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Journal articles on the topic 'Wireless computing'

To see the other types of publications on this topic, follow the link: Wireless computing.

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2023

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1

Li, Fan, Jinyuan Chen, and Zhiying Wang. "Wireless MapReduce Distributed Computing." IEEE Transactions on Information Theory 65, no. 10 (October 2019): 6101–14. http://dx.doi.org/10.1109/tit.2019.2924621.

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2

Goel, Shipra. "Grid Computing integrated with Mobile computing wireless devices." International Journal of Mobile Network Communications & Telematics 1, no. 2 (December 31, 2011): 39–48. http://dx.doi.org/10.5121/ijmnct.2011.1203.

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3

WAKAMIYA, Naoki. "Neural Computing on Wireless Networks." IEICE ESS Fundamentals Review 14, no. 4 (April 1, 2021): 308–17. http://dx.doi.org/10.1587/essfr.14.4_308.

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4

Manvi, Sunilkumar S., and Mahantesh N. Birje. "Wireless Grid Computing: A Survey." IETE Journal of Education 50, no. 3 (September 2009): 119–31. http://dx.doi.org/10.1080/09747338.2009.10876059.

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5

Master, Neal, Aditya Dua, Dimitrios Tsamis, Jatinder Pal Singh, and Nicholas Bambos. "Adaptive Prefetching in Wireless Computing." IEEE Transactions on Wireless Communications 15, no. 5 (May 2016): 3296–310. http://dx.doi.org/10.1109/twc.2016.2519882.

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6

Lefor, Alan T., and Maarten K. Lefor. "Wireless computing in health care." Current Surgery 60, no. 4 (July 2003): 477–79. http://dx.doi.org/10.1016/s0149-7944(03)00087-4.

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7

Schumny, Harald. "Bluetooth, wireless mobile computing, eBooks." Computer Standards & Interfaces 24, no. 3 (July 2002): 189–91. http://dx.doi.org/10.1016/s0920-5489(02)00033-8.

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8

Patton, Janice K. "Wireless Computing in the Library." Community & Junior College Libraries 10, no. 3 (March 2002): 11–16. http://dx.doi.org/10.1300/j107v10n03_03.

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9

Rhyu, Sunkyung, and SangYeob Oh. "ICT-Based Wireless Personal Computing." Wireless Personal Communications 93, no. 1 (January 23, 2017): 1–5. http://dx.doi.org/10.1007/s11277-017-3955-3.

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10

Wang, Xingzhu. "A Collaborative Detection Method of Wireless Mobile Network Intrusion Based on Cloud Computing." Wireless Communications and Mobile Computing 2022 (October 19, 2022): 1–12. http://dx.doi.org/10.1155/2022/1499736.

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In order to improve the communication security of wireless mobile network, a collaborative intrusion detection method based on cloud computing is studied. The mobile terminal and the cloud computing platform are connected by the wireless mobile network. The cloud computing platform authentication server adopts a dual server and multifactor authentication scheme for mobile cloud computing to provide authentication services for mobile terminal users. The web server of the cloud computing platform uses the intrusion node detection protocol of the neighbor classification mechanism to provide a communication security protocol for users; Using the HMM algorithm, the intrusion detection module of the computing platform realizes the intrusion detection of wireless mobile network. Finally, using authentication service, security protocol, and intrusion detection module completes the cooperative detection of mobile network intrusion. The experimental results show that this method can realize the cooperative detection of wireless mobile network intrusion, and the detection accuracy is as high as 98%, which ensures the communication security of wireless mobile network.

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11

Feng, Hao, Jaime Llorca, Antonia M. Tulino, and Andreas F. Molisch. "Optimal Control of Wireless Computing Networks." IEEE Transactions on Wireless Communications 17, no. 12 (December 2018): 8283–98. http://dx.doi.org/10.1109/twc.2018.2875740.

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12

Satoh, I. "Software testing for wireless mobile computing." IEEE Wireless Communications 11, no. 5 (October 2004): 58–64. http://dx.doi.org/10.1109/mwc.2004.1351682.

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13

Duchamp, Daniel. "Systems Software for Wireless Mobile Computing." ACM SIGOPS Operating Systems Review 26, no. 2 (April 1992): 10. http://dx.doi.org/10.1145/142111.964563.

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14

Bahli, Bouchaib, and Younes Benslimane. "An exploration of wireless computing risks." Information Management & Computer Security 12, no. 3 (July 2004): 245–54. http://dx.doi.org/10.1108/09685220410542606.

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15

Duchamp, D., S. K. Feiner, and G. Q. Maguire. "Software technology for wireless mobile computing." IEEE Network 5, no. 6 (November 1991): 12–18. http://dx.doi.org/10.1109/65.103804.

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16

Periola, Ayodele A., and Olabisi E. Falowo. "Hybrid wireless aided volunteer computing paradigm." Wireless Networks 26, no. 7 (June 24, 2020): 5355–69. http://dx.doi.org/10.1007/s11276-020-02395-z.

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17

Kakalik, John S., and Marie A. Wright. "Privacy and Security in Wireless Computing." Network Security 2000, no. 12 (December 2000): 12–15. http://dx.doi.org/10.1016/s1353-4858(00)12018-5.

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18

Papageorgiou, Apostolos. "Service-oriented computing with wireless participants." ACM SIGMultimedia Records 4, no. 2 (July 24, 2012): 9–10. http://dx.doi.org/10.1145/2350204.2350211.

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19

Shaheen, Javed Ahmad. "Mobile Computing: Wireless Networking Security Issues." International Journal of Grid and Distributed Computing 9, no. 10 (October 31, 2016): 273–82. http://dx.doi.org/10.14257/ijgdc.2016.9.10.24.

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20

Ahuja, Sanjay P., and Jack R. Myers. "A Survey on Wireless Grid Computing." Journal of Supercomputing 37, no. 1 (July 2006): 3–21. http://dx.doi.org/10.1007/s11227-006-3845-z.

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21

Wu, Xintao, Jie Gan, Shiyong Chen, Xu Zhao, and Yucheng Wu. "Optimization Strategy of Task Offloading with Wireless and Computing Resource Management in Mobile Edge Computing." Wireless Communications and Mobile Computing 2021 (November 11, 2021): 1–11. http://dx.doi.org/10.1155/2021/8288836.

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Mobile edge computing (MEC) provides user equipment (UE) with computing capability through wireless networks to improve the quality of experience (QoE). The scenario with multiple base stations and multiple mobile users is modeled and analyzed. The optimization strategy of task offloading with wireless and computing resource management (TOWCRM) in mobile edge computing is considered. A resource allocation algorithm based on an improved graph coloring method is used to allocate wireless resource blocks (RBs). The optimal solution of computing resource is obtained by using KKT conditions. To improve the system utility, a semi-distributed TOWCRM strategy is proposed to obtain the task offloading decision. Theoretical simulations under different system parameters are executed, and the proposed semi-distributed TOWCRM strategy can be completed with finite iterations. Simulation results have verified the effectiveness of the proposed algorithm.

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22

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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23

Liu, Jie, and Li Zhu. "Joint Resource Allocation Optimization of Wireless Sensor Network Based on Edge Computing." Complexity 2021 (March 29, 2021): 1–11. http://dx.doi.org/10.1155/2021/5556651.

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Resource allocation has always been a key technology in wireless sensor networks (WSN), but most of the traditional resource allocation algorithms are based on single interface networks. The emergence and development of multi-interface and multichannel networks solve many bottleneck problems of single interface and single channel networks, it also brings new opportunities to the development of wireless sensor networks, but the multi-interface and multichannel technology not only improves the performance of wireless sensor networks but also brings great challenges to the resource allocation of wireless sensor networks. Edge computing changes the traditional centralized cloud computing processing method into a method that reduces computing storage capacity to the edge of the network and faces users and terminals. Realize the advantages of lower latency, higher bandwidth, and fast response. Therefore, this paper proposes a joint optimization algorithm of resource allocation based on edge computing. We establish a wireless sensor allocation model and then propose our algorithm model combined with the advantages of edge computing. Compared with the traditional allocation algorithm (PCOA, MCMH, and TDMA), it can further improve the resource utilization, reduce the network energy consumption, increase network capacity, and reduce the complexity of the schemes.

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24

Ceballos, Henry Zárate, and Jorge Eduardo Ortiz Triviño. "S.O.V.O.R.A.: A Distributed Wireless Operating System." Information 11, no. 12 (December 14, 2020): 581. http://dx.doi.org/10.3390/info11120581.

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Due to the growth of users and linked devices in networks, there is an emerging need for dynamic solutions to control and manage computing and network resources. This document proposes a Distributed Wireless Operative System on a Mobile Ad-hoc Network (MANET) to manage and control computing resources in relation to several virtual resources linked in a wireless network. This prototype has two elements: a local agent that works on each physical node to manage the computing resources (e.g., virtual resources and distributed applications) and an orchestrator agent that monitors, manages, and deploys policies on each physical node. These elements arrange the local and global computing resources to provide a quality service to the users of the Ad-hoc cluster. The proposed S.O.V.O.R.A. model (Operating Virtualized System oriented to Ad-hoc networks) defines primitives, commands, virtual structures, and modules to operate as a distributed wireless operating system.

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25

Hatti, Daneshwari I., and Ashok V. Sutagundar. "Nature Inspired Computing for Wireless Networks Applications." International Journal of Applied Evolutionary Computation 10, no. 1 (January 2019): 1–29. http://dx.doi.org/10.4018/ijaec.2019010101.

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Nature inspired computing (NIC) is a computing paradigm inspired by the attractive behavior of nature. NIC has influenced the researchers to perform optimization in many approaches using physics/chemistry-based algorithms and biology-based algorithms. Physics/chemistry-based algorithms include the water cycle, a galaxy base, or gravitational-based algorithms. Biology-based algorithms, namely bio-inspired and swarm intelligence-related algorithms are discussed with their importance in the field of wireless networks. A wireless network such as MANET's, VANET, AdHoc, and IoT are playing a vital role in all sectors. Some of the issues such as finding the optimal path in routing, clustering, dynamic allocation of motes, energy and lifetime of the network pertaining to a wireless network can be solved using an NIC approach. Algorithms derived by the inspiration from nature are discussed briefly in this article.

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26

Liu, Chuanyi, and Xiaoyong Li. "Fast, Resource-Saving, and Anti-Collaborative Attack Trust Computing Scheme Based on Cross-Validation for Clustered Wireless Sensor Networks." Sensors 20, no. 6 (March 12, 2020): 1592. http://dx.doi.org/10.3390/s20061592.

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The trust computing mechanism has an increasing role in the cooperative work of wireless sensor networks. However, the computing speed, resource overhead, and anti-collaborative attack ability of a trust mechanism itself are three key challenging issues for any open and resource-constrained wireless sensor networks. In this study, we propose a fast, resource-saving, and anti-collaborative attack trust computing scheme (FRAT) based on across-validation mechanism for clustered wireless sensor networks. First, according to the inherent relationship among three network entities (which are made up of three types of network nodes, namely base stations, cluster heads, and cluster members), we propose the cross-validation mechanism, which is effective and reliable against collaborative attacks caused by malicious nodes. Then, we adopt a fast and resource-saving trust computing scheme for cooperation between between cluster heads or cluster members. This scheme is suitable for wireless sensor networks because it facilitates resource-saving. Through theoretical analysis and experiments, the feasibility and effectiveness of the trust computing scheme proposed in this study are verified.

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27

Hawkes, Mark, and Claver Hategekimana. "Impacts of Mobile Computing on Student Learning in the University: A Comparison of Course Assessment Data." Journal of Educational Technology Systems 38, no. 1 (September 2009): 63–74. http://dx.doi.org/10.2190/et.38.1.g.

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This study focuses on the impact of wireless, mobile computing tools on student assessment outcomes. In a campus-wide wireless, mobile computing environment at an upper Midwest university, an empirical analysis is applied to understand the relationship between student performance and Tablet PC use. An experimental/control group comparison of mobile computing enabled learning outcomes in selected courses showed that the integration of wireless technology and highly functional computing tools did not have a negative effect on student assessment results. Out of the four courses evaluated, none of the revealed test scores were statistically different between non-using and mobile computer using groups, indicating no negative impacts of the introduction of ubiquitous technology into the classroom. A freshman-level college math course showed statistically significantly positive differences in course assessment scores when mobile computing was implemented over the same timeline. Results are discussed.

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28

Kumar, Sunil, Kashyap Kambhatla, Fei Hu, Mark Lifson, and Yang Xiao. "Ubiquitous Computing for Remote Cardiac Patient Monitoring: A Survey." International Journal of Telemedicine and Applications 2008 (2008): 1–19. http://dx.doi.org/10.1155/2008/459185.

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New wireless technologies, such as wireless LAN and sensor networks, for telecardiology purposes give new possibilities for monitoring vital parameters with wearable biomedical sensors, and give patients the freedom to be mobile and still be under continuous monitoring and thereby better quality of patient care. This paper will detail the architecture and quality-of-service (QoS) characteristics in integrated wireless telecardiology platforms. It will also discuss the current promising hardware/software platforms for wireless cardiac monitoring. The design methodology and challenges are provided for realistic implementation.

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29

Lei, Wen Li, Xin Cheng Ren, and Yan Hu Fan. "Research of Mine Wireless Video Monitoring Sub-Station Based on WIFI." Applied Mechanics and Materials 214 (November 2012): 605–9. http://dx.doi.org/10.4028/www.scientific.net/amm.214.605.

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With the development of country's attention to safe production of mine, electronic technology and network technology begin to get widespread application in the mine exploration and monitoring. It has been brought forward a new method of mine wireless video monitoring sub-station based on WIFI wireless network technology, it send the collected video data to the ground monitoring equipment through wireless video capture terminals and wireless access point AP which has been installing in the mine tunnel, with a router connected to the cloud computing platform that enables remote monitoring terminal access, and it can intelligently analyze, search, data mining and other complex calculations by using of super-computing ability of cloud computing , in order to achieve the monitoring of mine production site.

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30

Dong, Xiaogang, Zheng Wan, Changshou Deng, Wenying Wen, and Yuxuan Luo. "Intelligent Time Allocation for Wireless Power Transfer in Wireless-Powered Mobile Edge Computing." Wireless Communications and Mobile Computing 2022 (August 24, 2022): 1–13. http://dx.doi.org/10.1155/2022/6722848.

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Wireless-powered mobile edge computing is a new network computing paradigm that combines with the advantages of wireless power transfer and mobile edge computing. When the harvest-then-offload protocol is adopted in this network, the time of wireless power transfer has a significant impact on system performance. If the time is too short, the user cannot harvest enough energy. If it is too long, the user will not have enough time to complete the task offloading. Both result in many of user tasks being discarded. To address this problem, DEWPT, a differential evolution-based optimization scheme for wireless power transfer time, is proposed in this paper. DEWPT is designed with a hybrid mutation operator and a perturbation-based binomial crossover operator. The hybrid mutation operator combines the benefits of two mutation operators with distinct characteristics, so that DEWPT not only has a strong exploration ability but also can quickly converge. Meanwhile, the perturbation-based binomial crossover operator improves DEWPT’s ability to exploit local space. These two improvements effectively enhance DEWPT’s optimization performance, which is beneficial to find the optimal time for wireless power transfer. Furthermore, to improve the optimization efficiency, micro-population is introduced into DEWPT. Finally, the computation completion ratio maximization model is used to validate the performance of DEWPT in the wireless-powered mobile edge computing network with multiple edge servers. Numerical results show that the computation offloading scheme integrating with DEWPT can achieve a higher computation completion rate than three benchmark schemes, and is competitive in complexity. This demonstrates that DEWPT is an effective time allocation scheme for wireless power transfer.

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31

Liu, Ji. "Optimization and Management in Order to Drive Targeted Networking Memory Database." Applied Mechanics and Materials 727-728 (January 2015): 965–68. http://dx.doi.org/10.4028/www.scientific.net/amm.727-728.965.

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In today's vehicle networking system architecture is mainly composed of four parts: sensor networks, wireless communication networks, cloud computing platforms and vehicle terminal. Wireless sensor network is responsible for the front of the real-time collection of traffic information, a wireless communication network to send information to the backend of the cloud computing platform, cloud computing platform to handle a large number of vehicles to collect real-time information from the front, and finally sends the information to the end user. In this thesis, this car networking research background, analyze vehicle networking system architecture consisting of performance indicators for each part of the system recognize cloud platform for large data processing efficiency as well as room for improvement. Then put forward the traditional computing platform I / O disk database with in-memory database to replace the cloud to enhance cloud computing platform for large data processing efficiency.

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32

Han, Songyue, Dawei Ma, Chao Kang, Wei Huang, Chaoying Lin, and Chunyuan Tian. "Optimization of Mobile Edge Computing Offloading Model for Distributed Wireless Sensor Devices." Journal of Sensors 2022 (February 28, 2022): 1–9. http://dx.doi.org/10.1155/2022/9047737.

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The development and popularization of mobile Internet and wireless communication technology have spawned a large number of computation-intensive and delay-intensive applications. Limited computing resources and existing technologies cannot meet the performance requirements of new applications. Mobile edge computing technology can use wireless communication technology to offload data to be stored and computing tasks to the nearby assistant or edge server with idle resources. Based on the data offloading of distributed wireless sensor device to device communication, the architecture is designed and the basic framework of distributed mobile edge computing is constructed. To solve the problem of high mobile cloud computing technology, the offloading model of optimized mobile edge computing was proposed, and the stability and convergence of the proposed algorithm were proved. Finally, the system performance of the proposed algorithm is verified by simulation. The results show that the proposed algorithm can converge within a finite number of steps. Compared with other benchmark schemes, the proposed algorithm has better performance in reducing system energy consumption, reducing moving edge response delay and system total delay.

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33

Zha, Yukun, Hui Zhi, and Xiaotong Fang. "Cooperative computing schemes in wireless sensor networks." IET Communications 14, no. 21 (December 2020): 3784–90. http://dx.doi.org/10.1049/iet-com.2019.1162.

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34

Fan, Lisheng, Junhui Zhao, George K. Karagiannidis, and Rose Qingyang Hu. "Edge Caching and Computing for Wireless Networks." Wireless Communications and Mobile Computing 2022 (April 21, 2022): 1–2. http://dx.doi.org/10.1155/2022/9756304.

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35

Park, Chongmyung, Chungsan Lee, Youngtae Jo, and Inbum Jung. "Distributed Computing Models for Wireless Sensor Networks." Journal of KIISE 41, no. 11 (November 15, 2014): 958–66. http://dx.doi.org/10.5626/jok.2014.41.11.958.

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36

Li, Songze, Qian Yu, Mohammad Ali Maddah-Ali, and A. Salman Avestimehr. "A Scalable Framework for Wireless Distributed Computing." IEEE/ACM Transactions on Networking 25, no. 5 (October 2017): 2643–54. http://dx.doi.org/10.1109/tnet.2017.2702605.

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37

Makoond, B., S. Khaddaj, D. CC Ong, R. Oudrhiri, and M. Tunnicliffe. "Distributed Computing Techniques for Wireless Messaging Systems." Journal of Algorithms & Computational Technology 2, no. 3 (September 2008): 429–46. http://dx.doi.org/10.1260/174830108785302823.

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38

Wang, Kun, Yunqi Wang, Xiaoxuan Hu, Yanfei Sun, Der-Jiunn Deng, Alexey Vinel, and Yan Zhang. "Wireless Big Data Computing in Smart Grid." IEEE Wireless Communications 24, no. 2 (April 2017): 58–64. http://dx.doi.org/10.1109/mwc.2017.1600256wc.

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39

Lorenz, Pascal, Sofiane Hamrioui, and Abbas Jamalipour. "Guest Editorial: Next Generation Wireless Computing Systems." IEEE Transactions on Emerging Topics in Computing 6, no. 4 (October 1, 2018): 551–52. http://dx.doi.org/10.1109/tetc.2018.2872298.

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40

Hu, Xiaoyan, Kai-Kit Wong, and Kun Yang. "Wireless Powered Cooperation-Assisted Mobile Edge Computing." IEEE Transactions on Wireless Communications 17, no. 4 (April 2018): 2375–88. http://dx.doi.org/10.1109/twc.2018.2794345.

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41

Sarma, E., and Pillai Pillai. "Soft Computing for Robust Secure Wireless Reception." Defence Science Journal 59, no. 5 (September 24, 2009): 517–23. http://dx.doi.org/10.14429/dsj.59.1554.

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42

Imielinski, Tomasz, and B. R. Badrinath. "Mobile wireless computing: challenges in data management." Communications of the ACM 37, no. 10 (October 1994): 18–28. http://dx.doi.org/10.1145/194313.194317.

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43

Alfaqawi, Mohammed, Mohamed Hadi Habaebi, Md Rafiqul Islam, and Mohammad Umar Siddiqi. "Energy Harvesting Network With Wireless Distributed Computing." IEEE Systems Journal 13, no. 3 (September 2019): 2605–16. http://dx.doi.org/10.1109/jsyst.2019.2893248.

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44

Kenyeres, M., J. Kenyeres, and V. Skorpil. "Split Distributed Computing in Wireless Sensor Networks." Radioengineering 24, no. 3 (September 15, 2015): 749–56. http://dx.doi.org/10.13164/re.2015.0749.

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45

Drew, Wilfred (Bill). "Wireless networks: new meaning to ubiquitous computing." Journal of Academic Librarianship 29, no. 2 (February 2003): 102–6. http://dx.doi.org/10.1016/s0099-1333(02)00420-2.

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46

Datla, Dinesh, Haris I. Volos, S. M. Hasan, Jeffrey H. Reed, and Tamal Bose. "Wireless distributed computing in cognitive radio networks." Ad Hoc Networks 10, no. 5 (July 2012): 845–57. http://dx.doi.org/10.1016/j.adhoc.2011.04.002.

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47

Li, Feng, Shibo He, Jun Luo, Mohan Gurusamy, and Junshan Zhang. "Editorial: Green computing in Wireless Sensor Networks." Computer Networks 150 (February 2019): 266–68. http://dx.doi.org/10.1016/j.comnet.2018.11.007.

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48

Schiller, J. "Review: Mobile Commerce and Wireless Computing Systems." Computer Journal 47, no. 2 (February 1, 2004): 272–73. http://dx.doi.org/10.1093/comjnl/47.2.272.

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49

Hills, A. "Wireless Andrew [mobile computing for university campus]." IEEE Spectrum 36, no. 6 (June 1999): 49–53. http://dx.doi.org/10.1109/6.769269.

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50

Chien, Charles, Sean Nazareth, Paul Lettieri, Stephen Molloy, Brian Schoner, Walter A. Boring IV, Joey Chen, Christopher Deng, William H. Mangione-Smith, and Rajeev Jain. "An integrated testbed for wireless multimedia computing." Journal of VLSI signal processing systems for signal, image and video technology 13, no. 2-3 (August 1996): 105–24. http://dx.doi.org/10.1007/bf01130401.

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