony algorithm

Abstract

The optimization of nodes deployment is one of the most active research areas in wireless sensor networks. In this paper, we propose an improved culture algorithm–ant colony algorithm (CA–ACA) to solve the problem of nodes deployment. Double evolution mechanism of culture algorithm is integrated into the improved ant colony optimization algorithm within the population space as an evolutionary strategy, and then directs the search of population space through the elites of continuous evolution in belief space. The introduction of culture algorithm makes the search for optimization faster and better stability of CA–ACA than traditional ones. In addition, greedy strategy is introduced for the situation of sparsely monitored points, which makes CA–ACA be suitable for any environment. Furthermore, we also investigate the convergence judging method which makes CA–ACA avoid premature convergence so as to achieve the purpose of global optimization. A large number of simulation experiments have been conducted and the results not only demonstrate the validity of CA–ACA, but also verify that CA–ACA algorithm can optimize the number of sensors deployed in network under the conditions of guaranteed connectivity and coverage. Current results are of great significance to effectively design the optimal deployment of nodes in wireless and mobile sensor networks.