“IoCT”: Internet of Complex Things
The proposal aims finally to build a distributed network management framework for dense, distributed and heterogeneous IoT networks based on complex systems science paradigms. There are many communication technologies and testbeds but they do not talk the same language and solutions cannot be tested on multiple platforms, at the same time, in order to extract actionable knowledge the elements of a network and their interactions need to be modelled efficiently and complex systems science is very much a science which studies interactions between processes. The first part of the work deals with building a powerful and flexible taxonomy to describe how elements of an IoT network, interact with one another in a system-of-systems framework.
Subsequently, a cross layer simulation frameworks will be designed to allow the study of interactions between all the network variables form PHY to application. A time-series causality analysis will be implemented to determine how a local action at a specific time can impact global behaviour in the future. Finally, the network behaviour is going to be studied with complex systems science to determine the most relevant processes in the network and develop distributed solutions once all the actionable variables are identified and the figures of merit are defined (by the data itself rather than by prior expert knowledge).
The proposed research builds upon the concrete work done in the biostatistics fields about structure learning (network inference), in complex systems science about inter-node interactions in time and space for causality, in practical network testing using real-life testbeds and IoT protocol design.
The framework envisioned will be used to study and redefine how heterogeneous dense networks are designed and tested to allow seamless integrations of technologies and services.