1,524 research outputs found
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
Reference Nodes Selection for Anchor-Free Localization in Wireless Sensor Networks
DizertaÄnĂ prĂĄce se zabĂœvĂĄ nĂĄvrhem novĂ©ho bezkotevnĂho lokalizaÄnĂho algoritmu slouĆŸĂcĂho pro vĂœpoÄet pozice uzlĆŻ v bezdrĂĄtovĂœch senzorovĂœch sĂtĂch. ProvedenĂ© studie ukĂĄzaly, ĆŸe dosavadnĂ bezkotevnĂ lokalizaÄnĂ algoritmy, pracujĂcĂ v paralelnĂm reĆŸimu, dosahujĂ malĂœch lokalizaÄnĂch chyb. Jejich nevĂœhodou ovĆĄem je, ĆŸe pĆi sestavenĂ mnoĆŸiny referenÄnĂch uzlu spotĆebovĂĄvajĂ daleko vÄtĆĄĂ mnoĆŸstvĂ energie neĆŸ algoritmy pracujĂcĂ v inkrementĂĄlnĂm reĆŸimu. ParalelnĂ lokalizaÄnĂ algoritmy vyuĆŸĂvajĂ pro urÄenĂ pozice referenÄnĂ uzly nachĂĄzejĂcĂ se na protilehlĂœch hranĂĄch bezdrĂĄtovĂ© sĂtÄ. NovĂœ lokalizaÄnĂ algoritmus oznaÄenĂœ jako BRL (Boundary Recognition aided Localization) je zaloĆŸen na myĆĄlence decentralizovanÄ detekovat uzly leĆŸĂcĂ na hranici sĂti a pouze z tĂ©to mnoĆŸiny vybrat potĆebnĂœ poÄet referenÄnĂch uzlu. PomocĂ navrĆŸenĂ©ho pĆĂstupu lze znaĆŸnÄ snĂĆŸit mnoĆŸstvĂ energie spotĆebovanĂ© v prĆŻbÄhu procesu vĂœbÄru referenÄnĂch uzlĆŻ v senzorovĂ©m poli. DalĆĄĂm pĆĂnosem ke snĂĆŸenĂ energetickĂœch nĂĄroku a zĂĄroveĆ zachovĂĄnĂ nĂzkĂ© lokalizaÄnĂ chyby je vyuĆŸitĂ procesu multilaterace se tĆemi, eventuĂĄlnÄ ÄtyĆmi referenÄnĂmi body. V rĂĄmci prĂĄce byly provedeny simulace nÄkolika dĂlÄĂch algoritmu a jejich funkÄnost byla ovÄĆena experimentĂĄlnÄ v reĂĄlnĂ© senzorovĂ© sĂti. NavrĆŸenĂœ algoritmus BRL byl porovnĂĄn z hlediska lokalizaÄnĂ chyby a poÄtu zpracovanĂœch paketĆŻ s nÄkolika znĂĄmĂœmi lokalizaÄnĂmi algoritmy. VĂœsledky simulacĂ dokĂĄzaly, ĆŸe navrĆŸenĂœ algoritmus pĆedstavuje efektivnĂ ĆeĆĄenĂ pro pĆesnou a zĂĄroveĆ nĂzkoenergetickou lokalizaci uzlĆŻ v bezdrĂĄtovĂœch senzorovĂœch sĂtĂch.The doctoral thesis is focused on a design of a novel anchor free localization algorithm for wireless sensor networks. As introduction, the incremental and concurrent anchor free localization algorithms are presented and their performance is compared. It was found that contemporary anchor free localization algorithms working in the concurrent manner achieve a low localization error, but dissipate signicant energy reserves. A new Boundary Recognition Aided Localization algorithm presented in this thesis is based on an idea to recognize the nodes placed on the boundary of network and thus reduce the number of transmission realized during the reference nodes selection phase of the algorithm. For the position estimation, the algorithm employs the multilateration technique that work eectively with the low number of the reference nodes. Proposed algorithms are tested through the simulations and validated by the real experiment with the wireless sensor network. The novel Boundary Recognition Aided Localization algorithm is compared with the known algorithms in terms of localization error and the communication cost. The results show that the novel algorithm presents powerful solution for the anchor free localization.
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 reÂŹstricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted CenÂŹtroid 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
Selected RSSI-based DV-Hop Localization for Wireless Sensor Networks
With the increasing demand on wireless sensor networks (WSNs) applications, acquiring the information of sensor node locations becomes one of the most important issues. Up to now, available localization approaches can be categorized into range-free and range-based methods. Range-free localizations are being pursued as a more cost-effective method. However, range-based schemes have better localization accuracy. This paper proposes the selected RSSI-based DV-Hop localization, which improves localization accuracy from the existing schemes by applying a combined technique that inherits the benefits from both methods. Our proposed technique firstly employs the DV-Hop approach of range-free algorithms, then uses the received signal strength indicator (RSSI) estimation technique of range-based algorithms to estimate the distances of selected hops. This paper also includes basic studies, which have been performed via computer simulations as well as testbed experiments, for distance calculation from RSSI measurement and location estimation in order to prove the credibility of our simulator. The proposed technique is implemented and tested via our developed WSN simulation model. Results in terms of distance error in comparison with traditional DV-Hop, RDV-Hop, and weighted RSSI algorithms show significant performance improvement by using our proposed method for both low-density and high-density wireless sensor network test scenarios
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
Group behavior impact on an opportunistic localization scheme
In this paper we tackled the localization problem from an opportunistic perspective, according to which a node can infer its own spatial position by exchanging data with passing by nodes, called peers. We consider an opportunistic localization algorithm based on the linear matrix inequality (LMI) method coupled with a weighted barycenter algorithm. This scheme has been previously analyzed in scenarios with random deployment of peers, proving its effectiveness. In this paper, we extend the
analysis by considering more realistic mobility models for peer nodes. More specifically, we consider two mobility models, namely the Group Random Waypoint Mobility Model and the Group Random Pedestrian Mobility Model, which is an
improvement of the first one. Hence, we analyze by simulation the opportunistic localization algorithm for both the models, in order to gain insights on the impact of nodes mobility pattern onto the localization performance. The simulation results show that the mobility model has non-negligible effect on the final localization error, though the performance of the opportunistic localization scheme remains acceptable in all the considered scenarios
LIS: Localization based on an intelligent distributed fuzzy system applied to a WSN
The localization of the sensor nodes is a fundamental problem in wireless sensor networks.
There are a lot of different kinds of solutions in the literature. Some of them use external
devices like GPS, while others use special hardware or implicit parameters in wireless
communications.
In applications like wildlife localization in a natural environment, where the power available
and the weight are big restrictions, the use of hungry energy devices like GPS or hardware
that add extra weight like mobile directional antenna is not a good solution.
Due to these reasons it would be better to use the localizationâs implicit characteristics in
communications, such as connectivity, number of hops or RSSI. The measurement related
to these parameters are currently integrated in most radio devices. These measurement
techniques are based on the beaconsâ transmissions between the devices.
In the current study, a novel tracking distributed method, called LIS, for localization of
the sensor nodes using moving devices in a network of static nodes, which have no additional
hardware requirements is proposed.
The position is obtained with the combination of two algorithms; one based on a local
node using a fuzzy system to obtain a partial solution and the other based on a centralized
method which merges all the partial solutions. The centralized algorithm is based on the
calculation of the centroid of the partial solutions.
Advantages of using fuzzy system versus the classical Centroid Localization (CL)
algorithm without fuzzy preprocessing are compared with an ad hoc simulator made for
testing localization algorithms.
With this simulator, it is demonstrated that the proposed method obtains less localization
errors and better accuracy than the centroid algorithm.Junta de AndalucĂa P07-TIC-0247
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