Comparative Study on Ant Colony Optimization (ACO) and K-Means Clustering Approaches for Jobs Scheduling and Energy Optimization Model in Internet of Things (IoT)

Abstract

T latest IoT applications depend on promotion of wireless sensor networks (WSNs) with expert of engineering. These IoT applications contain a large number of devices, connected with different requirements and technologies. Such kinds of IoT applications do the sensing and collection of data with transmission of data to the administrator nodes for other possible operations and even a cloud at the backdrop for data analytics. These processes require routing protocols for their completion. Routing protocols have two major challenges. The first challenge is to improve data transmission and scalability whereas the second challenge is to minimize energy consumption. In an IoT application, network nodes under different network topology collect different kind of data so that an IoT application produces an enormous amount of data. The heterogeneity in network topology restricts the TCP/IP to become the best policy for proper resource allocation to computing and routing [1]-[3], [27]-[29]. Owing to the above-mentioned challenges, different persons view IoT in different ways, based on their perception and requirements. A routing protocol includes the multiple job scheduling methodologies. These job scheduling methodologies are reported as either heuristic or metaheuristic-based approaches. Heuristic-based methodologies are comparatively more helpful when we look for a local optimum whereas metaheuristic methodologies further try to explore the solution DOI: 10.9781/ijimai.2020.01.003