Tracking Irregularly Moving Objects based on Alert-enabling Sensor Model in Sensor Networks

2005 ◽  
Author(s):  
Chao-Chun Chen ◽  
Yu-Chi Chung
Keyword(s):  
Sensor Networks ◽  
Moving Objects ◽  
Sensor Model

Multi-Sensor Fusion in Dynamic Environment using Evidential Grid Mapping

2020 ◽  
Vol 2020 (16) ◽  
pp. 255-1-255-7
Author(s):  
G. M. Dilshan P. Godaliyadda ◽  
Vijay Pothukuchi ◽  
JuneChul Roh
Keyword(s):  
Autonomous Navigation ◽  
Moving Objects ◽  
Dynamic Environment ◽  
Lidar Measurement ◽  
Seamless Integration ◽  
Dempster Shafer Theory ◽  
Sensor Model ◽  
Grid Mapping ◽  
Stationary Objects ◽  
Shafer Theory

Grid mapping is widely used to represent the environment surrounding a car or a robot for autonomous navigation. This paper describes an algorithm for evidential occupancy grid (OG) mapping that fuses measurements from different sensors, based on the Dempster-Shafer theory, and is intended for scenes with stationary and moving (dynamic) objects. Conventional OGmapping algorithms tend to struggle in the presence of moving objects because they do not explicitly distinguish between moving and stationary objects. In contrast, evidential OG mapping allows for dynamic and ambiguous states (e.g. a LIDAR measurement: cannot differentiate between moving and stationary objects) that are more aligned with measurements made by sensors. In this paper, we present a framework for fusing measurements as they are received from disparate sensors (e.g. radar, camera and LIDAR) using evidential grid mapping. With this approach, we can form a live map of the environment, and also alleviate the problem of having to synchronize sensors in time. We also designed a new inverse sensor model for radar that allows us to extract more information from object level measurements, by incorporating knowledge of the sensor’s characteristics. We have implemented our algorithm in the OpenVX framework to enable seamless integration into embedded platforms. Test results show compelling performance especially in the presence of moving objects.

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.

Initializing Sensor Networks of Non-uniform Density in the Weak Sensor Model

Algorithmica ◽  
2014 ◽  
Vol 73 (1) ◽  
pp. 87-114 ◽  
Author(s):  
Martín Farach-Colton ◽  
Miguel A. Mosteiro
Keyword(s):  
Sensor Networks ◽  
Uniform Density ◽  
Sensor Model

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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