A Compilation Framework for Macroprogramming Networked Sensors

Abstract. Macroprogramming—the technique of specifying the behavior of the system, as opposed to the constituent nodes—provides application developers with high level abstractions that alleviate the programming burden in develop- ing wireless sensor network (WSN) applications. However, as the semantic gap between macroprogramming abstractions and node-level code is considerably wider than in traditional programming, converting the high level specification to running code is a daunting process, and a major hurdle to the acceptance of macroprogramming. In this paper, we propose a general compilation framework for a data-driven macroprogramming language that allows for plugging in different modules implementing various stages of compilation. We also demonstrate an actual instantiation of our framework by showing an end-to-end solution for compiling macro- programs. Our compiler provides the final code to be deployed on real nodes as well as an estimate of the costs the running system will incur, e.g., in terms of messages exchanged. We compared the auto-generated code against a hand- coded version for the same application behavior to verify the outcome of our compiler

Programming models for sensor networks: a survey

Sensor networks have a significant potential in diverse applications some of which are already beginning to be deployed in areas such as environmental monitoring. As the application logic becomes more complex, programming difficulties are becoming a barrier to adoption of these networks. The difficulty in programming sensor networks is not only due to their inherently distributed nature but also the need for mechanisms to address their harsh operating conditions such as unreliable communications, faulty nodes, and extremely constrained resources. Researchers have proposed different programming models to overcome these difficulties with the ultimate goal of making programming easy while making full use of available resources. In this article, we first explore the requirements for programming models for sensor networks. Then we present a taxonomy of the programming models, classified according to the level of abstractions they provide. We present an evaluation of various programming models for their responsiveness to the requirements. Our results point to promising efforts in the area and a discussion of the future directions of research in this area.

Scalable and Energy Efficient Software Architecture for Human Behavioral Measurements

Understanding human behavior is central to many professions including engineering, health and the social sciences, and has typically been measured through surveys, direct observation and interviews. However, these methods are known to have drawbacks, including bias, problems with recall accuracy, and low temporal fidelity. Modern mobile phones have a variety of sensors that can be used to find activity patterns and infer the underlying human behaviors, placing a heavy load on the phone's battery. Social science researchers hoping to leverage this new technology must carefully balance the fidelity of the data with the cost in phone performance. Crucially, many of the data collected are of limited utility because they are redundant or unnecessary for a particular study question. Previous researchers have attempted to address this problem by modifying the measurement schedule based on sensed context, but a complete solution remains elusive. In the approach described here, measurement is made contingent on sensed context and measurement objectives through extensions to a configuration language, allowing significant improvement to flexibility and reliability. Empirical studies indicate a significant improvement in energy efficiency with acceptable losses in data fidelity