A butterfly optimization approach for improving the performance of futuristic internet-of-things

• Published in: Evolving systems Vol. 15; no. 3; pp. 1057 - 1071
• Main Authors: Arya, Anju, Pahwa, Kanika, Gunjan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2024; Springer Nature B.V
• Subjects: 5G mobile communication; 6G mobile communication; Algorithms; Artificial Intelligence; Clustering; Communication; Complex Systems; Complexity; Edge computing; End users; Energy; Energy consumption; Energy efficiency; Engineering; Genetic algorithms; Heuristic; Heuristic methods; Internet of Things; Load balancing; Microprocessors; Network latency; Optimization; Optimization algorithms; Optimization techniques; Original Paper; Prairie dogs; Quality of service; Quality of service architectures; Routing (telecommunications); Servers; Wireless networks; Internet of things; 6G communications; Lifetime; Power efficiency; Quality of service; Latency
• ISSN: 1868-6478; 1868-6486
• DOI: 10.1007/s12530-023-09539-4

Although the fifth generation (5G) is crucial to the current Internet of Things (IoT), the increase of automated IoT systems and data-centric services that need thousands of microseconds of latency, terabytes of data every second, and more than 10 7 IoT connections per km 2 would be beyond the capability of current 5G networks. In order to fulfil the requirements of future IoT networks, this research presents an intelligent clustering and routing approach for IoT networks (ICR-IoT) that minimizes energy consumption and server latency, which are crucial factors in ensuring quality of service to the end users. Load balancing and energy efficiency in IoT-edge computing systems are NP- hard problems. This paper presents a novel approach called ICR-IoT for intelligent clustering and routing in internet of things networks, with the goal of minimizing energy consumption and server latency to ensure quality of service for end users. The approach uses a metaheuristic called the Butterfly Optimization Algorithm (BOA) to solve the NP-hard problems of load balancing and energy efficiency in IoT edge computing systems, and a dynamic routing approach to handle changing network conditions such as node energy. ICR-IoT presents novel parameters, packet uniformity (Pu) and Lifetime uniformity (Lu) of the data aggregators to improve the overall 6G IoT network performance. An experiment set is created to thoroughly assess the performance of the proposed work with various sensor node and gateway configurations. We have implemented our code in Python 3.10 on a 64-bit system having 8 GB RAM and 2.00 GHz processor. The proposed approach was tested and found to perform better than the BPSO approach, with improvements in lifetime and energy uniformity of the network. The lifetime and energy uniformity has been improved by 13.6% and 27.07%, respectively. In general, the ICR-IoT approach has the potential to enhance routing and clustering performance, as well as quality of service and network lifetime, in 6G-IoT networks.