Tiny intermittently powered computers can monitor objects in hard to reach places maintenance free for decades by leaving batteries behind and surviving off energy harvested from the environmentavoiding the cost of replacing and disposing of billions or trillions of dead batteries. However, creating programs for these sensors is difficult. Energy harvesting is inconsistent, energy storage is scarce, and batteryless sensors can lose power at any point in timecausing volatile memory, execution progress, and time to reset. In response to these disruptions, developers must write unwieldy programs attempting to protect against failures, instead of focusing on sensing goals, defining tasks, and generating useful data in a timely manner. To address these shortcomings, we have designed Mayfly, a language and runtime for timely execution of sensing tasks on tiny, intermittently-powered, energy harvesting sensing devices. Mayfly is a coordination language and runtime built on top of Embedded-C that combines intermittent execution fragments to form coherent sensing schedules-maintaining forward progress, data consistency, data freshness, and data utility across multiple power failures. Mayfly makes the passing of time explicit, binding data to the time it was gathered, and keeping track of data and time through power failures. We evaluated Mayfly against state-of-the art systems, conducted a user study, and implemented multiple real world applications across application domains in inventory tracking, and wearables. CCS CONCEPTS • Computer systems organization → Embedded systems; Architectures; • Human-centered computing → Ubiquitous and mobile computing systems and tools; • Software and its engineering → Context specific languages;

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Tiny energy harvesting sensors that operate intermittently, without batteries, have become an increasingly appealing way to gather data in hard to reach places at low cost. Frequent power failures make forward progress, data preservation and consistency, and timely operation challenging. Unfortunately, state-of-the-art systems ask the programmer to solve these challenges, and have high memory overhead, lack critical programming features like pointers and recursion, and are only dimly aware of the passing of time and its effect on application quality. We present Time-sensitive Intermittent Computing System (TICS), a new platform for intermittent computing, which provides simple programming abstractions for handling the passing of time through intermittent failures, and uses this to make decisions about when data can be used or thrown away. Moreover, TICS provides predictable checkpoint sizes by keeping checkpoint and restore times small and reduces the cognitive burden of rewriting embedded code for intermittency without limiting expressibility or language functionality, enabling numerous existing embedded applications to run intermittently. CCS Concepts. • Computer systems organization → Embedded software; • Hardware → Emerging architectures; Impact on the environment; • Software and its engineering → Runtime environments; Source code generation.

If people are not in control of the collection and sharing of their personal health information collected using mobile health (mHealth) devices and applications, privacy concerns could limit their willingness to use and reduce potential benefits provided via mHealth. We investigated users' willingness to share their personal information, collected using mHealth sensing devices, with their family, friends, third parties, and the public. Previous work employed hypothetical scenarios, surveys and interviews to understand people's information-sharing behavior; to the best of our knowledge, ours is the first privacy study where participants actually have the option to share their own information with real people. We expect our results can guide the development of privacy controls for mobile devices and applications that collect any personal and activity information, not restricted to health or fitness information.Our study revealed three interesting findings about people's privacy concerns regarding their sensed health information: 1) We found that people share certain health information less with friends and family than with strangers, but more with specific third parties than the public. 2) Information that people were less willing to share could be information that is indirectly collected by the mobile devices. 3) We confirmed that privacy concerns are not static; mHealth device users may change their sharing decisions over time. Based on our findings, we emphasize the need for sensible default settings and flexible privacy controls to allow people to choose different settings for different recipients, and to change their sharing settings at any time.

Sensing platforms are becoming batteryless to enable the vision of the Internet of Things, where trillions of devices collect data, interact with each other, and interact with people. However, these batteryless sensing platforms—that rely purely on energy harvesting—are rarely able to maintain a sense of time after a power failure. This makes working with sensor data that is time sensitive especially difficult. We propose two novel, zero-power timekeepers that use remanence decay to measure the time elapsed between power failures. Our approaches compute the elapsed time from the amount of decay of a capacitive device, either on-chip Static Random-Access Memory (SRAM) or a dedicated capacitor. This enables hourglass-like timers that give intermittently powered sensing devices a persistent sense of time. Our evaluation shows that applications using either timekeeper can keep time accurately through power failures as long as 45s with low overhead.