Energy-Aware Real-Time Data Processing for IoT Systems

In many real-time processing systems for the Internet of Things (IoT), the correctness of real-time data objects that model physical world entities, such as the status of mobile robotics, depends not only on the functional correctness, but also on the temporal consistency. Maintaining temporal consistency of real-time data while reducing energy cost is of critical importance when designing such IoT systems. In this paper, we formulate the energy-aware real-time data processing problem on multicore platforms and prove it to be NP-hard. In view of the intractability of the problem, we adopt a divide-and-conquer strategy. We first propose a per-CPU solution, which can result in significant power savings. Next, in order to save energy in a fine-grained granularity, we propose an efficient per-Task solution by adopting the per-CPU solution as a building block. Finally, by developing new energy-aware mapping techniques, we further explore energy savings on multicore platforms. Extensive simulation results show that the proposed methods offer remarkable performance improvement in terms of energy savings, as compared to the state-of-the-art schemes.