ArticleSensors (Basel, Switzerland)2023
Enhancing Localization Efficiency and Accuracy in Wireless Sensor Networks.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Integration of multi agent reinforcement learning with golden jackal optimization for predicting average localization error in wireless sensor networks.Scientific reports · 2025Article
- Wireless Sensor Network Deployment: Architecture, Objectives, and Methodologies.Sensors (Basel, Switzerland) · 2025Review
- Energy efficient and robust node localization in WSNs using LSTM optimized DV hop framework to mitigate multihop localization errors.Scientific reports · 2025Article
- Node Localization Method in Wireless Sensor Networks Using Combined Crow Search and the Weighted Centroid Method.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
Accuracy is the vital indicator in location estimation used in many scenarios, such as warehousing, tracking, monitoring, security surveillance, etc., in a wireless sensor network (WSN). The conventional range-free DV-Hop algorithm uses hop distance to estimate sensor node positions but has limitations in terms of accuracy. To address the issues of low accuracy and high energy consumption of DV-Hop-based localization in static WSNs, this paper proposes an enhanced DV-Hop algorithm for efficient and accurate localization with reduced energy consumption. The proposed method consists of three steps: first, the single-hop distance is corrected using the RSSI value for a specific radius; second, the average hop distance between unknown nodes and anchors is modified based on the difference between actual and estimated distances; and finally, the least-squares approach is used to estimate the location of each unknown node. The proposed algorithm, named Hop-correction and energy-efficient DV-Hop (HCEDV-Hop), is executed and evaluated in MATLAB to compare its performance with benchmark schemes. The results show that HCEDV-Hop improves localization accuracy by an average of 81.36%, 77.99%, 39.72%, and 9.96% compared to basic DV-Hop, WCL, improved DV-maxHop, and improved DV-Hop, respectively. In terms of message communication, the proposed algorithm reduces energy usage by 28% compared to DV-Hop and 17% compared to WCL.
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Registered trials
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