Real-Time Scheduling in Drop Computing

The IoT world evolves at a tremendously fast rate, leaving some of the currently employed technologies behind. The modern day user has more and more expectations: high availability and performance, low energy consumption and high privacy standards. If all of these things were achievable a decade ago, the world is now witnessing an explosion of interconnected, mobile devices, which consequently generate big volumes of data. In addition to this, an exponential increase in real-time based applications fuels even more the end users' demands. Edge and Fog computing paved the way to a new, different approach in the light of delegating not only the Cloud for computational and storage resources. By bringing processing resources closer to the source, a completely different perspective was offered to the research world. The emergence of these new models revealed the need for different approaches and marked the beginning of a major paradigm shift. Drop Computing expands the horizon of the previous technologies to another level, by introducing a decentralized model, based on opportunistic networks and geographical co-locations. In this paper, we aim to optimize the solution by introducing new scheduling strategies. We addressed the proliferation of real-time applications by shaping deadline focused algorithms and we analysed the results to evaluate the system's behaviour under various constraints.