Fast Operation Mode Selection for Highly Efficient IoT Edge Devices

In the emerging paradigm of edge computing (EC) for Internet of Things (IoT), data processing is pushed to the edge of the IoT network (e.g., gateways and embedded IoT devices). IoT devices must support multiple operation modes in order to adapt to varying runtime situations, like preserving energy at low battery, while still maintaining some crucial functionality, etc. Adapting the optimal operation mode is a challenge for edge devices given the limited resources at the edge of the network (both bandwidth and processing power of the shared gateway), various constraints (e.g., battery lifetime), etc. This paper proposes a fast and low-overhead scheme to determine and adapt the operation mode of edge devices at runtime and orchestrate devices in a way that the efficiency of IoT devices is optimized with respect to the gateway's resource constraints. The proposed scheme breaks the optimization problem into several smaller ones (i.e., subproblems) whose solutions are aggregated to find the final solution. We present a novel memoization technique that determines the solution to a range of subproblems based on subproblems that are already solved. In addition, we present a novel pruning technique that reduces the search space and consequently reduces both memory and execution time overhead. The experimental results show up to 50% reduction in memory overhead and 14× reduction in execution time overhead compared to the state-of-the-art solution which is a major step toward efficient EC for IoT.