ArticleSensors (Basel, Switzerland)2021
A Novel Location Source Optimization Algorithm for Low Anchor Node Density Wireless Sensor Networks.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Optimization of range based self-localization problem in wireless sensor networks using improved cuckoo search algorithm.Scientific reports · 2025Article
- A Quantum Annealing Bat Algorithm for Node Localization in Wireless Sensor Networks.Sensors (Basel, Switzerland) · 2023Article
- Wireless Sensor Network-Based Rigid Body Localization for NLOS Parameter Estimation.Sensors (Basel, Switzerland) · 2022Article
- An Improved Trilateration Positioning Algorithm with Anchor Node Combination and K-Means Clustering.Sensors (Basel, Switzerland) · 2022Article
- Indoor Positioning and Navigation.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Location information is one of the basic elements of the Internet of Things (IoT), which is also an important research direction in the application of wireless sensor networks (WSNs). Aiming at addressing the TOA positioning problem in the low anchor node density deployment environment, the traditional cooperative localization method will reduce the positioning accuracy due to excessive redundant information. In this regard, this paper proposes a location source optimization algorithm based on fuzzy comprehensive evaluation. First, each node calculates its own time-position distribute conditional posterior Cramer-Rao lower bound (DCPCRLB) and transfers it to neighbor nodes. Then collect the DCPCRLB, distance measurement, azimuth angle and other information from neighboring nodes to form a fuzzy evaluation factor set and determine the final preferred location source after fuzzy change. The simulation results show that the method proposed in this paper has better positioning accuracy about 33.9% with the compared method in low anchor node density scenarios when the computational complexity is comparable.
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Registered trials
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