3,417 research outputs found
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
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
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
A survey of localization in wireless sensor network
Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network
Opportunistic Localization Scheme Based on Linear Matrix Inequality
Enabling self-localization of mobile nodes is an important problem that has been widely studied in the literature.
The general conclusions is that an accurate localization
requires either sophisticated hardware (GPS, UWB, ultrasounds transceiver) or a dedicated infrastructure (GSM, WLAN). In this paper we tackle the problem from a different and rather new perspective: we investigate how localization performance can be improved by means of a cooperative and opportunistic data exchange among the nodes. We consider a target node, completely unaware of its own position, and a number of mobile nodes with some self-localization capabilities. When the opportunity occurs, the target node can exchange data with in-range mobile nodes. This opportunistic data exchange is then used by the target node to refine its position estimate by using a technique based on Linear Matrix Inequalities and barycentric algorithm. To investigate the performance of such an opportunistic localization algorithm, we define a simple mathematical model that describes the opportunistic interactions and, then, we run several computer simulations for analyzing the effect of the nodes duty-cycle and of the native self-localization error modeling considered. The results show that the opportunistic interactions can actually improve the self-localization accuracy of a strayed node in many different scenarios
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Distributed tuplespace and location management - an integrated perspective using Bluetooth
Location based or "context aware" computing is becoming increasingly recognized as a vital part of a mobile computing environment. As a consequence, the need for location-management middleware is widely recognized and actively researched. Location management is frequently offered to the application through an API where the location is given in the form of coordinates. It is the opinion of the authors that a localization API should offer localized data (e.g. direction to the nearest pharmacy) directly through a transparent and integrated API. Our proposed middleware for location and context management is built on top of Mobispace. Mobispace is a distributed tuplespace made for J2me units where replication between local replicas takes place with a central server (over GPRS) or with other mobile units (using Bluetooth). Since a Bluetooth connection indicates physical proximity to another node, a set of stationary nodes may distribute locality information over Bluetooth connections, and this information may be retrieved through the ordinary tuplespace AP
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