Monitoring applications define an important class of applications used in wireless sensor networks. In these applications the network perceives the environment and searches for event occurrences (phenomena) by sensing different physical properties, such as temperature, humidity, pressure, ambient light, movement, and presence (for target tracking). In such cases the location information of both phenomena and nodes is usually required for tracking and correlation purposes. In this work we summarize most of the concepts related to localization systems for WSNs as well as how to localize the nodes in these networks (which allows the localization of phenomena). By dividing the localization systems into three distinct components -distance/angle estimation, position computation, and localization algorithm - besides providing a didactic viewpoint, we show that these components can be seen as subareas of the localization problem that need to be analyzed and studied separately.

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 discuss their applicability in the context of wireless sensor networks.

Localization systems play a major role in many applications for Vehicular Ad Hoc Networks (VANETs). One of the most interesting problems to be solved in vehicular networks is how to provide anywhere and anytime highly accurate and reliable localization information. Unique characteristics of VANETs such as mobility constraints, driver’s behavior and the high-speed displacement nature of vehicles cause rapid and constant changes in network topology, leading to dissemination of outdated localization information. To circumvent this problem, an alternative is the use of predicted future locations of vehicles. The main idea of this approach is to use the localization prediction as an extension of a data fusion localization system. In such an approach, a future position of a vehicle is predicted for a given future time and used to take advantage of a future time-space window of a vectorial trajectory rather than a static localization point. In this paper, we discuss this subject by studying and analyzing the use of localization prediction as natural way to improve VANET applications. We survey proposed approaches for localization, target tracking and time series prediction techniques that can be used to estimate the future position of a vehicle. We also highlight their advantages and disadvantages through an analytical discussion visualizing its potential application scenarios in VANETs. We present a set of experiments that show the results of such techniques when applied to a realistic VANET scenario.

In the military and emergency preparedness class of applications, wireless sensor networks have a number of desirable characteristics, such as being autonomous systems that can be deployed in a remote - possibly hostile - environment and can perform tasks like battlefield surveillance or enemy tracking, as well as monitor the security of military facilities. One of the main challenges in this kind of application is security. Due to their key role in WSNs and also their fragility, localization systems can be the target of an attack that could compromise the entire functioning of a WSN and lead to incorrect military plans and decision making, among other problems. In this article we show how current localization systems are vulnerable to these security attacks, and how existing techniques can be used to prevent or impede these attacks in WSNs.

Target-tracking algorithms typically organize the network into a logical structure (e.g., tree, cluster, or faces) to enable data fusion and reduce communication costs. These algorithms often predict the target’s future position. In addition to using position forecasts for decision making, we can also use such information to save energy by activating only the set of sensors nearby the target’s trajectory. In this work, we survey of the state of the art of target-tracking techniques in sensor networks. We identify three different formulations for the target-tracking problem and classify the target-tracking algorithms based on common characteristics. Furthermore, for the sake of a better understanding of the target-tracking process, we organize this process in six components: target detection, node cooperation, position computation, future-position estimation, energy management, and target recovery. Each component has different solutions that affect the target-tracking performance.