COMPARISON OF EXISTING CLUSTERING AND OPTIMIZATION ALGORITHMS IN IoT NETWORKS

Abstract

The “wireless sensor networks (WSNs)” are the most important aspect of the growth of “Internet of Things (IoT)” over the recent years with an increasing range of applications like agriculture, healthcare, etc., especially for tracking and monitoring, which are usually associated with security issues. Sensors can be used in large, remote, and unpopulated areas in some applications and track congested and busy spaces. It is very important to cluster the nodes of a sensor network into multiple clusters for common scalability reasons. They can also devise usage or maintenance schedules that might improve the lifetime of the network.

The WSNs are an important aspect of the daily lives of people in the future. The sensor nodes need very little energy. Failure of a single node of the network or WSN can pose serious damage to the operation, especially when it is used in healthcare and the military. Power-saving is a major issue in wireless networks. Various optimization techniques can be used to get ample output in every situation where it is possible to save energy. Sensor deployment and routing are two major issues to get fruitful results with optimization models. This paper briefly discusses optimization algorithms used in IoT networks. This paper also compares existing and advanced clustering algorithms to analyze their performance.