Selfish Mules: Social Profit Maximization in Sparse Sensornets using Rationally-Selfish Human Relays

Abstract

Future smart cities will require sensing on a scale hitherto unseen. Fixed infrastructures have limitations regarding sensor maintenance, placement and connectivity. Employing the ubiquity of mobile phones is one approach to overcoming some of these problems. Here, mobility and social patterns of phone owners can be exploited to optimize data forwarding efficiency. The question remains, how can we stimulate phone owners to serve as data relays? In this paper, we combine network science principles and Lyapunov optimization techniques, to maximize global social profit across this hybrid sensor and mobile phone network. Sensor data packets are produced and traded (transmitted) over a virtual economic network using a lightweight social-economic-aware backpressure algorithm, combining rate control, routing, and resource pricing. Phone owners can get benefits through relaying sensor data. Our algorithm is fully distributed and makes no probabilistic/stochastic assumptions regarding mobility, topology, and channel conditions, nor does it require prediction. The global social profit achieved by our algorithm can perform close to (or better than) an ideal algorithm with perfect prediction- proven by rigorous theoretical analysis. Simulation results further demonstrate that the proposed algorithm outperforms pure backpressure and social-aware schemes; highlighting the advantage of building systems combining communication with other types of networks.