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.

蜂窝网络正在发展到第五代（5G）时代，在此期间，人们正在设想5G新无线电（NR）和长期演进：高级（LTE-A）Pro技术，以实现互联网智能和创新服务。物联网（IoT）。但是，由于各种IoT设备的异构特性，诸如组寻呼方法之类的现有LTE-A Pro协议仍然非常倾向于人与人之间的通信。由于大多数物联网设备都由电池供电，其功耗率决定电池寿命，因此，节能数据传输协议对于下一代物联网网络至关重要。组寻呼是一种已被广泛接受的提高IoT网络能源效率的机制。然而，物联网设备的分组方法仍然不是一个很热门的话题，尽管已经研究了一些新颖的分组寻呼方法，这些方法侧重于各种物联网特性和移动性；尽管这种方法在计算上并不高效，特别是对于大规模物联网部署。因此，本文提出了一种新颖的多参数进化优化方法，即基于遗传算法（GA）的分组方法，该方法还考虑了IoT特性，例如流量模式，时延要求和移动性模式。从仿真中获得的结果证明，我们提出的方法比随机分组方案和其他方法可以显着提高IoT设备的能源效率。尽管这种方法在计算上并不高效，特别是对于大规模物联网部署。因此，本文提出了一种新颖的多参数进化优化方法，即基于遗传算法（GA）的分组方法，该方法还考虑了IoT特性，例如流量模式，时延要求和移动性模式。从仿真中获得的结果证明，我们提出的方法比随机分组方案和其他方法可以显着提高IoT设备的能源效率。尽管这种方法在计算上并不高效，特别是对于大规模物联网部署。因此，本文提出了一种新颖的多参数进化优化方法，即基于遗传算法（GA）的分组方法，该方法还考虑了IoT特性，例如流量模式，时延要求和移动性模式。从仿真中获得的结果证明，我们提出的方法比随机分组方案和其他方法可以显着提高IoT设备的能源效率。