Cellular Internet Of Things Devices Research Articles

Deep reinforcement learning based mobility management in a MEC-Enabled cellular IoT network

Mobile Edge Computing (MEC) has paved the way for new Cellular Internet of Things (CIoT) paradigm, where resource constrained CIoT Devices (CDs) can offload tasks to a computing server located at either a Base Station (BS) or an edge node. For CDs moving in high speed, seamless mobility is crucial during the MEC service migration from one base station (BS) to another. In this paper, we investigate the problem of joint power allocation and Handover (HO) management in a MEC network with a Deep Reinforcement Learning (DRL) approach. To handle the hybrid action space (continuous: power allocation and discrete: HO decision), we leverage Parameterized Deep Q-Network (P-DQN) to learn the near-optimal solution. Simulation results illustrate that the proposed algorithm (P-DQN) outperforms the conventional approaches, such as the nearest BS +random power and random BS +random power, in terms of reward, HO cost, and total power consumption. According to simulation results, HO occurs almost in the edge point of two BS, which means the HO is almost perfectly managed. In addition, the total power consumption is around 0.151 watts in P-DQN while it is about 0.75 watts in nearest BS +random power and random BS +random power.

Survey of Secure IoT using Machine Learning Approach

The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives.[1] The participating nodes in IoT networks are usually resource constrained, which makes them luring targets for cyber-attacks [1]. Internet of Things security is attracting a growing attention from both academic and industry communities. Indeed, IoT devices are prone to various security attacks varying from Denial of Service (DoS) to network intrusion and data leakage [2]. The disruptive acceleration of Internet of Things (IoT)is drastically modifying the current ICT landscape with a massive number of cellular IoT devices expected to be deployed in the next few years. IoT devices are taking over a variety of aspects of our current lives, such as health care, transportation, and home environments [2]. Key Words: IOT, Machine Learning, Cyber Security, Security Attacks, Security, data privacy

Energy-Saving Solutions for Cellular Internet of Things–A Survey

The Cellular Internet of Things (CIoT), a new paradigm, paves the way for a large-scale deployment of IoT devices. CIoT promises enhanced coverage and massive deployment of low-cost IoT devices with an expected battery life of up to 10 years. However, such a long battery life can only be achieved provided the CIoT device is configured with energy efficiency in mind. This paper conducts a comprehensive survey on energy-saving solutions in 3GPP-based CIoT networks. In comparison to current studies, the contribution of this paper is the classification and an extensive analysis of existing energy-saving solutions for CIoT, e.g., configuration of particular parameter values and software modifications of transport- or radio-layer protocols, while also stressing key parameters impacting the energy consumption such as the frequency of data reporting, discontinuous reception cycles (DRX), and Radio Resource Control (RRC) timers. In addition, we discuss shortcomings, limitations, and possible opportunities which can be investigated in the future to reduce the energy consumption of CIoT devices.

Power Efficient Time-Division Random-Access Model Based in Wireless Communication Networks

The conventional infrastructure for mobile-communication is used for providing internet-of-things (IoT) services by the third-generation partnership project (3GPP) with the help of the recently developed cellular internet-of-things (CIoT) scheme. Random-access procedure can be used for connecting the large number of IoT devices using the CIoT systems. This process is advantages as the huge devices are accessed in a concurrent manner. When random access procedures are used simultaneously on a massive number of devices, the probability of congestion is high. This can be controlled to a certain extent through the time division scheme. A power efficient time-division random access model is developed in this paper to offer reliable coverage enhancement (CE) based on the coverage levels (CL). The quality of radio-channel is used for categorization of the CIoT devices after assigning them with CLs. The performance of random-access model can be improved and the instantaneous contention is relaxed greatly by distributing the loads based on their coverage levels into different time periods. Markov chain is used for mathematical analysis of the behavior and state of the devices. The probability of blocking access, success rate and collision control are enhanced by a significant level using this model in comparison to the conventional schemes.

A Novel Cross-Layer Authentication Protocol for the Internet of Things

An innovative cross-layer authentication protocol that integrates cryptography-based authentication and physical layer authentication (PLA) is proposed for massive cellular Internet of things (IoT) systems. Due to dramatic increases in the number of cellular IoT devices, a centralized authentication architecture in which a mobility management entity in core networks administers authentication of massive numbers of IoT devices may cause network congestion with large signaling overhead. Thus, a distributed authentication architecture in which a base station in radio access networks authenticates IoT devices locally is presented. In addition, a cross-layer authentication protocol is designed with a novel integration strategy under the distributed authentication architecture, where PLA, which employs physical features for authentication, is used as preemptive authentication in the proposed protocol. Theoretical analysis and numerical simulations were performed to analyze the trade-off between authentication performance and overhead in the proposed authentication method compared with existing authentication protocols. The results demonstrate that the proposed protocol outperforms conventional authentication and key agreement protocols in terms of overhead and computational complexity while guaranteeing low authentication error probability.

Time-division random-access scheme based on coverage level for cellular internet-of-things in 3GPP networks

Cellular internet-of-things (CIoT) systems are recently developed by the third-generation partnership project (3GPP) to support internet-of-things (IoT) services over the conventional mobile-communication infrastructures. The CIoT systems allow a large number of IoT devices to be connected through the random-access procedure, but the concurrent accesses of the massive devices make this procedure heavily competitive. In this article, we present an effective time-division random-access scheme built upon the coverage levels (CLs), where each CIoT device is assigned a CL and categorized based on its radio-channel quality. In our scheme, the random-access loads of device groups having different CLs are distributed into different time periods, which greatly relaxes instantaneous contention and improves random-access performance. To assess the performance of our scheme, we also introduce a mathematical model that expresses and analyzes the states and behaviors of CIoT devices using the Markov chain. Mathematical analysis and simulation results show that our scheme significantly outperforms the conventional scheme (without time-division control) in terms of collision probability, succeeding access rate, and access-blocking probability.

A Feasible Cellular Internet of Things: Enabling Edge Computing and the IoT in Dense Futuristic Cellular Networks

The cellular Internet of Things (C-IoT) with edge computing (EC) is one of the most promising technologies in fifth-generation (5G) cellular systems, which enables connecting everything to the Internet. C-IoT devices are low powered and support a longer transmission range, and they are developed to enable IoT in both dense and remote areas. Machine-type communication (MTC)/C-IoT devices need a ubiquitous connectivity, which is provided by the cellular infrastructure. As the density of these devices increases, it becomes more challenging to connect them to the cellular network within the stipulated amount of time. In this article, we look at two typical deployment scenarios of C-IoT with EC, identify the crucial challenges, and provide solutions. One solution is the RACH procedure, which enables a million devices to access the cellular network. Our emphasis is on a fast RACH procedure that allows C-IoT devices to perform a successful RACH with fewer attempts, which saves power.

Reducing State Information by Sharing IMSI for Cellular IoT Devices

To provide cellular communications for a large number of Internet of Things (IoT) devices, the mobile core system, called evolved packet core (EPC) in the case of a 4G system, requires a large amount of computational resources. This is because the EPC needs to maintain communication state information for IoT devices, even if they rarely send data. This paper proposes a method for reducing state information. The proposed method assigns the same international mobile subscriber identity to multiple IoT devices, which upload data with the same cycle, and manages devices uploading data at different timings. This paper investigates the impact of factors affecting the performance of the proposed method through several simulation-based verifications and shows that the proposal can reduce the required state information to less than 0.5% compared to the current EPC method.