5,354 research outputs found
A survey of localization in wireless sensor network
Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network
Distributed Regression in Sensor Networks: Training Distributively with Alternating Projections
Wireless sensor networks (WSNs) have attracted considerable attention in
recent years and motivate a host of new challenges for distributed signal
processing. The problem of distributed or decentralized estimation has often
been considered in the context of parametric models. However, the success of
parametric methods is limited by the appropriateness of the strong statistical
assumptions made by the models. In this paper, a more flexible nonparametric
model for distributed regression is considered that is applicable in a variety
of WSN applications including field estimation. Here, starting with the
standard regularized kernel least-squares estimator, a message-passing
algorithm for distributed estimation in WSNs is derived. The algorithm can be
viewed as an instantiation of the successive orthogonal projection (SOP)
algorithm. Various practical aspects of the algorithm are discussed and several
numerical simulations validate the potential of the approach.Comment: To appear in the Proceedings of the SPIE Conference on Advanced
Signal Processing Algorithms, Architectures and Implementations XV, San
Diego, CA, July 31 - August 4, 200
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
- …