1,510 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Distributed Inference over Linear Models using Alternating Gaussian Belief Propagation
We consider the problem of maximum likelihood estimation in linear models
represented by factor graphs and solved via the Gaussian belief propagation
algorithm. Motivated by massive internet of things (IoT) networks and edge
computing, we set the above problem in a clustered scenario, where the factor
graph is divided into clusters and assigned for processing in a distributed
fashion across a number of edge computing nodes. For these scenarios, we show
that an alternating Gaussian belief propagation (AGBP) algorithm that
alternates between inter- and intra-cluster iterations, demonstrates superior
performance in terms of convergence properties compared to the existing
solutions in the literature. We present a comprehensive framework and introduce
appropriate metrics to analyse AGBP algorithm across a wide range of linear
models characterised by symmetric and non-symmetric, square, and rectangular
matrices. We extend the analysis to the case of dynamic linear models by
introducing dynamic arrival of new data over time. Using a combination of
analytical and extensive numerical results, we show the efficiency and
scalability of AGBP algorithm, making it a suitable solution for large-scale
inference in massive IoT networks.Comment: 14 pages, 18 figure
Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G
The next wave of wireless technologies is proliferating in connecting things
among themselves as well as to humans. In the era of the Internet of things
(IoT), billions of sensors, machines, vehicles, drones, and robots will be
connected, making the world around us smarter. The IoT will encompass devices
that must wirelessly communicate a diverse set of data gathered from the
environment for myriad new applications. The ultimate goal is to extract
insights from this data and develop solutions that improve quality of life and
generate new revenue. Providing large-scale, long-lasting, reliable, and near
real-time connectivity is the major challenge in enabling a smart connected
world. This paper provides a comprehensive survey on existing and emerging
communication solutions for serving IoT applications in the context of
cellular, wide-area, as well as non-terrestrial networks. Specifically,
wireless technology enhancements for providing IoT access in fifth-generation
(5G) and beyond cellular networks, and communication networks over the
unlicensed spectrum are presented. Aligned with the main key performance
indicators of 5G and beyond 5G networks, we investigate solutions and standards
that enable energy efficiency, reliability, low latency, and scalability
(connection density) of current and future IoT networks. The solutions include
grant-free access and channel coding for short-packet communications,
non-orthogonal multiple access, and on-device intelligence. Further, a vision
of new paradigm shifts in communication networks in the 2030s is provided, and
the integration of the associated new technologies like artificial
intelligence, non-terrestrial networks, and new spectra is elaborated. Finally,
future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Secure Cloud-Edge Deployments, with Trust
Assessing the security level of IoT applications to be deployed to
heterogeneous Cloud-Edge infrastructures operated by different providers is a
non-trivial task. In this article, we present a methodology that permits to
express security requirements for IoT applications, as well as infrastructure
security capabilities, in a simple and declarative manner, and to automatically
obtain an explainable assessment of the security level of the possible
application deployments. The methodology also considers the impact of trust
relations among different stakeholders using or managing Cloud-Edge
infrastructures. A lifelike example is used to showcase the prototyped
implementation of the methodology
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