Mobile user localization in wireless sensor network using grey prediction method

Luo, Ren C.; Chen, Ogst; Pan, Shengyi

doi:10.1109/iecon.2005.1569330

Cited by 26 publications

(26 citation statements)

References 5 publications

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“…The mean position error achieved in RADAR [1] was 2.28 m. Paper [14] have achieved position error of 1.81 m. Paper [22] also used RSSI values and have achieved mean position error of 1.65 m. In this method, only RSSI is used to train neural networks. Since the RSSI suffers from reflections, refractions and scatting effects, location estimation based only on RSSI is expected to give less accurate results.…”

Section: Discussion Of the Resultsmentioning

confidence: 95%

“…In the second part, we will describe the experimental setup and precision error obtained using approaches [1,14,22] and [20].…”

Section: Resultsmentioning

confidence: 99%

“…Paper [14] determines the location of the mobile node by measuring the RSSI values at wireless devices. Grey prediction method [13] is used to predict the tendency of the RSSI values when a mobile node is moving.…”

Section: Indoor Localization Techniquesmentioning

confidence: 99%

“…When a new node comes in the experimental area, it communicates with the three reference nodes. The [14].…”

Section: Mobile User (Dtn Algorithm) [14]mentioning

confidence: 99%

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Neural-based approach for localization of sensors in indoor environment

et al. 2009

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Location of wireless sensor nodes is an important piece of information for many applications. There are many algorithms present in literature based on Received Signal Strength (RSSI) to estimate the location. However the radio signal propagation is easily influenced by diffraction, reflection and scattering. Therefore algorithms purely based on RSSI may not accurately predict the position of the node.In the present work, an algorithm for estimating the position of mobile nodes is proposed which is based on a combination of Received Signal Strength (RSSI) and Link Quality Indicator (LQI). Artificial Neural Networks are used to establish the relationship between the location of the mobile node and the experimentally obtained values of RSSI and LQI. Two different algorithms namely, Bayesian Regularization and Gradient Descent are used to develop the neural network model. Proposed algorithms improve the localization accuracy and perform better than other state-of-the-art algorithms.

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Section: Discussion Of the Resultsmentioning

confidence: 95%

“…In the second part, we will describe the experimental setup and precision error obtained using approaches [1,14,22] and [20].…”

Section: Resultsmentioning

confidence: 99%

Section: Indoor Localization Techniquesmentioning

confidence: 99%

“…When a new node comes in the experimental area, it communicates with the three reference nodes. The [14].…”

Section: Mobile User (Dtn Algorithm) [14]mentioning

confidence: 99%

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Neural-based approach for localization of sensors in indoor environment

et al. 2009

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“…We utilize the GM, which has been extensively used in handover, positioning and general forecasting algorithms [39]- [43], as prediction model. 1) GM Approach: In grey system theory, GM (n,m) denotes a grey model, wheren is the order of the differential equation andm is the number of variables.…”

Section: B Outage Detection Phasementioning

confidence: 99%

A Cell Outage Management Framework for Dense Heterogeneous Networks

Onireti

Zoha

Moysen

et al. 2016

IEEE Trans. Veh. Technol.

106

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Abstract-In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks (HetNets) with split control and data planes -a candidate architecture for meeting future capacity, quality of service and energy efficiency demands. In such architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, while the data BSs handle UE data. An implication of this split architecture is that, an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both the data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large scale minimize drive testing (MDT) reports data, and detects outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly detecting algorithms, i.e. k− nearest neighbor and local outlier factor based anomaly detector, within the control COD. On the other hand, for data cells COD, we propose a heuristic grey-prediction based approach, which can work with the small number of UEs in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity, by receiving a periodic update of the received signal reference power (RSRP) statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of residual error that is inherent to grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm which can be applied to both planes. Our COC solution utilizes an actor critic (AC) based reinforcement learning (RL) algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage, and also compensate for the detected outage in a reliable manner.

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Localization in mobile wireless sensor networks using drones

Kaushik

Lobiyal

2021

Trans Emerging Tel Tech

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Localization is one of the most important aspects of wireless sensor networks.Most of the localization algorithms proposed in the literature, work for static networks. However, the performance of these protocols in the mobile environment still needs to be investigated. Localization in a mobile environment may pose challenges of accuracy and computational complexity due to mobility of the nodes. In this article, we have proposed an algorithm for localization in mobile wireless sensor networks using drones (LMWSND). A drone-based algorithm is proposed to simulate the moving trajectory of the drones. The proposed drone-based algorithm uses the technique of received signal strength between the drone and unknown sensor nodes to find the distance between them. The distance thus obtained is used for localization of the unknown nodes. A new method is used to solve the system of distance equations to restrict the propagation of error. Furthermore, the mathematical analysis of the propagation error in LMWSND proves the superiority of our proposed algorithm in comparison to other algorithms. The results obtained through simulations further strengthen our conclusion that the proposed algorithm reduces the propagation of error.We have also proposed another algorithm to choose the nearby beacon points for better utilization of the network resources and to reduce the computational efforts.

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Mobile user localization in wireless sensor network using grey prediction method

Cited by 26 publications

References 5 publications

Neural-based approach for localization of sensors in indoor environment

Neural-based approach for localization of sensors in indoor environment

A Cell Outage Management Framework for Dense Heterogeneous Networks

Localization in mobile wireless sensor networks using drones

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