D2D-Based Grouped Random Access to Mitigate Mobile Access Congestion in 5G Sensor Networks

The Fifth Generation (5G) wireless service of sensor networks involves significant challenges when dealing with the coordination of ever-increasing number of devices accessing shared resources. This has drawn major interest from the research community as many existing works focus on the radio access network congestion control to efficiently manage resources in the context of device-to-device (D2D) interaction in huge sensor networks. In this context, this paper pioneers a study on the impact of D2D link reliability in group-assisted random access protocols, by shedding the light on beneficial performance and potential limitations of approaches of this kind against tunable parameters such as group size, number of sensors and reliability of D2D links. Additionally, we leverage on the association with a Geolocation Database (GDB) capability to assist the grouping decisions by drawing parallels with recent regulatory-driven initiatives around GDBs and arguing benefits of the suggested proposal. Finally, the proposed method is approved to significantly reduce the delay over random access channels, by means of an exhaustive simulation campaign.Comment: First submission to IEEE Communications Magazine on Oct.28.2017. Accepted on Aug.18.2019. This is the camera-ready versio

Enabling mobile and wireless technologies for smart cities - Part 2

[EN] The articles in this special section focus on communications technologies for use in smart cities. Due to advancements in communication and computing technologies, smart cities have become the main innovation agenda of research organizations, technology vendors, and governments. To make a city smart, a strong communications infrastructure is required for connecting smart objects, people, and sensors. Smart cities rely on wireless and mobile technologies for providing services such as healthcare assistance, security and safety, real-time traffic monitoring, and managing the environment, to name a few. Such applications have been a main driving force in the development of smart cities. Without the appropriate communication networks, it is really difficult for a city to facilitate its citizens in a sustainable, efficient, and safer manner/environment. Considering the significance of mobile and wireless technologies for realizing the vision of smart cities, there is a need to conduct research to further investigate the standardization efforts and explore different issues/challenges in wireless technologies, mobile computing, and smart environments.Ahmed, E.; Imran, M.; Guizani, M.; Rayes, A.; Lloret, J.; Han, G.; Guibene, W. (2017). Enabling mobile and wireless technologies for smart cities - Part 2. IEEE Communications Magazine. 55(3):12-13. https://doi.org/10.1109/MCOM.2017.7876850S121355

Enabling mobile and wireless technologies for smart cities

[EN] The articles in this special section focs on mobile and wireless communications technologies and services for deployment in smart citiesAhmed, E.; Imran, M.; Guizani, M.; Rayes, A.; Lloret, J.; Han, G.; Guibene, W. (2017). Enabling mobile and wireless technologies for smart cities. IEEE Communications Magazine. 55(1):74-75. https://doi.org/10.1109/MCOM.2017.7823341S747555

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