A Hybrid Model using Genetic Algorithm for Energy Optimization in Heterogeneous Internet of Blockchain Things (IoBT)

Abstract

Internet of Things (IoT) emerging is one of the promising technologies aspiring industries and public alike with its broad spectrum of applications adding intelligence to the real-life objects. Due to its resource-limited nature, and heterogeneity of the devices in IoT networks, data security and energy consumption is a burning issue. Security for sensitive data in the network is paramount and privacy and access control mechanisms should be in force. Also, for reliable application services, the optimized network operations in terms of energy are demanding needs nowadays. This paper proposed a novel energy optimization and node deployment strategy is proposed by amalgamating genetic algorithm (GA) for energy optimization and mixed integer linear programming (MILP) for strategic node replacement. GA-based optimization focuses on improving residual energy of the nodes in the network thereby enhancing the network life-time. The MILP based node deployment strategy focuses on selecting minimum node set while still servicing the entire network area. The potentiality of the Blockchain is used in the model to provide data privacy and access control to the sensitive data. The proposed model is then compared with the state-of-the-art models to validate the performance in terms of network life-time, and throughput. It is evident from the results that the proposed method outperforms the existing models and provides reliable and viable solutions for many applications running on the IoT networks