Wireless Sensor Network (WSN) is generally used for constructing an efficient network with minimum infrastructure. With this note, the WSN can be considered to construct smart communication in the existing city to facilitate the inhabitants. Nowadays in India, so many cities are going to be transformed into smart cities. In those cities, the maximum electronic gadgets will be IoT enabled. These IoT enabled gadgets should be connected through a robust network. The WSN can be an alternative for constructing a robust network between the electronic devices and the local server (Sink Node). The WSN can also sense many external environmental factors to facilitate the user, such as traffic status, rainfall, heat or smoke, vibration, and pollution detection. One of the major important challenges for the construction of an effective Wireless Sensor Network is to use the existing infrastructure of a city. In this paper, the effective Wireless Sensor Network has been constructed using the existing infrastructure and this is the major reason to choose the existing roads to deploy the WSN nodes. One can easily use the existing lamp posts of those roads to deploy the WSN nodes. In this paper, the modified Ant Colony Optimization (ACO) technique has been used to construct efficient WSN. The ACO is the probabilistic technique which is used to solve computational problems of choosing a minimized
In this paper, the authors have concentrated on the practical application of optimization problems related to the minimization of the energy consumption of WSN. Here a noble algorithm called Self-adaptive Multi-Objective Weighted Approach-Genetic Algorithm (SMOWA-GA) is proposed to resolve the optimization problem. A multi-objective optimization problem was chosen as the subject of this research. The main objective of the paper is to propose and apply different WSN node deployment strategies to design an efficient Wireless Sensor Network to minimize the energy consumption of the whole WSN. The statistical analysis also has been carried out on the obtained data of the optimization techniques. To analyze the obtained result a statistical tool, Wilcoxon rank-sum test has been used. The Wilcoxon rank-sum test assists in determining whether the population chosen for the experiment (SMOWA-GA) is accurate. The statistical analysis also will help the reader to gather a detailed analysis of obtained data from the Multi-objective energy-efficient optimization problem.
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