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.

Currently, companies are about to optimize their internal processes by monitoring items they handle with Radio Frequency Identification (RFID). However, there is a risk that sensitive information is disclosed when sharing RFID data with other companies. Therefore, companies are unwilling to share RFID data. At first glance, Secure Multi-Party Computation (SMC) might reconciliate data sharing with the privacy concerns. However, SMC requires the collaboration of all parties involved in a protocol. This prevents using SMC for many applications based on item-level RFID data collected in supply chains, since some parties may be competitors or have conflicting interests. We present protocols for securely and privately computing item-level metrics using only existing communication links (e.g., messages stored on RFID tags) and an oblivious third party. This enables optimizing the supply chain using novel item-level metrics without compromising sensitive information.