Wireless Sensor Networks (WSNs) are a valuable technology to support countless applications in different areas. Given the WSN nodes resource constrained characteristics, designing energy-aware applications, communication protocols and security mechanisms are critical. The operating system (OS) running on the WSN node also interferes with the node overall behavior, and its energy consumption. In this paper, we develop a comparison between two different operating systems (Contiki and TinyOS) running on the same hardware platform (Crossbow TelosB). Using a set of tasks, which includes sensing, communication and security mechanisms, we evaluate their behavior in terms of energy consumption and execution time.

Due to the inherent nature of their heterogeneity, resource scarcity and dynamism, the provision of middleware for future networked embedded environments is a challenging task. In this paper we present a middleware approach that addresses these key challenges; we also discuss its application in a realistic networked embedded environment. Our application scenario involves fire management in a road tunnel that is instrumented with networked sensor and actuator devices. These devices are able to reconfigure their behaviour and their information dissemination strategies as they become damaged under emergency conditions, and firefighters are able to coordinate their operations and manage sensors and actuators through dynamic reprogramming. Our supporting middleware is based on a two-level architecture: the foundation is a language-independent, component-based programming model that is sufficiently minimal to run on any of the devices typically found in networked embedded environments. Above this is a layer of software components that offer the necessary middleware functionality. Rather than providing a monolithic middleware 'layer', we separate orthogonal areas of middleware functionality into self-contained components that can be selectively and individually deployed according to current resource constraints and application needs. Crucially, the set of such components can be updated at runtime to provide the basis of a highly dynamic and reconfigurable system

Next generation embedded systems will be composed of large numbers of heterogeneous devices. These will typically be resource-constrained, will use different operating systems, and will be connected through different types of network interfaces. Additionally, they may be mobile and/or form ad-hoc networks with their peers, and will need to be adaptive to changing conditions based on context-awareness. As an example of these system we consider disaster recovery scenarios where large numbers of different devices need to interconnect in an ad-hoc manner. In this respect, our goal is the provisioning of a middleware framework for such system environments. Our approach is based on a small and efficient middleware kernel supporting highly modularised and customisable component-based middleware services. These services can be tailored for specific embedded environments, and are runtime-reconfigurable to support adaptivity. This paper describes a demonstration that highlights some of the features available in our middleware. In particular, we focus on heterogeneity handling by showing our middleware running on resource-rich as well as resource-constrained devices, and on adaptivity features by demonstrating runtime reprogramming and on-the-fly component deployment

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We study how Pastry performs in a heterogeneous network environment of varying size. The large traffic overhead for management traffic makes the overlay nonfunctional if it grows too large. This can be circumvented by partitioning the routing tables at the cost of increased path lengths and response times.

We present an architecture for content-based aggregation in peer-to-peer filesharing networks. It is designed to significantly reduce the amount of nodes that have to be queried, by introducing a distributed index into the system. This index allows content to be located in a peer-to-peer network without using broadcast-style techniques. We also show how this index can be created and maintained in a decentralized self-organizing fashion.