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Journal articles on the topic 'Querying (Computer science). Wireless sensor networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Ye, Mao, Ken C. K. Lee, Wang-Chien Lee, Xingjie Liu, and Meng-Chang Chen. "Querying Uncertain Minimum in Wireless Sensor Networks." IEEE Transactions on Knowledge and Data Engineering 24, no. 12 (December 2012): 2274–87. http://dx.doi.org/10.1109/tkde.2011.166.

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2

Demirbas, Murat, Anish Arora, and Vinodkrishnan Kulathumani. "Glance: A lightweight querying service for wireless sensor networks." Theoretical Computer Science 410, no. 6-7 (February 2009): 500–513. http://dx.doi.org/10.1016/j.tcs.2008.10.013.

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3

Joon Ahn and B. Krishnamachari. "Scaling Laws for Data-Centric Storage and Querying in Wireless Sensor Networks." IEEE/ACM Transactions on Networking 17, no. 4 (August 2009): 1242–55. http://dx.doi.org/10.1109/tnet.2008.2009220.

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4

Moro, Gianluca, and Gabriele Monti. "W-Grid: A scalable and efficient self-organizing infrastructure for multi-dimensional data management, querying and routing in wireless data-centric sensor networks." Journal of Network and Computer Applications 35, no. 4 (July 2012): 1218–34. http://dx.doi.org/10.1016/j.jnca.2011.05.002.

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5

Di Pietro, Roberto, and Alexandre Viejo. "Location privacy and resilience in wireless sensor networks querying." Computer Communications 34, no. 3 (March 2011): 515–23. http://dx.doi.org/10.1016/j.comcom.2010.05.014.

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6

Ahn, Joon, Shyam Kapadia, Sundeep Pattem, Avinash Sridharan, Marco Zuniga, Jung-Hyun Jun, Chen Avin, and Bhaskar Krishnamachari. "Empirical evaluation of querying mechanisms for unstructured wireless sensor networks." ACM SIGCOMM Computer Communication Review 38, no. 3 (July 2008): 17–26. http://dx.doi.org/10.1145/1384609.1384612.

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7

Stankovic, John A. "Wireless Sensor Networks." Computer 41, no. 10 (October 2008): 92–95. http://dx.doi.org/10.1109/mc.2008.441.

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8

Zorzi, M. "Wireless sensor networks." IEEE Wireless Communications 11, no. 6 (December 2004): 2. http://dx.doi.org/10.1109/mwc.2004.1368890.

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9

Havinga, P., J. C. Hou, and Feng Zhao. "Wireless sensor networks." IEEE Wireless Communications 11, no. 6 (December 2004): 4–5. http://dx.doi.org/10.1109/mwc.2004.1368892.

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10

Akan, O. B., M. T. Isik, and B. Baykal. "Wireless passive sensor networks." IEEE Communications Magazine 47, no. 8 (August 2009): 92–99. http://dx.doi.org/10.1109/mcom.2009.5181898.

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11

Perrig, Adrian, John Stankovic, and David Wagner. "Security in wireless sensor networks." Communications of the ACM 47, no. 6 (June 2004): 53–57. http://dx.doi.org/10.1145/990680.990707.

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12

Gavalas, D., G. Pantziou, and C. Konstantopoulos. "Mobility in Wireless Sensor Networks." Computer Journal 54, no. 12 (July 31, 2011): 1928–30. http://dx.doi.org/10.1093/comjnl/bxr074.

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13

Miyaji, Atsuko, and Kazumasa Omote. "Self‐healing wireless sensor networks." Concurrency and Computation: Practice and Experience 27, no. 10 (April 8, 2015): 2547–68. http://dx.doi.org/10.1002/cpe.3434.

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14

Terzis, Andreas, Razvan Musaloiu E., Joshua Cogan, Katalin Szlavecz, Alexander Szalay, Jim Gray, Stuart Ozer, Chieh Jan Mike Liang, Jayant Gupchup, and Randal Burns. "Wireless sensor networks for soil science." International Journal of Sensor Networks 7, no. 1/2 (2010): 53. http://dx.doi.org/10.1504/ijsnet.2010.031850.

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15

Anagnostopoulos, C., N. M. Adams, and D. J. Hand. "Streaming Covariance Selection with Applications to Adaptive Querying in Sensor Networks." Computer Journal 53, no. 9 (January 8, 2010): 1401–14. http://dx.doi.org/10.1093/comjnl/bxp123.

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16

Hill, Jason, Mike Horton, Ralph Kling, and Lakshman Krishnamurthy. "The platforms enabling wireless sensor networks." Communications of the ACM 47, no. 6 (June 2004): 41–46. http://dx.doi.org/10.1145/990680.990705.

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17

Yu-Chee Tseng, Meng-Shiuan Pan, and Yuen-Yung Tsai. "Wireless Sensor Networks for Emergency Navigation." Computer 39, no. 7 (July 2006): 55–62. http://dx.doi.org/10.1109/mc.2006.248.

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18

Coulson, Geoff, Barry Porter, Ioannis Chatzigiannakis, Christos Koninis, Stefan Fischer, Dennis Pfisterer, Daniel Bimschas, et al. "Flexible experimentation in wireless sensor networks." Communications of the ACM 55, no. 1 (January 2012): 82–90. http://dx.doi.org/10.1145/2063176.2063198.

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19

Nagy, Naya, Marius Nagy, and Selim G. Akl. "Quantum security in wireless sensor networks." Natural Computing 9, no. 4 (April 22, 2010): 819–30. http://dx.doi.org/10.1007/s11047-010-9190-4.

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20

Sarkar, Sanchita Mal, Iftikhar U. Sikder, Chansu Yu, and Vijay K. Konangi. "Uncertainty-aware Wireless Sensor Networks." International Journal of Mobile Communications 7, no. 3 (2009): 330. http://dx.doi.org/10.1504/ijmc.2009.023675.

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21

PICOVICI, D., M. BERGLUND, and J. NELSON. "Voice Controlled Wireless Sensor Networks." Advances in Electrical and Computer Engineering 7, no. 1 (2007): 3–8. http://dx.doi.org/10.4316/aece.2007.01001.

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22

Xiaojiang Du and Hsiao-Hwa Chen. "Security in wireless sensor networks." IEEE Wireless Communications 15, no. 4 (August 2008): 60–66. http://dx.doi.org/10.1109/mwc.2008.4599222.

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23

Liming Zhou, Qiaoyan Wen, Hua Zhang, and Lin Cheng. "Enhancing Sensor Location Privacy in Wireless Sensor Networks." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 5, no. 10 (May 31, 2013): 789–96. http://dx.doi.org/10.4156/aiss.vol5.issue10.92.

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24

Delaët, Sylvie, Partha Sarathi Mandal, Mariusz A. Rokicki, and Sébastien Tixeuil. "Deterministic secure positioning in wireless sensor networks." Theoretical Computer Science 412, no. 35 (August 2011): 4471–81. http://dx.doi.org/10.1016/j.tcs.2011.04.010.

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25

Aliouat, Zibouda, and Saad Harous. "Energy efficient clustering for wireless sensor networks." International Journal of Pervasive Computing and Communications 10, no. 4 (October 28, 2014): 469–80. http://dx.doi.org/10.1108/ijpcc-05-2014-0033.

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Purpose – The purpose of this paper is to design a hierarchical routing protocol. Wireless sensor network (WSN) consists of a set of miniature sensor nodes powered by a low-capacity energy battery. This limitation requires that energy is used in an efficient way and kept as long as possible to allow the WSN to accomplish its mission. Thus, energy conservation is a very important problem facing researchers in this context. Because sending and receiving messages is the activity that consumes the most energy in a WSN, so when designing routing protocols, this problem is targeted specifically. The aim of this paper is to propose a solution to this problem by designing a hierarchical routing protocol. Design/methodology/approach – The authors started by designing a protocol called efficient energy-aware distributed clustering (EEADC). Simulation result showed EEADC might generate clusters with very small or very large size. To solve this problem, the authors designed a new algorithm called fixed efficient energy-aware distributed clustering (FEEADC). They concluded from the simulation result that cluster-heads (CHs) far away from the base station die faster than the ones closer to it. To remedy this problem, they propose multi-hop fixed efficient energy-aware distributed clustering (M-FEEADC). It is based on a new fixed clustering mechanism, which aims to create a balanced distribution of CHs. It uses data aggregation and sleep/wakeup techniques. Findings – The simulation results show a significant improvement in terms of energy consumption and network lifetime over the well-known low-energy adaptive clustering hierarchy and threshold-sensitive energy-efficient protocols. Originality/value – The authors propose M-FEEADC. It is based on a new fixed clustering mechanism, which aims to create a balanced distribution of CHs. It uses data aggregation and sleep/wakeup techniques.

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26

Zhang, Mingze, Mun Choon Chan, and A. L. Ananda. "Connectivity monitoring in wireless sensor networks." Pervasive and Mobile Computing 6, no. 1 (February 2010): 112–27. http://dx.doi.org/10.1016/j.pmcj.2009.07.009.

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27

Bahi, Jacques, Wiem Elghazel, Christophe Guyeux, Mourad Hakem, Kamal Medjaher, and Noureddine Zerhouni. "Reliable diagnostics using wireless sensor networks." Computers in Industry 104 (January 2019): 103–15. http://dx.doi.org/10.1016/j.compind.2018.10.006.

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28

Voronenko, Oleksandr, Igor Galelyuka, and Volodymyr Romanov. "Wireless sensor networks for smart agriculture." International Journal of Reasoning-based Intelligent Systems 13, no. 3 (2021): 147. http://dx.doi.org/10.1504/ijris.2021.10040159.

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29

Romanov, Volodymyr, Igor Galelyuka, and Oleksandr Voronenko. "Wireless sensor networks for smart agriculture." International Journal of Reasoning-based Intelligent Systems 13, no. 3 (2021): 147. http://dx.doi.org/10.1504/ijris.2021.117079.

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30

Shaoguo Xie, Yanjun Hu, Yi Wang, Lele Qiu, and Yingguan Wang. "Probability-based Localization in Wireless Sensor Networks." International Journal of Advancements in Computing Technology 5, no. 9 (May 31, 2013): 670–78. http://dx.doi.org/10.4156/ijact.vol5.issue9.79.

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31

Sneha, V., and M. Nagarajan. "Localization in Wireless Sensor Networks: A Review." Cybernetics and Information Technologies 20, no. 4 (November 1, 2020): 3–26. http://dx.doi.org/10.2478/cait-2020-0044.

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AbstractWireless Sensor Network (WSN) has been a source of attraction for many researchers as well as common people for the past few years. The use of WSN in various environmental applications like monitoring of weather, temperature, humidity, military surveillance etc. is not limited. WSN is built on hundreds to thousands of nodes where each node is a sensor whose main role is to sense data. These nodes are restricted to various constraints like power, energy, efficiency and deployment. The location of deployment influences the efficiency of data transmission. In this paper we briefly discuss on localization process in WSN and the classification of localization methodologies, namely centralized localization and distributed localization. The various techniques like ToA, TDoA, AoA and RSSI that are used to estimate the distance among the nodes are studied in detail. The localization issues categorized under proximity-based, range-based and range-free localization are discussed in detail. This paper also focuses on how the nodes with GPS can contribute to the localization process. The merits and demerits of using GPS have also been looked into. The various approaches of range-based techniques like Bounding box, SumDistMinMax, geometric methods, general techniques have been discussed briefly. We will also discuss on how the factors like path loss, noise, propagation, device measurements, connectivity, power control and tracking can influence the measurements in localization. In the tracking process we have briefly discussed about the variants of Kalman filter that can be used in detecting the direct path, strongest path and undirected path. This paper as a whole is just a brush up of the localization methodologies used in wireless sensor networks. This paper may give idea to the researchers to develop efficient algorithms to localize nodes with accuracy adapting to different techniques with respect to the environment and applications to be designed.

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32

Bordim, Jacir Luiz, Koji Nakano, and Hong Shen. "Sorting on Single-Channel Wireless Sensor Networks." International Journal of Foundations of Computer Science 14, no. 03 (June 2003): 391–403. http://dx.doi.org/10.1142/s0129054103001807.

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A Wireless Sensor Network (WSN) is a distributed system consisting of a large number of wireless sensing devices and a base station. Due to their compactness and low-cost, sensor networks can be distributed at a fraction of the cost of conventional wired sensors and actuator systems. The physical world generates an unlimited amount of data that can be observed and monitored. Hence, designing protocols to coordinate WSNs with hundreds, or even thousands, of sensors will face many challenges. In this work we focus on the design of protocols that enable the sensor nodes to coordinate among themselves to achieve a larger task. From this standpoint, we present a sorting protocol for wireless sensor networks. We show that in a WSN consisting of n sensor nodes, where each sensor stores an element and has a fixed transmission range r. sorting can be performed in [Formula: see text] time slots when [Formula: see text]. We also reason that future applications of wireless sensor networks are very likely to employ short-range radio communications (i.e., r less than 100 meters). If this is the case, the time complexity of our sorting protocol is optimal.

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33

Bok, Kyoungsoo, Eunkyung Ryu, Junho Park, Jaijin Jung, and Jaesoo Yoo. "Multimedia congestion control in wireless sensor networks." Computer Science and Information Systems 12, no. 2 (2015): 801–21. http://dx.doi.org/10.2298/csis141009027b.

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In this paper, we propose a new congestion control scheme to minimize data loss and maintain data quality in wireless multimedia sensor networks. The proposed scheme extracts and transfers dynamic regions by considering monitoring characteristics over multimedia sensor network environments to reduce the transferred data. Furthermore, it can reduce the packet size by deleting and transferring low-priority bit data by considering multimedia data characteristics during congestion situations to minimize packet loss. To show the superiority of the proposed scheme, we compare it with the existing congestion control schemes through simulation.

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34

Weimin, Gao, and Zhu Lingzhi. "Distributed Data Storage in Wireless Sensor Networks." International Journal of Database Theory and Application 8, no. 4 (August 30, 2015): 179–82. http://dx.doi.org/10.14257/ijdta.2015.8.4.18.

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35

Ji, W. W., and Z. Liu. "Locating ineffective sensor nodes in wireless sensor networks." IET Communications 2, no. 3 (2008): 432. http://dx.doi.org/10.1049/iet-com:20060044.

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36

Zhang, Fenghui, Anxiao (Andrew) Jiang, and Jianer Chen. "On the Planarization of Wireless Sensor Networks." Algorithmica 60, no. 3 (December 14, 2010): 593–608. http://dx.doi.org/10.1007/s00453-010-9476-z.

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37

Gandino, Filippo, and Antonio Servetti. "Key Recoverability in Wireless Sensor Networks." IEEE Access 7 (2019): 164407–17. http://dx.doi.org/10.1109/access.2019.2952945.

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38

Lee, Sang Hoon, Hyeokman Kim, and Lynn Choi. "Sleep Control Game for Wireless Sensor Networks." Mobile Information Systems 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/3085408.

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In wireless sensor networks (WSNs), each node controls its sleep to reduce energy consumption without sacrificing message latency. In this paper we apply the game theory, which is a powerful tool that explains how each individual acts for his or her own economic benefit, to analyze the optimal sleep schedule for sensor nodes. We redefine this sleep control game as a modified version of the Prisoner’s Dilemma. In the sleep control game, each node decides whether or not it wakes up for the cycle. Payoff functions of the sleep control game consider the expected traffic volume, network conditions, and the expected packet delay. According to the payoff function, each node selects the best wake-up strategy that may minimize the energy consumption and maintain the latency performance. To investigate the performance of our algorithm, we apply the sleep control game to X-MAC, which is one of the recent WSN MAC protocols. Our detailed packet level simulations confirm that the proposed algorithm can effectively reduce the energy consumption by removing unnecessary wake-up operations without loss of the latency performance.

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39

Siqueira, Isabela G., Linnyer Beatrys Ruiz, Antonio A. F. Loureiro, and José Marcos Nogueira. "Coverage area management for wireless sensor networks." International Journal of Network Management 17, no. 1 (January 2007): 17–31. http://dx.doi.org/10.1002/nem.604.

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40

PICOVICI, D., A. V. CONTIU, A. TOPA, and J. NELSON. "Virtual Lab for Wireless Sensor Networks." Advances in Electrical and Computer Engineering 8, no. 2 (2008): 37–42. http://dx.doi.org/10.4316/aece.2008.02007.

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41

Yen, Y. S., S. Hong, R. S. Chang, and H. C. Chao. "Controlled deployments for wireless sensor networks." IET Communications 3, no. 5 (2009): 820. http://dx.doi.org/10.1049/iet-com.2008.0262.

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42

Boukerche, Azzedine, and Eduardo Nakamura. "Localization systems for wireless sensor networks." IEEE Wireless Communications 14, no. 6 (December 2007): 6–12. http://dx.doi.org/10.1109/mwc.2007.4407221.

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43

Rajasegarar, S., C. Leckie, and M. Palaniswami. "Anomaly detection in wireless sensor networks." IEEE Wireless Communications 15, no. 4 (August 2008): 34–40. http://dx.doi.org/10.1109/mwc.2008.4599219.

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44

Prasad, Neeli R., and Mahbubul Alam. "Security Framework for Wireless Sensor Networks." Wireless Personal Communications 37, no. 3-4 (May 16, 2006): 455–69. http://dx.doi.org/10.1007/s11277-006-9044-7.

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45

Pandey, Manjusha, and Shekhar Verma. "Privacy Provisioning in Wireless Sensor Networks." Wireless Personal Communications 75, no. 2 (September 23, 2013): 1115–40. http://dx.doi.org/10.1007/s11277-013-1411-6.

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46

Li, Jason H., and Miao Yu. "Sensor coverage in wireless ad hoc sensor networks." International Journal of Sensor Networks 2, no. 3/4 (2007): 218. http://dx.doi.org/10.1504/ijsnet.2007.013202.

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47

Leone, Pierre, and José Rolim. "Towards a dynamical model for wireless sensor networks." Theoretical Computer Science 344, no. 1 (November 2005): 69–85. http://dx.doi.org/10.1016/j.tcs.2005.06.027.

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48

Àlvarez, Carme, Josep Díaz, Jordi Petit, José Rolim, and Maria Serna. "High level communication functionalities for wireless sensor networks." Theoretical Computer Science 406, no. 3 (October 2008): 240–47. http://dx.doi.org/10.1016/j.tcs.2008.06.055.

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49

Hwang, Shiow‐Fen, Kun‐Hsien Lu, Tsung‐Hsiang Chang, and Chyi‐Ren Dow. "Hierarchical data gathering schemes in wireless sensor networks." International Journal of Pervasive Computing and Communications 4, no. 3 (September 5, 2008): 299–321. http://dx.doi.org/10.1108/17427370810911649.

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50

Li, Xin, Bei Hua, Yi Shang, and Yan Xiong. "A robust localization algorithm in wireless sensor networks." Frontiers of Computer Science in China 2, no. 4 (July 30, 2008): 438–50. http://dx.doi.org/10.1007/s11704-008-0018-7.

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