1,447 research outputs found
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
A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms
Many Internet of Things (IoT) networks are created as an overlay over traditional ad-hoc networks such as Zigbee. Moreover, IoT networks can resemble ad-hoc networks over networks that support device-to-device (D2D) communication, e.g., D2D-enabled cellular networks and WiFi-Direct. In these ad-hoc types of IoT networks, efficient topology management is a crucial requirement, and in particular in massive scale deployments. Traditionally, clustering has been recognized as a common approach for topology management in ad-hoc networks, e.g., in Wireless Sensor Networks (WSNs). Topology management in WSNs and ad-hoc IoT networks has many design commonalities as both need to transfer data to the destination hop by hop. Thus, WSN clustering techniques can presumably be applied for topology management in ad-hoc IoT networks. This requires a comprehensive study on WSN clustering techniques and investigating their applicability to ad-hoc IoT networks. In this article, we conduct a survey of this field based on the objectives for clustering, such as reducing energy consumption and load balancing, as well as the network properties relevant for efficient clustering in IoT, such as network heterogeneity and mobility. Beyond that, we investigate the advantages and challenges of clustering when IoT is integrated with modern computing and communication technologies such as Blockchain, Fog/Edge computing, and 5G. This survey provides useful insights into research on IoT clustering, allows broader understanding of its design challenges for IoT networks, and sheds light on its future applications in modern technologies integrated with IoT.acceptedVersio
Application of learning algorithms to traffic management in integrated services networks.
SIGLEAvailable from British Library Document Supply Centre-DSC:DXN027131 / BLDSC - British Library Document Supply CentreGBUnited Kingdo
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 enhanced Mobile
Broadband (eMBB), massive Machine Type Communications
(mMTC) and Ultra-Reliable and Low Latency Communications
(URLLC), the mMTC brings the unique technical challenge of
supporting a huge number of MTC devices in cellular networks,
which is the main focus of this paper. The related challenges
include Quality of Service (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 Narrowband IoT (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 scenario along with the recent advances towards
enhancing its learning performance and convergence. Finally,
we discuss some open research challenges and promising future
research directions
Achieving Energy-Efficient Uplink URLLC with MIMO-Aided Grant-Free Access
The optimal design of the energy-efficient multiple-input multiple-output
(MIMO) aided uplink ultra-reliable low-latency communications (URLLC) system is
an important but unsolved problem. For such a system, we propose a novel
absorbing-Markov-chain-based analysis framework to shed light on the puzzling
relationship between the delay and reliability, as well as to quantify the
system energy efficiency. We derive the transition probabilities of the
absorbing Markov chain considering the Rayleigh fading, the channel estimation
error, the zero-forcing multi-user-detection (ZF-MUD), the grant-free access,
the ACK-enabled retransmissions within the delay bound and the interactions
among these technical ingredients. Then, the delay-constrained reliability and
the system energy efficiency are derived based on the absorbing Markov chain
formulated. Finally, we study the optimal number of user equipments (UEs) and
the optimal number of receiving antennas that maximize the system energy
efficiency, while satisfying the reliability and latency requirements of URLLC
simultaneously. Simulation results demonstrate the accuracy of our theoretical
analysis and the effectiveness of massive MIMO in supporting large-scale URLLC
systems.Comment: 14 pages, 9 figures, accepted to appear on IEEE Transactions on
Wireless Communications, Aug. 202
Data-Intensive Computing in Smart Microgrids
Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area
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