Optical Fibre Distributed Sensors And Sensor Networks

Author(s):  
D. E. N. Davies
1990 ◽  
Vol 1 (9) ◽  
pp. 908-916 ◽  
Author(s):  
P Ferdinand ◽  
Y Denayrolles ◽  
C Mersier ◽  
J Plantey ◽  
N Recrosio ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2830
Author(s):  
Sili Wang ◽  
Mark P. Panning ◽  
Steven D. Vance ◽  
Wenzhan Song

Locating underground microseismic events is important for monitoring subsurface activity and understanding the planetary subsurface evolution. Due to bandwidth limitations, especially in applications involving planetarily-distributed sensor networks, networks should be designed to perform the localization algorithm in-situ, so that only the source location information needs to be sent out, not the raw data. In this paper, we propose a decentralized Gaussian beam time-reverse imaging (GB-TRI) algorithm that can be incorporated to the distributed sensors to detect and locate underground microseismic events with reduced usage of computational resources and communication bandwidth of the network. After the in-situ distributed computation, the final real-time location result is generated and delivered. We used a real-time simulation platform to test the performance of the system. We also evaluated the stability and accuracy of our proposed GB-TRI localization algorithm using extensive experiments and tests.


2005 ◽  
Author(s):  
Sylvie Delepine-Lesoille ◽  
Erick Merliot ◽  
Marie Delaveau ◽  
Alain Courteville ◽  
Lionel Quetel

Author(s):  
Torsten Licht ◽  
Abhijit Deshmukh

As sensor hardware becomes more sophisticated, smaller in size and increasingly affordable, use of large scale sensor networks is bound to become a reality in several application domains, such as vehicle condition monitoring, environmental sensing and security assessment. The ability to incorporate communication and decision capabilities in individual or groups of sensors, opens new opportunities for distributed sensor networks to monitor complex engineering systems. In such large scale sensor networks, the ability to integrate observations or inferences made by distributed sensors into a single hypothesis about the state of the system is critical. This paper addresses the sensor integration issue in hierarchically organized sensor networks. We propose a multi-agent architecture for distributed sensor networks. We present a new formalism to represent causal relations and prior beliefs of hierarchies of sensors, called Hierarchically Organized Bayesian Networks (HOBN), which is a semantic extension of Multiply Sectioned Bayesian Networks (MSBN). This formalism allows a sensor to reason about the integrity of a sensed signal or the integrity of neighboring sensors. Furthermore, we can also evaluate the consistency of local observations with respect to the knowledge of the system gathered up to that point.


A Wireless Sensor Network may often consist of hundreds of distributed sensors. Our goal is to formulate wireless sensor networks (WSNs) fault identification problem in terms of pattern classification and to introduce a newly developed algorithm, neighbor node hidden conditional algorithm (NHCA) to determine the unknown path through which packets are transmitted from source to destination. We propose a concept of fault recovery in WSN using clustering. This includes the protocols of Dynamic Delegation based Efficient Broadcast and Neighborhood Hidden Conditional Random Field Algorithm. The history of transmission is classified according to the pattern, sorted and ranked along with the cluster information. The data privacy is maintained with in the cluster during the packet transmission apart from the destination which may present outside the cluster. The leader of the cluster is restricted only to view the transmission path in order to maintain the confidentiality of the data transmitted. Our simulation results strongly enforce fault recovery in quick time and also maintain the confidentiality of data.


2021 ◽  
Vol 17 (7) ◽  
pp. 155014772110363
Author(s):  
Lizhen Cui ◽  
Jian Cao ◽  
Zhulin An ◽  
Yong Yang ◽  
Qianqian Guo

Time synchronicity works as a popular requirement in wireless sensor networks. Pulse-coupled oscillators similar to firefly flashing and synchronization via discrete pulse coupling are widely used in wireless sensor networks. In this article, we have studied the time synchronization with communication delay in the nearest neighbor network of distributed sensors, based on the pulse-coupled oscillators model of synchronicity achieved by biological systems. First, we present a linear pulse-coupled oscillators model with coupling delay and the model is used to analyze the wireless sensor networks synchronization with communication delay. Second, we mathematically analyze the firing behaviors in the linear pulse-coupled oscillators network using the delayed excitatory coupling and track the synchronization process of the two and multi-oscillators and obtain the synchronization conditions from the regression mapping. Finally, through the proposed model implementation in the wireless sensor networks simulation framework, we demonstrate that the multi-oscillators system can be synchronized from a random starting stage distribution under linear phase responding functions and the nearest neighbor communication. The results show that our approach can achieve clock synchronization in wireless sensor networks with delayed nearest neighbor communication.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hongbo Zhu ◽  
Jiabao Ding

Wireless sensor networks (WSNs) have been spawning many new applications where cooperative state estimation is essential. In this paper, the problem of performing cooperative state estimation for a discrete linear stochastic dynamical system over wireless sensor networks with a limitation on the sampling and communication rate is considered, where distributed sensors cooperatively sense a linear dynamical process and transmit observations each other via a common wireless channel. Firstly, a novel dynamic variance-based triggering scheme (DVTS) is designed to schedule the sampling of each sensor and the transmission of its local measurement. In contrast to the existing static variance-based triggering scheme (SVTS), the newly proposed DVTS can lead to the larger average intertrigger time interval and thus fewer total triggering number with almost approximate estimation accuracy. Second, a new Riccati equation of the prediction variance iteration for each estimator is obtained, which switches dynamically among the modes related to the variance of the previous step and the recently received measurements from other sensors. Furthermore, the stability issue is also mainly investigated. Finally, simulation results show the effectiveness and advantage of the proposed strategy.


1996 ◽  
Author(s):  
Bill.P. Petreski ◽  
Peter.M. Farrell ◽  
Greg.W. Baxter ◽  
Stephen.F. Collins

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