Understanding Energy Efficiency in IoT App Executions

Billions of Internet-of-Things (IoT) devices such as sensors, actuators, computing units, etc., are connected to form IoT platforms. However, it is observed that such hardware platforms today spend a significant proportion of their energy in communication between the CPU and the sensors (which are controlled by micro-controller unit (MCU)). Motivated by this observation, two simple, yet effective, optimizations are proposed to minimize the energy consumption. The first optimization, called Batching, interrupts the CPU after collecting multiple sensor data points at the MCU (instead of only 1), and thus, minimizes the interrupt overheads. The second optimization, called Computation Offloading to MCU (COM), offloads app-specific computations to the MCU to minimize data transfer overheads, and makes use of the relatively low energy footprint, and low-compute capabilities of the MCU in place of the CPU in the hub. However, questions such as why these two schemes are needed, where the energy benefit comes from, which IoT apps are suitable for these optimizations, etc., remain unclear. To better understand the Batching and COM approaches towards energy efficiency in IoT app executions, we characterize ten representative workloads on a Raspberry Pi and ESP8266 MCU platform, and evaluate the energy savings using these two optimizations and illustrate that for light-weight workloads (where COM is applicable), Batching and COM reduce the energy consumption by 52% and 85%, respectively when compared to the baseline. And for heavy-weight apps (where COM is not possible due to limited capacity of MCU), by offloading the light-weight apps and batching for the heavy-weight, Batching + COM (BCOM) benefits 10% energy savings compared to the baseline.