PROJECT TITLE :
An Ant Colony Optimization Based Approach for Minimum Cost Coverage on 3-D Grid in Wireless Sensor Networks - 2018
The application of swarm intelligence algorithms to wireless sensor networks (WSNs) deployment has been the focus of analysis community for past few years. One such algorithm is ant colony optimization (ACO), whose application in reducing the value of WSNs in terms of deployed sensor nodes has recently attracted attention of the researchers. In this letter, we have a tendency to propose an ACO-primarily based framework for WSN deployment in a very realistic three-D surroundings, by creating modifications to the standard ACO algorithm. The simulation results cause the conclusion that the proposed framework achieves higher performance compared with the state-of-the-art ACO-primarily based algorithms in terms of size of the solution for node deployment. As well, during a three-D environment, time overhead drawback arises in commonplace ACO-based mostly algorithms since they require a large variety of iterations to achieve better solutions. In contrast, the performance of the proposed approach will not degrade with reduction in number of iterations, which enables the algorithm to realize quick convergence.
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