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Journal articles on the topic 'Distributed Sensor Network'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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1

Bao, Xi Rong, Yue Huang, and Shi Zhang. "A Distributed Motion Algorithm for Mobile Sensor in Hybrid Wireless Sensor Networks." Applied Mechanics and Materials 719-720 (January 2015): 812–17. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.812.

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Constructing a hybrid wireless sensor networks comprising a mix of static sensors and mobile sensors can achieve a balance between improving coverage and reducing the cost of the network. In order to achieve high network coverage, mobile sensor move from a small to a big size of coverage hole in the hybrid wireless sensor networks. Due to the energy of the mobile sensor is limited, how to reduce the moving distance of the mobile sensor and reduce the energy consumption in the process of moving is a very important issue. This paper proposes a distributed minimum cost matching algorithm (DMMA) to redeploy mobile sensor, which can make the level of network coverage to meet the requirement of the environment, while effectively reducing the number of sensors. In our method, static sensors detect coverage hole by Voronoi diagrams, coverage holing sensors and mobile sensors by using DMMA to excellently heal the large coverage holes. Simulation results show that our method can effectively improve the coverage rate of the WSNs, while save the energy of mobile sensors.
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Chaczko, Zenon, Christopher Chiu, Shahrzad Aslanzadeh, and Toby Dune. "Sensor-Actor Network Solution for Scalable Ad-hoc Sensor Networks." International Journal of Electronics and Telecommunications 58, no. 1 (March 1, 2012): 55–62. http://dx.doi.org/10.2478/v10177-012-0008-4.

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Sensor-Actor Network Solution for Scalable Ad-hoc Sensor NetworksArchitects of ad-hoc wireless Sensor-Actor Networks (SANETS) face various problems and challenges. The main limitations relate to aspects such as the number of sensor nodes involved, low bandwidth, management of resources and issues related to energy management. In order for these networks to be functionally proficient, the underlying software system must be able to effectively handle unreliable and dynamic distributed communication, power constraints of wireless devices, failure of hardware devices in hostile environments and the remote allocation of distributed processing tasks throughout the wireless network. The solution must be solved in a highly scalable manner. This paper provides the requirements analysis and presents the design of a software system middleware that provides a scalable solution for ad-hoc sensor network infrastructure made of both stationary and mobile sensors and actuators.
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Patan, Maciej. "Distributed scheduling of sensor networks for identification of spatio-temporal processes." International Journal of Applied Mathematics and Computer Science 22, no. 2 (June 1, 2012): 299–311. http://dx.doi.org/10.2478/v10006-012-0022-9.

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Distributed scheduling of sensor networks for identification of spatio-temporal processesAn approach to determine a scheduling policy for a sensor network monitoring some spatial domain in order to identify unknown parameters of a distributed system is discussed. Given a finite number of possible sites at which sensors are located, the activation schedule for scanning sensors is provided so as to maximize a criterion defined on the Fisher information matrix associated with the estimated parameters. The related combinatorial problem is relaxed through operating on the density of sensors in lieu of individual sensor positions. Then, based on the adaptation of pairwise communication algorithms and the idea of running consensus, a numerical scheme is developed which distributes the computational burden between the network nodes. As a result, a simple exchange algorithm is outlined to solve the design problem in a decentralized fashion.
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Singh, Mitali, and Viktor K. Prasanna. "A HIERARCHICAL MODEL FOR DISTRIBUTED COLLABORATIVE COMPUTATION IN WIRELESS SENSOR NETWORKS." International Journal of Foundations of Computer Science 15, no. 03 (June 2004): 485–506. http://dx.doi.org/10.1142/s012905410400256x.

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In-network collaborative computation is essential for implementation of a large number of sensor applications. We approach the problem of computation in sensor networks from a parallel and distributed system's perspective. We define COSMOS, the Cluster-based, heterOgeneouSMOdel for Sensor networks. The model abstracts the key features of the class of cluster-based sensor applications. It assumes a hierarchical network architecture comprising of a large number of low cost sensors with limited computation capability, and fewer number of powerful clusterheads, uniformly distributed in a two dimensional terrain. The sensors are organized into single hop clusters, each managed by a distinct clusterhead. The clusterheads are organized in a mesh-like topology. All sensors in a cluster are time synchronized, whereas the clusterheads communicate asynchronously. The sensors are assumed to have multiple power states and a wakeup mechanism to facilitate power management. To illustrate algorithm design using our model, we discuss implementation of algorithms for sorting and summing in sensor networks.
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B, Venkateswarulu Naik, and Dr S. Rama Krishna. "An Energy-aware Distributed Clustering Protocol in Wireless Sensor Network." International Journal of Trend in Scientific Research and Development Volume-1, Issue-6 (October 31, 2017): 1130–38. http://dx.doi.org/10.31142/ijtsrd5798.

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Wang, Hong Wei, Chun Lei Zhang, Xiao Ming Ni, Zhi Gang Gao, Wen Kai Zhang, Xiao Ni Wang, Zhi Tian Hao, and Ming Hui Wang. "Distributed Temperature Sensor Network System." Applied Mechanics and Materials 190-191 (July 2012): 968–71. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.968.

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The wireless sensor network (WSN) is the development trend of the technology of sensor. This paper, based on the nRF905 wireless module, introduces a wireless temperature gathering and transmitting system. From RF modules and master control module, the hardware platform has been designed, beyond that, the paper introduces the temperature sensor network software design. The test show that the system is stable, and datas are reliable.
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Li, Wenchao, Yonggui Yuan, Jun Yang, and Libo Yuan. "Review of Optical Fiber Sensor Network Technology Based on White Light Interferometry." Photonic Sensors 11, no. 1 (January 22, 2021): 31–44. http://dx.doi.org/10.1007/s13320-021-0613-x.

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AbstractOptical fiber sensor networks (OFSNs) provide powerful tools for large-scale buildings or long-distance sensing, and they can realize distributed or quasi-distributed measurement of temperature, strain, and other physical quantities. This article provides some optical fiber sensor network technologies based on the white light interference technology. We discuss the key issues in the fiber white light interference network, including the topology structure of white light interferometric fiber sensor network, the node connection components, and evaluation of the maximum number of sensors in the network. A final comment about further development prospects of fiber sensor network is presented.
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HAENGGI, MARTIN. "DISTRIBUTED SENSOR NETWORKS: A CELLULAR NONLINEAR NETWORK PERSPECTIVE." International Journal of Neural Systems 13, no. 06 (December 2003): 405–14. http://dx.doi.org/10.1142/s0129065703001686.

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Large-scale networks of integrated wireless sensors become increasingly tractable. Advances in hardware technology and engineering design have led to dramatic reductions in size, power consumption, and cost for digital circuitry, and wireless communications. Networking, self-organization, and distributed operation are crucial ingredients to harness the sensing, computing, and computational capabilities of the nodes into a complete system. This article shows that those networks can be considered as cellular nonlinear networks (CNNs), and that their analysis and design may greatly benefit from the rich theoretical results available for CNNs.
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Veeravalli, Venugopal V., and Pramod K. Varshney. "Distributed inference in wireless sensor networks." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 370, no. 1958 (January 13, 2012): 100–117. http://dx.doi.org/10.1098/rsta.2011.0194.

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Statistical inference is a mature research area, but distributed inference problems that arise in the context of modern wireless sensor networks (WSNs) have new and unique features that have revitalized research in this area in recent years. The goal of this paper is to introduce the readers to these novel features and to summarize recent research developments in this area. In particular, results on distributed detection, parameter estimation and tracking in WSNs will be discussed, with a special emphasis on solutions to these inference problems that take into account the communication network connecting the sensors and the resource constraints at the sensors.
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Lenzen, Christoph, and Roger Wattenhofer. "Distributed algorithms for sensor networks." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 370, no. 1958 (January 13, 2012): 11–26. http://dx.doi.org/10.1098/rsta.2011.0212.

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Distributed algorithms are an established tool for designing protocols for sensor networks. In this paper, we discuss the relation between distributed computing theory and sensor network applications. We also present a few basic and illustrative distributed algorithms.
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Patra, Chiranjib, Anjan Guha Roy, Samiran Chattopadhyay, and Parama Bhaumik. "Designing Energy-Efficient Topologies for Wireless Sensor Network: Neural Approach." International Journal of Distributed Sensor Networks 6, no. 1 (January 1, 2010): 216716. http://dx.doi.org/10.1155/2010/216716.

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Preserving energy or battery power of wireless sensor network is of major concern. As such type of network, the sensors are deployed in an ad hoc manner, without any deterministic way. This paper is concerned with applying standard routing protocols into wireless sensor network by using topology modified by neural network which proves to be energy efficient as compared with unmodified topology. Neural network has been proved to be a powerful tool in the distributed environment. Here, to capture the true distributed nature of the Wireless Sensor Network (WSN), neural network's Self-Organizing Feature Map (SOFM) is used.
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White, R. R., H. Davis, W. T. Vestrand, and P. R. Wozniak. "Distributed intelligence in an astronomical Distributed Sensor Network." Astronomische Nachrichten 329, no. 3 (March 2008): 278–79. http://dx.doi.org/10.1002/asna.200710956.

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13

Ramezani, Tayebeh, and Tahereh Ramezani. "A Distributed Method to Reconstruct Connection in Wireless Sensor Networks by Using Genetic Algorithm." Modern Applied Science 10, no. 6 (April 10, 2016): 50. http://dx.doi.org/10.5539/mas.v10n6p50.

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In recent years most of the research in the field of sensor networks is allocated to the wireless sensor and actor networks due to their complicacy and vastness of research area. This type of network is a group of sensors and actors wirelessly linked to each other. Sensors gather information of physical world while actors take appropriate decisions on the basis of gathered information and then perform proper actions upon the environment. In wireless sensor and actor networks, it is very important to maintain the connection between actors. Failure of one or more actors can break up the network into separated parts and this failure acts as a barrier to the network to perform its duties. The purpose of the present paper was to provide a genetic algorithm in wireless sensor and actor networks, to improve evaluation and to maintain the connection between actors’ networks. In order to evaluate strong points and weaknesses of the recommended approach, the OMNet++ simulation was used and the outcomes of the simulation were indicative of the recommended approach’s validity.
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14

Al-Asadi, Hamid Ali Abed. "A Novel and Enhanced Distributed Clustering Methodology for Large Scale Wireless Sensor Network Fields." Journal of Computational and Theoretical Nanoscience 16, no. 2 (February 1, 2019): 633–38. http://dx.doi.org/10.1166/jctn.2019.7782.

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Wireless sensor network (WSN) is a grid of sensors possessing processor unit and trivial memory unit implanted on them. Trustworthy packet forwarding from nodes to sink seems to be the most substantial purpose of this sensor network. The customary routing algorithms could not be employed at this juncture since the sensor battery power is limited. To provide energy proficiency, sensors are normally grouped as non-overlapping groups. This research work provides a transitory summary on clustering procedures in sensor networks. An energy-efficient distributed clustering approach for impenetrable sensor networks, the Weight based clustering Low Energy Adaptive Clustering Hierarchy (WC-LEACH) is proposed and the outcomes are assessed in contradiction with the prevailing Low Energy Adaptive Clustering Hierarchy (LEACH) and Hybrid Energy Efficient Distributed Clustering (HEED) methodologies. Simulation results obviously display an exceptional enhancement in packet delivery ratio, reduced packet loss, reduced energy consumption, increased throughput and increased lifetime for WSNs.
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Jia, Jie, Guiyuan Zhang, Xingwei Wang, and Jian Chen. "On Distributed Localization for Road Sensor Networks: A Game Theoretic Approach." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/640391.

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Road sensor network is an important part of vehicle networks system and is critical for many intelligent automobile scenarios, such as vehicle safety monitoring and transportation efficiency supporting. Localization of sensors is an active and crucial issue to most applications of road sensor network. Generally, given some anchor nodes’ positions and certain pairwise distance measurements, estimating the positions of all nonanchor nodes embodies a nonconvex optimization problem. However, due to the small number of anchor nodes and low sensor node connectivity degree in road sensor networks, the existing localization solutions are ineffective. In order to tackle this problem, a novel distributed localization method based on game theory for road sensor networks is proposed in this paper. Formally, we demonstrate that our proposed localization game is a potential game. Furthermore, we present several techniques to accelerate the convergence to the optimal solution. Simulation results demonstrate the effectiveness of our proposed algorithm.
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Wei, Guofeng, Bangning Zhang, Guoru Ding, Bing Zhao, Yimin Wei, and Daoxing Guo. "Massive MIMO-Based Distributed Signal Detection in Multi-Antenna Wireless Sensor Networks." Sensors 20, no. 7 (April 3, 2020): 2005. http://dx.doi.org/10.3390/s20072005.

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For massive multiple-input multiple-output (MIMO) distributed wireless sensor networks, this paper investigates the role of multi-antenna sensors in improving network perception performance. First, we construct a distributed multi-antenna sensor network based on massive MIMO. By using the anti-fading characteristics of multi-antennas, it is better to achieve accurate detection than the single-antenna sensor network. Based on this, we derive a closed-loop expression for the detection probability of the best detector. Then, we consider the case that the sensor power resources are limited, and thus we want to use finite power to achieve higher detection probability. For this reason, the power was optimized by the alternating direction method of multipliers (ADMM). Moreover, we also prove that only statistical channel state is needed in large-scale antenna scenarios, which avoid the huge overhead of channel state information. Finally, according to the simulation results, the multi-antenna sensor network has better detection performance than the single-antenna sensor network which demonstrates the improved performance of the proposed schemes and also validates the theoretical findings.
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Li, Xian Li, Jia Wei Zhang, and Hai Tao Zhang. "Distributed Data Aggregation Algorithm in Wireless Sensor Networks." Applied Mechanics and Materials 442 (October 2013): 526–31. http://dx.doi.org/10.4028/www.scientific.net/amm.442.526.

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Considering the limited resources and data transmission redundancy of wireless sensor networks, this paper proposes a distributed data aggregation algorithm based on lifting wavelet transform (DDAA-LWT), and carries out the rational design. The algorithm distributes the computing quantity which the lifting wavelet transform requires to all network nodes, eliminates the additional computing and wireless transmission, reduces the information redundancy of network, greatly prolongs the lifecycle of wireless sensor networks. Simulation results demonstrate that the distributed data aggregation algorithm based on lifting wavelet transform (DDAA-LWT) can effectively aggregate the original sensed data and decrease the energy consumption, it significantly outperforms the data aggregation algorithm based on traditional wavelet transform (DAA-WT).
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Shin, Huicheol, Yongjae Kim, Seungjae Baek, and Yujae Song. "Distributed Learning for Dynamic Channel Access in Underwater Sensor Networks." Entropy 22, no. 9 (September 7, 2020): 992. http://dx.doi.org/10.3390/e22090992.

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In this study, the problem of dynamic channel access in distributed underwater acoustic sensor networks (UASNs) is considered. First, we formulate the dynamic channel access problem in UASNs as a multi-agent Markov decision process, wherein each underwater sensor is considered an agent whose objective is to maximize the total network throughput without coordinating with or exchanging messages among different underwater sensors. We then propose a distributed deep Q-learning-based algorithm that enables each underwater sensor to learn not only the behaviors (i.e., actions) of other sensors, but also the physical features (e.g., channel error probability) of its available acoustic channels, in order to maximize the network throughput. We conduct extensive numerical evaluations and verify that the performance of the proposed algorithm is similar to or even better than the performance of baseline algorithms, even when implemented in a distributed manner.
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Du, Juan, and Fenfen Wu. "Distributed Data Mining in Wireless Sensor Networks." International Journal of Online Engineering (iJOE) 12, no. 11 (November 24, 2016): 68. http://dx.doi.org/10.3991/ijoe.v12i11.6224.

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With the rapid development and wide application of sensor network technology, consequently a huge volume of data would be continuously generated and collected. In order to process the data and analyze the data more accurate and efficient, the paper proposed a distributed data mining method in wireless sensor networks. Thus Smart Octopus, an open framework for seamlessly integrating sensor network and data mining technology, so that both of the huge amounts of data resource collected in sensor networks and the powerful knowledge discovery capability of data mining could be effectively and efficiently utilized, is discussed in this paper.
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Kim, Du Yong, and Moongu Jeon. "Robust Distributed Kalman Filter for Wireless Sensor Networks with Uncertain Communication Channels." Mathematical Problems in Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/238597.

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We address a state estimation problem over a large-scale sensor network with uncertain communication channel. Consensus protocol is usually used to adapt a large-scale sensor network. However, when certain parts of communication channels are broken down, the accuracy performance is seriously degraded. Specifically, outliers in the channel or temporal disconnection are avoided via proposed method for the practical implementation of the distributed estimation over large-scale sensor networks. We handle this practical challenge by using adaptive channel status estimator and robust L1-norm Kalman filter in design of the processor of the individual sensor node. Then, they are incorporated into the consensus algorithm in order to achieve the robust distributed state estimation. The robust property of the proposed algorithm enables the sensor network to selectively weight sensors of normal conditions so that the filter can be practically useful.
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Simonetto, Andrea, and Geert Leus. "Distributed Maximum Likelihood Sensor Network Localization." IEEE Transactions on Signal Processing 62, no. 6 (March 2014): 1424–37. http://dx.doi.org/10.1109/tsp.2014.2302746.

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Li, Chunguang, and Yiliang Luo. "Distributed Vector Quantization over Sensor Network." International Journal of Distributed Sensor Networks 10, no. 10 (January 2014): 189619. http://dx.doi.org/10.1155/2014/189619.

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Li, Chunguang, and Heyu Wang. "Distributed Frequency Estimation Over Sensor Network." IEEE Sensors Journal 15, no. 7 (July 2015): 3973–83. http://dx.doi.org/10.1109/jsen.2015.2407579.

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Saihi, Marwa, Ahmed Zouinkhi, Boumedyen Boussaid, Mohamed Naceur Abdelkarim, and Guillaume Andrieux. "Hidden Gaussian Markov model for distributed fault detection in wireless sensor networks." Transactions of the Institute of Measurement and Control 40, no. 6 (March 15, 2017): 1788–98. http://dx.doi.org/10.1177/0142331217691334.

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Wireless sensor networks are based on a large number of sensor nodes used to measure information like temperature, acceleration, displacement, or pressure. The measurements are used to estimate the state of the monitored system or area. However, the quality of the measurements must be guaranteed to ensure the reliability of the estimated state of the system. Actually, sensors can be used in a hostile environment such as, on a battle field in the presence of fires, floods, earthquakes. In these environments as well as in normal operation, sensors can fail. The failure of sensor nodes can also be caused by other factors like: the failure of a module (such as the sensing module) due to the fabrication process models, loss of battery power and so on. A wireless sensor network must be able to identify faulty nodes. Therefore, we propose a probabilistic approach based on the Hidden Markov Model to identify faulty sensor nodes. Our proposed approach predicts the future state of each node from its actual state, so the fault could be detected before it occurs. We use an aided judgment of neighbour sensor nodes in the network. The algorithm analyses the correlation of the sensors’ data with respect to its neighbourhood. A systematic approach to divide a network on cliques is proposed to fully draw the neighbourhood of each node in the network. After drawing the neighbourhood of each node (cliques), damaged cliques are identified using the Gaussian distribution theorem. Finally, we use the Hidden Markov Model to identify faulty nodes in the identified damaged cliques by calculating the probability of each node to stay in its normal state. Simulation results demonstrate our algorithm is efficient even for a huge wireless sensor network unlike previous approaches.
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WANG, BANG. "SENSOR PLACEMENT FOR COMPLETE INFORMATION COVERAGE IN DISTRIBUTED SENSOR NETWORKS." Journal of Circuits, Systems and Computers 17, no. 04 (August 2008): 627–36. http://dx.doi.org/10.1142/s0218126608004575.

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The sensor placement problem for complete information coverage in a distributed sensor network is studied. Sensors are assumed to be placed on the grid points of a grid in the sensor field and complete information coverage is claimed if all the grid points are information covered. This sensor placement problem is formulated as a constrained optimization problem where the objective is to minimize the total cost while guaranteeing certain coverage requirement. We propose a greedy algorithm to solve this problem. Computational results show that the proposed algorithm can efficiently obtain a good quality solution with greatly reduced computation complexity and the number of sensors can be greatly reduced for information coverage.
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Wang, Jing, In Soo Ahn, Yufeng Lu, Tianyu Yang, and Gennady Staskevich. "A Distributed Least-Squares Algorithm in Wireless Sensor Networks With Unknown and Limited Communications." International Journal of Handheld Computing Research 8, no. 3 (July 2017): 15–36. http://dx.doi.org/10.4018/ijhcr.2017070102.

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In this article, the authors propose a new distributed least-squares algorithm to address the sensor fusion problem in using wireless sensor networks (WSN) to monitor the behaviors of large-scale multiagent systems. Under a mild assumption on network observability, that is, each sensor can take the measurements of a limited number of agents but the complete multiagent systems are covered under the union of all sensors in the network, the proposed algorithm achieves the estimation consensus if local information exchange can be performed among sensors. The proposed distributed least-squares algorithm can handle the directed communication network by explicitly estimating the left eigenvector corresponding to the largest eigenvalue of the sensing/communication matrix. The convergence of the proposed algorithm is analyzed, and simulation results are provided to further illustrate its effectiveness.
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Ho, Jun-Won. "Distributed Software-Attestation Defense against Sensor Worm Propagation." Journal of Sensors 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/874782.

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Wireless sensor networks are vulnerable to sensor worm attacks in which the attacker compromises a few nodes and makes these compromised nodes initiate worm spread over the network, targeting the worm infection of the whole nodes in the network. Several defense mechanisms have been proposed to prevent worm propagation in wireless sensor networks. Although these proposed schemes use software diversity technique for worm propagation prevention under the belief that different software versions do not have common vulnerability, they have fundamental drawback in which it is difficult to realize the aforementioned belief in sensor motes. To resolve this problem, we propose on-demand software-attestation based scheme to defend against worm propagation in sensor network. The main idea of our proposed scheme is to perform software attestations against sensor nodes in on-demand manner and detect the infected nodes by worm, resulting in worm propagation block in the network. Through analysis, we show that our proposed scheme defends against worm propagation in efficient and robust manner. Through simulation, we demonstrate that our proposed scheme stops worm propagation at the reasonable overhead while preventing a majority of sensor nodes from being infected by worm.
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Zhang, Zhao Hui, Ming Ming Hu, Dong Li, and Xiao Gang Qi. "Distributed Malicious Nodes Detection in Wireless Sensor Networks." Applied Mechanics and Materials 519-520 (February 2014): 1243–46. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.1243.

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Data theft and node attack in wireless sensor networks causes great damage to the networks and the attacker destroys network and obtains the data of the network by malicious nodes distributed in the network. Therefore, it is necessary to detect these malicious nodes and to eliminate their influence. We propose a distributed malicious nodes detection protocol which called BMND based on Bayesian voting, every node determine its suspected malicious nodes by its request message and abnormal behavior. Also, we determine the malicious nodes by Bayesian voting, so that the network can protect itself from such malicious nodes influence. The simulation results show that our algorithm has good performance in both the detection rate and false positive rate.
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Noda, Tomoyuki, Takahiro Miyashita, Hiroshi Ishiguro, Kiyoshi Kogure, and Norihiro Hagita. "Detecting Feature of Haptic Interaction Based on Distributed Tactile Sensor Network on Whole Body." Journal of Robotics and Mechatronics 19, no. 1 (February 20, 2007): 42–51. http://dx.doi.org/10.20965/jrm.2007.p0042.

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To extract information about users contacting robots physically, the distribution density of tactile sensor elements, the sampling rate, and the resolution all must be high, increasing the volume of tactile information. In the self-organized skin sensor network we propose for dealing with a large number of tactile sensors embedded throughout a humanoid robot, each network node having a processing unit is connected to tactile sensor elements and other nodes. By processing tactile information in the network based on the situation, individual nodes process and reduce information rapidly in high sampling. They also secure information transmission routes to the host PC using a data transmission protocol for self-organizing sensor networks. In this paper, we verify effectiveness of our proposal through sensor network emulation and basic experiments in spatiotemporal calculation of tactile information using prototype hardware. As an emulation result of the self-organized sensor network, routes to the host PC are secured at each node, and a tree-like network is constructed recursively with the node as a root. As the basic experiments, we describe an edge detection as data processing and extraction for haptic interaction. In conclusion, local information processing is effective for detecting features of haptic interaction.
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Li, Zhu Guo, Bing Wen Wang, and Li Zhu Feng. "EDMC: An Energy-Efficient Distributed Multi-Hop Clustering Approach for Wireless Sensor Networks." Applied Mechanics and Materials 198-199 (September 2012): 1668–71. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1668.

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The past few years have witnessed increasing focus on the potential applications of wireless sensor networks. Sensors in these networks are expected to be remotely dispersed in large number and to operate autonomously and unattended. Clustering is a widely used technique that can enhance scalability and decrease energy consumption over sensor networks. We present an energy-efficient distributed multi-hop clustering approach for sensor networks, which combined multi-hop transmission with clustering method, aiming to balance the energy dissipation and prolong the whole network lifetime. Simulations showed that the protocol proposed worked nearly 100% more efficient compared with LEACH and HEED.
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Qu, Ming Zhe. "Research on the Applications and Characteristics of the Wireless Sensor Network." Applied Mechanics and Materials 538 (April 2014): 498–501. http://dx.doi.org/10.4028/www.scientific.net/amm.538.498.

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A wireless sensor network (WSN) consists of spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, pressure, etc. and to cooperatively pass their data through the network to a main location. The more modern networks are bi-directional, also enabling control of sensor activity. The development of wireless sensor networks was motivated by military applications such as battlefield surveillance; today such networks are used in many industrial and consumer applications, such as industrial process monitoring and control, machine health monitoring, and so on. The WSN is built of "nodes" – from a few to several hundreds or even thousands, where each node is connected to one (or sometimes several) sensors. Each such sensor network node has typically several parts: a radio transceiver with an internal antenna or connection to an external antenna, a microcontroller, an electronic circuit for interfacing with the sensors and an energy source, usually a battery or an embedded form of energy harvesting.
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Khaytbaev, A. E., and A. M. Eshmuradov. "APPLICATIONS OF NEURAL NETWORK TECHNOLOGIES IN WIRELESS SENSOR NETWORKS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 195 (September 2020): 46–51. http://dx.doi.org/10.14489/vkit.2020.09.pp.046-051.

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The purpose of the article is to study the possibilities of improving the efficiency of the sensory network management technique, using the neural network method. The presented model of the wireless sensor network takes into account the charging of the environment. The article also tests the hypothesis of the possibility of organizing distributed computing in wireless sensor networks. To achieve this goal, a number of tasks are allocated: review and analysis of existing methods for managing BSS nodes; definition of simulation model components and their properties of neural networks and their features; testing the results of using the developed method. The article explores the major historical insights of the application of the neural network technologies in wireless sensor networks in the following practical fields: engineering, farming, utility communication networks, manufacturing, emergency notification services, oil and gas wells, forest fires prevention equipment systems, etc. The relevant applications for the continuous monitoring of security and safety measures are critically analyzed in the context of the relevancy of specific decisions to be implemented within the system architecture. The study is focused on the modernization of methods of control and management for the wireless sensor networks considering the environmental factors to be allocated using senor systems for data maintenance, including the information on temperature, humidity, motion, radiation, etc. The article contains the relevant and adequate comparative analysis of the updated versions of node control protocols, the components of the simulation model, and the control method based on neural networks to be identified and tested within the practical organizational settings.
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Khaytbaev, A. E., and A. M. Eshmuradov. "APPLICATIONS OF NEURAL NETWORK TECHNOLOGIES IN WIRELESS SENSOR NETWORKS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 195 (September 2020): 46–51. http://dx.doi.org/10.14489/vkit.2020.09.pp.046-051.

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The purpose of the article is to study the possibilities of improving the efficiency of the sensory network management technique, using the neural network method. The presented model of the wireless sensor network takes into account the charging of the environment. The article also tests the hypothesis of the possibility of organizing distributed computing in wireless sensor networks. To achieve this goal, a number of tasks are allocated: review and analysis of existing methods for managing BSS nodes; definition of simulation model components and their properties of neural networks and their features; testing the results of using the developed method. The article explores the major historical insights of the application of the neural network technologies in wireless sensor networks in the following practical fields: engineering, farming, utility communication networks, manufacturing, emergency notification services, oil and gas wells, forest fires prevention equipment systems, etc. The relevant applications for the continuous monitoring of security and safety measures are critically analyzed in the context of the relevancy of specific decisions to be implemented within the system architecture. The study is focused on the modernization of methods of control and management for the wireless sensor networks considering the environmental factors to be allocated using senor systems for data maintenance, including the information on temperature, humidity, motion, radiation, etc. The article contains the relevant and adequate comparative analysis of the updated versions of node control protocols, the components of the simulation model, and the control method based on neural networks to be identified and tested within the practical organizational settings.
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Zhang, Yuan, Yue Liu, and Zhong Tian Jia. "A Sensor Data Management Scheme for Wireless Sensor Networks." Key Engineering Materials 467-469 (February 2011): 709–12. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.709.

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One of the major difficulties of wireless sensor network (WSN) applications is how to efficiently manage the large amount of data produced by sensors. The differences from standard database source pose challenges of sensor data management. In this paper, we propose a novel sensor data management architecture based on our extensive discussion on existing works. The hierarchical system model consists of sensor network layer and proxy network layer. Sensor network layer performs limited computation and communication while being managed intelligently by the proxy network. The proxy network receives sensor data, manages sensor data and processes queries in a distributed manner. We also provide insight into possible research directions in this area.
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Sun, Bao Shan, and Zhen Kai Wan. "Three-Dimensional Braided Composites Detection Method Based on Improved Chaotic Neural Network." Advanced Materials Research 562-564 (August 2012): 455–58. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.455.

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Three-dimensional braided composite materials for the use occurred during the fracture, deformation and other injury problems, a distributed Bragg grating sensor applied to detection of parts in health status. With three-dimensional braided composite weaving, research on the mechanical properties of small parts, sensors embedded in the sensor signal processing easy way. Embedded in three-dimensional composite parts of the sensor network signal processing method, the rapid implementation of distributed signal processing and the structure of large parts of the distributed monitoring.
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Heo, Taewook, Hyunhak Kim, Yoonmee Doh, Kwangsoo Kim, Jongjun Park, Naesoo Kim, JongArm Jun, and JeongGil Ko. "Multitiered and Distributed WSAN for Cooperative Indoors Environment Management." Mobile Information Systems 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/6979178.

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For the past decade, wireless sensor networks have focused primarily on data collection. As a result the network topology for these systems was usually heavily centralized. However, for these networks to form a full system, the introduction of proper actuation units and decision-making intelligence is inevitable. Such a new wireless sensor and actuator network system enables new architectural research issues that have not been previously studied. In this work, we introduce the DWSAN system architecture, which effectively combines both sensor and actuation hardware devices to a single network and manages this network so that the actuation decisions are made in a distributed manner and the topology of the network maintains a multitier architecture. Our intensive set of evaluations reveal that, compared to the centralized approach that has been used in most wireless sensor network systems until now, when actuation units are introduced to the system, the DWSAN architecture reduces the transmission load of the network and the actuation decision-making latency by close to twofold and threefold, respectively. Furthermore, we show that this benefit naturally leads to better scalability of the system, making it suitable for various sensing applications in different environments.
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Et. al., N. Srinivas Rao,. "Wireless Sensor Network routing for Life Time Maximization Using ANFIS Based Decision with Low Power Consumption." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 2893–900. http://dx.doi.org/10.17762/turcomat.v12i2.2324.

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Wireless sensor networks (WSNs) allocate thousands of cheap micro-sensor nodes to a hundreds to more than thousands of nodes in the reserved areas. In the WSN, sensor nodes control storage resources, calculating energy of nodes, power resources of nodes, and additional resources information on a sensor network. These micro-sensor nodes are key components of the Internet of Things (). WSNs are pre-arranged in clusters or groups to protect the ability for efficient data communication. Strong routing methods are required to maintain long network life and achieve high power usage. In this work, the new energy efficient ANFIS-based routing system for WSN enabled to improve network performance. The proposed ANFIS-based routing involves a novel distributed clustering mechanism that activates the local configuration of local node energy equally across all sensors. A new technique for replacing clusters and rotating nodes with a centroid-based cluster head (CH) to distribute loads. The simulation results show that the proposed program will surpass conventional methods with 78% improvement over the lifetime of the network and 26% improvement in performance.
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Dehnaw, Amare Mulatie, Yibeltal Chanie Manie, Ya Yu Chen, Po Han Chiu, Hung Wei Huang, Guan Wei Chen, and Peng Chun Peng. "Design Reliable Bus Structure Distributed Fiber Bragg Grating Sensor Network Using Gated Recurrent Unit Network." Sensors 20, no. 24 (December 21, 2020): 7355. http://dx.doi.org/10.3390/s20247355.

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The focus of this paper was designing and demonstrating bus structure FBG sensor networks using intensity wavelength division multiplexing (IWDM) techniques and a gated recurrent unit (GRU) algorithm to increase the capability of multiplexing and the ability to detect Bragg wavelengths with greater accuracy. Several Fiber Bragg grating (FBG) sensors are coupled with power ratios of 90:10 and 80:10, respectively in the suggested experimental setup. We used the latest IWDM multiplexing technique for the proposed scheme, as the IWDM system increases the number of sensors and allows us to alleviate the limited operational region drawback of conventional wavelength division multiplexing (WDM). However, IWDM has a crosstalk problem that causes high-sensor signal measurement errors. Thus, we proposed the GRU model to overcome this crosstalk or overlapping problem by converting the spectral detection problem into a regression problem and considered the sequence of spectral features as input. By feeding this sequential spectrum dataset into the GRU model, we trained the GRU system until we achieved optimal efficiency. Consequently, the well-trained GRU model quickly and accurately identifies the Bragg wavelength of each FBG from the overlapping spectra. The Bragg wavelength detection performance of our proposed GRU model is tested or validated using different numbers of FBG sensors, such as 3-FBG, 5-FBG, 7-FBG, and 10-FBG, separately. As a result, the experiment result proves that the well-trained GRU model accurately identifies each FBG Bragg wavelength, and even the number of FBG sensors increase, as well as the spectra of FBGs, which are partially or fully overlapped. Therefore, to boost the detection efficiency, reliability, and to increase the multiplexing capabilities of FBG sensor networks, the proposed sensor system is better than the other previously proposed methods.
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Dai, Liang, Zhong Shen, and Yi Lin Chang. "DMTA: A Task Scheduling Algorithm in Multi-Sinks Wireless Sensor Networks Based on Divisible Load Theory." Advanced Materials Research 143-144 (October 2010): 143–47. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.143.

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Multi-Sinks wireless sensor networks, a current research focus, has better stability and effectiveness compared to the traditional single-SINK structure. To solve the problem how to complete the tasks within the possibly shortest time, a task scheduling algorithm(DMTA) based on divisible load theory in multi-Sinks wireless sensor networks is proposed. In DMTA, the tasks are distributed to wireless sensor network based on the processing and communication capacity of each sensor by multiple Sinks respectively. By removing communications interference between each sensor, reduced task completion time and improved network resource utilization achieved. Simulation results show that DMTA reasonably distributes tasks to each node in wireless sensor networks, and effectively reduces the time-consuming of task completion.
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Prabhu, Boselin, and Bala Kumar. "HIGHLY DISTRIBUTED AND ENERGY EFFICIENT CLUSTERING ALGORITHM FOR WIRELESS SENSOR NETWORKS." International Journal of Research -GRANTHAALAYAH 4, no. 9 (September 30, 2016): 30–38. http://dx.doi.org/10.29121/granthaalayah.v4.i9.2016.2531.

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Wireless sensor network (WSN) is a low-powered prestigious network fashioned by sensor nodes that treasures application in civilian, military, visual sense models and many others. Reduced energy utilization is an exigent task for these sensor networks. By the data aggregation procedure, needless communication between sensor nodes, cluster head and the base station is eluded. An evaluation of energy efficient optical low energy adaptive clustering hierarchy has been performed and the enactments have been compared with the prevailing low energy adaptive clustering hierarchy algorithm, between two detached wireless sensor network fields. The proposed clustering procedure has been primarily implemented to join two distinct wireless sensor fields. An optical fiber is used to join two reserved wireless sensor fields. This distributed clustering methodology chiefly targets in exploiting the parameters like network lifetime, throughput and energy efficiency of the whole wireless sensor system.
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Lee, Jongmin, Cihan Tepedelenlioglu, Andreas Spanias, and Gowtham Muniraju. "Distributed Quantiles Estimation of Sensor Network Measurements." International Journal of Smart Security Technologies 7, no. 2 (July 2020): 38–61. http://dx.doi.org/10.4018/ijsst.2020070103.

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A quantile is a value below which random draws from a distribution falls with a given probability. In a centralized setting where the cumulative distribution function (CDF) is unknown, empirical CDF can be used to estimate quantiles after data aggregation. In a distributed sensor network, it is challenging to estimate quantiles, as each sensor observes local measurement data with limited storage and transmission power, which makes it difficult to obtain the global ECDF. This paper proposes consensus-based quantile estimation for such networks, even when communication links are corrupted by independent random noise. The state-values are recursively updated with two steps: a local update based on measurement data and current state and averaging updated states with local nodes. The estimated state sequence is shown to be asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Applications on the distributed estimation of trimmed mean; computation of median, maximum, or minimum values; and identification of outliers through simulation are also provided.
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Chen, Xiyuan, Loic Maxwell, Franklin Li, Amrita Kumar, Elliot Ransom, Tanay Topac, Sera Lee, Mohammad Faisal Haider, Sameh Dardona, and Fu-Kuo Chang. "Design and Integration of a Wireless Stretchable Multimodal Sensor Network in a Composite Wing." Sensors 20, no. 9 (April 29, 2020): 2528. http://dx.doi.org/10.3390/s20092528.

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This article presents the development of a stretchable sensor network with high signal-to-noise ratio and measurement accuracy for real-time distributed sensing and remote monitoring. The described sensor network was designed as an island-and-serpentine type network comprising a grid of sensor “islands” connected by interconnecting “serpentines.” A novel high-yield manufacturing process was developed to fabricate networks on recyclable 4-inch wafers at a low cost. The resulting stretched sensor network has 17 distributed and functionalized sensing nodes with low tolerance and high resolution. The sensor network includes Piezoelectric (PZT), Strain Gauge (SG), and Resistive Temperature Detector (RTD) sensors. The design and development of a flexible frame with signal conditioning, data acquisition, and wireless data transmission electronics for the stretchable sensor network are also presented. The primary purpose of the frame subsystem is to convert sensor signals into meaningful data, which are displayed in real-time for an end-user to view and analyze. The challenges and demonstrated successes in developing this new system are demonstrated, including (a) developing separate signal conditioning circuitry and components for all three sensor types (b) enabling simultaneous sampling for PZT sensors for impact detection and (c) configuration of firmware/software for correct system operation. The network was expanded with an in-house developed automated stretch machine to expand it to cover the desired area. The released and stretched network was laminated into an aerospace composite wing with edge-mount electronics for signal conditioning, processing, power, and wireless communication.
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43

Ishak, Ruzana, Shaharuddin Salleh, Stephan Olariu, and Mohd Ismail Abdul Aziz. "SPLAI: Computational Finite Element Model for Sensor Networks." Mobile Information Systems 2, no. 1 (2006): 77–92. http://dx.doi.org/10.1155/2006/672524.

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Wireless sensor network refers to a group of sensors, linked by a wireless medium to perform distributed sensing task. The primary interest is their capability in monitoring the physical environment through the deployment of numerous tiny, intelligent, wireless networked sensor nodes. Our interest consists of a sensor network, which includes a few specialized nodes called processing elements that can perform some limited computational capabilities. In this paper, we propose a model called SPLAI that allows the network to compute a finite element problem where the processing elements are modeled as the nodes in the linear triangular approximation problem. Our model also considers the case of some failures of the sensors. A simulation model to visualize this network has been developed using C++ on the Windows environment.
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44

Bein, Doina, Yicheng Wen, Shashi Phoha, Bharat B. Madan, and Asok Ray. "Distributed network control for mobile multi-modal wireless sensor networks." Journal of Parallel and Distributed Computing 71, no. 3 (March 2011): 460–70. http://dx.doi.org/10.1016/j.jpdc.2010.08.016.

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45

Yang, Wen, Ling Shi, Ye Yuan, Xiaofan Wang, and Hongbo Shi. "Network design for distributed consensus estimation over heterogeneous sensor networks." IFAC Proceedings Volumes 47, no. 3 (2014): 5550–55. http://dx.doi.org/10.3182/20140824-6-za-1003.02093.

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46

Wang, Lili, and Xiaobei Wu. "Distributed prevention mechanism for network partitioning in wireless sensor networks." Journal of Communications and Networks 16, no. 6 (December 2014): 667–76. http://dx.doi.org/10.1109/jcn.2014.000113.

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47

Madda, Dheeraj R. "Distributed Environmental Monitoring using Wireless Sensor Network." International Journal for Research in Applied Science and Engineering Technology 7, no. 7 (July 31, 2019): 633–36. http://dx.doi.org/10.22214/ijraset.2019.7101.

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48

Liu, Xiang Yang, Fang Cui, and Wan Li Kou. "Detection Performance for Distributed Radar Sensor Network." Applied Mechanics and Materials 427-429 (September 2013): 1272–76. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1272.

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The need for mobility and the rapid deployment motivates the research of distributed radar sensor network withNTtransmitters,NRreceivers and a fusion center, where the fusion center and the receivers are communicated via wireless signaling. With the assumption of orthogonal waveforms for sensing and circular complex Gaussian noise channel model for communication, the formulae of detection probability and the false alarm probability are derived given the sensing power and communication power. Numerical results show that with the increase of the total power, the ratio between the best sensing power and the best communication power will increase. Moreover, the shape of the region in which the target detection probability is larger than a predetermined value is not regular, which should be taken care in case of network planning.
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49

Liang, Jing, and Chengchen Mao. "Distributed compressive sensing in heterogeneous sensor network." Signal Processing 126 (September 2016): 96–102. http://dx.doi.org/10.1016/j.sigpro.2015.10.026.

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50

Pennisi, A., F. Previtali, F. Ficarola, D. D. Bloisi, L. Iocchi, and A. Vitaletti. "Distributed Sensor Network for Multi-robot Surveillance." Procedia Computer Science 32 (2014): 1095–100. http://dx.doi.org/10.1016/j.procs.2014.05.538.

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