9,045 research outputs found
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
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
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
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
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.
A mosaic of eyes
Autonomous navigation is a traditional research topic in intelligent robotics and vehicles, which requires a robot to perceive its environment through onboard sensors such as cameras or laser scanners, to enable it to drive to its goal. Most research to date has focused on the development of a large and smart brain to gain autonomous capability for robots. There are three fundamental questions to be answered by an autonomous mobile robot: 1) Where am I going? 2) Where am I? and 3) How do I get there? To answer these basic questions, a robot requires a massive spatial memory and considerable computational resources to accomplish perception, localization, path planning, and control. It is not yet possible to deliver the centralized intelligence required for our real-life applications, such as autonomous ground vehicles and wheelchairs in care centers. In fact, most autonomous robots try to mimic how humans navigate, interpreting images taken by cameras and then taking decisions accordingly. They may encounter the following difficulties
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
mTOSSIM: A simulator that estimates battery lifetime in wireless sensor networks
Knowledge of the battery lifetime of the wireless sensor network is important for many situations,
such as in evaluation of the location of nodes or the estimation of the connectivity,
along time, between devices. However, experimental evaluation is a very time-consuming
task. It depends on many factors, such as the use of the radio transceiver or the distance
between nodes. Simulations reduce considerably this time. They allow the evaluation of
the network behavior before its deployment. This article presents a simulation tool which
helps developers to obtain information about battery state. This simulator extends the
well-known TOSSIM simulator. Therefore it is possible to evaluate TinyOS applications
using an accurate model of the battery consumption and its relation to the radio power
transmission. Although an specific indoor scenario is used in testing of simulation, the simulator
is not limited to this environment. It is possible to work in outdoor scenarios too.
Experimental results validate the proposed model.Junta de AndalucĂa P07-TIC-02476Junta de AndalucĂa TIC-570
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