A HYBRID MODEL USING GENETIC ALGORITHM FOR ENERGY OPTIMIZATION IN HETEROGENEOUS INTERNET OF BLOCKCHAIN THINGS

The Internet of Things (IoT) is a promising technology inspiring industries and the public alike with its broad spectrum of applications adding intelligence to real-life objects. Due to its resource-limited nature and heterogeneity of the devices in IoT networks, data security and energy consumption is an important 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, optimized network operations in terms of energy are demanding needs. This paper proposes a novel energy optimization and node deployment strategy 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 lifetime. The MILP-based node deployment strategy focuses on selecting a 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 sensitive data. The proposed model is then compared with state-ofthe-art models to validate the performance in terms of network lifetime and throughput. It is evident from the results that the proposed method outperforms existing models and provides reliable and viable solutions for many applications running on IoT networks. © 2024 by Begell House, Inc.