Regularized Least Square Multi-Hops Localization Algorithm for Wireless Sensor Networks

Liouane, Hend; Messous, Salma; Cheikhrouhou, Omar; Baz, Mohammed; Hamam, Habib

doi:10.1109/access.2021.3116767

Cited by 11 publications

(16 citation statements)

References 39 publications

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“…Through the inter-node hop count constraint mechanism and anchor node pair reliability parameter in Section III.A, the to-be-localized nodes can be given more accurate anchor nodes selected for localization. Meanwhile, to further improve the accuracy of inter-node distance estimation, regularized least squares [34,35] is used to compute the distance between the node pairs to be localized and the anchor nodes based on equal constraints and generalization performance.…”

Section: B Inter-node Distance Estimation Based On Regularized Least ...mentioning

confidence: 99%

Node Localization Algorithm for Irregular Regions Based on Particle Swarm Optimization Algorithm and Reliable Anchor Node Pairs

Li,

Liu,

Zou

et al. 2024

IEEE Access

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In wireless sensor networks (WSN), node localization is a key function, and only by knowing the coordinate positions of the nodes can correct decisions be made. In certain applications, such as smart cities, environmental monitoring, or industrial automation, irregular areas can be complex and affected by environmental complexity and inhomogeneity. Coverage gaps may exist between the nodes to be located and the anchor nodes, and communication paths between the nodes may deviate significantly from the ideal straight line, resulting in a large error between the final positioning result of the algorithm and the actual position. In order to solve this problem, this paper proposes a non-ranging node localization method (RANP-PSO) for irregular regions based on PSO algorithm and anchor node pair selection. The way firstly selects anchor node pairs with higher reliability parameters for the nodes to be located by introducing the hop count constraint mechanism for distance estimation; then uses the regularized least squares method for further constraints on the estimated distances; Finally, the PSO algorithm is utilized to optimize the coordinates of the target node, so as to solve to obtain the position of the node. When the proportion of anchor nodes is 20%, the communication radius of nodes is 30m, and the distribution density of nodes is 0.008, the proposed algorithm reduces the root mean square error by approximately 11.94% compared to AEML and LRAQS algorithms, 7.26% compared to the BDMCL algorithm, and 0.69% compared to the MSVR-DV-Hop algorithm. This demonstrates the advantage of the proposed algorithm in terms of localisation accuracy.

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Section: B Inter-node Distance Estimation Based On Regularized Least ...mentioning

confidence: 99%

Node Localization Algorithm for Irregular Regions Based on Particle Swarm Optimization Algorithm and Reliable Anchor Node Pairs

Li,

Liu,

Zou

et al. 2024

IEEE Access

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“…Based on the node movements, RESA-MCL enhances the performance of the localization of sensor nodes. In [21], an extension of the distance vector-hop algorithm (DV-Hop) localization method is introduced as a regularized least square DV-Hop localization algorithm to enhance node localization accuracy based on statistical filtering optimization and the least square localization method. In [22], an anchor-assisted localization method is adopted to detect precise positions of mobile sensor nodes based on the transmission power control mechanism.…”

Section: Introductionmentioning

confidence: 99%

High uncertainty aware localization and error optimization of mobile nodes for wireless sensor networks

Nandyala¹,

Gandhi²

2023

IJ-AI

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<p>The localization of mobile sensor nodes in a wireless sensor network (WSN) is a key research area for the speedy development of wireless communication and microelectronics. The localization of mobile sensor nodes massively depends upon the received signal strength (RSS). Recently, the least squared relative error (LSRE) measurements are optimized using traditional semidefinite programming (SDP) and the location of the mobile sensor nodes was determined using the previous localization methods like least squared relative error and semidefinite programming (LSRE-SDP), and approximate nonlinear least squares and semidefinite programming (ANLS-SDP). Therefore, in this work, a novel high uncertainty aware-localization error correction and optimization (HUA-LECO) model is employed to minimize the aforementioned problems regarding the localization of mobile sensor nodes and enhance the performance efficiency of root mean square error (RMSE) results. Here, the position of target mobile sensor nodes is evaluated based on the gathered measurements while discarding faulty data. Here, an iterative weight updation approach is utilized to perform localization based on Monte Carlo simulations. Simulation results show significant improvement in terms of RMSE results in comparison with traditional LSRE-SDP and ANLS-SDP methods under high uncertainty.</p>

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“…But this algorithm is highly susceptible to environmental factors in practical scenarios. Reference [17] presents an improved regularized least-squares DV-HOP algorithm by combining double least squares with a statistical filtering optimization strategy, which effectively reduces the errors generated in the algorithm localization process. Reference [18] proposed a new range-free iterative localization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Incremental Strategy-based Residual Regression Networks for Node Localization in Wireless Sensor Networks

2022

KSII TIIS

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The easy scalability and low cost of range-free localization algorithms have led to their wide attention and application in node localization of wireless sensor networks. However, the existing range-free localization algorithms still have problems, such as large cumulative errors and poor localization performance. To solve these problems, an incremental strategy-based residual regression network is proposed for node localization in wireless sensor networks. The algorithm predicts the coordinates of the nodes to be solved by building a deep learning model and fine-tunes the prediction results by regression based on the intersection of the communication range between the predicted and real coordinates and the loss function, which improves the localization performance of the algorithm. Moreover, a correction scheme is proposed to correct the augmented data in the incremental strategy, which reduces the cumulative error generated during the algorithm localization. The analysis through simulation experiments demonstrates that our proposed algorithm has strong robustness and has obvious advantages in localization performance compared with other algorithms.

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Regularized Least Square Multi-Hops Localization Algorithm for Wireless Sensor Networks

Cited by 11 publications

References 39 publications

Node Localization Algorithm for Irregular Regions Based on Particle Swarm Optimization Algorithm and Reliable Anchor Node Pairs

Node Localization Algorithm for Irregular Regions Based on Particle Swarm Optimization Algorithm and Reliable Anchor Node Pairs

High uncertainty aware localization and error optimization of mobile nodes for wireless sensor networks

Incremental Strategy-based Residual Regression Networks for Node Localization in Wireless Sensor Networks

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