3,516 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
GEAMS: a Greedy Energy-Aware Multipath Stream-based Routing Protocol for WMSNs
Because sensor nodes operate on power limited batteries, sensor
functionalities have to be designed carefully. In particular, designing
energy-efficient packet forwarding is important to maximize the lifetime of the
network and to minimize the power usage at each node. This paper presents a
Geographic Energy-Aware Multipath Stream-based (GEAMS) routing protocol for
WMSNs. GEAMS routing decisions are made online, at each forwarding node in such
a way that there is no need to global topology knowledge and maintenance. GEAMS
routing protocol performs load-balancing to minimize energy consumption among
nodes using twofold policy: (1) smart greedy forwarding and (2) walking back
forwarding. Performances evaluations of GEAMS show that it can maximize the
network lifetime and guarantee quality of service for video stream transmission
in WMSNs
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