A wide gap exists between the state of the art in developing Wireless Sensor Network (WSN) software and current practices concerning the design, execution, and maintenance of business processes. WSN software is most often developed based on low-level OS abstractions, whereas business process development leverages high-level languages and tools. This state of affairs places WSNs at the fringe of industry. The makeSense system addresses this problem by simplifying the integration of WSNs into business processes. Developers use BPMN models extended with WSN-specific constructs to specify the application behavior across both traditional business process execution environments and the WSN itself, which is to be equipped with application-specific software. We compile these models into a high-level intermediate language-also directly usable by WSN developers-and then into OS-specific deployment-ready binaries. Key to this process is the notion of meta-abstraction, which we define to capture fundamental patterns of interaction with and within the WSN. The concrete realization of meta-abstractions is application-specific; developers tailor the system configuration by selecting concrete abstractions out of the existing codebase or by providing their own. Our evaluation of makeSense shows that i) users perceive our approach as a significant advance over the state of the art, providing evidence of the increased developer productivity when using makeSense; ii) in large-scale simulations, our prototype exhibits an acceptable system overhead and good scaling properties, demonstrating the general applicability of makeSense; and, iii) our prototype-including the complete tool-chain and underlying system support-sustains a real-world deployment where estimates by domain specialists indicate the potential for drastic reductions in the total cost of ownership compared to wired and conventional WSN-based solutions.

Experiments under controlled radio interference are crucial to assess the robustness of low-power wireless protocols. While tools such as JamLab augment existing sensornet testbeds with realistic interference, it remains an error-prone and time-consuming task to manually select the set of nodes acting as jammers and their individual transmit powers. We present an automated configuration approach based on simulated annealing to overcome this problem. A preliminary evaluation based on two testbeds shows that our approach can find near-optimal solutions within at most a few hours. We believe our approach can facilitate the widespread adoption of controlled interference experiments by the sensornet community.

The industrial adoption of wireless sensor networks (WSNs) is hampered by two main factors. First, there is a lack of integration of WSNs with business process modeling languages and back-ends. Second, programming WSNs is still challenging as it is mainly performed at the operating system level. To this end, we provide makeSense: a unified programming framework and a compilation chain that, from high-level business process specifications, generates code ready for deployment on WSN nodes.

This paper describes a two-step process to infer specific technical constraints and parameters needed for a reliable mission-specific design of wireless sensor networks (WSN). As the first step, we propose a new requirement catalog helping end-users to formulate a complete and consistent specification of WSN mission requirements. Its generality allows the unambiguous characterization of a wide spectrum of applications from the end-user's perspective. As the second step, we introduce a methodology to deduce fine grained technical specifications from the general requirements. The proposed automatic graph-based requirement expansion approach translates the content of the catalog and additional requirements to specific technical terms, which provide the basis for an application-specific WSN design. A real-world use case - a new WSN application in the area of critical infrastructure protection - demonstrates the applicability of the presented approach.