Preparing opportunistic networks for smart cities collecting shared data with minimal knowledge

Abstract

Opportunistic Networks exploit portable handheld devices to collect delay-tolerant data from sensors to gateways for realizing various Smart City applications. To obtain knowledge for determining suitable routing paths as users go about their daily routine, nodes maintain history of every encounter and exchange the information through summary vectors. Due to large node populations, the size of summary vectors makes it challenging to implement real-world city-scale applications with the technology. In this paper, we take the technology a step towards real-world implementation by proposing a set of adaptive and privacy-preserving mechanisms that can be incorporated into existing encounter-based routing protocols to reduce summary vector sizes without compromising delivery guarantees. We validate our proposals with real-world human movement traces and simulation experiments. In terms of network performance, our proposals reduce the average summary vector size by 75% to achieve up to 21% less energy consumption with about 28% improvement in throughput.