Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application

Security protocols suite for machine-to-machine systems

Nowadays, the great diffusion of advanced devices, such as smart-phones, has shown that there is a growing trend to rely on new technologies to generate and/or support progress; the society is clearly ready to trust on next-generation communication systems to face today’s concerns on economic and social fields. The reason for this sociological change is represented by the fact that the technologies have been open to all users, even if the latter do not necessarily have a specific knowledge in this field, and therefore the introduction of new user-friendly applications has now appeared as a business opportunity and a key factor to increase the general cohesion among all citizens. Within the actors of this technological evolution, wireless machine-to-machine (M2M) networks are becoming of great importance. These wireless networks are made up of interconnected low-power devices that are able to provide a great variety of services with little or even no user intervention. Examples of these services can be fleet management, fire detection, utilities consumption (water and energy distribution, etc.) or patients monitoring. However, since any arising technology goes together with its security threats, which have to be faced, further studies are necessary to secure wireless M2M technology. In this context, main threats are those related to attacks to the services availability and to the privacy of both the subscribers’ and the services providers’ data. Taking into account the often limited resources of the M2M devices at the hardware level, ensuring the availability and privacy requirements in the range of M2M applications while minimizing the waste of valuable resources is even more challenging. Based on the above facts, this Ph. D. thesis is aimed at providing efficient security solutions for wireless M2M networks that effectively reduce energy consumption of the network while not affecting the overall security services of the system. With this goal, we first propose a coherent taxonomy of M2M network that allows us to identify which security topics deserve special attention and which entities or specific services are particularly threatened. Second, we define an efficient, secure-data aggregation scheme that is able to increase the network lifetime by optimizing the energy consumption of the devices. Third, we propose a novel physical authenticator or frame checker that minimizes the communication costs in wireless channels and that successfully faces exhaustion attacks. Fourth, we study specific aspects of typical key management schemes to provide a novel protocol which ensures the distribution of secret keys for all the cryptographic methods used in this system. Fifth, we describe the collaboration with the WAVE2M community in order to define a proper frame format actually able to support the necessary security services, including the ones that we have already proposed; WAVE2M was funded to promote the global use of an emerging wireless communication technology for ultra-low and long-range services. And finally sixth, we provide with an accurate analysis of privacy solutions that actually fit M2M-networks services’ requirements. All the analyses along this thesis are corroborated by simulations that confirm significant improvements in terms of efficiency while supporting the necessary security requirements for M2M networks