Device-free or passive localization techniques allow positioning of targets, without requiring them to carry any form of transceiver or tag. In this paper, a novel device-free visible light positioning technique is proposed. It exploits the variation of the ambient light levels caused by a moving entity. The target is localized by employing a system of artificial potential fields associated with a set of photodiodes embedded into an indoor environment. The system does not require the existing lighting infrastructure to be modified. It also employs a novel calibration procedure that does not require labelled training data, thus significantly reducing the calibration cost. The developed prototype system is installed in three typical indoor environments consisting of a corridor, foyer, and laboratory and was able to attain median errors of 0.68m, 1.20m and 0.84m respectively. Through experimental results, the proposed VLP technique is benchmarked against an existing wireless RSSI-based device-free localization approach, and was able to attain a median error 0.63m lower than the wireless technique.

Device-free localization (DFL) systems that rely on the wireless received signal strength indicator (RSSI) metric to localize targets with no device attached to them have been reported in the literature for almost a decade. Approaches using RSSI can be split into three main categories. Link-based approaches utilize weighted summation or probabilistic methods to infer location. Location-based approaches create a fingerprint map of an area. Radio Tomographic Imaging treat DFL as an imaging problem solved with a linear inverse. In this paper, we implement and investigate the performance of all three major RSSI approaches in two test environments. We demonstrate how different environments and walking trajectories can have significant effects on the localization accuracy. The experimental results lead us to the conclusion that without implementing and testing within the same environment for the same target trajectories, the performance of various classes of DFL systems cannot be reliably evaluated. Relying on the stated accuracy from the literature for comparison is a flawed premise.

Device-free Localization (DFL) algorithms using the Received Signal Strength Indicator (RSSI) metric, have become a popular research focus in recent years as they allow for location-based service using Commercial-off-the-shelf (COTS) wireless equipment. However, most existing DFL approaches have limited applicability in realistic smart home environments as they typically require extensive offline calibration, large node densities or use technology that is not readily available in commercial smart homes. In this paper, we introduce SpringLoc, a DFL algorithm that relies on simple parameter tuning and does not require offline measurements. It localizes and tracks an entity using an adaptive spring relaxation approach. The anchor points of the artificial springs are placed in regions containing the links that are affected by the entity. The affected links are determined by comparing the kernel-based histogram distance of successive RSSI values. SpringLoc is benchmarked against existing algorithms in two diverse and realistic environments, showing significant improvement over the state-of-the-art, especially in situations with low node deployment density.

Indoor localization based on visible light and Visible Light Communication (VLC) has become a viable alternative to radio frequency wireless based techniques. Modern Visible Light Position (VLP) systems have been able to attain sub-decimeter level accuracy within standard room environments. However a major limitation is their reliance on Line-Of-Sight (LOS) visibility between the tracked object and the lighting infrastructure. This paper introduces Falcon (Fused Application of Light based positioning Coupled with Onboard Network localization), a VLP system which incorporates Convolutional Neural Network (CNN) based wireless localization to remove this limitation. This system has been tested in real life scenarios that cause traditional VLP systems to lose accuracy. In a hallway with luminaires along one axis, Falcon managed to attain position estimates with a mean error of 0.09m. In a large room where only a few luminaires were visible or the receiver was completely occluded, the mean error was 0.12m. With the luminaires switched off, Falcon managed to correctly classify the target 99.59% of the time to within a 0.9m2 cell and with 100% accuracy within a1.6m2 cell in the room and hallway respectively.

In recent years, research into localization systems has become more popular as the proliferation of Wireless Sensor Networks (WSNs) grows. Wireless Localization can refer to either an “Active” system which tracks a mobile transceiver, or “Passive” localization which tracks a transceiver free entity by measuring the changes it makes to the surrounding wireless environment. Recent work has seen both of these systems implemented with Received Signal Strength Indication (RSSI) values from transceivers. Many algorithms and channel models have been presented to increase the accuracy of a Received Signal Strength (RSS) based system. In this paper we experimentally check whether RSSI values map to the expected RSS values within an IEEE 802.15.4 network. Indoor experiments are repeated within an ideal outdoor environment, with multiple device platforms, to eliminate indoor multipath propagation as the cause for inconsistent behavior of RSSI. We identify 3 key issues with raw RSSI values and present either a possible solution or a mitigation strategy to reduce their effect. We conclude that using raw RSSI values is flawed, as the premise that they uniquely map to the distance between transceivers is incorrect. However they may be calibrated to increase their accuracy, and therefore viability.

Indoor positioning systems (IPS) have gained a lot of traction within the research community in recent years. Received Signal Strength Indicator (RSSI) of wireless networks are the most commonly used metric for indoor localization. The objective of this paper is to see how Wi-Fi interferers of different data rates affect the packet RSSI values of TX-RX links in a ZigBee based indoor localization system. The factors we examine also include the rate of corrupted packets and the overall packet loss. We also explore whether a Microwave Oven, a common source of interference for the ISM Band in a dwelling, perturb RSSI values in a localization system.