A Hybrid Load Balancing Strategy For Cloud-Based Internet Of Things For Rural Societal Services

Abstract

The Internet of Things (IoT) has evolved to connect billions of disparate physical items in order to improve human lives by gathering data from their surroundings. However, there is a requirement for large amounts of data to be stored in large storage systems, as well as strong computing skills. Big data can be stored via cloud computing. IoT technology was originally used to create Smart Cities (urban settlements), but it may also be used to create Smart Villages (rural settlements), improving the lives of villagers and communities. Rural settlements, however, have distinct needs from metropolitan settlements. If IoT applications in Smart Cities is defined by densification of IoT to everyday life, then IoT enabled Smart Villages are frequently characterised by dispersion and inadequacy.Because such many users often request data from servers, there are numerous overloads on the cloud's VM. As a result, strategies, and algorithms to solve the overload problem are in high demand. Message Queuing Telemetry Transport (MQTT) and Hypertext Send Protocol (HTTP) are two protocols used to transfer data from IoT devices (HTTP). The goal of this study is to create a system that is high-performing and reliable by making efficient use of resources. Thus, load balancing in cloud computing uses two types of algorithms to dynamically spread demand across nodes to avoid overloading any single resource: dynamic algorithm (adaptive firefly) and Particle Swarm Optimization (PSO). The results reveal that resource utilisation has improved, productivity has grown, and reaction time has decreased.