Bayesian selection of non faulty sensors (SYS 6)

The identification of sensors returning unreliable data is an important task when working with sensor networks. The detection of these sensors while in the field can cue human involvement in repairing problem sensors. This ensures meaningful data is collected throughout the entire length of a sensor deployment. We present a method of selecting non-faulty sensors from a given set of sensors that are expected to behave similarly. We use a Bayesian approach to select a subset of sensors which give the best probability of being correct given the data. From this we can determine whether other sensors' readings fall out of a reasonable range for the sensor set. Using data collected in a test conditions and environment data collected in the forest we verify that our method successfully selects all sensors that are expected to be correct

On Scalability and Source/Channel Coding Decoupling in Large Scale Sensor Networks

Prior results have shown that for ad hoc networks with uniform source-destination probabilities, where each node generates trafc, the transport capacity for each node in the network declines with the network size n [1]. In this paper, we show that the nite per-node throughput and hence scalability is achievable with the adjustment of source-destination pair distributions. We also explore the rate-distortion bound in the context of sensor networks. Considering a network over a nite region, with nite Gaussian point sources and densely distributed sensors, the otherwise difcult to obtain data rate region under a delity criterion, reduces to a partial side information problem for Gaussian sources. The key concept in proving this is to consider source, sensor, and communications relay densities as separate quantities

Bayesian selection of non faulty sensors (SYS 6)

The identification of sensors returning unreliable data is an important task when working with sensor networks. The detection of these sensors while in the field can cue human involvement in repairing problem sensors. This ensures meaningful data is collected throughout the entire length of a sensor deployment. We present a method of selecting non-faulty sensors from a given set of sensors that are expected to behave similarly. We use a Bayesian approach to select a subset of sensors which give the best probability of being correct given the data. From this we can determine whether other sensors' readings fall out of a reasonable range for the sensor set. Using data collected in a test conditions and environment data collected in the forest we verify that our method successfully selects all sensors that are expected to be correct

Microsoft Word - ita2011v1.doc

Abstract-We consider the problem of fair resource allocation in interference channels, for the case of one-dimensional cells. We propose simple interference channel models based upon the deterministic channel model, assuming zero queuing delay. This enables study of how the interference induces coupling among the rates available in the channels and evaluation of resource allocation. A sequence of models of increasing complexity provides a simple means of dissecting the different effects, and rapid simulation. We show that there is a strong benefit in explicitly pairing channels to minimize interference to users with disadvantaged locations, as compared to an averaging approach such as CDMA. We also show the benefits of power and bandwidth allocation when fair allocations are desired. A distributed algorithm is proposed that embodies the principle of interference channel pairing. We discuss the consequences for cognitive and multi-band radios

On Scalability and Source/Channel Coding Decoupling in Large Scale Sensor Networks

Prior results have shown that for ad hoc networks with uniform source-destination probabilities, where each node generates trafc, the transport capacity for each node in the network declines with the network size n [1]. In this paper, we show that the nite per-node throughput and hence scalability is achievable with the adjustment of source-destination pair distributions. We also explore the rate-distortion bound in the context of sensor networks. Considering a network over a nite region, with nite Gaussian point sources and densely distributed sensors, the otherwise difcult to obtain data rate region under a delity criterion, reduces to a partial side information problem for Gaussian sources. The key concept in proving this is to consider source, sensor, and communications relay densities as separate quantities

Correlated Data Routing in Sensor Networks

We study the correlated information routing with source coding using explicit side information in wireless sensor networks. This problem belongs to the difficult joint compression/routing optimization, and has its connections to the network information theory. We present our network and rate models based on explicit side information available at both the encoder and decoder, then introduce the integer programming formulation with a few simplifying assumptions. A two-stage DPCM coding scheme that can take advantage of the explicit side information and report the additional spatial is also included