Improved Chan Algorithm Based Optimum UWB Sensor Node Localization Using Hybrid Particle Swarm Optimization
The localization of Wireless sensor networks (WSNs) has been recognized as one of the most challenging problems to overcome. Thus, much work has been given to solving this difficult problem. In emergency services, navigational systems, civil/military surveillance etc., locating the signal source in...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9729866/ |
| _version_ | 1828831945825452032 |
|---|---|
| author | Yedida Venkata Lakshmi Parulpreet Singh Mohamed Abouhawwash Shubham Mahajan Amit Kant Pandit Abeer B. Ahmed |
| author_facet | Yedida Venkata Lakshmi Parulpreet Singh Mohamed Abouhawwash Shubham Mahajan Amit Kant Pandit Abeer B. Ahmed |
| author_sort | Yedida Venkata Lakshmi |
| collection | DOAJ |
| description | The localization of Wireless sensor networks (WSNs) has been recognized as one of the most challenging problems to overcome. Thus, much work has been given to solving this difficult problem. In emergency services, navigational systems, civil/military surveillance etc., locating the signal source in a WSN is essential. A novel approach for sensor node localization using range-based localization methodology has been proposed to overcome this issue. The problem is expressed in the form of a maximum probability distribution function. The use of an RSSI-based Time Difference of Arrival (TDOA) measurement model, along with the Chan algorithm, is used to find the coordinates of unknown nodes has been proposed. With the help of ultra-wideband, this research aims to develop new and precise localization algorithms for wireless sensor networks (WSNs). This work offers localization using two-hybrid localization algorithms, i.e., ELPSO (Ensemble learning particle swarm optimization) and PSO- BPNN (Back-propagation neural network optimized by particle swarm optimization). Further, the error optimization accuracy has been compared between those algorithms using simulations. The proposed techniques consistently offer a better localization accuracy than the conventional algorithms available in the literature. The new localization methods with optimal techniques reduce the error value to a minimal distance. The distance value of localization error is nearly2.7cms compared to other designs from the literature. It is noted as significantly less. |
| first_indexed | 2024-12-12T16:42:17Z |
| format | Article |
| id | doaj.art-a229e083b7334fb9bf60c1831fdf790d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-12T16:42:17Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a229e083b7334fb9bf60c1831fdf790d2022-12-22T00:18:32ZengIEEEIEEE Access2169-35362022-01-0110325463256510.1109/ACCESS.2022.31577199729866Improved Chan Algorithm Based Optimum UWB Sensor Node Localization Using Hybrid Particle Swarm OptimizationYedida Venkata Lakshmi0Parulpreet Singh1https://orcid.org/0000-0001-7226-6148Mohamed Abouhawwash2https://orcid.org/0000-0003-2846-4707Shubham Mahajan3https://orcid.org/0000-0003-0385-3933Amit Kant Pandit4https://orcid.org/0000-0003-4866-3746Abeer B. Ahmed5Department of Electrical and Electronics Engineering, Lovely Professional University, Phagwara, Punjab, IndiaDepartment of Electrical and Electronics Engineering, Lovely Professional University, Phagwara, Punjab, IndiaDepartment of Mathematics, Faculty of Science, Mansoura University, Mansoura, EgyptShool of Electronics and Communication, Shri Mata Vaishno Devi University, Katra, IndiaShool of Electronics and Communication, Shri Mata Vaishno Devi University, Katra, IndiaDepartment of Computers & Information Systems, Sadat Academy for Management Sciences, Cairo, EgyptThe localization of Wireless sensor networks (WSNs) has been recognized as one of the most challenging problems to overcome. Thus, much work has been given to solving this difficult problem. In emergency services, navigational systems, civil/military surveillance etc., locating the signal source in a WSN is essential. A novel approach for sensor node localization using range-based localization methodology has been proposed to overcome this issue. The problem is expressed in the form of a maximum probability distribution function. The use of an RSSI-based Time Difference of Arrival (TDOA) measurement model, along with the Chan algorithm, is used to find the coordinates of unknown nodes has been proposed. With the help of ultra-wideband, this research aims to develop new and precise localization algorithms for wireless sensor networks (WSNs). This work offers localization using two-hybrid localization algorithms, i.e., ELPSO (Ensemble learning particle swarm optimization) and PSO- BPNN (Back-propagation neural network optimized by particle swarm optimization). Further, the error optimization accuracy has been compared between those algorithms using simulations. The proposed techniques consistently offer a better localization accuracy than the conventional algorithms available in the literature. The new localization methods with optimal techniques reduce the error value to a minimal distance. The distance value of localization error is nearly2.7cms compared to other designs from the literature. It is noted as significantly less.https://ieeexplore.ieee.org/document/9729866/Chan algorithmKalman filterPSOrange based3D-node localization |
| spellingShingle | Yedida Venkata Lakshmi Parulpreet Singh Mohamed Abouhawwash Shubham Mahajan Amit Kant Pandit Abeer B. Ahmed Improved Chan Algorithm Based Optimum UWB Sensor Node Localization Using Hybrid Particle Swarm Optimization IEEE Access Chan algorithm Kalman filter PSO range based 3D-node localization |
| title | Improved Chan Algorithm Based Optimum UWB Sensor Node Localization Using Hybrid Particle Swarm Optimization |
| title_full | Improved Chan Algorithm Based Optimum UWB Sensor Node Localization Using Hybrid Particle Swarm Optimization |
| title_fullStr | Improved Chan Algorithm Based Optimum UWB Sensor Node Localization Using Hybrid Particle Swarm Optimization |
| title_full_unstemmed | Improved Chan Algorithm Based Optimum UWB Sensor Node Localization Using Hybrid Particle Swarm Optimization |
| title_short | Improved Chan Algorithm Based Optimum UWB Sensor Node Localization Using Hybrid Particle Swarm Optimization |
| title_sort | improved chan algorithm based optimum uwb sensor node localization using hybrid particle swarm optimization |
| topic | Chan algorithm Kalman filter PSO range based 3D-node localization |
| url | https://ieeexplore.ieee.org/document/9729866/ |
| work_keys_str_mv | AT yedidavenkatalakshmi improvedchanalgorithmbasedoptimumuwbsensornodelocalizationusinghybridparticleswarmoptimization AT parulpreetsingh improvedchanalgorithmbasedoptimumuwbsensornodelocalizationusinghybridparticleswarmoptimization AT mohamedabouhawwash improvedchanalgorithmbasedoptimumuwbsensornodelocalizationusinghybridparticleswarmoptimization AT shubhammahajan improvedchanalgorithmbasedoptimumuwbsensornodelocalizationusinghybridparticleswarmoptimization AT amitkantpandit improvedchanalgorithmbasedoptimumuwbsensornodelocalizationusinghybridparticleswarmoptimization AT abeerbahmed improvedchanalgorithmbasedoptimumuwbsensornodelocalizationusinghybridparticleswarmoptimization |