activities. However, decentralized architectures are rapidly emerging, as witnessed by wireless sensor and actuator networks (WSANs), and in general by solutions involving multiple data sinks, heterogeneous nodes, and in-network coordination. These settings demand new programming abstractions to tame complexity without sacrificing efficiency. In this work we introduce the notion of logical neighborhood, which replaces the physical neighborhood provided by wireless broadcast with a higher-level, application-defined notion of proximity. The span of a logical neighborhood is specified declaratively based on the characteristics of nodes, along with requirements about communication costs. This paper presents the SPIDEY programming language for defining logical neighborhoods, and a routing strategy that efficiently supports the communication enabled by its programming constructs.

As sensor networks move towards increasing heterogeneity, the number of link layers, MAC protocols, and underlying transportation mechanisms increases. System developers must adapt their applications and systems to accommodate a wide range of underlying protocols and mechanisms. However, existing communication architectures for sensor networks are not designed for this heterogeneity and therefore the system developer must redevelop their systems for each underlying communication protocol or mechanism. To remedy this situation, we present a communication architecture that adapts to a wide range of underlying communication mechanisms, from the MAC layer to the transport layer, without requiring any changes to applications or protocols. We show that the architecture is expressive enough to accommodate typical sensor network protocols. Measurements show that the increase in execution time over a non-adaptive architecture is small. Dis-

Wireless sensor networks (WSNs) involving actuation are increasingly envisioned in a range of fields [1]. Among these, there is considerable interest in leveraging off WSNs to improve safety in road tunnels [6]. Researchers are envisioning tunnels equipped with WSN nodes that gather physical readings such as temperature and light, monitor the structural integrity of the tunnel, and sense the presence of vehicles to detect a possible traffic congestion. Based on sensed data, the system operates a variety of devices, such as ventilation fans inside the tunnel, and traffic lights at the entrances. For instance, when a sensor detects the presence of a fire in a sector, the fans in the same sector are activated, and the traffic lights are turned red to prevent further vehicles from entering the tunnel. To implement this class of systems, dedicated programming abstractions and communication protocols are needed. Indeed, the presence of heterogeneous nodes, coupled with a highly decentralized

It is vital to support concurrent applications sharing a wireless sensor network in order to reduce the deployment and administrative costs, thus increasing the usability and efficiency of the network. We describe Melete 1, a system that supports concurrent applications with efficiency, reliability, flexibility, programmability, and scalability. Our work is based on the Maté virtual machine [1] with significant modifications and enhancements. Melete enables reliable storage and execution of concurrent applications on a single sensor node. Dynamic grouping is used for flexible, on-the-fly deployment of applications based on contemporary status of the sensor nodes. The grouping procedure itself is programmed with the TinyScript language. A group-keyed code dissemination mechanism is also developed for reliable and efficient code distribution among sensor nodes. Both analytical and simulation results are presented to study the impact of several key parameters and optimization techniques on the code dissemination mechanism. Simulation results indicate satisfactory scalability of our techniques to both application code size and node density. The usefulness and effectiveness of Melete is also validated by empirical study.

Wireless sensor networks produce a large amount of data that needs to be processed, delivered, and assessed according to the application objectives. The way these data are manipulated by the sensor nodes is a fundamental issue. Information fusion arises as a response to process data gathered by sensor nodes and benefits from their processing capability. By exploiting the synergy among the available data, information fusion techniques can reduce the amount of data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. In this work, we survey the current state-of-the-art of information fusion by presenting the known methods, algorithms, architectures, and models of information fusion, and

Abstract –Methodologies for efficient software management in wireless sensor networks (WSN) need to be investigated for operating and maintaining large-scale sensor networks. Until now, some network-based approaches have been described that are limited in terms of scalability, i.e. dependency on reliable end-to-end communication, and security. In this paper, we describe a distributed software management architecture using profiling techniques. We exploit the advantages of robot-based reconfiguration and re-programming methods for efficient and secure software management. The developed methods are depicted in detail. Additionally, demonstrate their applicability and advantages.

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 paper, 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.

Wireless sensor networks (WSNs) are attracting great interest in a number of application domains concerned with monitoring and control of physical phenomena, as they enable dense and untethered deployments at low cost and with unprecedented flexibility. However, application development is still one of the main hurdles to a wide adoption of WSN technology. In current real-world WSN deployments, programming is typically carried out very close to the operating system, therefore requiring the programmer to focus on low-level system issues. This not only shifts the focus of the programmer away from the application logic, but also requires a technical background that is rarely found among application domain experts. The need for appropriate high-level programming abstractions, capable to simplify the programming chore without sacrificing efficiency, has been long recognized and several solutions have been hitherto proposed, which differ along many dimensions. In this paper, we survey the state-of-the-art in programming approaches for WSNs. We begin by presenting a taxonomy of WSN applications, to identify the fundamental requirements programming platforms must deal with. Then, we introduce a taxonomy of WSN programming approaches that captures the fundamental differences among existing solutions, and constitutes the core contribution of this paper. Our presentation style relies on concrete examples and code snippets taken from programming platforms representative of the taxonomy dimensions being discussed. We use the taxonomy to provide an exhaustive classification of existing approaches. Moreover, we also map existing approaches back to the application requirements, therefore providing not only a complete view of the state-of-the-art, but also useful insights for selecting the programming abstraction most appropriate to the application at hand.

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The most common programming languages and platforms for sensor networks foster a low-level programming style. This design provides fine-grained control over the underlying sensor devices, which is critical given their severe resource constraints. However, this design also makes programs difficult to understand, maintain, and debug. In this paper, we describe an approach to automatically recover the high-level system logic from such low-level programs, along with an instantiation of the approach for nesC programs running on top of the TinyOS operating system. We adapt the technique of symbolic execution from the program analysis community to handle the event-driven nature of TinyOS, providing a generic component for approximating the behavior of a sensor network application or system component. We then employ a form of predicate abstraction on the resulting information to automatically produce a finite state machine representation of the component. We have used our tool, called FSMGen, to automatically produce compact and fairly accurate state machines for several TinyOS applications and protocols. We illustrate how this high-level program representation can be used to aid programmer understanding, error detection, and program validation. 1.