6,705 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
Secure and Privacy-Preserving Data Aggregation Protocols for Wireless Sensor Networks
This chapter discusses the need of security and privacy protection mechanisms
in aggregation protocols used in wireless sensor networks (WSN). It presents a
comprehensive state of the art discussion on the various privacy protection
mechanisms used in WSNs and particularly focuses on the CPDA protocols proposed
by He et al. (INFOCOM 2007). It identifies a security vulnerability in the CPDA
protocol and proposes a mechanism to plug that vulnerability. To demonstrate
the need of security in aggregation process, the chapter further presents
various threats in WSN aggregation mechanisms. A large number of existing
protocols for secure aggregation in WSN are discussed briefly and a protocol is
proposed for secure aggregation which can detect false data injected by
malicious nodes in a WSN. The performance of the protocol is also presented.
The chapter concludes while highlighting some future directions of research in
secure data aggregation in WSNs.Comment: 32 pages, 7 figures, 3 table
Broadcasting in Noisy Radio Networks
The widely-studied radio network model [Chlamtac and Kutten, 1985] is a
graph-based description that captures the inherent impact of collisions in
wireless communication. In this model, the strong assumption is made that node
receives a message from a neighbor if and only if exactly one of its
neighbors broadcasts.
We relax this assumption by introducing a new noisy radio network model in
which random faults occur at senders or receivers. Specifically, for a constant
noise parameter , either every sender has probability of
transmitting noise or every receiver of a single transmission in its
neighborhood has probability of receiving noise.
We first study single-message broadcast algorithms in noisy radio networks
and show that the Decay algorithm [Bar-Yehuda et al., 1992] remains robust in
the noisy model while the diameter-linear algorithm of Gasieniec et al., 2007
does not. We give a modified version of the algorithm of Gasieniec et al., 2007
that is robust to sender and receiver faults, and extend both this modified
algorithm and the Decay algorithm to robust multi-message broadcast algorithms.
We next investigate the extent to which (network) coding improves throughput
in noisy radio networks. We address the previously perplexing result of Alon et
al. 2014 that worst case coding throughput is no better than worst case routing
throughput up to constants: we show that the worst case throughput performance
of coding is, in fact, superior to that of routing -- by a
gap -- provided receiver faults are introduced. However, we show that any
coding or routing scheme for the noiseless setting can be transformed to be
robust to sender faults with only a constant throughput overhead. These
transformations imply that the results of Alon et al., 2014 carry over to noisy
radio networks with sender faults.Comment: Principles of Distributed Computing 201
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