Passive, device-free localization of a person exploiting the Channel State Information (CSI) from Wi-Fi signals is quickly becoming a reality. While this capability would enable new applications and services, it also raises concerns about citizens’ privacy. In this work, we propose a carefully-crafted obfuscating technique against one of such CSI-based localization methods. In particular, we modify the transmitted I/Q samples by leveraging an irreversible randomized sequence. I/Q symbol manipulation at the transmitter distorts the location-specific information in the CSI while preserving communication, so that an attacker can no longer derive information on user's location. We test this technique against a Neural Network (NN)-based localization system and show that the randomization of the CSI makes undesired localization practically unfeasible. Both the localization system and the CSI randomization are implemented on real devices. The experimental results obtained in our laboratories show that the considered localization method works smoothly regardless of the environment, and that adding random information to the CSI prevents the localization, thus providing the community with a system that preserve location privacy and communication performance at the same time.

Passive device-free localization of a person exploiting the Channel State Information (CSI) from Wi-Fi signals is quickly becoming a reality. While this capability would enable new applications and services, it also raises concerns about citizens' privacy. In this work, we propose a carefully-crafted obfuscating technique against one of such CSI-based localization methods. In particular, we modify the transmitted I/Q samples by leveraging an irreversible randomized sequence. I/Q symbol manipulation at the transmitter distorts the location-specific information in the CSI while preserving communication, so that an attacker can no longer derive information on user's location. We test this technique against a Neural Network (NN)-based localization system and show that the randomization of the CSI makes undesired localization practically unfeasible. Both the localization system and the randomization CSI management are implemented in real devices. The experimental results obtained in our laboratory show that the considered localization method (first proposed in an MSc thesis) works smoothly regardless of the environment, and that adding random information to the CSI mess up the localization, thus providing the community with a system that preserve location privacy and communication performance at the same time.

Vehicular networks supporting cooperative driving are among the most interesting and challenging ad-hoc networks. Platooning, or the act of coordinating a set of vehicles through an ad-hoc network, promises to improve traffic safety, and at the same time reduce congestion and pollution. The design of the control system for this application is challenging, especially because the coordination and cooperation between vehicles is obtained through a wireless network. So far, control and network issues of platooning have been investigated separately, but this is definitely a sub-optimal approach, as constraints of the networked control system impose bounds on the network performance, and network impairments translate into disturbances on the controlled system. In this work we design a cooperative driving system from a joint network and control perspective, determining upper bounds on the error subject to packet losses in the network, so that the actual inter-vehicle gap can be tuned depending on vehicle or network performance. Extensive simulations show that the system is very robust to packet losses and that the derived bounds are never violated. In addition, since the leader control law is part of the proposed control approach, we show that, besides taking into account external events and reacting within the given constraints to ensure the overall road safety, the system can be easily integrated into global traffic optimization tools that mandate the platoon behavior.

Cooperative automatic driving, or platooning, is a promising solution to improve traffic safety, while reducing congestion and pollution. The design of a control system for this application is a challenging, multi-disciplinary problem, as cooperation between vehicles is obtained through wireless communication. So far, control and network issues of platooning have been investigated separately. In this work we design a cooperative driving system from a joint network and control perspective, determining worst-case upper bounds on the safety distance subject to network losses, so the actual inter-vehicle gap can be tuned depending on vehicle or network performance. By means of simulation, we show that the system is very robust to packet losses and that the derived bounds are never violated.