Accurate Wireless Sensor Localization Technique Based on Hybrid PSO-ANN Algorithm for Indoor and Outdoor Track Cycling

Gharghan, Sadik Kamel; Nordin, Rosdiadee; Ismail, Mahamod; Ali, Jamal Abd

doi:10.1109/jsen.2015.2483745

Cited by 156 publications

(86 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Major cost of the system is only on the antenna cost for beacon. Simulation results in [18] shows that the error in position estimation is as low as 5%. Stability is a problem of concern in this method for future work has been suggested by the authors.…”

Section: Related Workmentioning

confidence: 98%

“…Authors have investigated various positioning algorithms and discussed their theoretical limitations. Authors in [18] investigated determination of distance between a mobile and an anchor node by using two approaches. First method was based on channel propagation known as LNSM and the second method was based on PSO-ANN (particle swarm optimization-artificial neural networks).…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Wireless Sensor Node Localization based on LNSM and Hybrid TLBO- Unilateral technique for Outdoor Location

Kaundal

Sharma

Prateek

2017

International Journal of Electronics and Telecommunications

View full text Add to dashboard Cite

Abstract-The paper aims at localization of the anchor node (fixed node) by pursuit nodes (movable node) in outdoor location. Two methods are studied for node localization. The first method is based on LNSM (Log Normal Shadowing Model) technique to localize the anchor node and the second method is based on Hybrid TLBO (Teacher Learning Based Optimization Algorithm)-Unilateral technique. In the first approach the ZigBee protocol has been used to localize the node, which uses RSSI (Received Signal Strength Indicator) values in dBm. LNSM technique is implemented in the self-designed hardware node and localization is studied for Outdoor location. The statistical analysis using RMSE (root mean square error) for outdoor location is done and distance error found to be 35 mtrs. The same outdoor location has been used and statistical analysis is done for localization of nodes using Hybrid TLBO-Unilateral technique. The Hybrid-TLBO Unilateral technique significantly localizes anchor node with distance error of 0.7 mtrs. The RSSI values obtained are normally distributed and standard deviation in RSSI value is observed as 1.01 for outdoor location. The node becomes 100% discoverable after using hybrid TLBO-Unilateral technique.

show abstract

Section: Related Workmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Wireless Sensor Node Localization based on LNSM and Hybrid TLBO- Unilateral technique for Outdoor Location

Kaundal

Sharma

Prateek

2017

International Journal of Electronics and Telecommunications

View full text Add to dashboard Cite

show abstract

“…which however have low range (few meters) [9] and are not capable to establish wide area network. Though RSSI values and distance estimation through ZigBee is very noisy but can be solved with modeling the wireless channel established in between nodes like log normal shadowing model [10]. The wireless network for post disaster communication link established using two algorithms, Location Fingerprinting [11] and Unilateral [12].…”

Section: Introductionmentioning

confidence: 99%

Design, development and deployment of a RSSI based wireless network for post disaster management

Kaundal¹,

Sharma²,

Prateek³

et al. 2018

IJET

View full text Add to dashboard Cite

The wide spectrum of applications of wireless sensor networks in real time monitoring of disaster prone areas makes it very promising and reliable. The present work focuses on communication link establishment for the first 72 hours just after disaster, highlighting the capability of wireless sensor network especially in disaster prone area. The complete network consisting of 8 wireless nodes with integrated Xbee as a sensor to establish a communication link in between the pursuit team and trapped people. The network is deployed in Dunga Valley of Dehradun, Uttarakhand, a seismic zone with a population of more than 1 lakh. The nodes are capable of recognizing the traveler's location whenever they are passing by the disaster prone areas. The nodes gather RSSI values along with the estimated distances of the traveler (having anchor node) from the pursuit team (having pursuit node). The system has proven its validity by tracking the trapped people in communication deprived area in Dunga Valley by doing mock drill several times. The paper reports the design of wireless network as well as deployment aspects of the nodes in such disaster prone areas.

show abstract

“…Recently, PSO has been used in many issues of WSNs [17,18]. This paper focuses on the PSO-based localization algorithms for static WSNs, where all sensors are static after deployment.…”

Section: Introductionmentioning

confidence: 99%

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

Cui

Shu

Song

et al. 2017

Sensors

View full text Add to dashboard Cite

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

show abstract

Accurate Wireless Sensor Localization Technique Based on Hybrid PSO-ANN Algorithm for Indoor and Outdoor Track Cycling

Cited by 156 publications

References 50 publications

Wireless Sensor Node Localization based on LNSM and Hybrid TLBO- Unilateral technique for Outdoor Location

Wireless Sensor Node Localization based on LNSM and Hybrid TLBO- Unilateral technique for Outdoor Location

Design, development and deployment of a RSSI based wireless network for post disaster management

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

Contact Info

Product

Resources

About