Adaptive SSO based node selection for partial charging in wireless sensor network

2022 ◽  
Author(s):  
Devarapalli Prasannababu ◽  
Tarachand Amgoth
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Node Selection ◽  
Selection For

scholarly journals Efficient and Adaptive Node Selection for Target Tracking in Wireless Sensor Network

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Juan Feng ◽  
Hongwei Zhao ◽  
Baowang Lian
Keyword(s):  
Wireless Sensor Network ◽  
Target Tracking ◽  
Sensor Network ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Selection Strategy ◽  
Tracking Accuracy ◽  
Node Selection ◽  
Information Utility ◽  
Distance Weighted

In target tracking wireless sensor network, choosing the proper working nodes can not only minimize the number of active nodes, but also satisfy the tracking reliability requirement. However, most existing works focus on selecting sensor nodes which are the nearest to the target for tracking missions and they did not consider the correlation of the location of the sensor nodes so that these approaches can not meet all the goals of the network. This work proposes an efficient and adaptive node selection approach for tracking a target in a distributed wireless sensor network. The proposed approach combines the distance-based node selection strategy and particle filter prediction considering the spatial correlation of the different sensing nodes. Moreover, a joint distance weighted measurement is proposed to estimate the information utility of sensing nodes. Experimental results show that EANS outperformed the state-of-the-art approaches by reducing the energy cost and computational complexity as well as guaranteeing the tracking accuracy.

scholarly journals IMM Filter Based Human Tracking Using a Distributed Wireless Sensor Network

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Sen Zhang ◽  
Wendong Xiao ◽  
Jun Gong
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Sensor Node ◽  
Computing System ◽  
Human Motion ◽  
Wireless Sensor ◽  
Human Tracking ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Node Selection

This paper proposes a human tracking approach in a distributed wireless sensor network. Most of the efforts on human tracking focus on vision techniques. However, most vision-based approaches to moving object detection involve intensive real-time computations. In this paper, we present an algorithm for human tracking using low-cost range wireless sensor nodes which can contribute lower computational burden based on a distributed computing system, while the centralized computing system often makes some information from sensors delay. Because the human target often moves with high maneuvering, the proposed algorithm applies the interacting multiple model (IMM) filter techniques and a novel sensor node selection scheme developed considering both the tracking accuracy and the energy cost which is based on the tacking results of IMM filter at each time step. This paper also proposed a novel sensor management scheme which can manage the sensor node effectively during the sensor node selection and the tracking process. Simulations results show that the proposed approach can achieve superior tracking accuracy compared to the most recent human motion tracking scheme.

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