A Distributed Processing Platform With Reconfigurable Autonomous Nodes

Distributed processing is a fast growing area of interest due to the exploding popularity of Internet of Things (IoT) and Unmanned Aerial Vehicles (UAV) technologies. IoT is a distributed processing structure by nature, while UAVs evolve from single-UAV applications towards multiple-UAV (teams). The demand for processing capabilities is expanding as well. The general purpose processors (e.g. CPUs) can be used for any type of application, however this flexibility is at the cost of operational efficiency. Application Specific Integrated Circuits (ASICs) are designed for certain types of application and have great operational efficiency, but they rarely can be used for other applications. The reconfigurable chips – Field Programmable Gate Arrays (FPGAs) provide high operational efficiency along with the application flexibility – as they can be reprogrammed with the functionality that is required at the given time. All the above listed aspects are combined in the distributed processing system that is expected to consume low amount of electrical energy. This dissertation proposes a comprehensive solution for the problem of distributed processing equipped with reconfigurable units. The complete and detailed architecture is provided for each element. The design includes operational algorithms that together with the architecture constitute a complete solution for the stated problem. The design of the units is flexible and allows any number and combination of CPUs, ASICs or FPGAs. Units in the proposed design are autonomous – the decisions are taken by individual units, instead of the central node, which is marginalized. The decentralized and autonomous approach provides more flexible and reliable design that is especially important for IoT and teamed UAV applications. The efficiency of the proposed solutions is defined as electrical energy consumption and operation timespan, and is measured using dedicated experimentation system through numerous simulations