Collective Abstractions and Platforms for Large-Scale Self-Adaptive IoT

Abstract

On the way to the materialisation of the pervasive computing vision, the technological progress swelling from mobile computing and the Internet of Things (IoT) domain is already rich of missed opportunities. Firstly, coordinating large numbers of heterogeneous situated entities to achieve system-level goals in a resilient and self-adaptive way is complex and requires novel approaches to be seamlessly injected into mainstream distributed computing models. Secondly, achieving effective exploitation of computer resources is difficult, due to operational constraints resulting from current paradigms and uncomprehensive software infrastructures which hinder flexibility, adaptation, and smooth coordination of computational tasks execution. Indeed, building dynamic, context-oriented applications in small-or large-scale IoT with traditional abstractions is hard: even harder is to achieve opportunistic, QoS-and QoE-driven application task management across available hardware and networking infrastructure. In this insight paper, we analyse by the collective adaptation perspective the key directions of the impelling paradigm shift urged by forthcoming large-scale IoT scenarios. Specifically, we consider how collective abstractions and platforms can synergistically assist in such a transformation, by better capturing and enacting a notion of "collective service" as well as the dynamic, opportunistic, and context-driven traits of space-time-situated computations.