Academic literature on the topic 'Maritime wireless sensor networks'

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Journal articles on the topic "Maritime wireless sensor networks"

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Panić, Ivan, Jasmin Ćelić, and Aleksandar Cuculić. "Wireless Condition Monitoring of Machinery and Equipment in Maritime Industry." Pomorstvo 32, no. 2 (December 20, 2018): 201–10. http://dx.doi.org/10.31217/p.32.2.5.

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Seagoing vessels are highly complex systems. Major requirements of marine vessels are continuous running time and high production output. As such, these systems require high availability and reliability, and are dependent on preventive maintenance procedures. Development of diverse range of sensors, combined with overall reduction in price, enabled implementation of condition based maintenance in such systems. Large increases in fuel cost, environmental restrictions and further crew reduction are current trend in maritime industry. Considering marine sector emphasis on the reduction of fuel consumption, environmental restrictions, and reduction of crew size, implementation of condition based maintenance is favourable, especially with regard to lost man-hours. However, high initial cost of installation on moving vessels, necessary crew training and additional sensor maintenance inhibits implementation of condition based maintenance. Replacing wired monitoring system with wireless ship-board sensor network would mitigate the above mentioned problems. However, current research of wireless sensor networks is based on terrestrial installation. This paper analyses the application of wireless sensor network technology on board seagoing vessels. Practical engineering solutions, including sensor types, configurations and wireless network topologies have been identified and reviewed.
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Walid, Elgenaidi, Newe Thomas, O'Connell Eoin, and Dooly Gerard. "Trust security mechanism for maritime wireless sensor networks." Concurrency and Computation: Practice and Experience 29, no. 23 (September 16, 2016): e3945. http://dx.doi.org/10.1002/cpe.3945.

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Masnicki, Romuald, Janusz Mindykowski, and Przemyslaw Grala. "Towards Safety Improvement of Measurement and Control Signals Transmission in Marine Environment." Sensors 20, no. 6 (March 17, 2020): 1668. http://dx.doi.org/10.3390/s20061668.

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This paper is dedicated to some aspects of maritime on-board safety improvement. The contribution of this article is a proposal to extend wireless communication on the ship, supported by examples of solutions that have not previously been used on board. Analyzed aspects concern the measurement and control signal transmission in hostile marine environment. A problem to solve is formulated, how to reduce a negative impact of this environment on effectiveness and reliability of maritime on board communication. The proposed ways for solving a problem under consideration cover recommendations concerning some aspects of ship construction and implementation of wireless sensor network. The main topic faced by the paper is concentrated on case study-based ZigBee as well as WiFi networks implementation in the area of the acquisition of data from sensors and measuring transducers connected to the terminal network. The exemplary implementation of ZigBee network, elaborated in Gdynia Maritime University is related, firstly, to the configuration of a simple wireless measurement and control channel, and secondly, to wireless communication channel supported by autonomously working microprocessor measurement and control system. WiFi networks were also tested in the same area of application. Finally, some concluding remarks are formulated.
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Chen, Weigang, Dongming Sun, Changcai Han, Jinsheng Yang, Feng Gong, and Wei Wang. "Macrodiversity Reception with Distributed Hard-Decision Receivers for Maritime Wireless Sensor Networks." Sensors 20, no. 14 (July 15, 2020): 3925. http://dx.doi.org/10.3390/s20143925.

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Maritime wireless sensor networks are considered to be the primary means of monitoring methods in the marine environment. The transmission between sensor node and sink node in maritime wireless sensor networks is usually unreliable due to the harsh propagation environment. To extend the transmission range or to enhance the transmission reliability between sensor nodes and sink node, we propose a macrodiversity reception scheme in the sink node equipped with distributed multiple hard-decision receivers. Multiple receivers are divided into several clusters and placed at different locations to receive different signal copies suffering from different fadings. Furthermore, a cascaded combining strategy based on hard-decision information is used to reduce the overall complexity of receiving side. The experimental results in the ocean scenarios show that the macrodiversity reception scheme with two antenna clusters has a transmission gain of 3–4 dB compared with the single antenna reception when the package loss rate is 10 − 2 . The study casts a new method for reliable transmission in maritime wireless sensor networks using commercial transceivers which can only output hard-decision results.
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Luo, Hanjiang, Xu Wang, Ziyang Xu, Chao Liu, and Jeng-Shyang Pan. "A software-defined multi-modal wireless sensor network for ocean monitoring." International Journal of Distributed Sensor Networks 18, no. 1 (January 2022): 155014772110683. http://dx.doi.org/10.1177/15501477211068389.

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The software-defined networking paradigm enables wireless sensor networks as a programmable and reconfigurable network to improve network management and efficiency. However, several challenges arise when implementing the concept of software-defined networking in maritime wireless sensor networks, as the networks operate in harsh ocean environments, and the dominant underwater acoustic systems are with limited bandwidth and high latency, which render the implementation of software-defined networking central-control difficult. To cope with the problems and meet demand for high-speed data transmission, we propose a radio frequency–acoustic software-defined networking-based multi-modal wireless sensor network which leverages benefits of both radio frequency and acoustic communication systems for ocean monitoring. We first present the software-defined networking-based multi-modal network architecture, and then explore two examples of applications with this architecture: network deployment and coverage for intrusion detection with both grid-based and random deployment scenarios, and a novel underwater testbed design by incorporating radio frequency–acoustic multi-modal techniques to facilitate marine sensor network experiments. Finally, we evaluate the performance of deployment and coverage of software-defined networking-based multi-modal wireless sensor network through simulations with several scenarios to verify the effectiveness of the network.
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Dinis, Hugo, João Rocha, Tiago Matos, Luís M. Gonçalves, and Marcos Martins. "The Challenge of Long-Distance Over-the-Air Wireless Links in the Ocean: A Survey on Water-to-Water and Water-to-Land MIoT Communication." Applied Sciences 12, no. 13 (June 24, 2022): 6439. http://dx.doi.org/10.3390/app12136439.

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Robust wireless communication networks are a cornerstone of the modern world, allowing data to be transferred quickly and reliably. Establishing such a network at sea, a Maritime Internet of Things (MIoT), would enhance services related to safety and security at sea, environmental protection, and research. However, given the remote and harsh nature of the sea, installing robust wireless communication networks with adequate data rates and low cost is a difficult endeavor. This paper reviews recent MIoT systems developed and deployed by researchers and engineers over the past few years. It contains an analysis of short-range and long-range over-the-air radio-frequency wireless communication protocols and the synergy between these two in the pursuit of an MIoT. The goal of this paper is to serve as a go-to guide for engineers and researchers that need to implement a wireless sensor network at sea. The selection criterion for the papers included in this review was that the implemented wireless communication networks were tested in a real-world scenario.
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García, Óscar, Ricardo Alonso, Dante Tapia, and Fabio Guevara. "Wireless Sensor Networks and Real-Time Locating Systems to Fight against Maritime Piracy." International Journal of Interactive Multimedia and Artificial Intelligence 1, no. 5 (2012): 14. http://dx.doi.org/10.9781/ijimai.2012.152.

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Bouk, Safdar Hussain, Syed Hassan Ahmed, and Dongkyun Kim. "Delay Tolerance in Underwater Wireless Communications: A Routing Perspective." Mobile Information Systems 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/6574697.

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Similar to terrestrial networks, underwater wireless networks (UWNs) also aid several critical tasks including coastal surveillance, underwater pollution detection, and other maritime applications. Currently, once underwater sensor nodes are deployed at different levels of the sea, it is nearly impossible or very expensive to reconfigure the hardware, for example, battery. Taking this issue into account, considerable amount of research has been carried out to ensure minimum energy costs and reliable communication between underwater nodes and base stations. As a result, several different network protocols were proposed for UWN, including MAC, PHY, transport, and routing. Recently, a new paradigm was introduced claiming that the intermittent nature of acoustic channel and signal resulted in designing delay tolerant routing schemes for the UWN, known as an underwater delay tolerant network. In this paper, we provide a comprehensive survey of underwater routing protocols with emphasis on the limitations, challenges, and future open issues in the context of delay tolerant network routing.
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Wu, Huafeng, Lei Yang, Ling Liu, Ming Xu, and Xinping Guan. "Real-Time Localization Algorithm for Maritime Search and Rescue Wireless Sensor Network." International Journal of Distributed Sensor Networks 9, no. 3 (January 2013): 791981. http://dx.doi.org/10.1155/2013/791981.

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Kang, Hee Jin, Dongkon Lee, Jong Gye Shin, Gyeong Joong Lee, and Jin Choi. "Interactive Escape Route Control for Passenger Ships Using Emergency Lighting." Marine Technology Society Journal 44, no. 5 (September 1, 2010): 43–49. http://dx.doi.org/10.4031/mtsj.44.5.1.

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AbstractMaritime accidents lead to massive loss of life and property. In the case of passenger ships, the potential for damage is especially high. Although the Maritime Safety Committee (MSC) of the International Maritime Organization has recently increased the regulation of passenger ship safety, serious accidents continue to occur.A large passenger ship (e.g., a cruise ship) is very wide and contains a complex population of passengers; it is difficult for crew members to be aware of each passenger’s location and idiosyncrasies. Such knowledge could be used by crew members in normal conditions to control the passengers, thereby avoiding dangerous scenarios. In an emergency situation, the same knowledge can help crew members control the flow of escape and help passengers, including handicapped persons, children, and the elderly.In this paper, we examine the use of emergency lighting and wireless sensor networks to create a new methodology for interactive escape route control. The sensor network consists of a wireless system (i.e., ZigBee) that collects raw data from each passenger. These data are then used to determine escape routes, which are communicated to the crew via emergency lighting. We also include simulation results of the proposed interactive escape route control for selected scenarios.
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Dissertations / Theses on the topic "Maritime wireless sensor networks"

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Casias, Juan Francisco. "Performance of wireless unattended sensor networks in maritime applications." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Jun%5FCasias.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, June 2007.
Thesis Advisor(s): John C. McEachen. "June 2007." Includes bibliographical references (p. 75 -79). Also available in print.
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Yazar, Dogan. "RESTful Wireless Sensor Networks." Thesis, Uppsala University, Department of Information Technology, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-110353.

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Sensor networks have diverse structures and generally employ proprietary protocols to gather useful information about the physical world. This diversity generates problems to interact with these sensors since custom APIs are needed which are tedious, error prone and have steep learning curve. In this thesis, I present RESThing, a lightweight REST framework for wireless sensor networks to ease the process of interacting with these sensors by making them accessible over the Web. I evaluate the system and show that it is feasible to support widely used and standard Web protocols in wireless sensor networks. Being able to integrate these tiny devices seamlessly into the global information medium, we can achieve the Web of Things.

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Dogru, Sedat. "Sycophant Wireless Sensor Networks Tracked By Sparsemobile Wireless Sensor Networks While Cooperativelymapping An Area." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12615139/index.pdf.

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In this thesis the novel concept of Sycophant Wireless Sensors (SWS) is introduced. A SWS network is a static ectoparasitic clandestine sensor network mounted incognito on a mobile agent using only the agent&rsquo
s mobility without intervention. SWS networks not only communicate with each other through mobileWireless Sensor Networks (WSN) but also cooperate with them to form a global hybrid Wireless Sensor Network. Such a hybrid network has its own problems and opportunities, some of which have been studied in this thesis work. Assuming that direct position measurements are not always feasible tracking performance of the sycophant using range only measurements for various communication intervals is studied. Then this framework was used to create a hybrid 2D map of the environment utilizing the capabilities of the mobile network the sycophant. In order to show possible applications of a sycophant deployment, the sycophant sensor node was equipped with a laser ranger as its sensor, and it was let to create a 2D map of its environment. This 2D map, which corresponds to a height dierent than the follower network, was merged with the 2D map of the mobile network forming a novel rough 3D map. Then by giving up from the need to properly localize the sycophant even when it is disconnected to the rest of the network, a full 3D map of the environment is obtained by fusing 2D map and tracking capabilities of the mobile network with the 2D vertical scans of the environment by the sycophant. And finally connectivity problems that arise from the hybrid sensor/actuator network were solved. For this 2 new connectivity maintenance algorithms, one based on the helix structures of the proteins, and the other based on the acute triangulation of the space forming a Gabriel Graph, were introduced. In this new algorithms emphasis has been given to sparseness in order to increase fault tolerance to regional problems. To better asses sparseness a new measure, called Resistance was introduced, as well as another called updistance.
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Chraibi, Youssef. "Localization in Wireless Sensor Networks." Thesis, KTH, Reglerteknik, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-107528.

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Similar to many technological developments, wireless sensor networks have emerged from military needs and found its way into civil applications. Today, wireless sensor networks has become a key technology for different types of ”smart environments”, and an intense research effort is currently underway to enable the application of wireless sensor networks for a wide rangeof industrial problems. Wireless networks are of particular importance whena large number of sensor nodes have to be deployed, and/or in hazardous situations. Localization is important when there is an uncertainty of the exact location of some fixed or mobile devices. One example has been in the supervision of humidity and temperature in forests and/or fields, where thousands of sensors are deployed by a plane, giving the operator little or no possibility to influence the precise location of each node. An effective localization algorithm can then use all the available information from the wireless sensor nodes to infer the position of the individual devices. Another application is the positioning of a mobile robot based on received signal strength from a set of radio beacons placed at known locations on the factory floor. This thesis work is carried out on the wireless automation testbed at the S3. Focusing on localization processes, we will first give an overview of the state of the art in this area. From the various techniques, one idea was found to have significant bearing for the development of a new algorithm. We present analysis and simulations of the algorithms, demonstrating improved accuracy compared to other schemes although the accuracy is probably not good enough for some high-end applications. A third aspect of the work concerns the feasibility of approaches based on received signal strength indication (RSSI). Multiple measurement series have been collected in the lab with the MoteIV wireless sensor node platform. The measurement campaign indicates significant fluctuations in the RSSI values due to interference and limited repeatability of experiments, which may limit the reliability of many localization schemes, especially in an indoor environment.
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Cao, Hui. "Stabilization in wireless sensor networks." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1211079872.

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Tseng, Kuan-Chieh Robert. "Resilience of wireless sensor networks." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/33713.

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The coverage of a wireless sensor network is a measure of the quality of service. One type of coverage is k-barrier coverage. Given a starting region S and an ending region T , we say that a sensor network has k-barrier coverage with respect to S and T if any S−T path in the surveillance domain must intersect the coverage regions of at least k sensors. In this thesis, we focus on determining the resilience of a wireless sensor network. The resilience is defined to be the minimum number of sensors that need to be removed in order to ensure the existence of a S−T path that does not cross any sensor coverage region. A sensor network with resilience k constitutes a k-barrier coverage. We demonstrate that determining resilience of a wireless sensor network with 2D surveillance domain is NP-hard for the case when the sensor coverage regions are unit line segments. Furthermore, it is possible to extend the reduction to show that the problem remains NP-hard for other types of sensor coverage regions. In general, if the shape of the coverage region is non-symmetric, then determining resilience is NP-hard. We also investigate the problem of determining resilience of a wireless sensor network with 3D surveillance domain. In this case, we show that if the coverage regions of the sensors are unit spheres, then the problem is NP-hard.
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Saif, Waleed Abdulwahed. "Localization in wireless sensor networks." Thesis, University of Leeds, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.555845.

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In this thesis we examine localization in wireless sensor networks starting with a brief overview of the basics of radiolocation techniques and then look at some of the most well-known commercial positioning techniques and localization algorithms. We then concentrate on the application of the Fastmap (FM) algorithm in the field of wireless sensor localization. Our first contribution in this thesis is the mathematical analysis of the FM algorithm in terms of the mean squared error (MSE) of the coordinate estimate under a multiplicative noise model followed by the optimum placement of anchor nodes. The algorithm is compared to Linear Least Squares (LLS) algorithm, which is well known and has a similar complexity to that of FM. Another contribution is proposing the angle-projected FM algorithm for wireless sensor nodes localization in order to enhance the connectivity of the network and the overall performance. A comprehensive study and mathematical analysis in terms of the MSE for this algorithm is presented and it is also compared with the original FM algorithm. We also propose a weighted Fastmap (WFM) algorithm in which more than one pair of anchor nodes is used to evaluate the first coordinate (i.e., x-coordinate) of the unknown nodes in order to reduce the effect of error dependency in the y-coordinate estimation. (In the original FM algorithm only one pair of anchor nodes was employed.) The optimal WFM weights are determined via (constrained) minimization of the MSE of the estimated node coordinates. A simplification of the WFM is also introduced, called the averaged FM (AFM), where complexity is reduced at the expense of degradation in the overall WFM performance. Both the WFM and AFM exhibit improved performance over the original FM algorithm. Finally, an unbiased version of the WFM, AFM and FM is presented in which an estimate of the bias term is removed to improve the overall MSE performance. The effect of this modification on the algorithms' performance is then analysed and discussed.
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Li, Wei. "Cooperation arrayed wireless sensor networks." Thesis, Imperial College London, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.536033.

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Peng, Wei. "Optimisation of wireless sensor networks." Thesis, University of Oxford, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.543734.

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Cheng, King-yip, and 鄭勁業. "Localization in wireless sensor networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B38700189.

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Books on the topic "Maritime wireless sensor networks"

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S, Raghavendra C., Sivalingam Krishna M, and Znati Taieb F, eds. Wireless sensor networks. Boston: Kluwer Academic, 2004.

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Minoli, Daniel. Wireless Sensor Networks. New York: John Wiley & Sons, Ltd., 2007.

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Cui, Li, and Xiaolan Xie, eds. Wireless Sensor Networks. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-8174-5.

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Selmic, Rastko R., Vir V. Phoha, and Abdul Serwadda. Wireless Sensor Networks. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46769-6.

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Silva, Jorge Sá, Bhaskar Krishnamachari, and Fernando Boavida, eds. Wireless Sensor Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11917-0.

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Shen, Shikai, Kaiguo Qian, Shaojun Yu, and Wu Wang, eds. Wireless Sensor Networks. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6834-9.

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Picco, Gian Pietro, and Wendi Heinzelman, eds. Wireless Sensor Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28169-3.

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Akyildiz, Ian F., and Mehmet Can Vuran. Wireless Sensor Networks. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470515181.

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Yang, Shuang-Hua. Wireless Sensor Networks. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5505-8.

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Krishnamachari, Bhaskar, Amy L. Murphy, and Niki Trigoni, eds. Wireless Sensor Networks. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04651-8.

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Book chapters on the topic "Maritime wireless sensor networks"

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Higgins, Henry. "Wireless Communication." In Body Sensor Networks, 155–88. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6374-9_4.

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Fong, David Y. "WIRELESS SENSOR NETWORKS." In Internet of Things and Data Analytics Handbook, 197–213. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119173601.ch12.

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Buratti, Chiara, Marco Martalò, Roberto Verdone, and Gianluigi Ferrari. "Wireless Sensor Networks." In Signals and Communication Technology, 3–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17490-2_1.

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Lazarescu, Mihai Teodor, and Luciano Lavagno. "Wireless Sensor Networks." In Handbook of Hardware/Software Codesign, 1261–302. Dordrecht: Springer Netherlands, 2017. http://dx.doi.org/10.1007/978-94-017-7267-9_38.

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Lazarescu, Mihai Teodor, and Luciano Lavagno. "Wireless Sensor Networks." In Handbook of Hardware/Software Codesign, 1–42. Dordrecht: Springer Netherlands, 2016. http://dx.doi.org/10.1007/978-94-017-7358-4_38-1.

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Öztürk, Yusuf. "Wireless Sensor Networks." In Studies in Computational Intelligence, 47–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-44910-2_3.

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Jin, Miao, Xianfeng Gu, Ying He, and Yalin Wang. "Wireless Sensor Networks." In Conformal Geometry, 253–96. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75332-4_10.

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Lewis, Frank L. "Wireless Sensor Networks." In Smart Environments, 11–46. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/047168659x.ch2.

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Kale, Vivek. "Wireless Sensor Networks." In Agile Network Businesses, 417–43. New York : CRC Press, 2017.: Auerbach Publications, 2017. http://dx.doi.org/10.4324/9781315368559-22.

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Chai, Senchun, Zhaoyang Wang, Baihai Zhang, Lingguo Cui, and Runqi Chai. "Localization Technology for Wireless Sensor Networks." In Wireless Networks, 69–141. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5757-6_3.

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Conference papers on the topic "Maritime wireless sensor networks"

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Kavelaars, W., and M. Maris. "Wireless sensor networks in a maritime environment." In European Symposium on Optics and Photonics for Defence and Security, edited by Edward M. Carapezza. SPIE, 2005. http://dx.doi.org/10.1117/12.630837.

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Barbosa, Pedro N. E. S., Neil M. White, and Nick R. Harris. "Wireless Sensor Network for Localized Maritime Monitoring." In 22nd International Conference on Advanced Information Networking and Applications - Workshops (aina workshops 2008). IEEE, 2008. http://dx.doi.org/10.1109/waina.2008.273.

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Barbosa, P., N. M. White, and N. R. Harris. "Wireless Sensor Network for maritime deployment: Modeling and simulation." In Melecon 2010 - 2010 15th IEEE Mediterranean Electrotechnical Conference. IEEE, 2010. http://dx.doi.org/10.1109/melcon.2010.5475907.

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Manoufali, Mohamed, Peng-Yong Kong, and Shihab Jimaa. "Full-view coverage quasi-mobile camera sensor network for maritime surveillance." In 2014 International Wireless Communications and Mobile Computing Conference (IWCMC). IEEE, 2014. http://dx.doi.org/10.1109/iwcmc.2014.6906372.

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Elgenaidi, Walid, Thomas Newe, Eoin O'Connell, Daniel Toal, Gerard Dooly, and Joseph Coleman. "Memory storage administration of security encryption keys for line topology in maritime wireless sensor networks." In 2016 10th International Conference on Sensing Technology (ICST). IEEE, 2016. http://dx.doi.org/10.1109/icsenst.2016.7796268.

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Manoufali, Mohamed, Peng-Yong Kong, and Shihab Jimaa. "An analysis of uncovered area for camera sensor network in maritime environment." In 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE, 2013. http://dx.doi.org/10.1109/wimob.2013.6673351.

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Guan, Shan. "Current and Future Tendency of Monitoring Technology for Integrity Assessment and Life Extension of the Hull Structure of Floating Production, Storage and Offloading (FPSO) Vessel." In ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/omae2015-41020.

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This paper critically reviewed commercially available hull stress monitoring systems (HSM) and summarized notations for vessels with hull stress monitoring systems from major classification societies. The authors recommended that, for every FPSO vessel, at least one HSM should be equipped in compliance with regulation from the International Maritime Organization. Additionally, sixteen types of sensing technologies were identified for monitoring of FPSO Vessel. Depending on the technology matureness and readiness for application in marine structures, they were categorized into three groups: Proven, Un-Proven and State-of-the-art technology. Furthermore, challenges associated with FPSO hull monitoring including Sensor Systems, Communication and Data Analysis techniques were examined. It is concluded that multiple sensing technologies should be systematically combined to provide more accurate information. Based on these discussions, a feasible solution that integrates proper wireless sensor network technology and Bayesian Network modeling was proposed as the future direction of FPSO hull monitoring system.
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Manoufali, M., H. Alshaer, Peng-Yong Kong, and S. Jimaa. "Technologies and networks supporting maritime wireless mesh communications." In 2013 6th Joint IFIP Wireless and Mobile Networking Conference (WMNC 2013). IEEE, 2013. http://dx.doi.org/10.1109/wmnc.2013.6549005.

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Yang, Xing, Kezhong Liu, Yongqiang Cui, and Jinfen Zhang. "Modeling and simulation for Maritime Surveillance Sensor Networks." In 2010 10th International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2010. http://dx.doi.org/10.1109/iscit.2010.5664841.

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Quaglio, Federico, Maurzio Martina, Fabrizio Vacca, Guido Masera, Andrea Molino, Gianluca Piccinini, and Maurizio Zamboni. "Wireless sensor networks." In the 2003 ACM/SIGDA eleventh international symposium. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/611817.611878.

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Reports on the topic "Maritime wireless sensor networks"

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Lee, A. P., C. F. McConaghy, J. N. Simon, W. Benett, L. Jones, and J. Trevino. Sensor modules for wireless distributed sensor networks. Office of Scientific and Technical Information (OSTI), February 1999. http://dx.doi.org/10.2172/15005723.

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Raghavendra, Cauligi S., and Viktor K. Prasanna. Distributed Signal Processing in Wireless Sensor Networks. Fort Belvoir, VA: Defense Technical Information Center, August 2005. http://dx.doi.org/10.21236/ada437824.

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Berry, Nina M., and Teresa H. Ko. On computer vision in wireless sensor networks. Office of Scientific and Technical Information (OSTI), September 2004. http://dx.doi.org/10.2172/919195.

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Davis, William B. Graphical Model Theory for Wireless Sensor Networks. Office of Scientific and Technical Information (OSTI), December 2002. http://dx.doi.org/10.2172/833692.

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Stephenson, Andrew J. Sinkhole Avoidance Routing in Wireless Sensor Networks. Fort Belvoir, VA: Defense Technical Information Center, May 2011. http://dx.doi.org/10.21236/ada554671.

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Liu, Donggang. Secure and Robust Clustering in Wireless Sensor Networks. Fort Belvoir, VA: Defense Technical Information Center, July 2008. http://dx.doi.org/10.21236/ada500585.

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Cybenko, George, Dorothy Gramm, and Walter Gramm. Instrumentation for Wireless Agent Networks and Sensor Webs. Fort Belvoir, VA: Defense Technical Information Center, December 2001. http://dx.doi.org/10.21236/ada405520.

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Liang, Qilian. Energy Efficient Wireless Sensor Networks Using Fuzzy Logic. Fort Belvoir, VA: Defense Technical Information Center, June 2005. http://dx.doi.org/10.21236/ada434605.

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Liang, Qilian. Energy Efficient Wireless Sensor Networks Using Fuzzy Logic. Fort Belvoir, VA: Defense Technical Information Center, December 2003. http://dx.doi.org/10.21236/ada419061.

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Liang, Qilian. Energy Efficient Wireless Sensor Networks Using Fuzzy Logic. Fort Belvoir, VA: Defense Technical Information Center, June 2004. http://dx.doi.org/10.21236/ada423016.

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