Energy and delay aware massive access management in machine‐to‐machine communications

Abstract

Machine‐to‐machine (M2M) communication is a challenging topic in the Internet‐of‐Things era. The increasing growth of machines and high rate of packet production have result in massive access to the network. Therefore, one of the media access control (MAC) challenges in 5G cellular networks is the management of massive access to the wireless media regarding quality‐of‐service (QoS) requirements and battery restrictions of the machines. Delay is one of the QoS requirements that should be guaranteed in most applications. To the best of our knowledge, previous studies on scheduled MAC mechanisms have not addressed energy efficiency and delay requirements appropriately. In this paper, the problem of scheduled massive access management has been considered with the aim of simultaneously meeting delay requirements of machines and increasing energy efficiency. The proposed solution exploits gateways to reuse the spectrum dedicated to M2M communications and introduces a clustering algorithm to allocate radio resources to the machines appropriately. Moreover, an optimization problem is defined and solved using genetic algorithms (GA) to adjust the optimal transmission power of machines, aiming at maximizing the energy efficiency. To preserve delay constraints, the proposed method exploits an existing scheduling algorithm. Simulation results demonstrate more than 50% reduction in probability of packet delay violation for various classes of service and in mean packet delay compared to the baseline method. The results have also shown more than 20% improvement in energy efficiency of proposed method compared to the baseline.

A Survey on LTE/LTE-A Radio Resource Allocation Techniques for Machine-to-Machine Communication for B5G Networks

“…It offers comparable performance for low-population (<300 UEs) to the optimal solution while having comparatively lower time complexity. Tagarian et al [155] proposed a technique that aims to minimize energy consumption while maintaining delay QoS requirements of machines. The authors used a gateway-based approach where the use of clustering manages massive access.…”

Multiobjective ant lion optimizer based network planning for Internet of Things on TV white space

“…As shown in (21), random walking is used to simulate the movement of the gateway in the search space.…”

“…where rand is a random number generated uniformly distributed in the interval of [0, 1]. To ensure that the random walk of ants within the fixed area of the network planning, the random walk based on (21) needs to be normalized according to (23) in each iteration.…”

“…However, there exist some challenging issues in the application of free‐spectrum IoT technologies. As declared in Reference 21, though the traffic volume of each M2M end device is low, the number of M2M devices requiring access is high. However, taking the typical free‐spectrum IoT network, LoRa, as an example, the experiment shows that a typical smart city deployment can support at most 120 nodes per 3.8 hectares, which is not sufficient for future large‐scale M2M communication devices 22 .…”