Distributed Learning for Low Latency Machine Type Communication in a Massive Internet of Things

Abstract

The Internet of Things (IoT) will encompass a massive number of machine type devices that must wirelessly transmit, in near real-time, a diverse set of messages sensed from their environment. Designing resource allocation schemes to support such coexistent, heterogeneous communication is hence a key IoT challenge. In particular, there is a need for self-organizing resource allocation solutions that can account for unique IoT features, such as massive scale and stringent resource constraints. In this paper, a novel finite memory multi-state sequential learning framework is proposed to enable diverse IoT devices to share limited communication resources, while transmitting both delaytolerant, periodic messages and urgent, critical messages. The proposed learning framework enables the IoT devices to learn the number of critical messages and to reallocate the communication resources for the periodic messages to be used for the critical messages. Furthermore, the proposed learning framework explicitly accounts for IoT device limitations in terms of memory and computational capabilities. The convergence of the proposed learning framework is proved, and the lowest expected delay that the IoT devices can achieve using this learning framework is derived. Furthermore, the effectiveness of the proposed learning algorithm in IoT networks with different delay targets, network densities, probabilities of detection, and memory sizes is analyzed in terms of the probability of a successful random access request and percentage of devices that learned correctly. Simulation results show that, for a delay threshold of 1.25 ms, the average achieved delay is 0.71 ms and the delay threshold is satisfied with probability 0.87. Moreover, for a massive network, a delay threshold of 2.5 ms is satisfied with probability 0.92. The results also show that the proposed learning algorithm is very effective in reducing the delay of urgent, critical messages by intelligently reallocating the communication resources allocated to the delaytolerant, periodic messages.

Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research

“…2) Enabling 6G Technologies: There are several existing technologies that can directly be adopted to enable the mLLMT services in 6G ecosystem. For instance, Park et al [190], proposed a mechanism that may enable low latency machine type communication where the resources of IoE devices can be shared within a fraction of response time (in milliseconds). The authors designed a novel finite memory multi-state sequential learning framework that will suitably fulfill the requirements in several scenarios, such as delaytolerant applications, periodic messages delivery, and urgent and critical messages exchanges.…”

Autonomous IoT Device Management Systems: Structured Review and Generalized Cognitive Model

“…Another argument is that since the managing nodes also need to be managed autonomously, a star topology reduces overall complexity. Regarding the cluster topology, we see four benefits: 1) Clustering can help reducing latency, since processes are placed closer to the sensor devices [69]. 2) A topology that allows responsibility to be shared among the managing nodes in the network can be more suited to handle high variances in the network conditions or frequent changes in application requirements [13].…”

Insights on critical energy efficiency approaches in internet-of-things application

“…The study outcome has witnessed a higher accuracy in the presence of a different communication channel condition. The work carried out by Park, and Saad [43] has used a sequential learning model to support a restricted resource-based communication system over IoT devices. The authors have developed a mechanism that allows the devices to perform learning operation over critical messages for supporting seamless communication of sensitive nature.…”