Over the past decade, mobile computing and wireless communication have become increasingly important drivers of many new computing applications. The eld of wireless sensor networks particularly focuses on applications involving autonomous use of compute, sensing, and wireless communication devices for both scienti c and commercial purposes. This paper examines the research decisions and design tradeos that arise when applying wireless peer-to-peer networking techniques in a mobile sensor network designed to support wildlife tracking for biology research.

Systems composed of interacting autonomous agents offer a promising software engineering approach for developing applications in complex domains. However, this multiagent system paradigm introduces a number of new abstractions and design/development issues when compared with more traditional approaches to software development. Accordingly, new analysis and design methodologies, as well as new tools, are needed to effectively engineer such systems.

We consider the problem of self-deployment of a mobile sensor network. We are interested in a deployment strategy that maximizes the area coverage of the network with the constraint that each of the nodes has at least K neighbors, where K is a user-specified parameter. We propose an algorithm based on artificial potential fields which is distributed, scalable and does not require a prior map of the environment. Simulations establish that the resulting networks have the required degree with a high probability, are well connected and achieve good coverage. We present analytical results for the coverage achievable by uniform random and symmetrically tiled network configurations and use these to evaluate the performance of our algorithm.

Pervasive and mobile computing call for suitable middleware and programming models to support the activities of complex software systems in dynamic network environments. In this paper we present TOTA (“Tuples On The Air”), a novel middleware and programming approach for supporting adaptive context-aware activities in pervasive and mobile computing scenarios. The key idea in TOTA is to rely on spatially distributed tuples, adaptively propagated across a network on the basis of application-specific rules, for both representing contextual information and supporting uncoupled interactions between application components. TOTA promotes a simple way of programming that facilitates access to distributed information, navigation in complex environments, and achievement of complex coordination tasks in a fully distributed and adaptive way, mostly freeing programmers and system managers form the need to take care of lowlevel issues related to network dynamics. This paper includes both application examples to clarify concepts and performance figures to show the feasibility of the approach.

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Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algorithms for multi-signal ensembles that exploit both intra- and inter-signal correlation structures. The DCS theory rests on a new concept that we term the joint sparsity of a signal ensemble. We study in detail three simple models for jointly sparse signals, propose algorithms for joint recovery of multiple signals from incoherent projections, and characterize theoretically and empirically the number of measurements per sensor required for accurate reconstruction. We establish a parallel with the Slepian-Wolf theorem from information theory and establish upper and lower bounds on the measurement rates required for encoding jointly sparse signals. In two of our three models, the results are asymptotically best-possible, meaning that both the upper and lower bounds match the performance of our practical algorithms. Moreover, simulations indicate that the asymptotics take effect with just a moderate number of signals. In some sense DCS is a framework for distributed compression of sources with memory, which has remained a challenging problem for some time. DCS is immediately applicable to a range of problems in sensor networks and arrays.

The task of classifying the types of moving vehicles in a distributed, wireless sensor network is investigated. Specifically, based on an extensive real world experiment, we have compiled a dataset that consists of 820 MByte raw time series data, 70 MByte of pre-processed, extracted spectral feature vectors, and baseline classification results using the maximum likelihood classifier. The purpose of this paper is to detail the data collection procedure, the feature extraction and pre-processing steps, and baseline classifier development. The database is available for download at

Smart Dust sensor networks -- consisting of cubic millimeter scale sensor nodes capable of limited computation, sensing, and passive optical communication with a base station -- are envisioned to fulfill complex large scale monitoring tasks in a wide variety of application areas. In many potential Smart Dust applications such as object detection and tracking, fine-grained node localization plays a key role. However, due to the unique characteristics of Smart Dust, traditional localization systems cannot be used. In this paper we present and analyse the Lighthouse location systems, a novel laserbased location system for Smart Dust, which allows tiny dust nodes to autonomously estimate their location with high accuracy without additional infrastructure components besides a modified base station device. Using an early 2D prototype of the system, node locations could be estimated with an average accuracy of about 2% and an average standard deviation of about 0.7% of the node's distance to the base station.

Agent-based computing is a promising approach for developing applications in complex domains. However, despite the great deal of research in the area, a number of challenges still need to be faced (i) to make agent-based computing a widely accepted paradigm in software engineering practice, and (ii) to turn agent-oriented software abstractions into practical tools for facing the complexity of modern application areas. In this paper, after a short introduction to the key concepts of agent-based computing (as they pertain to software engineering), we characterise the emerging key issues in multiagent systems (MASs) engineering. In particular, we show that such issues can be analysed in terms of three different ‘‘scales of observation’’, i.e., in analogy with the scales of observation of physical phenomena, in terms of micro, macro, and meso scales. Based on this characterisation, we discuss, for each scale of observation, what are the peculiar engineering issues arising, the key research challenges to be solved, and the most promising research directions to be explored in the future.

Video-based sensor networks can provide important visual information in a number of applications including: environmental monitoring, health care, emergency response, and video security. This paper describes the Panoptes video-based sensor networking architecture, including its design, implementation, and performance. We describe two video sensor platforms that can deliver highquality video over 802.11 networks with a power requirement less than 5 watts. In addition, we describe the streaming and prioritization mechanisms that we have designed to allow it to survive longperiods of disconnected operation. Finally, we describe a sample application and bitmapping algorithm that we have implemented to show the usefulness of our platform. Our experiments include an in-depth analysis of the bottlenecks within the system as well as power measurements for the various components of the system.