Supporting Cyber-Physical Systems with Wireless Sensor Networks: An Outlook of Software and Services

Sensing, communication, computation and control technologies are the essential building blocks of a cyber-physical system (CPS). Wireless sensor networks (WSNs) are a way to support CPS as they provide fine-grained spatial-temporal sensing, communication and computation at a low premium of cost and power. In this article, we explore the fundamental concepts guiding the design and implementation of WSNs. We report the latest developments in WSN software and services for meeting existing requirements and newer demands; particularly in the areas of: operating system, simulator and emulator, programming abstraction, virtualization, IP-based communication and security, time and location, and network monitoring and management. We also reflect on the ongoing efforts in providing dependable assurances for WSN-driven CPS. Finally, we report on its applicability with a case-study on smart buildings

What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacy

Information about people’s movements and the locations they visit enables an increasing number of mobility analytics applications, e.g., in the context of urban and transportation planning, In this setting, rather than collecting or sharing raw data, entities often use aggregation as a privacy protection mechanism, aiming to hide individual users’ location traces. Furthermore, to bound information leakage from the aggregates, they can perturb the input of the aggregation or its output to ensure that these are differentially private. In this paper, we set to evaluate the impact of releasing aggregate location time-series on the privacy of individuals contributing to the aggregation. We introduce a framework allowing us to reason about privacy against an adversary attempting to predict users’ locations or recover their mobility patterns. We formalize these attacks as inference problems, and discuss a few strategies to model the adversary’s prior knowledge based on the information she may have access to. We then use the framework to quantify the privacy loss stemming from aggregate location data, with and without the protection of differential privacy, using two real-world mobility datasets. We find that aggregates do leak information about individuals’ punctual locations and mobility profiles. The density of the observations, as well as timing, play important roles, e.g., regular patterns during peak hours are better protected than sporadic movements. Finally, our evaluation shows that both output and input perturbation offer little additional protection, unless they introduce large amounts of noise ultimately destroying the utility of the data

Vehicular Networks and Outdoor Pedestrian Localization

This thesis focuses on vehicular networks and outdoor pedestrian localization. In particular, it targets secure positioning in vehicular networks and pedestrian localization for safety services in outdoor environments. The former research topic must cope with three major challenges, concerning users’ privacy, computational costs of security and the system trust on user correctness. This thesis addresses those issues by proposing a new lightweight privacy-preserving framework for continuous tracking of vehicles. The proposed solution is evaluated in both dense and sparse vehicular settings through simulation and experiments in real-world testbeds. In addition, this thesis explores the benefit given by the use of low frequency bands for the transmission of control messages in vehicular networks. The latter topic is motivated by a significant number of traffic accidents with pedestrians distracted by their smartphones. This thesis proposes two different localization solutions specifically for pedestrian safety: a GPS-based approach and a shoe-mounted inertial sensor method. The GPS-based solution is more suitable for rural and suburban areas while it is not applicable in dense urban environments, due to large positioning errors. Instead the inertial sensor approach overcomes the limitations of previous technique in urban environments. Indeed, by exploiting accelerometer data, this architecture is able to precisely detect the transitions from safe to potentially unsafe walking locations without the need of any absolute positioning systems