A Decentralized Mobile Computing Network for Multi-Robot Systems Operations

Collective animal behaviors are paradigmatic examples of fully decentralized operations involving complex collective computations such as collective turns in flocks of birds or collective harvesting by ants. These systems offer a unique source of inspiration for the development of fault-tolerant and self-healing multi-robot systems capable of operating in dynamic environments. Specifically, swarm robotics emerged and is significantly growing on these premises. However, to date, most swarm robotics systems reported in the literature involve basic computational tasks---averages and other algebraic operations. In this paper, we introduce a novel Collective computing framework based on the swarming paradigm, which exhibits the key innate features of swarms: robustness, scalability and flexibility. Unlike Edge computing, the proposed Collective computing framework is truly decentralized and does not require user intervention or additional servers to sustain its operations. This Collective computing framework is applied to the complex task of collective mapping, in which multiple robots aim at cooperatively map a large area. Our results confirm the effectiveness of the cooperative strategy, its robustness to the loss of multiple units, as well as its scalability. Furthermore, the topology of the interconnecting network is found to greatly influence the performance of the collective action.Comment: Accepted for Publication in Proc. 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conferenc

Supporting security-oriented, inter-disciplinary research: crossing the social, clinical and geospatial domains

How many people have had a chronic disease for longer than 5-years in Scotland? How has this impacted upon their choices of employment? Are there any geographical clusters in Scotland where a high-incidence of patients with such long-term illness can be found? How does the life expectancy of such individuals compare with the national averages? Such questions are important to understand the health of nations and the best ways in which health care should be delivered and measured for their impact and success. In tackling such research questions, e-Infrastructures need to provide tailored, secure access to an extensible range of distributed resources including primary and secondary e-Health clinical data; social science data, and geospatial data sets amongst numerous others. In this paper we describe the security models underlying these e-Infrastructures and demonstrate their implementation in supporting secure, federated access to a variety of distributed and heterogeneous data sets exploiting the results of a variety of projects at the National e-Science Centre (NeSC) at the University of Glasgow