954 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.
Distributed Recognition of Reference Nodes for Wireless Sensor Network Localization
All known localization techniques for wireless sensor and ad-hoc networks require certain set of reference nodes being used for position estimation. The anchor-free techniques in contrast to anchor-based do not require reference nodes called anchors to be placed in the network area before localization operation itself, but they can establish own reference coordinate system to be used for the relative position estimation. We observed that contemporary anchor-free localization algorithms achieve a low localization error, but dissipate significant energy reserves during the recognition of reference nodes used for the position estimation. Therefore, we have proposed the optimized anchor-free localization algorithm referred to as BRL (Boundary Recognition aided Localization), which achieves a low localization error and mainly reduces the communication cost of the reference nodes recognition phase. The proposed BRL algorithm was investigated throughout the extensive simulations on the database of networks with the different number of nodes and densities and was compared in terms of communication cost and localization error with the known related algorithms such as AFL and CRP. Through the extensive simulations we have observed network conditions where novel BRL algorithm excels in comparison with the state of art
Robust Localization from Incomplete Local Information
We consider the problem of localizing wireless devices in an ad-hoc network
embedded in a d-dimensional Euclidean space. Obtaining a good estimation of
where wireless devices are located is crucial in wireless network applications
including environment monitoring, geographic routing and topology control. When
the positions of the devices are unknown and only local distance information is
given, we need to infer the positions from these local distance measurements.
This problem is particularly challenging when we only have access to
measurements that have limited accuracy and are incomplete. We consider the
extreme case of this limitation on the available information, namely only the
connectivity information is available, i.e., we only know whether a pair of
nodes is within a fixed detection range of each other or not, and no
information is known about how far apart they are. Further, to account for
detection failures, we assume that even if a pair of devices is within the
detection range, it fails to detect the presence of one another with some
probability and this probability of failure depends on how far apart those
devices are. Given this limited information, we investigate the performance of
a centralized positioning algorithm MDS-MAP introduced by Shang et al., and a
distributed positioning algorithm, introduced by Savarese et al., called
HOP-TERRAIN. In particular, for a network consisting of n devices positioned
randomly, we provide a bound on the resulting error for both algorithms. We
show that the error is bounded, decreasing at a rate that is proportional to
R/Rc, where Rc is the critical detection range when the resulting random
network starts to be connected, and R is the detection range of each device.Comment: 40 pages, 13 figure
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
Secure Routing in Wireless Mesh Networks
Wireless mesh networks (WMNs) have emerged as a promising concept to meet the
challenges in next-generation networks such as providing flexible, adaptive,
and reconfigurable architecture while offering cost-effective solutions to the
service providers. Unlike traditional Wi-Fi networks, with each access point
(AP) connected to the wired network, in WMNs only a subset of the APs are
required to be connected to the wired network. The APs that are connected to
the wired network are called the Internet gateways (IGWs), while the APs that
do not have wired connections are called the mesh routers (MRs). The MRs are
connected to the IGWs using multi-hop communication. The IGWs provide access to
conventional clients and interconnect ad hoc, sensor, cellular, and other
networks to the Internet. However, most of the existing routing protocols for
WMNs are extensions of protocols originally designed for mobile ad hoc networks
(MANETs) and thus they perform sub-optimally. Moreover, most routing protocols
for WMNs are designed without security issues in mind, where the nodes are all
assumed to be honest. In practical deployment scenarios, this assumption does
not hold. This chapter provides a comprehensive overview of security issues in
WMNs and then particularly focuses on secure routing in these networks. First,
it identifies security vulnerabilities in the medium access control (MAC) and
the network layers. Various possibilities of compromising data confidentiality,
data integrity, replay attacks and offline cryptanalysis are also discussed.
Then various types of attacks in the MAC and the network layers are discussed.
After enumerating the various types of attacks on the MAC and the network
layer, the chapter briefly discusses on some of the preventive mechanisms for
these attacks.Comment: 44 pages, 17 figures, 5 table
1-D Coordinate Based on Local Information for MAC and Routing Issues in WSNs
More and more critical Wireless Sensor Networks (WSNs) applications are
emerging. Those applications need reliability and respect of time constraints.
The underlying mechanisms such as MAC and routing must handle such
requirements. Our approach to the time constraint problem is to bound the
hop-count between a node and the sink and the time it takes to do a hop so the
end-to-end delay can be bounded and the communications are thus real-time. For
reliability purpose we propose to select forwarder nodes depending on how they
are connected in the direction of the sink. In order to be able to do so we
need a coordinate (or a metric) that gives information on hop-count, that
allows to strongly differentiate nodes and gives information on the
connectivity of each node keeping in mind the intrinsic constraints of WSWs
such as energy consumption, autonomy, etc. Due to the efficiency and
scalability of greedy routing in WSNs and the financial cost of GPS chips,
Virtual Coordinate Systems (VCSs) for WSNs have been proposed. A category of
VCSs is based on the hop-count from the sink, this scheme leads to many nodes
having the same coordinate. The main advantage of this system is that the hops
number of a packet from a source to the sink is known. Nevertheless, it does
not allow to differentiate the nodes with the same hop-count. In this report we
propose a novel hop-count-based VCS which aims at classifying the nodes having
the same hop-count depending on their connectivity and at differentiating nodes
in a 2-hop neighborhood. Those properties make the coordinates, which also can
be viewed as a local identifier, a very powerful metric which can be used in
WSNs mechanisms.Comment: (2011
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