Our particular interest in the Edge Computing theme is one that is interested in exploring how highly-decentralised, lightweight techniques can maximise the highly heterogeneous computational mix of devices with differing capacities, within different environments. The particular techniques we wish to exploit centre around emergent solutions that provide the benefits of scale and agility necessary to make such systems live throughout its life-time and the many changes that one would expect within its cyber and physical environments.
To this end we are exploring the following:
Distributed Fair Scheduling: how to schedule a highly heterogeneous mix of systems with differing capacities and abilities in a formally fair manner. This work is very different from traditional scheduling because it goes beyond processor scheduling and assumes the weights of the requirements are not equal for all tasks.
In-network query processing: knowing where data is and deciding where to process it. Here we look at robust, scalable solutions that provide good (where not optimal) solutions to decide where to execute queries, taking minimising the resources and energy used to do this while maximising the systems’ performance. point to roman here.
Detecting and understanding anomalies: identifying anomalies and their potential sources and how errors in sensor readings impact on subsequent in-network processing of data values.
Older work examined underlying protocols to route, synchronize duty-cycles and manage edge systems like wireless sensor networks (please refer to related papers):
YA-MAC an agile Medium Access Control (MAC) protocol to provide high throughput
for both unicast and broadcast traffic in Duty-Cycled Multihop Wireless Sensor Networks.
VIBE Virtual-Infrastructure-Based Energy efficient routing: Self-Adaptive Minimum Energy Routing for WSN accounting for various application scenarios requiring schedule based,event-driven or on-demand routing.