Abstract
Tracking the locations and identities of moving targets in the surveillance area of wireless sensor networks is studied. In order to not rely on high-cost sensors that have been used in previous researches, we propose the integrated localization and classification based on semi-supervised learning, which uses both labeled and unlabeled data obtained from low-cost distributed sensor network. In our setting, labeled data are obtained by seismic and PIR sensors that contain information about the types of the targets. Unlabeled data are generated from the RF signal strength by applying Gaussian process, which represents the probability of predicted target locations. Finally, by using classified unlabeled data produced by semi-supervised learning, identities and locations of multiple targets are estimated. In addition, we consider a case when the labeled data are absent, which can happen due to fault or lack of the deployed sensor nodes and communication failure. We overcome this situation by defining artificial labeled data utilizing characteristics of support vector machine, which provides information on the importance of each training data point. Experimental results demonstrate the accuracy of the proposed tracking algorithm and its robustness to the absence of the labeled data thanks to the artificial labeled data.
Highlights
Wireless sensor networks (WSNs) consist of small, low-power sensor nodes that collect, process, and communicate the sensor data
This paper focuses on the problem of localization and classification of multiple targets moving within the area of deployed sensor nodes, which plays an important role in surveillance application [1], environmental monitoring [2,3,4], and traffic monitoring [5]
Because the received signal strength indication (RSSI) characteristics are affected by the motion of the flying aerial robot, its tracking results are worse than results of the ground robot
Summary
Wireless sensor networks (WSNs) consist of small, low-power sensor nodes that collect, process, and communicate the sensor data. There are existing classification methods such as fast Fourier transform (FFT) [13,14] and feature-aided tracking [15,16,17] in order to extract feature points from raw data These need heavy computational load and large memory, which are intractable for low-cost sensor nodes without additional digital signal co-processors. We propose a technique to deal with absence of labeled data, which can occur due to sensors fault, lack of the deployed sensors, or communication failure We overcome this problem by propagating support vectors of each time instant, i.e., essential data points during classification, to the time step and defining those that match with the incoming unlabeled data as artificial labeled data.
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