Wireless sensor networks are moving towards emerging standards such as IP, ZigBee and WirelessHART which makes interoperability testing important. Interoperability testing is performed today through black-box testing with vendors physically meeting to test their equipment. Black-box testing can test interoperability but gives no detailed information of the internals in the nodes during the testing. Blackbox testing is required because existing simulators cannot simultaneously simulate sensor nodes with different firmware. For standards such as IP and WirelessHART, a white-box interoperability testing approach is desired, since it gives details on both performance and clues about why tests succeeded or failed. To allow white-box testing, we propose a simulation-based approach to interoperability testing, where the firmware from different vendors is run in the same simulator. We extend our MSPSim emulator and COOJA wireless sensor network simulator to support interoperable simulation of sensor nodes with firmware from different vendors. To demonstrate both cross-vendor interoperability and the benefits of white-box interoperability testing, we run the state-of-the-art Contiki and TinyOS operating systems in a single simulation. Because of the white-box testing, we can do performance measurement and power profiling over both operating systems.

Abstract-With the proliferation of sensor networks and sensor network applications, the overall complexity of such systems is continuously increasing. Sensor networks are now heterogeneous in terms of their hardware characteristics and application requirements even within a single network. In addition, the requirements of currently supported applications are expected to change over time. All of this makes developing, deploying, and optimizing sensor network applications an extremely difficult task. In this paper, we present the architecture of TinyCubus, a flexible and adaptive cross-layer framework for TinyOSbased sensor networks that aims at providing the necessary infrastructure to cope with the complexity of such systems. TinyCubus consists of a data management framework that selects and adapts both system and data management components, a cross-layer framework that enables optimizations through cross-layer interactions, and a configuration engine that installs components dynamically. Furthermore, we show the feasibility of our architecture by describing and evaluating a code distribution algorithm that uses application knowledge about the sensor topology in order to optimize its behavior.

Boundary recognition is an important and challenging issue in wireless sensor networks when no coordinates or distances are available. The distinction between inner and boundary nodes of the network can provide valuable knowledge to a broad spectrum of algorithms. This article tackles the challenge of providing a scalable and range-free solution for boundary recognition that does not require a high node density. We explain the challenges of accurately defining the boundary of a wireless sensor network with and without node positions and provide a new definition of network boundary in the discrete domain. Our solution for boundary recognition approximates the boundary of the sensor network by determining the majority of inner nodes using geometric constructions, which guarantee that for a given d , a node lies inside of the construction for a d -quasi unit disk graph model of the wireless sensor network. Moreover, such geometric constructions make it possible to compute a guaranteed distance from a node to the boundary. We present a fully distributed algorithm for boundary recognition based on these concepts and perform a detailed complexity analysis. We provide a thorough evaluation of our approach and show that it is applicable to dense as well as sparse deployments.

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Structural health monitoring with wireless sensor networks has received much attention in recent years due to the ease of sensor installation and low deployment and maintenance costs. However, sensor network technology needs to solve numerous challenges in order to substitute conventional systems: large amounts of data, remote configuration of measurement parameters, on-site calibration of sensors and robust networking functionality for long-term deployments. We present a structural health monitoring network that addresses these challenges and is used in several deployments for monitoring of bridges and buildings. Our system supports a diverse set of sensors, a library of highly optimized processing algorithms and a lightweight solution to support a wide range of network runtime configurations. This allows flexible partitioning of the application between the sensor network and the backend software. We present an analysis of this partitioning and evaluate the performance of our system in three experimental network deployments on civil structures.