UAVs are deployed in various applications including disaster search-and-rescue, precision agriculture, law enforcement and first response. As UAV software systems grow more complex, the drawbacks of developing them in low-level languages become more pronounced. For example, the lack of memory safety in C implies poor isolation between the UAV autopilot and other concurrent tasks. As a result, the most crucial aspect of UAV reliability-timely control of the flight-could be adversely impacted by other tasks such as perception or planning. We introduce JCopter, an autopilot framework for UAVs developed in a managed language, i.e., a high-level language with built-in safe memory and timing management. Through detailed simulation as well as flight testing, we demonstrate how JCopter retains the timeliness of C-based autopilots while also providing the reliability of managed languages.

Rapid advancements in computing, communication, sensing, and actuation have seen the growth of Internet of Things (IoT) devices in our daily life. One of the fundamental constraints of a typical IoT device is energy as IoT devices rely on a battery. Therefore, it is crucial for their operating system (OS) to be able to accurately account for system-wide energy usage. Specifically, the OS should be able to attribute such accounted energy to the running applications accurately. Traditional OSs have limited capability when it comes to tracking components such as sensors, actuators and network interfaces, as they are often used in an asynchronous fashion. This would make it difficult to conduct energy accounting accurately. This paper proposes a new mechanism to accurately account for the asynchronous energy usage of resources in mobile systems and IoT devices. Our insight is that by accurately relating the application requests with kernel requests to device and corresponding device responses, we can accurately attribute time of use to the requesting process. However, resources such as WiFi reception violate this assumption. In such cases, we can measure usage by the number of bytes in each individual transaction. Using such a hybrid approach, we can account for energy usage with 94% accuracy and perform much better than using each of these models individually.

Advancements in the design of drones have led to their use in varied environments and applications such as battle field surveillance. In such scenarios, swarms of drones can coordinate to survey a given area. We consider the problem of covering an arbitrary area containing obstacles using multiple drones, i.e., the so-called coverage path planning (CPP) problem. The goal of the CPP problem is to find paths for each drone such that the entire area is covered. However, a major limitation in such deployments is drone flight time. To most efficiently use a swarm, we propose to minimize the maximum energy consumption among all drones' flight paths. We perform measurements to understand energy consumption of a drone. Using these measurements, we formulate an Energy Efficient Coverage Path Planning (EECPP) problem. We solve this problem in two steps: a load-balanced allocation of the given area to individual drones, and a minimum energy path planning (MEPP) problem for each drone. We conjecture that MEPP is NP-hard as it is similar to the Traveling Salesman Problem (TSP). We propose an adaptation of the well-known Lin-Kernighan heuristic for the TSP to efficiently solve the problem. We compare our solution to the recently proposed depth-limited search with back tracking algorithm, the optimal solution, and rastering as a baseline. Results show that our algorithm is more computationally efficient and provides more energy-efficient solutions compared to the other heuristics.

One essential functionality of a modern operating system is to accurately account for the resource usage of the underlying hardware. This is especially important for computing systems that operate on battery power, since energy management requires accurately attributing resource uses to processes. However, components such as sensors, actuators and specialized network interfaces are often used in an asynchronous fashion, and makes it difficult to conduct accurate resource accounting. For example, a process that makes a request to a sensor may not be running on the processor for the full duration of the resource usage; and current mechanisms of resource accounting fail to provide accurate accounting for such asynchronous uses. This paper proposes a new mechanism to accurately account for the asynchronous usage of resources in mobile systems. Our insight is that by accurately relating the user requests with kernel requests to device and corresponding device responses, we can accurately attribute resource use to the requesting process. Our prototype implemented in Linux demonstrates that we can account for the usage of asynchronous resources such as GPS and WiFi accurately.