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. The ability to update the program code installed on wireless sensor nodes plays an import role in the highly dynamic environments sensor networks are often deployed in. Such code update mechanisms should support flexible reconfiguration and adaptation of the sensor nodes but should also operate in an energy and time efficient manner. In this paper, we present FlexCup, a flexible code update mechanism that minimizes the energy consumed on each sensor node for the installation of arbitrary code changes. We describe two different versions of FlexCup and show, using a precise hardware emulator, that our mechanism is able to perform updates up to 8 times faster than related code update algorithms found in the literature, while consuming only an eighth of the energy.

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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.

In this paper we present Levels, a programming abstraction for energy-aware sensor network applications. Unlike most previous work it does not try to maximize network lifetime but rather helps to meet user-defined lifetime goals while maximizing application quality. Levels is targeted to applications where there is no redundancy and no node should fail early.With our programming abstraction the application developer defines so-called energy levels. These energy levels form a stack and can be deactivated from top to bottom if the lifetime goal cannot be met otherwise. Each code block within an energy level contains information about its energy consumption, which can be obtained from simulation tools without much effort. The runtime system then uses the data about the energy consumption of the different levels to compute an optimal level assignment for the time remaining. As we show in the evaluation, applications using Levels can accurately meet given lifetime goals and offer good application quality. In addition, the runtime overhead of our system is almost negligible.