Data Mules

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 devices is one approach to overcoming some of these problems. This work relates to general Delay Tolerant Networking and Opportunistic Networking principles but extends these notions to incorporate models of the behaviours of the environment and people that involved to optimise performance. Since our mobile devices carry data between sensors and their required destination we nick-name them Data Mules.

This project examines how our understanding of the environment in which sensing systems live can help improve the performance of that sensing system. Performance can be end-to-end data delivery times, guaranteed throughputs or life-time maximisation, among others. One approach is to exploit knowledge of mobility and the social patterns of phone owners to optimize data forwarding efficiency. However how can we stimulate phone owners to serve as data relays? To solve this we combine network science principles and Lyapunov optimization techniques, to maximize the global social profit of those acting as data mules across a hybrid network of sensors and mobile devices. Sensor data packets are produced and traded (transmitted) over a virtual economic network using a lightweight socio-economically aware backpressure algorithm, combining rate control, routing, and resource pricing. Here, phone owners can get benefits through relaying the sensor data. In line with the core principles of the AESE group’s principles, our algorithm is fully distributed and also makes no probabilistic/stochastic assumptions regarding mobility, topology, and channel conditions, nor does it require prediction. Common to the theme of this project, we find that our scheme outperform pure backpressure and social-aware schemes; highlighting the advantage of building systems combining communication with other types of network science principles.

Main Contributors