Due to the growing number of interconnected Internet of Things (IoT) devices along with a greater prevalence of data-intensive 5G applications, it has become increasingly clear that we need efficient methods to share a finite bandwidth among our growing needs. In this work, we examine the coexistence of these two types of applications and propose a method through which to improve its efficiency. To this end, we model the performance of NarrowBand IoT (NB-IoT) latency and 5G New Radio (NR) throughput for IoT and 5G applications performance respectively. Using these models, we formulate two methods through which to statically divide bandwidth between the two networks. Further, we provide a Dynamic Spectrum Sharing (DSS) scheme, allowing NB-IoT users to opportunistically utilize unused 5G spectrum, minimizing unused spectrum. Finally, we reconsider the static bandwidth division methods with our DSS scheme enabled, showing that the addition of our scheme allows more bandwidth to be allocated to the 5G network, resulting in a higher tolerance for variance in the notoriously volatile 5G traffic distributions.

Due to the exponential growth of endpoints in the Internet of Things (IoT), new protocols have been proposed to utilize cellular infrastructures, allowing a large amount of IoT devices to communicate through them. These novel protocols make up the Cellular IoT (C-IoT). In C-IoT, the energy efficiency of endpoints is essential in order to reduce both operational cost and required maintenance. One method of energy reduction is discontinuous reception (DRX). DRX allows a device’s radio frequency (RF) circuitry to turn off for brief periods of time. While off, the device experiences a tradeoff between saving energy and an increase in expected latency, which can be tuned by how long the device spends asleep. In this article, we model DRX as a Markov decision process (MDP). This MDP is solved using a low-complexity “DRX-aware” value iteration algorithm, then verified through simulation and analytical analysis. Further, the energy-latency tradeoff is explored by varying the device’s priority on either energy or latency in addition to varying the traffic intensity. Finally, a method of traffic estimation is applied, and the model’s performance in an environment with time-varying traffic intensity is explored. This approach is compared with a reinforcement learning approach, showing that the traffic estimation approach is better suited to the problem of DRX optimization.

Due to the exponential growth of endpoints in the Internet of Things (IoT), new protocols have been proposed to utilize cellular infrastructures, allowing a large amount of IoT devices to communicate through them. These novel protocols make up the Cellular IoT (C-IoT). In C-IoT, the energy efficiency of endpoints is essential in order to reduce both operational cost and required maintenance. One method of energy reduction is Discontinuous Reception (DRX). DRX allows a device's Radio Frequency (RF) circuitry to turn off for brief periods of time. While off, the device experiences a tradeoff between saving energy and an increase in expected latency, which can be tuned by how long the device spends asleep. In this paper, we model DRX as a Markov Decision Process (MDP). This MDP is solved using a dynamic programming approach and verified through simulation. Further, the energy-latency tradeoff is explored by varying the device's priority on either energy or network performance in addition to varying the traffic intensity.

Recent growth in the Internet of Things (IoT) has been remarkable. Among the solutions to accommodate such a growth is Cellular IoT (C-IoT), comprising a group of technologies extended from legacy cellular infrastructures. One of the key goals of C-IoT technologies is to extend the battery life of UEs (User Equipment) in the network. However, this often comes at the cost of degrading network performance. This work attempts to identify, categorize, and analyze the available literature on this problem. The literature is broadly categorized into three sections: scheduling, data processing, and sleep modes. In each of these sections, the literature is further sub categorized. Finally, a direction for future research is identified and discussed.

The massive introduction of traffic from the Internet of Things (IoT), particularly in smart-city scenarios, needs to be supported by a steady, pervasive and reliable communication infrastructure. Cellular networks, such as Long Term Evolution (LTE) and 5G, are considered a popular solution to support the increasing amount of traffic from IoT, especially in smart cities. However, a massive deployment of IoT devices in existing cellular infrastructures can jeopardize the communication of human users and the overall network performance. In this study, the coexistence of IoT traffic and human users in a smart-city LTE infrastructure was studied through simulation using the SimuLTE software. Real geographical data were employed on the position of LTE base stations and IoT devices, retrieved from publicly available sources. Key network indicators, such as user throughput and cell utilization, were adopted to analyze both network and user performance. Simulation results showed a considerable performance degradation when IoT traffic is introduced into the network.