62 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
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
Localization in Wireless Sensor Networks Using Heuristic Optimization Techniques, Journal of Telecommunications and Information Technology, 2011, nr 4
Many applications of wireless sensor networks (WSN) require information about the geographic location of each sensor node. Devices that form WSN are expected to be remotely deployed in large numbers in a sensing field, and to self-organize to perform sensing and acting task. The goal of localization is to assign geographic coordinates to each device with unknown position in the deployment area. Recently, the popular strategy is to apply optimization algorithms to solve the localization problem. In this paper, we address issues associated with the application of heuristic techniques to accurate localization of nodes in a WSN system. We survey and discuss the location systems based on simulated annealing, genetic algorithms and evolutionary strategies. Finally, we describe and evaluate our methods that combine trilateration and heuristic optimization
Computer Vision without Vision : Methods and Applications of Radio and Audio Based SLAM
The central problem of this thesis is estimating receiver-sender node positions from measured receiver-sender distances or equivalent measurements. This problem arises in many applications such as microphone array calibration, radio antenna array calibration, mapping and positioning using ultra-wideband and mapping and positioning using round-trip-time measurements between mobile phones and Wi-Fi-units. Previous research has explored some of these problems, creating minimal solvers for instance, but these solutions lack real world implementation. Due to the nature of using different media, finding reliable receiver-sender distances is tough, with many of the measurements being erroneous or to a worse extent missing. Therefore in this thesis, we explore using minimal solvers to create robust solutions, that encompass small erroneous measurements and work around missing and grossly erroneous measurements.This thesis focuses mainly on Time-of-Arrival measurements using radio technologies such as Two-way-Ranging in Ultra-Wideband and a new IEEE standard 802.11mc found on many WiFi modules. The methods investigated, also related to Computer Vision problems such as Stucture-from-Motion. As part of this thesis, a range of new commercial radio technologies are characterised in terms of ranging in real world enviroments. In doing so, we have shown how these technologies can be used as a more accurate alternative to the Global Positioning System in indoor enviroments. Further to these solutions, more methods are proposed for large scale problems when multiple users will collect the data, commonly known as Big Data. For these cases, more data is not always better, so a method is proposed to try find the relevant data to calibrate large systems
Enabling wireless sensors localization in dynamic indoor environments
Wireless sensors networks localization is an important area that attracts significant research interest. Localization is a fundamental problem that must be solved in order to support location-aware applications. The growing demand of location-aware applications requires the development of application-oriented localization solutions with appropriate trade offs between accuracy and costs. The present thesis seeks to enhance the performance of simple and low-cost propagation based localization solutions in dynamic indoor environments.
First, an overview of the different approaches in wireless sensors networks localization is provided. Next, sources of received signal strength variability are investigated. Then, the problems of the distance-dependant path loss estimation caused by the radio channel of dynamic indoor situations are empirically analyzed. Based on these previous theoretical and empirical analysis, the solution uses spatial and frequency diversity techniques, in addition to time diversity, in order to create a better estimator of the distance-dependent path loss by counteracting the random multipath effect. Furthermore, the solution attempts to account for the random shadow fading by using "shadowing-independent" path loss estimations in order to deduce distances. In order to find the unknown sensor's positions based on the distance estimates, the solution implements a weighted least-squares algorithm that reduces the impact of the distance estimates errors in the location estimate
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