Context-aware optimization of distributed resources in internet of things using key performance indicators

Abstract

The recent advancements in Internet of Things (IoT) show us a glimpse of a future in which all our devices are connected to the internet, providing users with services that make life easier, more comfortable and safer. Although this interconnectivity seems simple, in practice management of the IoT hardware and the enormous amounts of data it generates is challenging. To bring the connected future into reality and build advanced and useful services, better resource usage estimation (memory, bandwidth, storage etc.) and resource management is required. We propose a IoT optimization methodology, where resources are estimated at each level of the IoT architecture (i.e. nodes, edges and cloud). Using these estimates, the executed code is redistributed across the network in order to optimize the cost and efficiency of the IoT environment, while taking into a specific context (e.g. environment). Initially, we aim to apply this methodology for statically defined contexts. In our future research we aim to perform the optimization at runtime, redistributing tasks across the IoT network dynamically as the context of the nodes changes.