Cooperative localization and tracking in wireless sensor networks

Hehdly, Kaouther; Laaraiedh, Mohamed; Abdelkefi, Fatma; Siala, Mohamed

doi:10.1002/dac.3842

Cited by 7 publications

(10 citation statements)

References 26 publications

(48 reference statements)

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“…To solve the problem of the cooperative localization [12][13][14][15][16] of agents in a wireless environment, this paper uses the cooperative positioning strategy based on a factor graph to provide positioning for all agents. The proposed method uses the knowledge of factor graph theory and the sum product algorithm to construct a belief information [17][18] transfer model for all agents.…”

Section: Factor-graph-aided Three-dimensional Faster Cooperative Positioning Algorithmmentioning

confidence: 99%

“…This method suppresses the influence of reference nodes with large position errors, but ignores the influence of non-line-of-sight (NLOS) errors on the positioning results. A distributed particle filter algorithm for cooperative positioning and target tracking was proposed in [8]. This method is used to achieve target positioning and tracking in indoor environments.…”

Section: Introductionmentioning

confidence: 99%

“…However, the computational load of this method is too high to match the real-time demands of smart cities. In order to improve the positioning stability, a variety of Kalman filtering techniques are widely used in cooperative positioning [13][14][15][16]. For example, a new variational bayesian adaptive extended kalman filter is proposed in [13], distributed particle filteris proposed in [16].…”

Section: Introductionmentioning

confidence: 99%

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Factor-Graph-Aided Three-Dimensional Faster Cooperative Positioning Algorithm

et al. 2021

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With the development of smart cities and 5G applications, there is an increasingly urgent need for cooperative positioning among all kinds of intelligent terminals. Existing cooperative positioning technology is primarily designed for two-dimensional positions, and the computing speed and positioning accuracy cannot meet the needs of smart cities. To solve these problems, this paper proposes a factor-graphaided three-dimensional faster cooperative positioning algorithm (FG-3DCP) that combines a factor graph and sum product theory to establish a cooperative localization model with many nodes. To reduce computational complexity and describe fast positioning, the parameter independence of the factor graph is used, and the positioning data of each node coordinate axis are calculated independently. Then, the positioning result is obtained by fusion, and the computing speed is markedly improved. The proposed algorithm was simulated and analyzed in terms of ranging error, position ambiguity, network topology and the number of nodes. When the proposed algorithm was compared to the existing non-Bayesian estimation cooperative position methods, the position performance improved more than 20%, and the convergence rate was the fastest in the 3D environment.

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Section: Factor-graph-aided Three-dimensional Faster Cooperative Positioning Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Factor-Graph-Aided Three-Dimensional Faster Cooperative Positioning Algorithm

et al. 2021

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“…Under such condition, a small bias of the estimated angle may cause incorrect location detection. Therefore, transmitter localization with high accuracy can be an important task in specific applications such as radar, wireless communications, wireless sensor networks, and military purposes . Generally, such a process may involve joint estimation of frequencies, phase shifts, DOA, and time difference of arrival (TOA) .…”

Section: Introductionmentioning

confidence: 99%

A probabilistic‐based approach for direction‐of‐arrival estimation and localization of multiple sources

Abdelbari¹,

Bilgehan²

2020

Int J Communication

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Summary This article contributes to science at two points. The first contribution is at a point of introducing a novel direction‐of‐arrival (DOA) estimation method which based on subspaces methods called Probabilistic Estimation of Several Signals (PRESS). The PRESS method provides higher resolution and DOA accuracy than current models. Second contribution of the article is at a point of localizing the unknown signal source. The process of localization achieved by using DOA information for the first time. The importance of localization exists in a large area of engineering applications. The aim is to determine the location of multiple sources by using PRESS with minimum effort of computation. We used the maximum probabilistic process in this study. Initially, all the signals are collected by the array of sensors and accurately identified using the proposed algorithm. The receiver at the best in test estimates the source location using only the knowledge of the geographical latitude and longitude values of the array of sensors. Several test points with an accurately calculated angle of arrival enable us to draw linear lines towards the transmitter. The transmitter location can be accurately identified with the line of interceptions. Simulation and numerical results show the outstanding performance of both the DOA estimation method and transmitter localization approach compared with many classical and new DOA estimation methods. The PRESS localization method first tested at 19°, 26°, and 35° with an signal‐to‐noise ratio (SNR) value of ‐5 dB. The PRESS method produced results with an extremely low bias of 0 and 0.00080°. The simulation tests are repeated and produced results with zero bias, which give the exact location of the unknown source.

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“…Wireless sensor networks (WSNs) are widely applied in different systems, [1][2][3] for example, smart grid, 4 cooperative localization, 5 environmental monitoring, 6 and power system. 7 In the WSNs, sensor nodes gather, 8,9 transmit, 10,11 and fuse data.…”

Section: Introductionmentioning

confidence: 99%

An attack-defense game based reliability analysis approach for wireless sensor networks

Jiang

Deng

2019

International Journal of Distributed Sensor Networks

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Wireless sensor networks play an important role in daily life and have been applied in diverse fields. In practice, malware tries to infect sensor nodes, while wireless sensor network prevents from attacking. This article establishes a new attack-defense game based on Stackelberg game. In this game, malware selects its strategy of attacking first, and then wireless sensor network makes decision after observing the action of malware. This game considers the situation in which malware is more dominant than wireless sensor network. Then this game is solved and an equilibrium solution is calculated, which is the best strategy for malware and wireless sensor network to maximize their payoffs. With infection probability and defense probability, mean time to failure of a sensor node is computed and is used to analyze the reliability of wireless sensor network. The experiments show the influence of true-positive rate and false-positive rate on the equilibrium solution of game and the reliability of wireless sensor network. Finally, the proposed method is compared with the existing method based on a simultaneous game.

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Cooperative localization and tracking in wireless sensor networks

Cited by 7 publications

References 26 publications

Factor-Graph-Aided Three-Dimensional Faster Cooperative Positioning Algorithm

Factor-Graph-Aided Three-Dimensional Faster Cooperative Positioning Algorithm

A probabilistic‐based approach for direction‐of‐arrival estimation and localization of multiple sources

An attack-defense game based reliability analysis approach for wireless sensor networks

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