A novel multilayer neural network model for TOA-based localization in wireless sensor networks

A novel multilayer neural network model, called artificial synaptic network, was designed and implemented for single sensor localization with time-of-arrival (TOA) measurements. In the TOA localization problem, the location of a source sensor is estimated based on its distance from a number of anchor sensors. The measured distance values are noisy and the estimator should be able to handle different amounts of noise. Three neural network models: the proposed artificial synaptic network, a multi-layer perceptron network, and a generalized radial basis functions network were applied to the TOA localization problem. The performance of the models was compared with one another. The efficiency of the models was calculated based on the memory cost. The study result shows that the proposed artificial synaptic network has the lowest RMS error and highest efficiency. The robustness of the artificial synaptic network was compared with that of the least square (LS) method and the weighted least square (WLS) method. The Cramer-Rao lower bound (CRLB) of TOA localization was used as a benchmark. The model's robustness in high noise is better than the WLS method and remarkably close to the CRLB

A spatial role-based authorization framework for sensor network-assisted indoor WLANs

©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Article also available from publisher: http://dx.doi.org/10.1109/WIRELESSVITAE.2009.5172549In this paper, we propose a spatial role-based authorization framework which specifies authorization based on both role and location constrains in a wireless local area network with assistance from a sensor network. The framework performs a location-restricted verification scheme before granting a user with privileges for crucial resources access. Analysis and simulation results show that our framework can provide double-check safeguard to confidential information, so that potential attackers cannot access the resource outside the permitted region, even though their role is verified

Application of Neural Networks to Acoustic Localization

The intent of the work conducted was to build a neural network for the purposes of acoustic localization. The target of this localization is a sound source underwater. For our purposes, it is an acoustic pinger, as it produces consistent sound at a fixed rate making it ideal for testing. The network was intended to ingest raw data streams and output location information based on the arrangement of sensors employed. To achieve an accurate network, a simulation factoring in the environment was to be created to produce a data set large and diverse enough to describe the unique parameters of the signals, including: frequency, environmental reflections, and range. This problem will be approached in multiple steps. Initial models will consider simplified problem spaces, such as individual frequencies and less descriptive training sets. Through development, this will be refined and extended. Where required, simplifications will be kept managing the scope of the problem to allow for a demonstration of the technology to be made at all. Discussion of what is the root cause of the issue navigated will be presented when this occurs. Results will then be shown to demonstrate the performance of the network created as compared to the classical approach to this problem, time difference of arrival. This paper will demonstrate the performance of a neural network as applied to the problem of acoustic localization. The network developed can accurately localize an acoustic sound source to the same order of magnitude of accuracy and execution time as the current approaches to the problem. However, the network also showed a lacking in some areas of robustness due to training factors not considered, hampering the full potential

Doctor of Philosophy

dissertationThis work seeks to improve upon existing methods for device-free localization (DFL) using radio frequency (RF) sensor networks. Device-free localization is the process of determining the location of a target object, typically a person, without the need for a device to be with the object to aid in localization. An RF sensor network measures changes to radio propagation caused by the presence of a person to locate that person. We show how existing methods which use either wideband or narrowband RF channels can be improved in ways including localization accuracy, energy efficiency, and system cost. We also show how wideband and narrowband systems can combine their information to improve localization. A common assumption in ultra-wideband research is that to estimate the bistatic delay or range, "background subtraction" is effective at removing clutter and must first be performed. Another assumption commonly made is that after background subtraction, each individual multipath component caused by a person's presence can be distinguished perfectly. We show that these assumptions are often not true and that ranging can still be performed even when these assumptions are not true. We propose modeling the difference between a current set of channel impulse responses (CIR) and a set of calibration CIRs as a hidden Markov model (HMM) and show the effectiveness of this model over background subtraction. The methods for performing device-free localization by using ultra-wideband (UWB) measurements and by using received signal strength (RSS) measurements are often considered separate topic of research and viewed only in isolation by two different communities of researchers. We consider both of these methods together and propose methods for combining the information obtained from UWB and RSS measurements. We show that using both methods in conjunction is more effective than either method on its own, especially in a setting where radio placement is constrained. It has been shown that for RSS-based DFL, measuring on multiple channels improves localization accuracy. We consider the trade-o s of measuring all radio links on all channels and the energy and latency expense of making the additional measurements required when sampling multiple channels. We also show the benefits of allowing multiple radios to transmit simultaneously, or in parallel, to better measure the available radio links