11,954 research outputs found
Distributed Optimal Rate-Reliability-Lifetime Tradeoff in Wireless Sensor Networks
The transmission rate, delivery reliability and network lifetime are three
fundamental but conflicting design objectives in energy-constrained wireless
sensor networks. In this paper, we address the optimal
rate-reliability-lifetime tradeoff with link capacity constraint, reliability
constraint and energy constraint. By introducing the weight parameters, we
combine the objectives at rate, reliability, and lifetime into a single
objective to characterize the tradeoff among them. However, the optimization
formulation of the rate-reliability-reliability tradeoff is neither separable
nor convex. Through a series of transformations, a separable and convex problem
is derived, and an efficient distributed Subgradient Dual Decomposition
algorithm (SDD) is proposed. Numerical examples confirm its convergence. Also,
numerical examples investigate the impact of weight parameters on the rate
utility, reliability utility and network lifetime, which provide a guidance to
properly set the value of weight parameters for a desired performance of WSNs
according to the realistic application's requirements.Comment: 27 pages, 10 figure
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
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