584 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
IoT無線ネットワークの電力効率化・セキュア化を実現する通信システムの設計に関する研究
Tohoku University加藤寧課
Internet of Things and Sensors Networks in 5G Wireless Communications
The Internet of Things (IoT) has attracted much attention from society, industry and academia as a promising technology that can enhance day to day activities, and the creation of new business models, products and services, and serve as a broad source of research topics and ideas. A future digital society is envisioned, composed of numerous wireless connected sensors and devices. Driven by huge demand, the massive IoT (mIoT) or massive machine type communication (mMTC) has been identified as one of the three main communication scenarios for 5G. In addition to connectivity, computing and storage and data management are also long-standing issues for low-cost devices and sensors. The book is a collection of outstanding technical research and industrial papers covering new research results, with a wide range of features within the 5G-and-beyond framework. It provides a range of discussions of the major research challenges and achievements within this topic
Internet of Things and Sensors Networks in 5G Wireless Communications
This book is a printed edition of the Special Issue Internet of Things and Sensors Networks in 5G Wireless Communications that was published in Sensors
Internet of Things and Sensors Networks in 5G Wireless Communications
This book is a printed edition of the Special Issue Internet of Things and Sensors Networks in 5G Wireless Communications that was published in Sensors
5GAuRA. D3.3: RAN Analytics Mechanisms and Performance Benchmarking of Video, Time Critical, and Social Applications
5GAuRA deliverable D3.3.This is the final deliverable of Work Package 3 (WP3) of the 5GAuRA project, providing a report on the project’s developments on the topics of Radio Access Network (RAN) analytics and application performance benchmarking. The focus of this deliverable is to extend and deepen the methods and results provided in the 5GAuRA deliverable D3.2 in the context of specific use scenarios of video, time critical, and social applications. In this respect, four major topics of WP3 of 5GAuRA – namely edge-cloud enhanced RAN architecture, machine learning assisted Random Access Channel (RACH) approach, Multi-access Edge Computing (MEC) content caching, and active queue management – are put forward.
Specifically, this document provides a detailed discussion on the service level agreement between tenant and service provider in the context of network slicing in Fifth Generation (5G) communication networks. Network slicing is considered as a key
enabler to 5G communication system. Legacy telecommunication networks have been providing various services to all kinds of customers through a single network infrastructure. In contrast, by deploying network slicing, operators are now able to
partition one network into individual slices, each with its own configuration and Quality of Service (QoS) requirements. There are many applications across industry that open new business opportunities with new business models. Every application instance requires an independent slice with its own network functions and features, whereby every single slice needs an individual Service Level Agreement (SLA). In D3.3, we propose a comprehensive end-to-end structure of SLA between the tenant and the service provider of sliced 5G network, which balances the interests of both sides. The proposed SLA defines reliability, availability, and performance of delivered telecommunication services in order to ensure that right information is delivered to the right destination at right time, safely and securely. We also discuss the metrics of slicebased network SLA such as throughput, penalty, cost, revenue, profit, and QoS related metrics, which are, in the view of 5GAuRA, critical features of the agreement.Peer ReviewedPostprint (published version
The role of telecommunication companies in Internet of things
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementInternet of Things is pervading the enterprise and consumer worlds. This technological concept is
leading towards pervasive connectivity and encompasses everything connected to the Internet, being
used for defining objects that ‘talk’ with one another. As stated by many reports, it is expected to
generate a huge amount of added value through applications and the resulting services.
Telecommunication companies with their central role, and their well established ability to connect
millions of devices, are in a distinctive situation brought by the opportunity of new revenue streams
and new challenges resulting from this revolution of connectivity. Their share of the added value
market that is generated by the IoT is going to be dependent on the role they are going to play in the
value chain.
The purpose of this work is to investigate the impact Internet of Things will have on the
telecommunications industry and to identify and analyse the possible directions for
telecommunication companies as they take their role in IoT. In addition the IoT value chain is
examined, possible business models for telecommunication companies in IoT are identified, and
technology and business related challanges are elaborated. Furthermore in this work, a case study on
Makedonski Telekom AD is presented.
Telecommunication companies, being in the process of planning and/or implementing IoT have several
business models to consider. The ones that will position themselves highly on the value chain, will have
to play smart in order not to focus too heavily on industry specific solutions and balance in adding
highly differentiated vertical offers where feasible on one hand, and facilitate third‐party vertical
solutions where differentiation is more difficult and the investment is too great on the other, so they
do not underplay their hand in the connectivity and life cycle management layer
Towards efficient support for massive Internet of Things over cellular networks
The usage of Internet of Things (IoT) devices over cellular networks is seeing tremendous
growth in recent years, and that growth in only expected to increase in the near
future. While existing 4G and 5G cellular networks offer several desirable features for
this type of applications, their design has historically focused on accommodating traditional
mobile devices (e.g. smartphones). As IoT devices have very different characteristics
and use cases, they create a range of problems to current networks which often
struggle to accommodate them at scale. Although newer cellular network technologies,
such as Narrowband-IoT (NB-IoT), were designed to focus on the IoT characteristics,
they were extensively based on 4G and 5G networks to preserve interoperability, and
decrease their deployment cost. As such, several inefficiencies of 4G/5G were also
carried over to the newer technologies.
This thesis focuses on identifying the core issues that hinder the large scale deployment
of IoT over cellular networks, and proposes novel protocols to largely alleviate
them. We find that the most significant challenges arise mainly in three distinct areas:
connection establishment, network resource utilisation and device energy efficiency.
Specifically, we make the following contributions. First, we focus on the connection
establishment process and argue that the current procedures, when used by IoT devices,
result in increased numbers of collisions, network outages and a signalling overhead
that is disproportionate to the size of the data transmitted, and the connection duration
of IoT devices. Therefore, we propose two mechanisms to alleviate these inefficiencies.
Our first mechanism, named ASPIS, focuses on both the number of collisions
and the signalling overhead simultaneously, and provides enhancements to increase the
number of successful IoT connections, without disrupting existing background traffic.
Our second mechanism focuses specifically on the collisions at the connection establishment
process, and used a novel approach with Reinforcement Learning, to decrease
their number and allow a larger number of IoT devices to access the network with fewer
attempts.
Second, we propose a new multicasting mechanism to reduce network resource
utilisation in NB-IoT networks, by delivering common content (e.g. firmware updates)
to multiple similar devices simultaneously. Notably, our mechanism is both more efficient
during multicast data transmission, but also frees up resources that would otherwise
be perpetually reserved for multicast signalling under the existing scheme.
Finally, we focus on energy efficiency and propose novel protocols that are designed
for the unique usage characteristics of NB-IoT devices, in order to reduce the
device power consumption. Towards this end, we perform a detailed energy consumption
analysis, which we use as a basis to develop an energy consumption model for
realistic energy consumption assessment. We then take the insights from our analysis,
and propose optimisations to significantly reduce the energy consumption of IoT
devices, and assess their performance
- …