1,974 research outputs found
A Resource Intensive Traffic-Aware Scheme for Cluster-based Energy Conservation in Wireless Devices
Wireless traffic that is destined for a certain device in a network, can be
exploited in order to minimize the availability and delay trade-offs, and
mitigate the Energy consumption. The Energy Conservation (EC) mechanism can be
node-centric by considering the traversed nodal traffic in order to prolong the
network lifetime. This work describes a quantitative traffic-based approach
where a clustered Sleep-Proxy mechanism takes place in order to enable each
node to sleep according to the time duration of the active traffic that each
node expects and experiences. Sleep-proxies within the clusters are created
according to pairwise active-time comparison, where each node expects during
the active periods, a requested traffic. For resource availability and recovery
purposes, the caching mechanism takes place in case where the node for which
the traffic is destined is not available. The proposed scheme uses Role-based
nodes which are assigned to manipulate the traffic in a cluster, through the
time-oriented backward difference traffic evaluation scheme. Simulation study
is carried out for the proposed backward estimation scheme and the
effectiveness of the end-to-end EC mechanism taking into account a number of
metrics and measures for the effects while incrementing the sleep time duration
under the proposed framework. Comparative simulation results show that the
proposed scheme could be applied to infrastructure-less systems, providing
energy-efficient resource exchange with significant minimization in the power
consumption of each device.Comment: 6 pages, 8 figures, To appear in the proceedings of IEEE 14th
International Conference on High Performance Computing and Communications
(HPCC-2012) of the Third International Workshop on Wireless Networks and
Multimedia (WNM-2012), 25-27 June 2012, Liverpool, U
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
Mobile Edge Computing
This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks. The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and distributed machine learning (e.g., federated learning). We also identify the emerging applications of MEC in pandemic, industrial Internet of Things and disaster management. The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of MEC. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists
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