Energy management can extend the lifetime of batteryless, energy-harvesting systems by judiciously utilizing the energy available. Duty cycling of such systems is especially challenging for event detection, as events arrive sporadically and energy availability is uncertain. If the node sleeps too much, it may miss important events; if it depletes energy too quickly, it will stop operating in low energy conditions and miss events. Thus, accurate event prediction is important in making this tradeoff. We propose Ember, an energy management system based on deep reinforcement learning to duty cycle event-driven sensors in low energy conditions. We train a policy using historical real-world data traces of motion, temperature, humidity, pressure, and light events. The resulting policy can learn to capture up to 95% of the events without depleting the node. Without historical data for training when deploying a node at a new location, we propose a self-supervised mechanism to collect ground-truth data while learning from the data at the same time. Ember learns to capture the majority of events within a week without any historical data and matches the performance of the policies trained with historical data in a few weeks. We deployed 40 nodes running Ember for indoor sensing and demonstrate that the learned policies generalize to real-world settings as well as outperform state-of-the-art techniques.

The need for associating a time with the arrival of data is prevalent in many applications but this is even more the case in Cyber Physical Systems (CPS) which measure quantities from the real world. One common attribute of any measured real world quantity is the time of when it was acquired. Automating the process of time stamping data upon arrival frees the programmer from having to deal with this task manually thus reducing the number of errors, shrinking the code size and making the code more readable and maintainable. This paper explores the concept of variables that are automatically time stamped by the runtime system and its impact in building real-time applications. Such time stamping support enables seamless integration of a time model that is kept updated by real-time events within well-defined synchronization time bounds. This paper discusses the design of the Automatic Time Stamping (ATS) framework and the prototype implementation of such variables using open source programming language Swift. Our results demonstrate the viability of the ATS framework for most real-time applications. In doing this work we have learned that the abstraction of time in software programming models still have much room for improvements from the operating system level all the way up to the programming languages and runtimes used by the developers.