We study the tradeoffs involved in the energy-efficient localization and tracking of mobile targets by a wireless sensor network. Our work focuses on building a framework for evaluating the fundamental performance of tracking strategies in which only a small portion of the network is activated at any point in time. We first compare naive network operation with random activation and selective activation. In these strategies the gains in energy-savings come at the expense of increased uncertainty in the location of the target, resulting in reduced quality of tracking. We show that selective activation with a good prediction algorithm is a dominating strategy that can yield orders-of-magnitude energy savings with negligible difference in tracking quality. We then consider duty-cycled activation and show that it offers a flexible and dynamic tradeoff between energy expenditure and tracking error when used in conjunction with selective activation.

Mobile sensors cover more area over a period of time than the same number of stationary sensors. However, the quality of coverage achieved by mobile sensors depends on the velocity, mobility pattern, number of mobile sensors deployed and the dynamics of the phenomenon being sensed. The gains attained by mobile sensors over static sensors and the optimal motion strategies for mobile sensors are not well understood. In this paper we consider the problem of event capture using mobile sensors. The events of interest arrive at certain points in the sensor field and fade away according to arrival and departure time distributions. An event is said to be captured if it is sensed by one of the mobile sensors before it fades away. For this scenario we analyze how the quality of coverage scales with the velocity, path and number of mobile sensors. We characterize the cases where the deployment of mobile sensors has

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

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Low-connectivity and full-coverage three dimensional Wireless Sensor Networks (WSNs) have many real-world applications. By low connectivity, we mean there are at least k disjoint paths between any two sensor nodes in a WSN, where k ≤ 4. In this paper, we design a set of patterns for these networks. In particular, we design and prove the optimality of 1- and2-connectivity patterns under any value of the ratio of communication range rc over sensing range rs, amongregular lattice deployment patterns. We further propose a set of patterns to achieve 3- and4-connectivity patterns and investigate the evolutions among all the proposed low-connectivity patterns. Finally, we study the proposed patterns under several practical settings.

In this paper we present a new tool called BeanWatcher that allows the semi-automatic generation of multimedia monitoring and management applications for wireless sensor networks. Thus, particularities of multimedia management and wireless sensor networks were taken into account. The architecture of the tool is based on a component model flexible enough to allow the creation of new components and the optimization of the components currently provided. BeanWatcher was designed to o#er a development environment suitable for both expert an beginner users allowing them to choose the programming language that better fits the application requirements.

Abstract—In this paper, we study deployment patterns to achieve full coverage and k-connectivity (k 6) under different ratios of the sensor communication range (denoted by Rc)to the sensing range (denoted by Rs) for homogeneous wireless sensor networks (WSNs). In particular, we propose new patterns for 3- and 5-connectivity. We also discover that there exists a hexagon-based universally elemental pattern that can generate all known optimal patterns. The previously proposed Voronoi-based approach cannot be applied to prove the optimality of the new patterns due to their special features. We propose a new deployment-polygon-based methodology. We prove the optimality of deployment patterns to achieve 3-connectivity, 4-connectivity, and 5-connectivity for certain ranges of Rc=Rs, respectively, and prove the optimality of deployment patterns to achieve 6-connectivity under all ranges of Rc=Rs. Index Terms—Connectivity, coverage, network topology, optimal deployment pattern, wireless sensor networks (WSNs). I.

Energy-efficient tracking of a target using a sensor network has received significant attention in recent research. Our earlier study on energy-quality tradeoffs in target tracking with binary sensors showed that optimal selective activation of sensor nodes based on prediction of the target's trajectory could achieve orders of magnitude savings in the energy expenditure over naive and random activation, while achieving almost the same tracking quality. In this paper, we consider a more realistic sensor model and extend the analysis of activation strategies to account for the presence of noise in sensor measurements. Our results confirm that the best quality of tracking that can be obtained with selective activation depends on the noise level in sensor measurements and that the optimal radius of activation depends on the noise level and the density of deployment. We also show how duty cycling with selective activation can be used to obtain flexible tradeoffs between the energy expenditure and quality of tracking.

Abstract. Although, traditionally, mobility has not been supported in Wireless Sensor Networks, many applications in these environments need to interact with each other in highly dynamic scenarios, where configurations can vary from static to dynamic. Sensors phenomena, observers and even the network itself can present different levels of mobility. This mobility in Wireless Sensor Networks can be adopted through different methods based on the characteristics of their elements. This paper applies, compares and evaluates energy parameters in mobile Wireless Sensor Networks using traditional IP ad-hoc protocols. 1

Abstract In this chapter, the sensing coverage area of surveillance wireless sensor networks is considered. The sensing coverage is determined by applying Neyman-Pearson detection and defining the breach probability on a grid-modeled field. Using a graph model for the perimeter, Dijkstra’s shortest path algorithm is used to find the weakest breach path. The breach probability is linked to parameters such as the false alarm rate, size of the data record and the signal-to-noise ratio. Consequently, the required number of sensor nodes and the surveillance performance of the network are determined. For target tracking applications, small wireless sensors provide accurate information since they can be deployed and operated near the phenomenon. These sensing devices have the opportunity of collaboration amongst themselves to improve the target localization and tracking accuracies. Distributed data fusion architecture provides a collaborative tracking framework. Due to the present energy constraints of these small sensing and wireless communicating devices, a common trend is to put some of them into a dormant state. We adopt a mutual information based metric to select the most informative subset of the sensors to achieve reduction in the energy consumption, while preserving the desired accuracies of the target position estimation. 1.