848 research outputs found
Goodbye, ALOHA!
©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The vision of the Internet of Things (IoT) to interconnect and Internet-connect everyday people, objects, and machines poses new challenges in the design of wireless communication networks. The design of medium access control (MAC) protocols has been traditionally an intense area of research due to their high impact on the overall performance of wireless communications. The majority of research activities in this field deal with different variations of protocols somehow based on ALOHA, either with or without listen before talk, i.e., carrier sensing multiple access. These protocols operate well under low traffic loads and low number of simultaneous devices. However, they suffer from congestion as the traffic load and the number of devices increase. For this reason, unless revisited, the MAC layer can become a bottleneck for the success of the IoT. In this paper, we provide an overview of the existing MAC solutions for the IoT, describing current limitations and envisioned challenges for the near future. Motivated by those, we identify a family of simple algorithms based on distributed queueing (DQ), which can operate for an infinite number of devices generating any traffic load and pattern. A description of the DQ mechanism is provided and most relevant existing studies of DQ applied in different scenarios are described in this paper. In addition, we provide a novel performance evaluation of DQ when applied for the IoT. Finally, a description of the very first demo of DQ for its use in the IoT is also included in this paper.Peer ReviewedPostprint (author's final draft
CogCell: Cognitive Interplay between 60GHz Picocells and 2.4/5GHz Hotspots in the 5G Era
Rapid proliferation of wireless communication devices and the emergence of a
variety of new applications have triggered investigations into next-generation
mobile broadband systems, i.e., 5G. Legacy 2G--4G systems covering large areas
were envisioned to serve both indoor and outdoor environments. However, in the
5G-era, 80\% of overall traffic is expected to be generated in indoors. Hence,
the current approach of macro-cell mobile network, where there is no
differentiation between indoors and outdoors, needs to be reconsidered. We
envision 60\,GHz mmWave picocell architecture to support high-speed indoor and
hotspot communications. We envisage the 5G indoor network as a combination of-,
and interplay between, 2.4/5\,GHz having robust coverage and 60\,GHz links
offering high datarate. This requires an intelligent coordination and
cooperation. We propose 60\,GHz picocellular network architecture, called
CogCell, leveraging the ubiquitous WiFi. We propose to use 60\,GHz for the data
plane and 2.4/5GHz for the control plane. The hybrid network architecture
considers an opportunistic fall-back to 2.4/5\,GHz in case of poor connectivity
in the 60\,GHz domain. Further, to avoid the frequent re-beamforming in 60\,GHz
directional links due to mobility, we propose a cognitive module -- a
sensor-assisted intelligent beam switching procedure -- which reduces the
communication overhead. We believe that the CogCell concept will help future
indoor communications and possibly outdoor hotspots, where mobile stations and
access points collaborate with each other to improve the user experience.Comment: 14 PAGES in IEEE Communications Magazine, Special issue on Emerging
Applications, Services and Engineering for Cognitive Cellular Systems
(EASE4CCS), July 201
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
Application Protocols enabling Internet of Remote Things via Random Access Satellite Channels
Nowadays, Machine-to-Machine (M2M) and Internet of Things (IoT) traffic rate
is increasing at a fast pace. The use of satellites is expected to play a large
role in delivering such a traffic. In this work, we investigate the use of two
of the most common M2M/IoT protocols stacks on a satellite Random Access (RA)
channel, based on DVB-RCS2 standard. The metric under consideration is the
completion time, in order to identify the protocol stack that can provide the
best performance level
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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
Wearable Communications in 5G: Challenges and Enabling Technologies
As wearable devices become more ingrained in our daily lives, traditional
communication networks primarily designed for human being-oriented applications
are facing tremendous challenges. The upcoming 5G wireless system aims to
support unprecedented high capacity, low latency, and massive connectivity. In
this article, we evaluate key challenges in wearable communications. A
cloud/edge communication architecture that integrates the cloud radio access
network, software defined network, device to device communications, and
cloud/edge technologies is presented. Computation offloading enabled by this
multi-layer communications architecture can offload computation-excessive and
latency-stringent applications to nearby devices through device to device
communications or to nearby edge nodes through cellular or other wireless
technologies. Critical issues faced by wearable communications such as short
battery life, limited computing capability, and stringent latency can be
greatly alleviated by this cloud/edge architecture. Together with the presented
architecture, current transmission and networking technologies, including
non-orthogonal multiple access, mobile edge computing, and energy harvesting,
can greatly enhance the performance of wearable communication in terms of
spectral efficiency, energy efficiency, latency, and connectivity.Comment: This work has been accepted by IEEE Vehicular Technology Magazin
Performance Comparison of Contention- and Schedule-based MAC Protocols in Urban Parking Sensor Networks
Network traffic model is a critical problem for urban applications, mainly
because of its diversity and node density. As wireless sensor network is highly
concerned with the development of smart cities, careful consideration to
traffic model helps choose appropriate protocols and adapt network parameters
to reach best performances on energy-latency tradeoffs. In this paper, we
compare the performance of two off-the-shelf medium access control protocols on
two different kinds of traffic models, and then evaluate their application-end
information delay and energy consumption while varying traffic parameters and
network density. From the simulation results, we highlight some limits induced
by network density and occurrence frequency of event-driven applications. When
it comes to realtime urban services, a protocol selection shall be taken into
account - even dynamically - with a special attention to energy-delay tradeoff.
To this end, we provide several insights on parking sensor networks.Comment: ACM International Workshop on Wireless and Mobile Technologies for
Smart Cities (WiMobCity) (2014
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