Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Dynamic fine-grained localization in Ad-Hoc networks of sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Range-free localization schemes for large scale sensor networks
Proceedings of the 9th annual international conference on Mobile computing and networking
Using proximity and quantized RSS for sensor localization in wireless networks
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
Distributed online localization in sensor networks using a moving target
Proceedings of the 3rd international symposium on Information processing in sensor networks
A probabilistic approach to inference with limited information in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Impact of radio irregularity on wireless sensor networks
Proceedings of the 2nd international conference on Mobile systems, applications, and services
Localization in sensor networks
Wireless sensor networks
Localization for mobile sensor networks
Proceedings of the 10th annual international conference on Mobile computing and networking
Models and solutions for radio irregularity in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Organizing a global coordinate system from local information on an ad hoc sensor network
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Positioning in ad hoc sensor networks
IEEE Network: The Magazine of Global Internetworking
OTMCL: orientation tracking-based Monte Carlo localization for mobile sensor networks
INSS'09 Proceedings of the 6th international conference on Networked sensing systems
TDOA positioning in NLOS scenarios by particle filtering
Wireless Networks
Hi-index | 0.01 |
Node localization in wireless sensor networks is essential to many applications such as routing protocol, target tracking and environment surveillance. Many localization schemes have been proposed in the past few years and they can be classified into two categories: range-based and range-free. Since range-based techniques need special hardware, which increases the localization cost, many researchers now focus on the range-free techniques. However, most of the range-free localization schemes assume that the sensor nodes are static, the network topology is known in advance, and the radio propagation is perfect circle. Moreover, many schemes need densely distributed anchor nodes whose positions are known in advance in order to estimate the positions of the unknown nodes. These assumptions are not practical in real network. In this paper, we consider the sensor networks with sparse anchor nodes and irregular radio propagation. Based on Sequential Monte Carlo method, we propose an alterative localization method--Sequential Monte Carlo Localization scheme (SMCL). Unlike many previously proposed methods, our work takes the probabilistic approach, which is suitable for the mobile sensor networks because both anchors and unknown nodes can move, and the network topology need not be formed beforehand. Moreover, our algorithm is scalable and can be used in large-scale sensor networks. Simulation results show that SMCL has better localization accuracy and it can localize more sensor nodes when the anchor density is low. The communication overhead of SMCL is also lower than other localization algorithms.