5,479 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
Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks
Soaring capacity and coverage demands dictate that future cellular networks
need to soon migrate towards ultra-dense networks. However, network
densification comes with a host of challenges that include compromised energy
efficiency, complex interference management, cumbersome mobility management,
burdensome signaling overheads and higher backhaul costs. Interestingly, most
of the problems, that beleaguer network densification, stem from legacy
networks' one common feature i.e., tight coupling between the control and data
planes regardless of their degree of heterogeneity and cell density.
Consequently, in wake of 5G, control and data planes separation architecture
(SARC) has recently been conceived as a promising paradigm that has potential
to address most of aforementioned challenges. In this article, we review
various proposals that have been presented in literature so far to enable SARC.
More specifically, we analyze how and to what degree various SARC proposals
address the four main challenges in network densification namely: energy
efficiency, system level capacity maximization, interference management and
mobility management. We then focus on two salient features of future cellular
networks that have not yet been adapted in legacy networks at wide scale and
thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and
device-to-device (D2D) communications. After providing necessary background on
CoMP and D2D, we analyze how SARC can particularly act as a major enabler for
CoMP and D2D in context of 5G. This article thus serves as both a tutorial as
well as an up to date survey on SARC, CoMP and D2D. Most importantly, the
article provides an extensive outlook of challenges and opportunities that lie
at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201
Cross-layer Balanced and Reliable Opportunistic Routing Algorithm for Mobile Ad Hoc Networks
For improving the efficiency and the reliability of the opportunistic routing
algorithm, in this paper, we propose the cross-layer and reliable opportunistic
routing algorithm (CBRT) for Mobile Ad Hoc Networks, which introduces the
improved efficiency fuzzy logic and humoral regulation inspired topology
control into the opportunistic routing algorithm. In CBRT, the inputs of the
fuzzy logic system are the relative variance (rv) of the metrics rather than
the values of the metrics, which reduces the number of fuzzy rules
dramatically. Moreover, the number of fuzzy rules does not increase when the
number of inputs increases. For reducing the control cost, in CBRT, the node
degree in the candidate relays set is a range rather than a constant number.
The nodes are divided into different categories based on their node degree in
the candidate relays set. The nodes adjust their transmission range based on
which categories that they belong to. Additionally, for investigating the
effection of the node mobility on routing performance, we propose a link
lifetime prediction algorithm which takes both the moving speed and moving
direction into account. In CBRT, the source node determines the relaying
priorities of the relaying nodes based on their utilities. The relaying node
which the utility is large will have high priority to relay the data packet. By
these innovations, the network performance in CBRT is much better than that in
ExOR, however, the computation complexity is not increased in CBRT.Comment: 14 pages, 17 figures, 31 formulas, IEEE Sensors Journal, 201
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