Node localization using mobile robots in delay-tolerant sensor networks (original) (raw)
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Demo Abstract: The REKF Localization System: Node Localization Using Mobile Robots
2004
Localization of small wireless sensor devices, with the deployment of the minimal infrastructure or hardware, has been the topic of significant research over the past few years. We have developed the Robust Extended Kalman Filter (REKF) localization system[1], which enables a mobile, data gathering robot to localize static sensor devices, by combining the RSSI data received from the motes, with estimates of its trajectory. The REKF localization system is particularly well suited to delay-tolerant sensor networks, where node positions need not be known in real time. We have observed accuracies ranging from approximately 30cm to 1m in practice.
Localization of a Wireless Sensor Network with Unattended Ground Sensors and Some Mobile Robots
2006 IEEE Conference on Robotics, Automation and Mechatronics, 2006
A range-free approach for adaptive localization of un-localized sensor nodes employing a mobile robot with GPS is detailed. A mobile robot navigates through the sensor deployment area broadcasting its positional estimate and the uncertainty in its estimate. Distributed computationallyinexpensive, discrete-time Kalman Filters, implemented on each static sensor node, fuse information obtained over time from the robot to decrease the uncertainty in each node's location estimate. On the other hand, due to dead reckoning and other systematic errors, the robot loses positional accuracy over time. Updates from GPS and from the localized sensor nodes serve in improving the localization uncertainty of the robot. A Continuous-Discrete Extended Kalman Filter (CD EKF) running on the mobile robot fuses information from multiple distinct sources (GPS, various sensors nodes) for robot navigation. This two-part procedure achieves simultaneous localization of the sensor nodes and the mobile robot.
An Interlaced Extended Kalman Filter for sensor networks localisation
International Journal of Sensor Networks, 2009
Sensor networks have become a widely used technology for applications ranging from military surveillance to industrial fault detection. So far, the evolution in microelectronics have made it possible to build networks of inexpensive nodes characterised by modest computation and storage capability as well as limited battery life. In such a context, having an accurate knowledge about nodes position is fundamental to achieve almost any task. Several techniques to deal with the localisation problem have been proposed in literature: most of them rely on a centralised approach, whereas others work in a distributed fashion. However, a number of approaches do require a prior knowledge of particular nodes, i.e. anchors, whereas others can face the problem without relying on this information. In this paper, a new approach based on an Interlaced Extended Kalman Filter (IEKF) is proposed: the algorithm, working in a distributed fashion, provides an accurate estimation of node poses with a reduced computational complexity. Moreover, no prior knowledge for any nodes is required to produce an estimation in a relative coordinate system. Exhaustive experiments, carried on MICAz nodes, are shown to prove the effectiveness of the proposed IEKF.
2012 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2012
In this paper we propose a distributed approach for localization in wireless sensor networks using a mobile beacon based on Particle Filter (PF) and Extended Kalman Filter (EKF). The algorithm has two phases. In the first phase, each node localizes itself using messages received from the mobile beacon. In the second phase nodes cooperate with each other to improve their estimations of their locations. In the first phase particle filter is used for localization due to its multi-modal/multi-hypothesis nature. In the later EKF is used, due to the nonlinearity nature of the problem in hand, to improve the estimation. On the other hand, using EKF, instead of PF, in the inter-node communication reduces the communication bandwidth tremendously. The mobile beacon's trajectory is based on Hilbert curve which has shown better accuracy in localizing the nodes in the sensor network. The simulation results show better performance than the previous approaches, that either uses inter-node communication or mobile beacon localization.
Application of Kalman Filter to estimate position of a mobile node in Indoor environments
2016
Estimating the position of mobile agents in indoor environments is a challenging problem especially when the estimates must be obtained using commercial, low cost devices. The work in this thesis presents experimental results that demonstrate the effectiveness of our approach, that integrates the signal strength data with sensed values of acceleration and angular velocity. A well-known framework called the Kalman Filter is used. To cope with the noise in the measured values, different versions of the Kalman Filter had to be used such as the Extended Kalman Filter and the Unscented Kalman Filter. This framework allowed us to systematically fuse the data from multiple sources to improve the accuracy of the position estimates. Our results demonstrate that positional accuracy of 0.8m within an 30m x 10m area is achieved. In the future, this work can be extended to further reduce the error in the location estimates by inclusion of encoders.
A probabilistic framework for entire WSN localization using a mobilerobot
Robotics and Autonomous Systems, 2008
This paper presents a new method for the localization of a Wireless Sensor Network (WSN) by means of collaboration with a robot within a Network Robot System (NRS). The method employs the signal strength as input, and has two steps: an initial estimation of the position of the nodes is obtained centrally by one robot and is based on particle filtering. It does not require any prior information about the position of the nodes. In the second stage, the nodes refine their position estimates employing a decentralized information filter. The paper shows how the method is able to recover the 3D position of the nodes, and is very suitable for WSN outdoor applications. The paper includes several implementation aspects and experimental results.
EURASIP Journal on Wireless Communications and Networking, 2012
Since many range-free localization algorithms depend on only a few anchors and implicit range estimations, they produce poor results. In this article, we propose a distributed range-free algorithm to improve localization accuracy by using one-hop neighbors as well as anchors. When an unknown node knows which nodes it can directly communicate with, but does not know how far they are exactly placed, the node should have a location having the average distance to all neighbors since the location minimizes the sum of squares of hop distance errors. In the proposed algorithm, each node initializes its location using the information of anchors and updates it based on mass spring method and Kalman filtering with the location estimates of one-hop neighbors until the equilibrium is achieved. Subsequently, the network has the shape of isotropic graph with minimized variance of links between one-hop neighbors. We evaluate our algorithm and compare it with other range-free algorithms through simulations under varying node density, anchor ratio, and node deployment method.
Accurate and Efficient Node Localization for Mobile Sensor Networks
Mobile Networks and Applications, 2013
In this paper, we propose a range-free cooperative localization algorithm for mobile sensor networks by combining hop-distance measurements with particle filtering. In the hop-distance measurement step, we design a differential-error correction scheme to reduce the positioning error accumulated over multiple hops. We also introduce a backoff-based broadcast mechanism in our localization algorithm. It efficiently suppresses redundant broadcasts and reduces message overhead. The proposed localization method has fast convergence with small location estimation error. We verify our algorithm in various scenarios and compare it with conventional localization methods. Simulation results show that our proposed method has similar or superior performance when compared to other stateof-the-art localization algorithms.
Robust Algorithms for Localizing Moving Nodes in Wireless Sensor Networks
arXiv (Cornell University), 2018
The vivid success of the emerging wireless sensor technology (WSN) gave rise to the notion of localization in the communications field. Indeed, the interest in localization grew further with the proliferation of the wireless sensor network applications including medicine, military as well as transport. By utilizing a subset of sensor terminals, gathered data in a WSN can be both identified and correlated which helps in managing the nodes distributed throughout the network. In most scenarios presented in the literature, the nodes to be localized are often considered static. However, as we are heading towards the 5 th generation mobile communication, the aspect of mobility should be regarded. Thus, the novelty of this research relies in its ability to merge the robotics as well as WSN fields creating a state of art for the localization of moving nodes. The challenging aspect relies in the capability of merging these two platforms in a way where the limitations of each is minimized as much as possible. A hybrid technique which combines both the Particle Filter (PF) method and the Time Difference of Arrival Technique (TDOA) is presented. Simulation results indicate that the proposed approach outperforms other techniques in terms of accuracy and robustness.
Noise Tolerant Localization for Sensor Networks
IEEE/ACM Transactions on Networking, 2018
Most range-based localization approaches for wireless sensor networks (WSNs) rely on accurate and sufficient range measurements, yet noise and data missing are inevitable in distance ranging. Existing localization approaches often suffer from unsatisfied accuracy in the coexistence of incomplete and corrupted range measurements. In this paper, we propose LoMaC, a noise-tolerant localization scheme, to address this problem. Specifically, we first employ Frobenius-norm and L1norm to formulate the reconstruction of noisy and missing Euclidean Distance Matrix (EDM) as a Norm-Regularized Matrix Completion (NRMC) problem. Secondly, we design an efficient algorithm based on Alternating Direction Method of Multiplier (ADMM) to solve the NRMC problem. Thirdly, based on the completed EDM, we further employ a Multi-Dimension Scaling (MDS) method to localize unknown nodes. Meanwhile, to accelerate our algorithm, we also adopt some acceleration techniques to reduce the computation cost. Finally, extensive experimental results show that, our algorithm not only achieves significantly better localization performance than prior algorithms, but also provides an accurate position prediction of outlier, which are useful for malfunction diagnosis in WSNs. Index Terms-Wireless sensor networks; localization. I. INTRODUCTION L OCALIZATION is a core problem in Wireless Sensor Networks (WSNs) as the location information is critical for many WSN applications, such as environment monitoring, geographical routing, and data gathering [1]. In a WSN, typically only a few nodes are equipped with GPS devices, and they are called anchor nodes. The purpose of a localization algorithm is to determine the positions of all unknown nodes based on anchor nodes positions and inter-node measurements. Existing localization algorithms in WSNs can be classified into Manuscript