11,927 research outputs found
RSSI-Based Self-Localization with Perturbed Anchor Positions
We consider the problem of self-localization by a resource-constrained mobile
node given perturbed anchor position information and distance estimates from
the anchor nodes. We consider normally-distributed noise in anchor position
information. The distance estimates are based on the log-normal shadowing
path-loss model for the RSSI measurements. The available solutions to this
problem are based on complex and iterative optimization techniques such as
semidefinite programming or second-order cone programming, which are not
suitable for resource-constrained environments. In this paper, we propose a
closed-form weighted least-squares solution. We calculate the weights by taking
into account the statistical properties of the perturbations in both RSSI and
anchor position information. We also estimate the bias of the proposed solution
and subtract it from the proposed solution. We evaluate the performance of the
proposed algorithm considering a set of arbitrary network topologies in
comparison to an existing algorithm that is based on a similar approach but
only accounts for perturbations in the RSSI measurements. We also compare the
results with the corresponding Cramer-Rao lower bound. Our experimental
evaluation shows that the proposed algorithm can substantially improve the
localization performance in terms of both root mean square error and bias.Comment: Accepted for publication in 28th Annual IEEE International Symposium
on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC 2017
DILAND: An Algorithm for Distributed Sensor Localization with Noisy Distance Measurements
In this correspondence, we present an algorithm for distributed sensor
localization with noisy distance measurements (DILAND) that extends and makes
the DLRE more robust. DLRE is a distributed sensor localization algorithm in
introduced in \cite{usman_loctsp:08}. DILAND operates
when (i) the communication among the sensors is noisy; (ii) the communication
links in the network may fail with a non-zero probability; and (iii) the
measurements performed to compute distances among the sensors are corrupted
with noise. The sensors (which do not know their locations) lie in the convex
hull of at least anchors (nodes that know their own locations.) Under
minimal assumptions on the connectivity and triangulation of each sensor in the
network, this correspondence shows that, under the broad random phenomena
described above, DILAND converges almost surely (a.s.) to the exact sensor
locations.Comment: Submitted to the IEEE Transactions on Signal Processing. Initial
submission on May 2009. 12 page
Multi-mode Tracking of a Group of Mobile Agents
We consider the problem of tracking a group of mobile nodes with limited
available computational and energy resources given noisy RSSI measurements and
position estimates from group members. The multilateration solutions are known
for energy efficiency. However, these solutions are not directly applicable to
dynamic grouping scenarios where neighbourhoods and resource availability may
frequently change. Existing algorithms such as cluster-based GPS duty-cycling,
individual-based tracking, and multilateration-based tracking can only
partially deal with the challenges of dynamic grouping scenarios. To cope with
these challenges in an effective manner, we propose a new group-based
multi-mode tracking algorithm. The proposed algorithm takes the topological
structure of the group as well as the availability of the resources into
consideration and decides the best solution at any particular time instance. We
consider a clustering approach where a cluster head coordinates the usage of
resources among the cluster members. We evaluate the energy-accuracy trade-off
of the proposed algorithm for various fixed sampling intervals. The evaluation
is based on the 2D position tracks of 40 nodes generated using Reynolds'
flocking model. For a given energy budget, the proposed algorithm reduces the
mean tracking error by up to in comparison to the existing
energy-efficient cooperative algorithms. Moreover, the proposed algorithm is as
accurate as the individual-based tracking while using almost half the energy.Comment: Accepted for publication in the 20th international symposium on
wireless personal multimedia communications (WPMC-2017
Connection Between System Parameters and Localization Probability in Network of Randomly Distributed Nodes
This article deals with localization probability in a network of randomly
distributed communication nodes contained in a bounded domain. A fraction of
the nodes denoted as L-nodes are assumed to have localization information while
the rest of the nodes denoted as NL nodes do not. The basic model assumes each
node has a certain radio coverage within which it can make relative distance
measurements. We model both the case radio coverage is fixed and the case radio
coverage is determined by signal strength measurements in a Log-Normal
Shadowing environment. We apply the probabilistic method to determine the
probability of NL-node localization as a function of the coverage area to
domain area ratio and the density of L-nodes. We establish analytical
expressions for this probability and the transition thresholds with respect to
key parameters whereby marked change in the probability behavior is observed.
The theoretical results presented in the article are supported by simulations.Comment: To appear on IEEE Transactions on Wireless Communications, November
200
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