Dead Reckoning Localization Technique for Mobile Wireless Sensor Networks

Localization in wireless sensor networks not only provides a node with its geographical location but also a basic requirement for other applications such as geographical routing. Although a rich literature is available for localization in static WSN, not enough work is done for mobile WSNs, owing to the complexity due to node mobility. Most of the existing techniques for localization in mobile WSNs uses Monte-Carlo localization, which is not only time-consuming but also memory intensive. They, consider either the unknown nodes or anchor nodes to be static. In this paper, we propose a technique called Dead Reckoning Localization for mobile WSNs. In the proposed technique all nodes (unknown nodes as well as anchor nodes) are mobile. Localization in DRLMSN is done at discrete time intervals called checkpoints. Unknown nodes are localized for the first time using three anchor nodes. For their subsequent localizations, only two anchor nodes are used. The proposed technique estimates two possible locations of a node Using Bezouts theorem. A dead reckoning approach is used to select one of the two estimated locations. We have evaluated DRLMSN through simulation using Castalia simulator, and is compared with a similar technique called RSS-MCL proposed by Wang and Zhu .Comment: Journal Paper, IET Wireless Sensor Systems, 201

Efficient Range-Free Monte-Carlo-Localization for Mobile Wireless Sensor Networks

Das Hauptproblem von Lokalisierungsalgorithmen für WSNs basierend auf Ankerknoten ist die Abhängigkeit von diesen. Mobilität im Netzwerk kann zu Topologien führen, in denen einzelne Knoten oder ganze Teile des Netzwerks temporär von allen Ankerknoten isoliert werden. In diesen Fällen ist keine weitere Lokalisierung möglich. Dies wirkt sich primär auf den Lokalisierungsfehler aus, der in diesen Fällen stark ansteigt. Des weiteren haben Betreiber von Sensornetzwerken Interesse daran, die Anzahl der kosten- und wartungsintensiveren Ankerknoten auf ein Minimum zu reduzieren. Dies verstärkt zusätzlich das Problem von nicht verfügbaren Ankerknoten während des Netzwerkbetriebs. In dieser Arbeit werden zunächst die Vor- und Nachteile der beiden großen Hauptkategorien von Lokalisierungsalgorithmen (range-based und range-free Verfahren) diskutiert und eine Studie eines oft für range-based Lokalisierung genutzten Distanzbestimmungsverfahren mit Hilfe des RSSI vorgestellt. Danach werden zwei neue Varianten für ein bekanntes range-free Lokalisierungsverfahren mit Namen MCL eingeführt. Beide haben zum Ziel das Problem der temporär nicht verfügbaren Ankerknoten zu lösen, bedienen sich dabei aber unterschiedlicher Mittel. SA-MCL nutzt ein dead reckoning Verfahren, um die Positionsschätzung vom letzten bekannten Standort weiter zu führen. Dies geschieht mit Hilfe von zusätzlichen Sensorinformationen, die von einem elektronischen Kompass und einem Beschleunigungsmesser zur Verfügung gestellt werden. PO-MCL hingegen nutzt das Mobilitätsverhalten von einigen Anwendungen in Sensornetzwerken aus, bei denen sich alle Knoten primär auf einer festen Anzahl von Pfaden bewegen, um den Lokalisierungsprozess zu verbessern. Beide Methoden werden durch detaillierte Netzwerksimulationen evaluiert. Im Fall von SA-MCL wird außerdem eine Implementierung auf echter Hardware vorgestellt und eine Feldstudie in einem mobilen Sensornetzwerk durchgeführt. Aus den Ergebnissen ist zu sehen, dass der Lokalisierungsfehler in Situationen mit niedriger Ankerknotendichte im Fall von SA-MCL um bis zu 60% reduziert werden kann, beziehungsweise um bis zu 50% im Fall von PO-MCL.

Sparse Localization with a Mobile Beacon Based on LU Decomposition in Wireless Sensor Networks

Node localization is the core in wireless sensor network. It can be solved by powerful beacons, which are equipped with global positioning system devices to know their location information. In this article, we present a novel sparse localization approach with a mobile beacon based on LU decomposition. Our scheme firstly translates node localization problem into a 1-sparse vector recovery problem by establishing sparse localization model. Then, LU decomposition pre-processing is adopted to solve the problem that measurement matrix does not meet the re¬stricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted Cen¬troid scheme is introduced to accurately locate the node. Simulation and analysis show that our scheme has better localization performance and lower requirement for the mobile beacon than MAP+GC, MAP-M, and MAP-M&N schemes. In addition, the obstacles and DOI have little effect on the novel scheme, and it has great localization performance under low SNR, thus, the scheme proposed is robust

Wireless Sensor Networks for Underwater Localization: A Survey

Autonomous Underwater Vehicles (AUVs) have widely deployed in marine investigation and ocean exploration in recent years. As the fundamental information, their position information is not only for data validity but also for many real-world applications. Therefore, it is critical for the AUV to have the underwater localization capability. This report is mainly devoted to outline the recent advance- ment of Wireless Sensor Networks (WSN) based underwater localization. Several classic architectures designed for Underwater Acoustic Sensor Network (UASN) are brie y introduced. Acoustic propa- gation and channel models are described and several ranging techniques are then explained. Many state-of-the-art underwater localization algorithms are introduced, followed by the outline of some existing underwater localization systems

Target Tracking in Confined Environments with Uncertain Sensor Positions

To ensure safety in confined environments such as mines or subway tunnels, a (wireless) sensor network can be deployed to monitor various environmental conditions. One of its most important applications is to track personnel, mobile equipment and vehicles. However, the state-of-the-art algorithms assume that the positions of the sensors are perfectly known, which is not necessarily true due to imprecise placement and/or dropping of sensors. Therefore, we propose an automatic approach for simultaneous refinement of sensors' positions and target tracking. We divide the considered area in a finite number of cells, define dynamic and measurement models, and apply a discrete variant of belief propagation which can efficiently solve this high-dimensional problem, and handle all non-Gaussian uncertainties expected in this kind of environments. Finally, we use ray-tracing simulation to generate an artificial mine-like environment and generate synthetic measurement data. According to our extensive simulation study, the proposed approach performs significantly better than standard Bayesian target tracking and localization algorithms, and provides robustness against outliers.Comment: IEEE Transactions on Vehicular Technology, 201