31,123 research outputs found
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
Clustering Algorithms for Scale-free Networks and Applications to Cloud Resource Management
In this paper we introduce algorithms for the construction of scale-free
networks and for clustering around the nerve centers, nodes with a high
connectivity in a scale-free networks. We argue that such overlay networks
could support self-organization in a complex system like a cloud computing
infrastructure and allow the implementation of optimal resource management
policies.Comment: 14 pages, 8 Figurs, Journa
Introducing mobile edge computing capabilities through distributed 5G Cloud Enabled Small Cells
Current trends in broadband mobile networks are addressed towards the placement of different capabilities at the edge of the mobile network in a centralised way. On one hand, the split of the eNB between baseband processing units and remote radio headers makes it possible to process some of the protocols in centralised premises, likely with virtualised resources. On the other hand, mobile edge computing makes use of processing and storage capabilities close to the air interface in order to deploy optimised services with minimum delay. The confluence of both trends is a hot topic in the definition of future 5G networks. The full centralisation of both technologies in cloud data centres imposes stringent requirements to the fronthaul connections in terms of throughput and latency. Therefore, all those cells with limited network access would not be able to offer these types of services. This paper proposes a solution for these cases, based on the placement of processing and storage capabilities close to the remote units, which is especially well suited for the deployment of clusters of small cells. The proposed cloud-enabled small cells include a highly efficient microserver with a limited set of virtualised resources offered to the cluster of small cells. As a result, a light data centre is created and commonly used for deploying centralised eNB and mobile edge computing functionalities. The paper covers the proposed architecture, with special focus on the integration of both aspects, and possible scenarios of application.Peer ReviewedPostprint (author's final draft
Mobile Computing in Physics Analysis - An Indicator for eScience
This paper presents the design and implementation of a Grid-enabled physics
analysis environment for handheld and other resource-limited computing devices
as one example of the use of mobile devices in eScience. Handheld devices offer
great potential because they provide ubiquitous access to data and
round-the-clock connectivity over wireless links. Our solution aims to provide
users of handheld devices the capability to launch heavy computational tasks on
computational and data Grids, monitor the jobs status during execution, and
retrieve results after job completion. Users carry their jobs on their handheld
devices in the form of executables (and associated libraries). Users can
transparently view the status of their jobs and get back their outputs without
having to know where they are being executed. In this way, our system is able
to act as a high-throughput computing environment where devices ranging from
powerful desktop machines to small handhelds can employ the power of the Grid.
The results shown in this paper are readily applicable to the wider eScience
community.Comment: 8 pages, 7 figures. Presented at the 3rd Int Conf on Mobile Computing
& Ubiquitous Networking (ICMU06. London October 200
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