A genetic algorithm based energy efficient group paging approach for IoT over 5G

Abstract

Abstract Cellular networks are evolving to the era of 5th Generation (5G), where 5G new radio (NR) and Long-Term Evolution: Advanced (LTE-A) Pro technologies are being envisioned for enabling smart and innovative services of the Internet-of-Things (IoT). However, existing LTE-A Pro protocols such as the group paging approach is still heavily inclined towards human-to-human communications owing to the heterogeneous characteristics of a wide variety of IoT devices. Since most IoT devices are battery operated and their power consumption rate decides the battery lifetime, hence energy-efficient data transfer protocols are of paramount importance for next-generation IoT networks. Group paging is one such mechanism that has been widely accepted to improve energy efficiency of IoT networks. However, grouping approach for IoT devices is still not a much addressed topic, though a few novel group paging approaches have been studied that focus on varied IoT characteristics and mobility; though such approaches are not computationally efficient particularly for massive IoT deployments. Therefore, this paper proposes a novel multi-parameter evolutionary optimization, namely, genetic algorithm (GA) based grouping approach that also considers IoT features such as traffic patterns, delay requirements, and mobility patterns. Results obtained from simulations validate that our proposed method can significantly improve IoT devices’ energy efficiency over random grouping schemes and other approaches.