Data fusion in wireless sensor networks

University of Technology, Sydney. Faculty of Engineering and Information Technology.Wireless Sensor Networks (WSNs) are deployed for the purpose of monitoring an area of interest. Even when the sensors are properly calibrated at the time of deployment, they develop drift in their readings leading to erroneous network inferences. Traditionally, such errors are corrected by site visits where the sensors are calibrated against an accurately calibrated sensor. For large scale sensor networks, the process is manually intensive and economically infeasible. This imposes finding automatic procedures for continuous calibration. Noting that a physical phenomenon in a certain area follows some spatio-temporal correlation, we assume that the sensors readings in that area are correlated. We also assume that measurement errors due to faulty equipment are likely to be uncorrelated. Based on these assumptions, we follow a Bayesian framework to solve the drift and bias problem in WSNs. In the case of densely deployed WSN, neighbouring sensors are assumed to be close to each other that they observe the same phenomenon. Hence, the average of their corrected readings is taken as a basis for each sensor to self-assess its measurement, estimate its drift and to correct the measurement using a Kalman Filter (KF) in the case of smooth drift, and the Interacting Multiple Model algorithm (IMM) in the case of unsmooth drift. The solutions are computationally simple, decentralised and also scalable. Any new node joining the neighbourhood needs only to obtain the corrected readings of its neighbours to find the average and apply the KF iterative procedure. On the other hand, when the sensors are not densely deployed, Support Vector Regression (SVR) is used to model the interrelationships of sensor measurements in a neighbourhood. This enables the incorporation of the spatio-temporal correlation of neighbouring sensors, to predict future measurements. The SVR predicted value is used by a KF to estimate the actual drift and correct the measurement. Unfortunately, the KF introduces some system errors when used with nonlinear systems. The use of Unscented Kalman filter (UKF) instead, considerably reduces the system error and results in a better drift correction. The use of IMM with the SVRUKF framework allows for reducing the sampling rate which eventually reduces the communication overhead among the sensors and saves the communication energy. In this thesis, we present several solutions for the random and systematic (drift and bias) errors in sensors measurements, for different sensor deployment scenarios. We also consider two drift scenarios, namely smooth and unsmooth drifts. We evaluate the presented algorithms on simulated and real data obtained from the Intel Berkeley Research Laboratory sensor deployment. The results show that our algorithms successfully detect and correct systematic errors (drift and bias) developed in sensors and filters out the noise. Thereby, prolonging the effective lifetime of the network

A Wireless Sensor Data Fusion Framework for Contaminant Detection

In recent years, much research has been done on wireless sensor networks and sensor data fusion, however there has been limited work regarding implementation of real systems that are capable of providing a highly connected sensor network for data logging and data fusion applications. This paper describes the design and implementation of a wireless, portable, and reconfigurable sensor network framework. This sensor node design has proven to be effective for monitoring environmental conditions of aircraft cabins and is well suited to environmental monitoring and detection of contaminants in large areas when utilizing sensor data fusion features

Overlapping Coalition Formation for Efficient Data Fusion in Multi-Sensor Networks

This paper develops new algorithms for coalition formation within multi-sensor networks tasked with performing wide-area surveillance. Specifically, we cast this application as an instance of coalition formation, with overlapping coalitions. We show that within this application area sub-additive coalition valuations are typical, and we thus use this structural property of the problem to we derive two novel algorithms (an approximate greedy one that operates in polynomial time and has a calculated bound to the optimum, and an optimal branch-and-bound one) to find the optimal coalition structure in this instance. We empirically evaluate the performance of these algorithms within a generic model of a multi-sensor network performing wide area surveillance. These results show that the polynomial algorithm typically generated solutions much closer the optimal than the theoretical bound, and prove the effectiveness of our pruning procedure

Dataflow-Oriented Provenance System for Multifusion Wireless Sensor Networks

We present a dataflow-oriented provenance system for data fusion sensor networks. This model works best with net- works sensing dynamic objects and although our system is generic, we model it on a proximity binary sensor network. We introduce a network-level fault-tolerance mechanism by using the cognitive strength of provenance models. Our provenance model reduce the limitations of a sensor’s capability and decrease the error-prone nature of wireless sen- sor networks. In addition provenance data is used in order to efficiently build the dynamic data fusion scenario and to adjust the network such as turning of some sensors. In a fault-tolerant, self-adjusting sensor network, sensor data produce more accurate results and with the improvements, tasks such as target localization is more precisely done. One other aspect of our network is that by having computation nodes spread to the network, the computation is done in a distributed manner and as nodes make decisions based on the provenance and fusion data available, the network has a distributed intelligence. Keywords: Multifusion, Wireless Sensor Networks, Open Provenance Mode