scholarly journals Distributed Continuous k Nearest Neighbors Search over Moving Objects on Wireless Sensor Networks

2013 ◽  
Vol 9 (12) ◽  
pp. 125145 ◽  
Author(s):  
Chuan-Ming Liu ◽  
Chuan-Chi Lai
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Moving Objects ◽  
Nearest Neighbors ◽  
Wireless Sensor ◽  
K Nearest Neighbors

scholarly journals REEP : a data-centric, reliable and energy-efficient routing protocol for wireless sensor networks

2021 ◽  
Author(s):  
Farhana Zabin
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Routing Protocols ◽  
Energy Efficient ◽  
Moving Objects ◽  
Wireless Sensor ◽  
Sensor Technology ◽  
Energy Awareness ◽  
Energy Efficient Routing

Recent advances in sensor technology and wireless communications have led to many new data dissemination routing protocols, especially designed for wireless sensor networks, where energy awareness is the most important consideration. The focus of this thesis is on the area of routing protocols for wireless sensor networks, especially for those applications where, the whole sensor field need to be taken under observation to detect available different types of moving objects. Besides the efficient use of limited energy, reliability is another important issue in sensor communication, where the network is susceptible to environmental factors. In this thesis, the design of a new energy-efficient data-centric routing protocol, named Reliable and Energy-Efficient Protocol (REEP), is proposed. We have used MATLAB 7.4 for our implementation. The performance of REEP has been compared with Directed Diffusion (DD) for the aforementioned sensor network application. Our simulations and experimental results show that REEP performs better than DD.

scholarly journals Indoor positioning system for wireless sensor networks based on two-stage fuzzy inference

2018 ◽  
Vol 14 (5) ◽  
pp. 155014771878064 ◽  
Author(s):  
Chia-Hsin Cheng ◽  
Yi Yan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fuzzy Inference ◽  
Indoor Positioning ◽  
Wireless Sensor ◽  
Target Node ◽  
Two Stage ◽  
K Nearest Neighbors ◽  
Positioning Systems ◽  
Indoor Space

Wireless indoor positioning systems are susceptible to environmental distortion and attenuation of the signal, which can affect positioning accuracy. In this article, we present a two-stage indoor positioning scheme using a fuzzy-based algorithm aimed at minimizing uncertainty in received signal strength indicator measures from reference nodes in wireless sensor networks. In the first stage, the indoor space is divided into several zones and a fuzzy-based indoor zone-positioning scheme is used to identify the zone in which the target node is located via zone splitting. In the second stage, adaptive trilateration is used to position the target node within the zone identified in the first stage. Simulation results demonstrate that the proposed two-stage fuzzy rectangular splitting outperforms non-fuzzy-based algorithms, including K-Nearest Neighbors–based localization, and traditional triangular splitting schemes. We also developed an expanded positioning scheme to facilitate the selection of a positioning map for large indoor spaces, thereby overcoming the limitations imposed by the size of the positioning area while maintaining high positioning resolution.

scholarly journals A Moving Object Detection Method Using Deep Learning-Based Wireless Sensor Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Linghua Zhao ◽  
Zhihua Huang
Keyword(s):  
Wireless Sensor Networks ◽  
Deep Learning ◽  
Sensor Networks ◽  
Object Detection ◽  
Moving Objects ◽  
Detection Method ◽  
Learning Algorithm ◽  
Wireless Sensor ◽  
Moving Target ◽  
Deep Learning Algorithm

Aiming at the problem of real-time detection and location of moving objects, the deep learning algorithm is used to detect moving objects in complex situations. In this paper, based on the deep learning algorithm of wireless sensor networks, a novel target motion detection method is proposed. This method uses the deep learning model to extract visual potential representation features through offline similarity function ranking learning and online model incremental update and uses the hierarchical clustering algorithm to achieve target detection and positioning; the low-precision histogram and high-precision histogram cascade the method which determines the correct position of the target and achieves the purpose of detecting the moving target. In order to verify the advantages and disadvantages of the deep learning algorithm compared with traditional moving object detection methods, a large number of comparative experiments are carried out, and the experimental results were analyzed qualitatively and quantitatively from a statistical perspective. The results show that, compared with the traditional methods, the deep learning algorithm based on the wireless sensor network proposed in this paper is more efficient. The detection and positioning method do not produce the error accumulation phenomenon and has significant advantages and robustness. The moving target can be accurately detected with a small computational cost.

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