Energy-Efficient Radio Selection and Data Partitioning for Real-Time Data Transfer

The importance of real-time wireless data transfer is rapidly increasing for Internet of Things (IoT) applications. For example, smart glasses worn by a doctor need to transmit real-time data to a hospital information system, which performs face detection and recognition, for real-time interaction with recognized patients within a certain deadline, which is ideally a few hundred milliseconds. Other emerging IoT applications, e.g., structural health monitoring, clinical monitoring, and industrial process automation, also require real-time wireless data transfer. Those applications have critical demands for real-time and energy-efficient communication through wireless medium. However, it is very challenging to support stringent timing constraints energy-efficiently through wireless medium due to its inherent unreliability and timing-unpredictability. Fortunately, heterogeneous radios are becoming increasingly available in modern embedded devices, offering new opportunities to use multiple wireless technologies to accommodate the needs of real-time applications. In this paper, we first formulate the runtime radio selection and data partitioning for real-time IoT applications as an Integer Linear Programming (ILP) problem and then present (1) an optimal algorithm that makes quick and optimal decisions when selecting between two radios and (2) a heuristic algorithm for the platforms with more radios. Experimental results show that our heuristic algorithm provides optimal selections to 94.4% of the cases and makes the decisions 336~1412 times faster than an ILP problem solver.