Heterogeneous Wireless Sensor Network with Grid Based Node Deployment Strategy

The wireless sensor network accumulates and processes data in a cooperative way to manage a variety of wireless sensor networks, such habitat and environmental monitoring. In WSN applications, sensor placement is a crucial task. The deployment's performance, precision, and cost will be determined by the number of sensors and where they are placed. Node deployment dictates where the sensor nodes should be located to achieve the required objectives, such as maximizing the effective minimizing network size and cost of the coverage area ratio is a challenge in the application of WSNs. The coverage offered by the sensors deployment strategy heavily influences how effective these networks are. We presented the details of a grid-based heterogeneous node deployment in this thesis, in which sensor nodes have various processing and hardware functionality by node in a hex scheme, cover a limited number of sensor nodes, and reduce sensor node prices. In comparison to the previous system, the proposed technique used smaller sensor nodes to cover the area than the previous one. For the proposed system's implementation and performance evaluation, we used MATLAB. Moreover, we examine the performance of heterogeneous WSNs protocols in this paper under multi-level and three-level heterogeneous networks. Based on the evaluation results, we compared the efficiency of DEEC, DDEEC, and EDEEC, where, for example, when the number of iterations is 5000, DEEC, DDEEC, and EDEEC packets are sent to the base station in percentages of 80.1%, 86.3%, and 91.2%, respectively. The results obtained by the implementation of proposed node deployment algorithm with EDEEC routing algorithm are compared with previous deployment approach (existing state of art work) using mathematical modeling, theoretical analysis and formula deduction. The results show that the proposed approach consumes minimum number of node and minimum amount of deployment cost and better network lifetime.