Relay node placement for wireless sensor networks deployed in tunnels

Node placement plays a significant role in the effective and successful deployment of Wireless Sensor Networks (WSNs), i.e., meeting design goals such as cost effectiveness, coverage, connectivity, lifetime and data latency. In this paper, we propose a new strategy to assist in the placement of Relay Nodes (RNs) for a WSN monitoring underground tunnel infrastructure. By applying for the first time an accurate empirical mean path loss propagation model along with a well fitted fading distribution model specifically defined for the tunnel environment, we address the RN placement problem with guaranteed levels of radio link performance. The simulation results show that the choice of appropriate path loss model and fading distribution model for a typical environment is vital in the determination of the number and the positions of RNs. Furthermore, we adapt a two-tier clustering multi-hop framework in which the first tier of the RN placement is modelled as the minimum set cover problem, and the second tier placement is solved using the search-and-find algorithm. The implementation of the proposed scheme is evaluated by simulation, and it lays the foundations for further work in WSN planning for underground tunnel applications. © 2010 IEEE.

Simulation-based optimal sensor scheduling with application to observer trajectory planning

The sensor scheduling problem can be formulated as a controlled hidden Markov model and this paper solves the problem when the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. The aim is to minimise the variance of the estimation error of the hidden state w.r.t. the action sequence. We present a novel simulation-based method that uses a stochastic gradient algorithm to find optimal actions. © 2007 Elsevier Ltd. All rights reserved.

A resonant micromachined electrostatic charge sensor

A micromachined electrometer, based on the concept of a variable capacitor, has been designed, modeled, fabricated, and tested. The device presented in this paper functions as a modulated variable capacitor, wherein a dc charge to be measured is up-modulated and converted to an ac voltage output, thus improving the signal-to-noise ratio. The device was fabricated in a commercial standard SOI micromachining process without the need for any additional processing steps. The electrometer was tested in both air and vacuum at room temperature. In air, it has a charge-to-voltage conversion gain of 2.06 nV/e, and a measured charge noise floor of 52.4 e/rtHz. To reduce the effects of input leakage current, an electrically isolated capacitor has been introduced between the variable capacitor and input to sensor electronics. Methods to improve the sensitivity and resolution are suggested while the long-term stability of these sensors is modeled and discussed. © 2006 IEEE.

Fabrication and bio-functionalization of tetrahedral amorphous carbon thin films for bio sensor applications

Tetrahedral amorphous carbon (ta-C) thin films are a promising material for use as biocompatible interfaces in applications such as in-vivo biosensors. However, the functionalization of ta-C film surface, which is a pre-requisite for biosensors, remains a big challenge due to its chemical inertness. We have investigated the bio-functionalization of ta-C films fabricated under specific physical conditions through the covalent attachment of functional biomolecular probes of peptide nucleic acid (PNA) to ta-C films, and the effect of fabrication conditions on the bio-functionalization. The study showed that the functional bimolecular probes such as protected long-chain ω-unsaturated amine (TFAAD) can be covalently attached to the ta-C surface through a well-defined structure. With the given fabrication process, electrochemical methods can be applied to the detection of biomolecular interaction, which establishes the basis for the development of stable, label-free biosensors. © 2011 Elsevier B.V. All rights reserved.

Distributed maximum likelihood for self-localization in sensor networks

We show that the sensor localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we develop fully decentralized versions of the Recursive Maximum Likelihood and the Expectation-Maximization algorithms to localize the network. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a message passing algorithm to propagate the derivatives of the likelihood. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we show that the developed algorithms are able to learn the localization parameters well.