Node localisation in wireless ad hoc networks

Wireless ad hoc networks often require a method for estimating their nodes' locations. Typically this is achieved by the use of pair-wise measurements between nodes and their neighbours, where a number of nodes already accurately know their location and the remaining nodes must calculate theirs using these known locations. Typically, a minimum mean square estimate (MMSE), or a maximum likelihood estimate (MLE) is used to generate the unknown node locations, making use of range estimates derived from measurements between the nodes. In this paper we investigate the efficacy of using radio frequency, received signal strength (RSS) measurements for the accurate location of the transmitting nodes over long ranges. We show with signal strength measurements from three or more wireless probes in noisy propagation conditions, that by using a weighted MMSE approach we can obtain significant improvements in the variance of the location estimate over both the standard MMSE and MLE approaches.Jon Arnold, Nigel Bean, Miro Kraetzl, Matthew Rougha

Non-linear echo cancellation - a Bayesian approach

Echo cancellation literature is reviewed, then a Bayesian model is introduced and it is shown how how it can be used to model and fit nonlinear channels. An algorithm for cancellation of echo over a nonlinear channel is developed and tested. It is shown that this nonlinear algorithm converges for both linear and nonlinear channels and is superior to linear echo cancellation for canceling an echo through a nonlinear echo-path channel

Occupancy Detection using Wireless Sensor Network in Indoor Environment

Occupancy detection plays an important role in many smart buildings such as reducing building energy usage by controlling heating, ventilation and air conditioning (HVAC) systems, monitoring systems and managing lighting systems, tracking people in hospitals for medical issues, advertising to people in malls, and to search and rescue missions. The global positioning system (GPS) is used most widely as a localization system but highly inaccurate for indoor applications. The indoor environment is difficult to handle because along with the loss of signals, privacy is a major concern. Indoor tracking has many aspects in common with sensor localization in Wireless Sensor Networks (WSN). The contribution of this work is the demonstration of a nonintrusive approach to detect an occupancy in a building using wireless sensor networks to detect energy from cell phones in a secure facility and perform indoor localization based on the minimum mean square error (MMSE). To estimate the occupancy, the detected cellular signals information such as signal amplitude, frequency, power and detection time is sent to a fusion server, matched the detected signals by time and channel information, performed localization to estimate a location, and finally estimated the occupancy of rooms in a building from the estimated locations