Node placement with evolutionary algorithms for maximum coverage of heterogeneous WSNs

In consequence of advances in wireless communication, digital systems and micro-electronic-mechanical system technologies wireless sensor networks (WSNs) have been developed and applied in lots of different real-world applications such as military, industrial, environmental, health, etc. Due to their limited resources and constraints, WSNs face with several problems such as energy consumption, node deployment, data aggregation and transmission. Node deployment affects other problem domains in a direct or indirect way. Therefore, in this study, we deal with this problem and try to increase the coverage area of WSN system with an organized deployment approach. This problem is an NP-Hard problem, and it is hard to solve with standard mathematical formulations. Also this problem is known as maximum coverage sensor deployment problem (MCSDP) in literature. In this paper, worked on the topology that consists from sensor nodes which have the different sensing ranges — heterogeneous. A novel GA is developed for deploying sensor nodes on 2 dimensional area. Studied problem, proposed genetic algorithm and novel local optimization algorithm are described deeply. The result of applying GA to initial deployment topology, increase of coverage are due to iteration count and results of experiments with local optimization algorithm are represented. Organized and random deployment techniques are described briefly and fitness function of developed GA is applied to test data. According to results of experiments; proposed GA is performed effective and got desired coverage.