17,069 research outputs found
Localisation of mobile nodes in wireless networks with correlated in time measurement noise.
Wireless sensor networks are an inherent part of decision making, object tracking and location awareness systems. This work is focused on simultaneous localisation of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multi model auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localisation accuracy is demonstrated
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
Reference Nodes Selection for Anchor-Free Localization in Wireless Sensor Networks
DizertaÄnĂ prĂĄce se zabĂ˝vĂĄ nĂĄvrhem novĂŠho bezkotevnĂho lokalizaÄnĂho algoritmu slouĹžĂcĂho pro vĂ˝poÄet pozice uzlĹŻ v bezdrĂĄtovĂ˝ch senzorovĂ˝ch sĂtĂch. ProvedenĂŠ studie ukĂĄzaly, Ĺže dosavadnĂ bezkotevnĂ lokalizaÄnĂ algoritmy, pracujĂcĂ v paralelnĂm reĹžimu, dosahujĂ malĂ˝ch lokalizaÄnĂch chyb. Jejich nevĂ˝hodou ovĹĄem je, Ĺže pĹi sestavenĂ mnoĹžiny referenÄnĂch uzlu spotĹebovĂĄvajĂ daleko vÄtĹĄĂ mnoĹžstvĂ energie neĹž algoritmy pracujĂcĂ v inkrementĂĄlnĂm reĹžimu. ParalelnĂ lokalizaÄnĂ algoritmy vyuĹžĂvajĂ pro urÄenĂ pozice referenÄnĂ uzly nachĂĄzejĂcĂ se na protilehlĂ˝ch hranĂĄch bezdrĂĄtovĂŠ sĂtÄ. NovĂ˝ lokalizaÄnĂ algoritmus oznaÄenĂ˝ jako BRL (Boundary Recognition aided Localization) je zaloĹžen na myĹĄlence decentralizovanÄ detekovat uzly leĹžĂcĂ na hranici sĂti a pouze z tĂŠto mnoĹžiny vybrat potĹebnĂ˝ poÄet referenÄnĂch uzlu. PomocĂ navrĹženĂŠho pĹĂstupu lze znaĹžnÄ snĂĹžit mnoĹžstvĂ energie spotĹebovanĂŠ v prĹŻbÄhu procesu vĂ˝bÄru referenÄnĂch uzlĹŻ v senzorovĂŠm poli. DalĹĄĂm pĹĂnosem ke snĂĹženĂ energetickĂ˝ch nĂĄroku a zĂĄroveĹ zachovĂĄnĂ nĂzkĂŠ lokalizaÄnĂ chyby je vyuĹžitĂ procesu multilaterace se tĹemi, eventuĂĄlnÄ ÄtyĹmi referenÄnĂmi body. V rĂĄmci prĂĄce byly provedeny simulace nÄkolika dĂlÄĂch algoritmu a jejich funkÄnost byla ovÄĹena experimentĂĄlnÄ v reĂĄlnĂŠ senzorovĂŠ sĂti. NavrĹženĂ˝ algoritmus BRL byl porovnĂĄn z hlediska lokalizaÄnĂ chyby a poÄtu zpracovanĂ˝ch paketĹŻ s nÄkolika znĂĄmĂ˝mi lokalizaÄnĂmi algoritmy. VĂ˝sledky simulacĂ dokĂĄzaly, Ĺže navrĹženĂ˝ algoritmus pĹedstavuje efektivnĂ ĹeĹĄenĂ pro pĹesnou a zĂĄroveĹ nĂzkoenergetickou lokalizaci uzlĹŻ v bezdrĂĄtovĂ˝ch senzorovĂ˝ch 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.
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
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
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