Localisation of mobile nodes in wireless networks with correlated in time measurement noise.

Wireless sensor networks are an inherent part of decision making, object tracking and location awareness systems. This work is focused on simultaneous localisation of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multi model auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localisation accuracy is demonstrated

Location based services in wireless ad hoc networks

In this dissertation, we investigate location based services in wireless ad hoc networks from four different aspects - i) location privacy in wireless sensor networks (privacy), ii) end-to-end secure communication in randomly deployed wireless sensor networks (security), iii) quality versus latency trade-off in content retrieval under ad hoc node mobility (performance) and iv) location clustering based Sybil attack detection in vehicular ad hoc networks (trust). The first contribution of this dissertation is in addressing location privacy in wireless sensor networks. We propose a non-cooperative sensor localization algorithm showing how an external entity can stealthily invade into the location privacy of sensors in a network. We then design a location privacy preserving tracking algorithm for defending against such adversarial localization attacks. Next we investigate secure end-to-end communication in randomly deployed wireless sensor networks. Here, due to lack of control on sensors\u27 locations post deployment, pre-fixing pairwise keys between sensors is not feasible especially under larger scale random deployments. Towards this premise, we propose differentiated key pre-distribution for secure end-to-end secure communication, and show how it improves existing routing algorithms. Our next contribution is in addressing quality versus latency trade-off in content retrieval under ad hoc node mobility. We propose a two-tiered architecture for efficient content retrieval in such environment. Finally we investigate Sybil attack detection in vehicular ad hoc networks. A Sybil attacker can create and use multiple counterfeit identities risking trust of a vehicular ad hoc network, and then easily escape the location of the attack avoiding detection. We propose a location based clustering of nodes leveraging vehicle platoon dispersion for detection of Sybil attacks in vehicular ad hoc networks --Abstract, page iii

Hearing What You Cannot See: Acoustic Vehicle Detection Around Corners

This work proposes to use passive acoustic perception as an additional sensing modality for intelligent vehicles. We demonstrate that approaching vehicles behind blind corners can be detected by sound before such vehicles enter in line-of-sight. We have equipped a research vehicle with a roof-mounted microphone array, and show on data collected with this sensor setup that wall reflections provide information on the presence and direction of occluded approaching vehicles. A novel method is presented to classify if and from what direction a vehicle is approaching before it is visible, using as input Direction-of-Arrival features that can be efficiently computed from the streaming microphone array data. Since the local geometry around the ego-vehicle affects the perceived patterns, we systematically study several environment types, and investigate generalization across these environments. With a static ego-vehicle, an accuracy of 0.92 is achieved on the hidden vehicle classification task. Compared to a state-of-the-art visual detector, Faster R-CNN, our pipeline achieves the same accuracy more than one second ahead, providing crucial reaction time for the situations we study. While the ego-vehicle is driving, we demonstrate positive results on acoustic detection, still achieving an accuracy of 0.84 within one environment type. We further study failure cases across environments to identify future research directions.Comment: Accepted to IEEE Robotics & Automation Letters (2021), DOI: 10.1109/LRA.2021.3062254. Code, Data & Video: https://github.com/tudelft-iv/occluded_vehicle_acoustic_detectio

Reconfigurable and Static EM Skins on Vehicles for Localization

Electromagnetic skins (EMSs) have been recently considered as a booster for wireless sensing, but their usage on mobile targets is relatively novel and could be of interest when the target reflectivity can/must be increased to improve its detection or the estimation of parameters. In particular, when illuminated by a wide-bandwidth signal (e.g., from a radar operating at millimeter waves), vehicles behave like \textit{extended targets}, since multiple parts of the vehicle's body effectively contribute to the back-scattering. Moreover, in some cases perspective deformations challenge the correct localization of the vehicle. To address these issues, we propose lodging EMSs on vehicles' roof to act as high-reflectivity planar retro-reflectors toward the sensing terminal. The advantage is twofold: \textit{(i)} by introducing a compact high-reflectivity structure on the target, we make vehicles behave like \textit{point targets}, avoiding perspective deformations and related ranging biases and \textit{(ii)} we increase the reflectivity the vehicle, improving localization performance. We detail the EMS design from the system-level to the full-wave-level considering both reconfigurable intelligent surfaces (RIS) and cost-effective static passive electromagnetic skins (SP-EMSs). Localization performance of the EMS-aided sensing system is also assessed by Cram\'er-Rao bound analysis in both narrowband and spatially wideband operating conditions