Opportunistic Tracking in Cyber-Physical Systems

Cyber-Physical Systems raise a new dimension of security concerns as they open up the opportunity for attackers to affect a real-world environment. These systems are often applied in specific environments with special requirements and a common issue is to keep track of movements in a mobile system, e.g., involving autonomous robots, drones or sensory I/O devices. In Opportunistic Networks, nodes are usually mobile, forwarding messages from one device to another, not relying on external infrastructure like WiFi. Due to compact and convenient wearability, the nodes of an OppNet might be used to detect the absence and presence of devices or even people in an area where classical networks may not be reliable enough. In this paper, we combine opportunistic network technology with cyber-physical systems and propose a reliable routing algorithm for nodes tracking. Our real-world setup implements hardware sensor tags to evaluate the algorithm in a state-of-the-art environment. Efficiency and performance are compared with established algorithms, i.e., Epidemic and Prophet, in terms of latency, network overhead, as well as message delivery probability, and to evaluate the algorithm's scalability, we simulate the tracking in a huge environment.