Internet of Everything

Internet of Things (IoT) has become ubiquitous, particularly in domains like agriculture, transport and Industry 4.0. Distributed sensing and computing at the network edge face challenges: limited energy availability, unreliable network links, need for autonomous operation, and demands that can vary dramatically.  Resources like energy (typically, battery power), bandwidth and computing capacity might be in short supply locally, but elsewhere in the network there could be a surplus. Better resource allocation models (including scale-out capacity) support more efficient operational procedures, so the network achieves its objectives. We develop such models and validate them with simulation and prototypes.

Key areas of expertise:

• Energy-aware sensing in agriculture is a critical concern for smart agriculture adoption. Large numbers of sensors are deployed in inaccessible locations. Improved energy management protocols enable operations to continue for longer between maintenance interventions.

• Using communication infrastructure for sensing helps to reduce the apparent capital expenditure of both smart agriculture and 5G roll-out in rural areas, because the infrastructure has shared use, enabled by new measurement and communication protocols.

• Machine Learning for data from sensors is a typical use case for IoT. Data from sensors placed on animals can be used for animal health monitoring and early veterinary intervention. Smart city data can be used to predict public transport demand. Aligning communication, sensing and AI yields better outcomes.

• Opportunistic sensing at the network edge is an extension of the “sharing economy”. As mobile devices (in handsets, wearables and vehicles, etc.) gain more capabilities, new over-the-top services might arise, using these capabilities and paying owners for the privilege, rather than installing specific infrastructure for occasional use. Opportunistic, transient, resource-task allocation models are needed.