MAllEC: Fast and Optimal Scheduling of Energy Consumption for Energy Harvesting Devices

Energy consumption scheduling algorithms allow energy harvesting Internet of Things (IoT) devices to maximize the amount of harvested energy that they consume, while maintaining uninterrupted, indefinite, operation. The existing works in the area show a tradeoff between solution quality and computational complexity. At one end fast but suboptimal algorithms can lead to energy waste and power outages. At the other, optimal algorithms are computationally prohibitive for the constrained hardware of the IoT. This paper argues that the tradeoff can be avoided, and presents the MAllEC energy consumption scheduler that maximizes the allowed energy consumption while minimizing energy waste and power outages, with linear time complexity. MAllEC is compared against the state of the art through simulations using long term (14 years) traces of solar irradiance, and shown to consistently achieve the minimum energy waste and power outage. The linear time complexity of MAllEC is measured on constrained IoT hardware (8-bit Tmote Sky) to be low enough so that MAllEC can be executed unintrusively. This paper provides proof of MAllEC's optimality and shows that, in an application with dynamic, adjustable packet rate, MAllEC can maintain indefinite, uninterruptible, operation at an average rate of almost 100 packets per minute, where a 3-Ah battery powered device, at the same rate, would deplete after less than 200 days.