Journal articles: 'Wireless data networks'

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Prathap, Jomma, and Ramavath Janu. "All Data Aggregation in Wireless Sensor Networks." International Journal of Innovative Research in Engineering & Management 4, no. 6 (November 2017): 757–60. http://dx.doi.org/10.21276/ijirem.2017.4.6.2.

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Hadi, Teeb Hussein. "Types of Attacks in Wireless Communication Networks." Webology 19, no. 1 (January 20, 2022): 718–28. http://dx.doi.org/10.14704/web/v19i1/web19051.

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One of the most important types that used to transfer data between nodes without using wires is a “wireless communication network”, where the transmission of data is censored remotely by using electromagnetic waves such as radio waves that usually implemented in the physical layer of the network. Continuous improvements in wireless network technology have reduced the security and speed differences between types of networks (wired and wireless), but in turn, increased security threats to wirelessly transmitted data. Wireless networks are weak in terms of "privacy protection" because anyone who is within the coverage area of a wireless network can attempt to break into that network. Hacking incidents have been reported frequently in places with shared free networks, and it has been observed that the places of open distributed networks of the Internet are most at risk of complete penetration of your phone or PC data. To solve this problem, several programs have been developed that provide protection for wireless networks that differ in terms of security. Some of them did not provide sufficient protection for wireless networks, such as Wired Equivalent Privacy (WEP), and others made progress in preventing intrusions compared to their predecessors, such as Wi-Fi Protected Access (WPA).

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Dohare, Anand, Tulika, and Mallikarjuna B. "Data Collection in Wireless Sensor Networks Using Prediction Method." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (September 25, 2019): 815–20. http://dx.doi.org/10.5373/jardcs/v11/20192637.

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Liu, Wei, Qin Sheng Du, and Le Le Wang. "Study of Data Fusion Mechanism Based on the WSN." Applied Mechanics and Materials 321-324 (June 2013): 600–603. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.600.

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Wireless sensor networks integrated four technologies including sensor, embedded computing, network technology and wireless communication. It is a new type of non-infrastructure wireless network. In this paper, a data fusion method has been brought forward based on wireless sensor networks, and through an algorithm simulation test, It is proved that the algorithm is effective to reduce the energy consumption of the network, and extend the lifetime of the network.

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Ma, Lili, Jiangping Liu, and Jidong Luo. "Method of Wireless Sensor Network Data Fusion." International Journal of Online Engineering (iJOE) 13, no. 09 (September 22, 2017): 114. http://dx.doi.org/10.3991/ijoe.v13i09.7589.

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In order to better deal with large data information in computer networks, a large data fusion method based on wireless sensor networks is designed. Based on the analysis of the structure and learning algorithm of RBF neural networks, a heterogeneous RBF neural network information fusion algorithm in wireless sensor networks is presented. The effectiveness of information fusion processing methods is tested by RBF information fusion algorithm. The proposed algorithm is applied to heterogeneous information fusion of cluster heads or sink nodes in wireless sensor networks. The simulation results show the effectiveness of the proposed algorithm. Based on the above finding, it is concluded that the RBF neural network has good real-time performance and small network delay. In addition, this method can reduce the amount of information transmission and the network conflicts and congestion.

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Zhao, Ying, Ru Kun Li, and Kun Le Xu. "The Design of Wireless Sensor Networks and Data Analysis in Smart Grids." Advanced Materials Research 978 (June 2014): 257–60. http://dx.doi.org/10.4028/www.scientific.net/amr.978.257.

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Smart grids have a close relationship to advanced sensing technology and communication technology and also have provided a new application platform for wireless sensor networks. From wireless sensor networks research development and characteristics, combined with current status of wireless sensor networks applications in power system, this paper describes its key technologies of wireless sensor networks, then analyze applications and data analysis of the wireless sensor network in the smart grids.

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Firooz, Mohammad Hamed, and Sumit Roy. "Data Dissemination in Wireless Networks with Network Coding." IEEE Communications Letters 17, no. 5 (May 2013): 944–47. http://dx.doi.org/10.1109/lcomm.2013.031313.121994.

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Qiu, X., K. Chawla, J. C. I. Chuang, and N. Sollenberger. "Network-assisted resource management for wireless data networks." IEEE Journal on Selected Areas in Communications 19, no. 7 (July 2001): 1222–34. http://dx.doi.org/10.1109/49.932691.

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Luo, Li. "Data Aggregation in Wireless Sensor Networks." International Journal of Online Engineering (iJOE) 12, no. 11 (November 24, 2016): 28. http://dx.doi.org/10.3991/ijoe.v12i11.6233.

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In order to solve the problem of low efficiency and high energy consumption in data processing, In this paper, for reducing the communication overhead of data aggregation, we put forward a cluster division based data aggregation scheme for wireless sensor networks in which the cluster is divided into three subareas and some report nodes are assigned to each subarea. Simulation results show that, compared with LEACH algorithm, BP neural network and PSO-BP algorithm, more than 50% lower compression error is decreased and the simplicity of the improved algorithm makes it suitable for massive and the long term environmental monitoring network.

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Kumar, N. B. S. Vijay, D. Venkatesh D. Venkatesh, and K. Ramesh K. Ramesh. "Rapid Data Collection in Wireless Sensor Networks Organized as Trees." International Journal of Scientific Research 2, no. 5 (June 1, 2012): 223–26. http://dx.doi.org/10.15373/22778179/may2013/74.

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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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Lei, Shi. "Design of Data Acquisition System Based on Zigbee for Wireless Sensor Networks." MATEC Web of Conferences 246 (2018): 03036. http://dx.doi.org/10.1051/matecconf/201824603036.

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With the continuous development of modern network technology, sensor technology and wireless communication technologies, the emergence and development of wireless sensor networks has been promoted. Among many wireless communication technologies, wireless communication technology featuring low power consumption, close proximity and low cost for ZigBee has attracted the research interest of researchers. Based on the current status of communication systems, based on the review and system analysis of wireless sensor networks, this paper proposes a ZigBee-based wireless sensor network data acquisition system, which avoids the reliability and installation of traditional wired sensor network data acquisition systems and shortcomings such as high maintenance costs and large wiring.

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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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Liu, Yong, Baohua Liang, and Jiabao Jiang. "Information Processing and Data Management Technology in Wireless Sensor Networks." International Journal of Online Engineering (iJOE) 14, no. 09 (September 30, 2018): 66. http://dx.doi.org/10.3991/ijoe.v14i09.8270.

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The wireless sensor network is essentially a data-centric network that processes the continuous stream of data, which is collected by different sensors. Therefore, the existing data management technologies regard the wireless sensor network, which is named WSN as a distributed database, and it is composed of continuous data streams from the physical world. Wireless sensor networks are emerging next-generation sensor networks, but their transmission of information is highly dependent. The wireless sensor network processes the continuous stream of data collected by the sensor. Based on the features of wireless sensor networks, this paper presents a topology-dependent model of cluster evolution with fault tolerance. Through the limited data management, resources have reasonably configured, while also saving energy. The model is based on the energy-aware routing protocol in its network layer protocols. The key point is the energy routing principle. According to its own local view, the cluster head node builds the inter-cluster topology to achieve fault-tolerant and energy-saving goals. Simulation results show that the model has good fault tolerance and energy efficiency.

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Gong, Ting, and Hui Yan. "A Study on Data Fusion of Wireless Sensor Networks Security." Applied Mechanics and Materials 341-342 (July 2013): 1124–27. http://dx.doi.org/10.4028/www.scientific.net/amm.341-342.1124.

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Due to the advantage of data fusion in deleting redundant information and extending lifetime of network, data fusion has become one of the important ways of effectively relieving the bottleneck of wireless sensor networks resources, which has been widely used in wireless sensor networks. Security mechanism of transmission encryption of network is introduced to protect the security of data transmission of network.

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Lv, Hong, and Weina Huang. "The Application of Wireless Network in the Process of Remote Data Collection." Journal of Physics: Conference Series 2074, no. 1 (November 1, 2021): 012031. http://dx.doi.org/10.1088/1742-6596/2074/1/012031.

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Abstract The use of wireless network for remote data collection can provide a fast and reliable wireless data transmission channel for those monitoring points involving a wide area and scattered equipment layout with the help of its large coverage and high communication quality. This article analyzes the characteristics and advantages of wireless networks, and then discusses the networking scheme for remote data collection using wireless networks, and analyzes the reliability of network transmission. Finally, some program fragments on the server side are given.

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Li, Jun Xia. "Research on Data Compression of WSN Based on Compressed Sensing." Applied Mechanics and Materials 686 (October 2014): 423–28. http://dx.doi.org/10.4028/www.scientific.net/amm.686.423.

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For Wireless Sensor Networks (WSN) is responsible for sensing, collecting, processing and monitoring of environmental data, but it might be limited in resources. This paper describes in detail the compressed sensing theory, study the wireless sensor network data conventional compression and network coding method. The linear network coding scheme based on sparse random projection theory of compressed sensing. Simulation results show that this system satisfies the requirements of the reconstruction error of packets needed to reduce the number of nodes to the total number of 30%, improves the efficiency of data communications in wireless sensor network, reduce the energy consumption of the system. With other wireless sensor network data compression algorithm, the proposed algorithm has the advantages of simple realization, the compression effect is good, especially suitable for resource limited, and the accuracy requirements are not particularly stringent in wireless sensor networks.

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Materuhin, A. V., V. V. Shakhov, and O. D. Sokolova. "Spatial-temporal data collection process models using mobile sinks." Geodesy and Cartography 942, no. 12 (January 20, 2019): 22–28. http://dx.doi.org/10.22389/0016-7126-2018-942-12-22-28.

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Optimization of energy consumption in geosensor networks is a very important factor in ensuring stability, since geosensors used for environmental monitoring have limited possibilities for recharging batteries. The article is a concise presentation of the research results in the area of increasing the energy consumption efficiency for the process of collecting spatio-temporal data with wireless geosensor networks. It is shown that in the currently used configurations of geosensor networks there is a predominant direction of the transmitted traffic, which leads to the fact that through the routing nodes that are close to the sinks, a much more traffic passes than through other network nodes. Thus, an imbalance of energy consumption arises in the network, which leads to a decrease in the autonomous operation time of the entire wireless geosensor networks. It is proposed to use the possible mobility of sinks as an optimization resource. A mathematical model for the analysis of the lifetime of a wireless geosensor network using mobile sinks is proposed. The model is analyzed from the point of view of optimization energy consumption by sensors. The proposed approach allows increasing the lifetime of wireless geosensor networks by optimizing the relocation of mobile sinks.

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Kaur, Chamanpreet, and Vikramjit Singh. "A REVIEW ON DATA COLLECTION USING MOBILE NODES IN WSN." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 16, no. 5 (July 20, 2017): 6926–32. http://dx.doi.org/10.24297/ijct.v16i5.6263.

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Wireless sensor networks have become increasingly popular due to their wide range of application. Clustering sensor nodes organizing them hierarchically have proven to be an effective method to provide better data aggregation and scalability for the sensor network while conserving limited energy. Minimizing the energy consumption of a wireless sensor network application is crucial for effective realization of the intended application in terms of cost, lifetime, and functionality. However, the minimizing task is hardly possible as no overall energy cost function is available for optimization. The need for energy-efficient infrastructures for sensor networks is becoming increasingly important. Wireless sensor networks are networks consisting of many sensor nodes that communicate over a wireless media. A sensor node is equipped with a sensor module, a processor, a radio module and a battery. Since the battery limits the lifetime of the sensor nodes it also limits the lifetime of the sensor network, thus energy efficiency is a major issue for sensor networks. An important goal in many sensor networks is to monitor an area as long time as possible. Hence, it is important to distribute energy consumption evenly across the network. When the energy consumption is evenly distributed, the major part of the sensor nodes will stay alive approximately equally long time.

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Xu, Xia, Jin Tang, and Hua Xiang. "Data Transmission Reliability Analysis of Wireless Sensor Networks for Social Network Optimization." Journal of Sensors 2022 (January 6, 2022): 1–12. http://dx.doi.org/10.1155/2022/3842722.

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With the rapid development of the Internet in recent years, people are using the Internet less and less frequently. People publish and obtain information through various channels on the Internet, and online social networks have become one of the most important channels. Many nodes in social networks and frequent interactions between nodes create great difficulties for privacy protection, and some of the existing studies also have problems such as cumbersome computational steps and low efficiency. In this paper, we take the complex environment of social networks as the research background and focus on the key issues of mobile wireless sensor network reliability from the mobile wireless sensor networks that apply to large-scale, simpler information, and delay tolerance. By introducing intelligent learning methods and swarm intelligence bionic optimization algorithms, we address reliability issues such as mobile wireless sensor network fault prediction methods and topology reliability assessment methods in industrial application environments, the impact of mobile path optimization of mobile wireless sensor networks on data collection efficiency and network reliability, reliable data transmission based on data fusion methods, and intelligent fault tolerance strategies for multipath routing to ensure mobile wireless sensor networks operate energy-efficiently and reliably in complex industrial application environments.

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Gowrishankar and P. S. Satyanarayana. "Neural Network Based Traffic Prediction for Wireless Data Networks." International Journal of Computational Intelligence Systems 1, no. 4 (2008): 379. http://dx.doi.org/10.2991/ijcis.2008.1.4.9.

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Gowrishankar and P. S. Satyanarayana. "Neural Network Based Traffic Prediction for Wireless Data Networks." International Journal of Computational Intelligence Systems 1, no. 4 (December 2008): 379–89. http://dx.doi.org/10.1080/18756891.2008.9727633.

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S, Umamaheswari. "Capsule network-based data pruning in wireless sensor networks." International Journal of Communication Systems 33, no. 5 (August 18, 2019): e4145. http://dx.doi.org/10.1002/dac.4145.

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Verma, Richa, and Ravindara Bhatt. "Systems using Wireless Sensor Networks for Big Data." Computer Science & Engineering: An International Journal 12, no. 6 (December 30, 2022): 35–42. http://dx.doi.org/10.5121/cseij.2022.12605.

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Wireless sensor networks are continually developing in the big data world and are widely employed in many aspects of life. In the monitoring region, the WSN gathers, analyses, and sends information about the detected item. In recent years, WSN has also made important strides in the management of critical data protection, traffic monitoring, and climate - change detection. The rich big data contributors known as wireless sensor networks provide a significant amount of data from numerous sensor nodes in large-scale networks (WSNs), which are among the numerous potential datasets. However, unlike traditional wireless networks, suffer from significant constraints in communication and data dependability due to the cluster’s constraints. This paper gives a detailed assessment of cutting-edge research on using WSN into large data systems Potential network and effective deployment and scientific problems are presented and discussed in the context of the study topics and aim. Finally, unresolved issues are addressed in order to discuss interesting future research possibilities.

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Zhang, Wen Chao, and Yu Zhen Liu. "Research on Data Fusion Technology in WSN." Applied Mechanics and Materials 148-149 (December 2011): 75–81. http://dx.doi.org/10.4028/www.scientific.net/amm.148-149.75.

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Wireless Sensor Networks is a novel technology of information acquisition and processing. It integrates the technologies of sensor, wireless communication, and microelectronics. It can sense and collect environmental parameters in the range of the network, and then process the parameters cooperatively in real time. We discuss the design principle and performance evaluation method of the data fusion algorithm for wireless sensor networks in detail. We introduce the concept of fusion cost, and emphasize that the data fusion algorithm in wireless sensor networks must be combined with specific application background. Finally, we emphasize the proposed algorithm which combined reduced-rank filtering with adaptive weighted. The fact that it can effectively eliminate the effect of abnormal noise on fusion performance is illustrated through examples. The results showed that this algorithm occupies obviously the advantage in the aspect of fault-tolerant ability.

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Krivchenkov, Aleksandr, and Dmitry Sedykh. "Analysis Of Packets Delay In Wireless Data Networks." Transport and Telecommunication Journal 16, no. 4 (December 1, 2015): 330–40. http://dx.doi.org/10.1515/ttj-2015-0030.

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Abstract The networks with wireless links for automation control applications traffic transmission when packets have small size and application payload is predictable are under consideration. Analytical model for packets delay on their propagation path through the network is proposed. Estimations for network architectures based on WiFi and Bluetooth wireless technologies are made. The specifications for physical layer 802.11 a/b/g/n and 802.15.1 are under consideration. Analytical and experimental results for delivered network bandwidth for different network architecture, traffic structure and wireless technologies were compared to validate that basic mechanisms are correctly taken into account in the model. It is shown that basic effects are taken into account and further accuracy “improvement” of the model will give not more than 5%. As a result that is important for automation control applications we have reliably received the lowest possible level for packets delay in one wireless link. For 802.11 it is of order of 0.2 ms, for 802.15.1 it is 1.25 ms and is true when application packet can be transferred by one data frame.

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Liu, Yun, Qing-An Zeng, and Ying-Hong Wang. "Data Fusion in Wireless Sensor Networks." International Journal of Distributed Sensor Networks 10, no. 4 (January 2014): 131870. http://dx.doi.org/10.1155/2014/131870.

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Kim, S., S. H. Son, J. A. Stankovic, and Y. Choi. "Data Dissemination Over Wireless Sensor Networks." IEEE Communications Letters 8, no. 9 (September 2004): 561–63. http://dx.doi.org/10.1109/lcomm.2004.833810.

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Wang, Jian, Zhipeng Wu, Yanheng Liu, Weiwen Deng, and Heekuck Oh. "Computational data privacy in wireless networks." Peer-to-Peer Networking and Applications 10, no. 4 (January 25, 2016): 865–73. http://dx.doi.org/10.1007/s12083-016-0435-6.

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patel, Shubham. "DATA COLLECTION IN WIRELESS SENSOR NETWORKS." International Journal of Recent Advancement in Engineering & Research 2, no. 5 (May 14, 2017): 23. http://dx.doi.org/10.24128/ijraer.2017.ij9de.

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Singh, Rajinder, and Er Nidhi Bhalla. "Path Mechanism to reduce packet data loss in Wireless Mesh Networks." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 3 (June 30, 2012): 134–41. http://dx.doi.org/10.24297/ijct.v2i3c.6779.

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A wireless mesh network (WMN) is a communicationnetwork made up of radio nodes organized in a meshtopology. Wireless mesh network often consists of meshclients, mesh routers and gateways. A wireless Mesh networkuses multi-hop communication. Due to multi-hop architectureand wireless nature, Mesh networks are vulnerable to varioustypes of Denial of Services attack. It suffers from Packetdropping at Routing layer. Client nodes are unable to getservices from gateway nodes, hence network gets down. ThePaper emphasis on the developing of a path protocol when theminimun possible packet dropp occurs in wireless meshnetworks. Due to packet droping occurrences the networkperformance degrades. In the work, we have evaluated thePerformance of WMN under packet dropping on the basis oftheir throughput and Data packet loss.

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Arslan tariq and Rafaqat Alam. "A Review on Skin Cancer Data Using Image Processing." Lahore Garrison University Research Journal of Computer Science and Information Technology 2, no. 3 (September 28, 2018): 29–32. http://dx.doi.org/10.54692/lgurjcsit.2018.020351.

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The wireless industry is going very fast nowadays. We can easily see the evolution from 2G to 3G and now advance to the 4G and 5G network. Before wireless networks, wired networks were commonly used in every field. But there were some disadvantages regarding mobility, quality of service and connectivity. Wired network bounded the region of the working area for the internet and it requires multiple wires to connect computer from one device to another. While on the other hand wireless network is an open source for everyone to use the internet. There is no limitation of the region and no issue regarding connectivity because data is transfer through signal which includes frequency in the form of waves. But there are also some disadvantages of wireless network regarding cost, speed, coverage, bandwidth etc. If we talk about the better network so it depends on the situation and problem.

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Lu, Xinghua, Jiahao Huang, and Guohua Luo. "Data detection technology of wireless sensor network based on non-stationary filtering." Journal of Physics: Conference Series 2113, no. 1 (November 1, 2021): 012023. http://dx.doi.org/10.1088/1742-6596/2113/1/012023.

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Abstract By optimizing the data detection performance of distributed wireless sensor networks, the data sensing and collecting ability of wireless sensor networks can be improved. Traditional methods adopt statistical characteristic parameter detection algorithm for distributed wireless sensor network data detection. Distributed wireless sensor network data has strong time-frequency coupling, so it is difficult to realize frequency domain spatial parameter clustering in frequency domain, and the detection performance is not good. A distributed wireless sensor network data detection algorithm based on non-stationary filtering and high-order statistical feature peak retrieval is proposed. The data model of distributed wireless sensor network is constructed under the interference of color noise. The weak vibration signal is subjected to time-frequency analysis and noise separation by non-stationary filtering, and the spectral peak of distributed wireless sensor network data is searched by the fourth-order cumulant slice post-operator to realize the optimal detection of signals. The simulation results show that the algorithm has a high probability of accurate detection, and has a good ability of suppressing noise and noise sidelobe information interference, which improves the probability of accurate detection of distributed wireless sensor network data under low signal-to-noise ratio.

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Huang, Sun, and Yang. "Data Uploading Strategy for Underwater Wireless Sensor Networks." Sensors 19, no. 23 (November 29, 2019): 5265. http://dx.doi.org/10.3390/s19235265.

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Underwater wireless sensor networks (UWSNs) have become a popular research topic due to the challenges of underwater communication. The existing mechanisms for collecting data from UWSNs focus on reducing the data redundancy and communication energy consumption, while ignoring the problem of energy-saving transmission after compression. In order to improve the efficiency of data collection, we propose a data uploading decision-making strategy based on the high similarity of the collected data and the energy consumption of the high similarity data compression. This decision-making strategy efficiently optimizes the energy consumption of the networks. By analyzing the data similarity, the quality of network communication, and uploading energy consumption, the decision-making strategy provides an energy-efficient data upload strategy for underwater nodes, which reduces the energy consumption in various network settings. The simulation results show that compared with several existing data compression and uploading methods, the proposed data upload methods has better energy saving effect in different network scenarios.

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Li, Qiangyi, and Ningzhong Liu. "Coverage Blind Area Repair Based on Perceived Multimedia Data Driven in Mobile Wireless Sensor Networks." Advances in Multimedia 2022 (April 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/2354024.

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Wireless sensor networks deploy a large number of wireless sensor nodes in the monitoring area to complete the target monitoring task. The coverage effect is an important index to evaluate wireless sensor networks. The effect of network coverage is realized by the wireless sensor nodes deployed in the monitoring area, because the reasonable deployment of sensor nodes can affect the accuracy, reliability, real time, and integrity of monitoring target data collection. The main purpose of network coverage is to use the least number of wireless sensor nodes to complete the monitoring task of targets in the monitoring area and to avoid the emergence of coverage blind areas. Random node deployment, electronic equipment failure, software error, fire spread, flood, and other phenomena may lead to large-scale failure of wireless sensor nodes. For the failure of some nodes, the phenomenon of coverage blind area appears in the network. In order to repair the coverage blind area in the network, a coverage blind area repair algorithm for wireless sensor networks is proposed in this article. The coverage blind area attracts the nodes, makes the nodes move, and repairs the coverage blind area in the network.

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Hills, A., and D. B. Johnson. "Seamless access to multiple wireless data networks. A wireless data network infrastructure at Carnegie Mellon University." IEEE Personal Communications 3, no. 1 (1996): 56–63. http://dx.doi.org/10.1109/98.486976.

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Ye, Qian, Meng Wu, and Yu Fei Wang. "A Survey on QoS-Aware Secure Data Transmission in Multimedia Sensor Networks." Applied Mechanics and Materials 511-512 (February 2014): 120–25. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.120.

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QoS assurance and information security are important for multimedia data transmission in wireless multimedia sensor networks. First, the status of multimedia sensor networks was introduced. Then research status of QoS assurance and information security of wireless multimedia sensor networks was surveyed, and the related open issues were also analyzed. Finally, the technology routes to solve the open issues were also studied and discussed. We argue that trusted QoS architecture considering various applications with different QoS requirements is important for wireless multimedia sensor networks. And network conditions and protocol layers must be well designed in QoS-aware secure multimedia routing mechanism to satisfy QoS requirements of multiple traffic classes. Technologies of information hiding, multimedia data resolution, and multi-routing can be used for privacy protection in wireless multimedia sensor networks.

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Diachok, Roman, and Halyna Klym. "DATA CLEANING METHOD IN WIRELESS SENSOR-BASED ON INTELLIGENCE TECHNOLOGY." Measuring Equipment and Metrology 83, no. 2 (2022): 5–10. http://dx.doi.org/10.23939/istcmtm2022.02.005.

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The method of cleaning management data in wireless sensor networks based on intelligence technology has been studied. Specific forms of application of wireless sensor networks are analyzed. The characteristics of the structure of wireless sensor networks are presented and the data cleaning technology based on the clustering model is offered. An algorithm for deleting a cluster-based replication record is proposed and the accuracy of data cleaning methods is tested. The obtained results testify to the efficiency of using the studied method.

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Prabhakar, Abhishek, Amod Tiwari, and Vinay Kumar Pathak. "Demand of Wireless Network and Security in Current Research." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 14, no. 6 (April 19, 2015): 5809–13. http://dx.doi.org/10.24297/ijct.v14i6.1918.

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Wireless security is the prevention of unauthorized access to computers using wireless networks .The trends in wireless networks over the last few years is same as growth of internet. Wireless networks have reduced the human intervention for accessing data at various sites .It is achieved by replacing wired infrastructure with wireless infrastructure. Some of the key challenges in wireless networks are Signal weakening, movement, increase data rate, minimizing size and cost, security of user and QoS (Quality of service) parameters... The goal of this paper is to minimize challenges that are in way of our understanding of wireless network and wireless network performance.

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HUANG, GUANGYAN, XIAOWEI LI, JING HE, and XIN LI. "DATA MINING VIA MINIMAL SPANNING TREE CLUSTERING FOR PROLONGING LIFETIME OF WIRELESS SENSOR NETWORKS." International Journal of Information Technology & Decision Making 06, no. 02 (June 2007): 235–51. http://dx.doi.org/10.1142/s0219622007002538.

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Clustering is applied in wireless sensor networks for increasing energy efficiency. Clustering methods in wireless sensor networks are different from those in traditional data mining systems. This paper proposes a novel clustering algorithm based on Minimal Spanning Tree (MST) and Maximum Energy resource on sensors named MSTME. Also, specified constrains of clustering in wireless sensor networks and several evaluation metrics are given. MSTME performs better than already known clustering methods of Low Energy Adaptive Clustering Hierarchy (LEACH) and Base Station Controlled Dynamic Clustering Protocol (BCDCP) in wireless sensor networks when they are evaluated by these evaluation metrics. Simulation results show MSTME increases energy efficiency and network lifetime compared with LEACH and BCDCP in two-hop and multi-hop networks, respectively.

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Zhang, Chen, Yuanzhu Chen, and Cheng Li. "TCP adaptation with network coding and opportunistic data forwarding in multi-hop wireless networks." PeerJ Computer Science 2 (October 3, 2016): e89. http://dx.doi.org/10.7717/peerj-cs.89.

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Opportunistic data forwarding significantly increases the throughput in multi-hop wireless mesh networks by utilizing the broadcast nature of wireless transmissions and the fluctuation of link qualities. Network coding strengthens the robustness of data transmissions over unreliable wireless links. However, opportunistic data forwarding and network coding are rarely incorporated with TCP because the frequent occurrences of out-of-order packets in opportunistic data forwarding and long decoding delay in network coding overthrow TCP’s congestion control. In this paper, we propose a solution dubbed TCPFender, which supports opportunistic data forwarding and network coding in TCP. Our solution adds an adaptation layer to mask the packet loss caused by wireless link errors and provides early positive feedbacks to trigger a larger congestion window for TCP. This adaptation layer functions over the network layer and reduces the delay of ACKs for each coded packet. The simulation results show that TCPFender significantly outperforms TCP/IP in terms of the network throughput in different topologies of wireless networks.

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Zong Chen, Dr Joy Iong, and Lu-Tsou Yeh. "Data Forwarding in Wireless Body Area Networks." June 2020 2, no. 2 (June 1, 2020): 80–87. http://dx.doi.org/10.36548/jei.2020.2.002.

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One of the most crucial application of Wireless Body Area Networks in healthcare applications is the process of monitoring human bodies and gather physiological data. Network performance degradation in the form of energy efficiency and latency are caused because of energy depletions which arises due to limited energy resource availability. The heterogeneity of body sensors will lead to variation in the rate of energy consumption. Based on this, a novel Data Forwarding Strategy is presented in this research work to enhance collaborative WBAN operations, improve network lifetime and restrict energy consumption of the sensors. In this paper, we have contributed towards reducing the size of data to be transmitted by compressed sensing and selection of relay sensor based on sampling frequency, energy levels and sensor importance. Using the proposed methodology, it is possible to improve both reliability and energy-efficiency of WBAN data transmission. moreover, it is also possible to adapt to the changing WBAN topologies when the proposed methodology is used, balancing energy efficiency and consumption.

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Wang, Xinheng. "Wireless mesh networks." Journal of Telemedicine and Telecare 14, no. 8 (December 2008): 401–3. http://dx.doi.org/10.1258/jtt.2008.008003.

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Wireless telemedicine using GSM and GPRS technologies can only provide low bandwidth connections, which makes it difficult to transmit images and video. Satellite or 3G wireless transmission provides greater bandwidth, but the running costs are high. Wireless networks (WLANs) appear promising, since they can supply high bandwidth at low cost. However, the WLAN technology has limitations, such as coverage. A new wireless networking technology named the wireless mesh network (WMN) overcomes some of the limitations of the WLAN. A WMN combines the characteristics of both a WLAN and ad hoc networks, thus forming an intelligent, large scale and broadband wireless network. These features are attractive for telemedicine and telecare because of the ability to provide data, voice and video communications over a large area. One successful wireless telemedicine project which uses wireless mesh technology is the Emergency Room Link (ER-LINK) in Tucson, Arizona, USA. There are three key characteristics of a WMN: self-organization, including self-management and self-healing; dynamic changes in network topology; and scalability. What we may now see is a shift from mobile communication and satellite systems for wireless telemedicine to the use of wireless networks based on mesh technology, since the latter are very attractive in terms of cost, reliability and speed.

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ABIDI, Bahae, Abdelillah JILBAB, and Mohamed EL HAZITI. "Security in wireless sensor networks." International Journal of Informatics and Communication Technology (IJ-ICT) 8, no. 1 (April 1, 2019): 13. http://dx.doi.org/10.11591/ijict.v8i1.pp13-18.

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Even in difficult places to reach, the new networking technique allows the easy deployment of sensor networks, although these wireless sensor networks confront a lot of constraints. The major constraint is related to the quality of information sent by the network. The wireless sensor networks use different methods to achieve data to the base station. Data aggregation is an important one, used by these wireless sensor networks. But this aggregated data can be subject to several types of attacks and provides security is necessary to resist against malicious attacks, secure communication between severely resource constrained sensor nodes while maintaining the flexibility of the topology changes. Recently, several secure data aggregation schemes have been proposed for wireless sensor networks, it provides better security compared with traditional aggregation. In this paper, we try to focus on giving a brief statement of the various approaches used for the purpose of secure data aggregation in wireless sensor networks.

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Huang, Yudi, Junjie Tan, and Ying-Chang Liang. "Wireless big data: transforming heterogeneous networks to smart networks." Journal of Communications and Information Networks 2, no. 1 (March 2017): 19–32. http://dx.doi.org/10.1007/s41650-017-0002-1.

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S, Sweta, and Balajee Maram. "Underwater Wireless Sensor Networks." JOIV : International Journal on Informatics Visualization 2, no. 1 (January 5, 2018): 10. http://dx.doi.org/10.30630/joiv.2.1.99.

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There are a plenty of unexploited resources that lies underwater that covers almost 75% of the earth.In order to utilise them,the ﬁeld of underwater wireless sensor networks (UWSN) is attracting the researchers to extend their thoughts in this field. The wireless sensor networks are heavy networks that consist of small low cost sensors that have a large amount of solving ability and energy resources which can be applicable in any type of irregular environments irrespective of changing conditions. Keeping in view of the real-time remote data transferring requirements, underwater acoustic sensor networks (UASN) has been recognised as a preferred network because it satisfies all aspects of data transfer. In UASN, the required availability and recycling of energy resources along with specified utilisation of data with the help of utilized sensor nodes for energy requirements that are necessary are done for the development of further theories in these contexts. Due to these causes, the maximum underwater resources utilisation techniques mainly depends on UAN (Underwater Acoustic Networks).Underwater wireless sensor networks (UWSNs) suitable for applications on submarine detection and monitoring,where nodes collect data with a mobile autonomous underwater vehicle (AUV) via optical communications, and applied accordingly to deal with further approaches. They provide continuous monitoring for various applications like ocean sampling network, pollution monitoring, submarine detection, disaster prevention etc.This paper particularly deals with a brief collection of the UWSN applications and some of the algorithms for the path finding in order to pass maximum valued information(VOI) among the different nodes.

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Saluja, Arleen, Pruthikrai Mahatanankoon, and Tibor Gyires. "Users' Perceptions of Wireless Networks Usage." International Journal of Interdisciplinary Telecommunications and Networking 2, no. 2 (April 2010): 67–78. http://dx.doi.org/10.4018/jitn.2010040105.

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Wireless networks provide the most convenient way to access the Internet. This paper determines how the preliminary and advanced knowledge of wireless networks affect users’ perception and usage. Based on their knowledge, users develop certain emotional responses, which could affect their wireless network usage. This study identifies the effect of these emotional responses on users’ degree of use of wireless networks. Using data collected from 143 respondents, regression analyses reveal that wireless network usage is affected less by advanced wireless network knowledge and more by preliminary knowledge. The findings also show that the degree of usage depends on users’ ability to understand basic wireless network functionality and security. To motivate wireless usage, developers and designers of future wireless technologies do not need to overwhelm users with technical detail.

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Iqbal, Naveed, Abdulmajid Lawal, and Azzedine Zerguine. "MitigatingWireless Channel Impairments in Seismic Data Transmission Using Deep Neural Networks." Sensors 21, no. 18 (September 12, 2021): 6105. http://dx.doi.org/10.3390/s21186105.

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The traditional cable-based geophone network is an inefficient way of seismic data transmission owing to the related cost and weight. The future of oil and gas exploration technology demands large-scale seismic acquisition, versatility, flexibility, scalability, and automation. On the one hand, a typical seismic survey can pile up a massive amount of raw seismic data per day. On the other hand, the need for wireless seismic data transmission remains immense. Moving from pre-wired to wireless geophones faces major challenges given the enormous amount of data that needs to be transmitted from geophones to the on-site data collection center. The most important factor that has been ignored in the previous studies for the realization of wireless seismic data transmission is wireless channel effects. While transmitting the seismic data wirelessly, impairments like interference, multi-path fading, and channel noise need to be considered. Therefore, in this work, a novel amalgamation of blind channel identification and deep neural networks is proposed. As a geophone already is responsible for transmitting a tremendous amount of data under tight timing constraints, the proposed setup eschews sending any additional training signals for the purpose of mitigating the channel effects. Note that the deep neural network is trained only on synthetic seismic data without the need to use real data in the training process. Experiments show that the proposed method gives promising results when applied to the real/field data set.

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Liu, Zhou-zhou, and Shi-ning Li. "Sensor-cloud data acquisition based on fog computation and adaptive block compressed sensing." International Journal of Distributed Sensor Networks 14, no. 9 (September 2018): 155014771880225. http://dx.doi.org/10.1177/1550147718802259.

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The emergence of sensor-cloud system has completely changed the one-to-one service mode of traditional wireless sensor networks, and it greatly expands the application field of wireless sensor networks. As the high delay of large-scale data processing tasks in sensor-cloud, a sensor-cloud data acquisition scheme based on fog computing and adaptive block compressive sensing is proposed. First, the sensor-cloud framework based on fog computing is constructed, and the fog computing layer includes many wireless mobile nodes, which helps to realize the implementation of information transfer management between lower wireless sensor networks layer and upper cloud computing layer. Second, in order to further reduce network traffic and improve data processing efficiency, an adaptive block compressed sensing data acquisition strategy is proposed in the lower wireless sensor networks layer. By dynamically adjusting the size of the network block and building block measurement matrix, the implementation of sensor compressed sensing data acquisition is achieved; in order to further balance the lower wireless sensor networks’ node energy consumption, reduce the time delay of data processing task in fog computing layer, the mobile node data acquisition path planning strategy and multi-mobile nodes collaborative computing system are proposed. Through the introduction of the fitness value constraint transformation processing technique and parallel discrete elastic collision optimization algorithm, the efficient processing of the fog computing layer data is realized. Finally, the simulation results show that the sensor-cloud data acquisition scheme can effectively achieve large-scale sensor data efficient processing. Moreover, compared with cloud computing, the network traffic is reduced by 20% and network task delay is reduced by 12.8%–20.1%.

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Liang, Xiurong, and You Qian. "Energy Balance Routing Protocol for Wireless Sensor Networks Based on Fuzzy Control Strategy." Wireless Communications and Mobile Computing 2022 (May 27, 2022): 1–12. http://dx.doi.org/10.1155/2022/4597992.

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The existing routing protocols for wireless sensor networks were not reasonable in design, which limited their application. Most of the existing studies did not take into account the energy consumption of the network and the balanced use of the energy of sensor nodes, which led to the unsatisfactory application effect of wireless sensor networks in some fields. Therefore, from the perspective of energy balance in wireless sensor networks, this paper proposed a construction method of an energy balance routing protocol in wireless sensor networks based on a fuzzy control strategy. Firstly, based on the analysis of the basic composition of wireless sensor networks and the structure of sensor nodes, this paper expounded the basic process of wireless data transmission and summarized the classification and characteristics of routing protocols in wireless sensor networks from different angles. Secondly, according to the node data transmission characteristics of wireless sensor networks, the energy balance use model of sensor nodes was established, and the design method of the energy balance routing protocol based on fuzzy control strategy was proposed, and the data transmission link was optimized. Finally, through experimental comparative analysis, the results showed that the energy balanced routing protocol proposed in this paper can effectively realize the energy balanced use of the network data transmission process. Compared with other common routing protocols, the wireless sensor network routing protocol proposed in this paper can not only improve the data transmission efficiency and reduce the data redundancy but also save energy consumption and prolong the network running time. The design method of routing protocol proposed in this paper will be conducive to the optimization and application of routing protocol in wireless sensor networks and provide a theoretical basis for the related research of wireless sensor networks.

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Journal articles on the topic 'Wireless data networks'

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