A Model-Driven Workflow for Energy-Aware Scheduling Analysis of IoT-Enabled Use Cases

Internet of Things (IoT)-enabled applications are gaining inroads in various domains. Several IoT platforms, with support for rich set of device libraries, facilitate rapid development of embedded IoT applications. But, none of the approaches deal with an early analysis of performance characteristics such as energy and timing properties. Such analysis (and feedback) during early design stages would help in identifying problems in meeting the performance attributes, reduce development time/effort, and deliver better quality without budget overshoot. Addressing this gap, a novel, generic workflow to carry out a quick, early, model-driven, system-level energy-aware timing validation of IoT-enabled hand-written code in specialized timing analysis tools, such as SymTA/S, is proposed in this paper. A prototype of the workflow is realized using a light-weight interfacing tool framework (with plug-ins) employing the Eclipse environment. A discussion on the various implementation aspects of the workflow and its qualitative and quantitative analysis based on experiments on real-world, model-based, IoT-enabled use cases of varying complexities is presented. The IoT-enabled emission monitoring system described in this paper is a full-fledged use case (in its completeness) and tested including live data captured from emission monitoring with the IBM Watson IoT platform. A tradeoff analysis among the energy and timing characteristics of an IoT-enabled automotive real-time emission monitoring use case, with the aid of the workflow/prototype implementation is presented in a timing-energy analysis tool. The main benefit of the proposed workflow is the feedback regarding energy and timing characteristics of the IoT-enabled application, early during development stages. Such analysis results providing early feed back regarding performance characteristics (load and schedulability) of the IoT application software system can be termed as a breakthrough for software architects and design engineers toward realizing reliable and cutting edge IoT-enabled systems.