Optimized Resource Allocation in IoT Using Fuzzy Logic and Bio-Inspired Algorithms

Abstract

IoT smart devices are a confuence of microprocessors, sensors, power source and trans ceiver modules to efectively sense, communicate and transfer data. Energy efciency is a key governing value of the network performance of smart devices in distributed IoT networks. Low and discrete power and limited amount of memory and fnite number of resources form some major bottlenecks in the workfow. Dynamic load balancing, reli ability and fexibility are heavily relied upon by cloud computing for its accessibility. Resources are dynamically provided to the end client in an as-come on-demand fashion with the global network that is the Internet. Proportionally the need for services is increas ing at a rate that is astonishing compared to any other forms of development. Load balanc ing seems a major challenge faced due to the architecture and the modular nature of our cloud environment. Loads need to be distributed dynamically to all the nodes. In this paper, we have introduced a technique that combines fuzzy logic with various nature inspired algorithms—grey wolf algorithm and frefy algorithm to efectively balance the load in a network of IoT devices. The performances of various nature inspired algorithms are com pared with a brute force approach based on energy efciency, network lifetime maximiza tion, node failure rate and packet delivery ratio.