Relevant bibliographies by topics / Sensor network

Academic literature on the topic 'Sensor network'

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Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Sensor network"

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Chan, Tung Jung, Ching Mu Chen, and Tsair Rong Chen. "A Forwarding Station Integrated with Optimal Cluster Number Selection in Wireless Sensor Networks." Applied Mechanics and Materials 201-202 (October 2012): 745–48. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.745.

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In wireless sensor networks, power consumption is the most important issue. That is wireless sensors are normally deployed into unattended places where power of sensors is hard to be charged. Indeed, the network lifetime of wireless sensor networks equipped with city power or deployed into attended place is much longer than those wireless sensors equipped with batteries. In general, wireless sensor nodes are connected together and become a network after deployed into certain places. With the certain range places that wireless senor nodes deployed into, finding the optimal clusters can increase the entire network lifetime. Also, adding the forwarding station extends the network lifetime. Therefore, this paper proposes the integration of both the forwarding station and optimal clusters in ad-hoc wireless sensor networks. Simulation results show that the entire network lifetime proposed is extended in this paper compared to both optimal cluster number selection and normal forwarding station.
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Samara, Ghassan, Mohammad Hassan, and Yahya Zayed. "An Intelligent Vice Cluster Head Election Protocol in WSN." International Journal of Advances in Soft Computing and its Applications 13, no. 3 (November 28, 2021): 202–22. http://dx.doi.org/10.15849/ijasca.211128.14.

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Wireless sensor networks (WSNs) has a practical ability to link a set of sensors to build a wireless network that can be accessed remotely; this technology has become increasingly popular in recent years. Wi-Fi-enabled sensor networks (WSNs) are used to gather information from the environment in which the network operates. Many obstacles prevent wireless sensor networks from being used in a wide range of fields. This includes maintaining network stability and extending network life. In a wireless network, sensors are the most essential component. Sensors are powered by a battery that has a finite amount of power. The battery is prone to power loss, and the sensor is therefore rendered inoperative as a result. In addition, the growing number of sensor nodes off-site affects the network's stability. The transmission and reception of information between the sensors and the base consumes the most energy in the sensor. An Intelligent Vice Cluster Head Selection Protocol is proposed in this study (IVC LEACH). In order to achieve the best performance with the least amount of energy consumption, the proposed hierarchical protocol relies on a fuzzy logic algorithm using four parameters to calculate the value of each node in the network and divides them into three hierarchical levels based on their value. This improves network efficiency and reliability while extending network life by 50 percent more than the original Low Energy Adaptive Clustering Hierarchy protocol. Keywords: Wireless Sensor Networks, Sensors, Communication Protocol, Fuzzy logic, Leach protocol.
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Vino, T., S. S. Sivaraju, R. V. V. Krishna, T. Karthikeyan, Yogesh kumar Sharma, K. G. S. Venkatesan, G. Manikandan, R. Selvameena, and Mebratu Markos. "Multicluster Analysis and Design of Hybrid Wireless Sensor Networks Using Solar Energy." International Journal of Photoenergy 2022 (October 11, 2022): 1–8. http://dx.doi.org/10.1155/2022/1164613.

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A wireless touch network is a distributed, self-organizing network of multiple sensors and actuators in combination with multiple sensors and a radio channel. Also, the security area of such a network can be several meters to several meters. The main difference between wireless sensor networks from traditional computer and telephone networks is the lack of a fixed infrastructure owned by a specific operator or provider. Each user terminal in a touch network is capable of acting as a terminal device only. Despite the long history of sensor networks, the concept of building a sensor network is not finally imposed and expressed in some software and hardware (platform) solutions. In this paper, the design and analysis of multicluster model of the sensor nodes in wireless sensor network with the help of solar energy. This proposed model provides the required energy to transmit the information between two end nodes in different cluster. The communication between the end to end clusters was increased based on this design. The implementation of sensory networks at the current stage depends largely on the specific needs of the industrial problem. The architecture, software, and hardware implementation technology is at an intensive development stage, attracting the attention of developers looking for a technological niche of future makers.
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Ahmed, Ayam Tawfeek, Ahmed Noori Rashid, and Khalid Shaker. "Localization in Wireless Sensor Network." Webology 19, no. 1 (January 20, 2022): 692–704. http://dx.doi.org/10.14704/web/v19i1/web19049.

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The major problems in a Wireless Sensor Networks (WSNs) is the localization problem, that relates to how an area covers by the sensor nodes. In this study, the problem formulates as the decision problem, that takes the best location for all sensors in the sensor field. Butterfly Optimization Algorithm (BOA), proposes to calculate the estimate locations for all sensors. Simulating the BOA with using number of sensors from 25 to 150 sensors and number of the anchor nodes. The distance between sensors and anchors measures by Received Signal Strength (RSS) so, this strategy is known as RSS-BOA. The obtained results shed that, the performance of the proposed algorithm is more accurate in comparing with BOA approach in the term sensor's location and the average error.
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THATIPAMULA RAJU, THATIPAMULA RAJU, and D. DEEPIKA RANI D. DEEPIKA RANI. "Achieving Network Level Privacy in Wireless Sensor Networks." International Journal of Scientific Research 2, no. 8 (June 1, 2012): 183–87. http://dx.doi.org/10.15373/22778179/aug2013/61.

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Shukur, Marwan Ihsan. "S-CDCA: a semi-cluster directive-congestion protocol for priority-based data in WSNs." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 1 (July 1, 2021): 438. http://dx.doi.org/10.11591/ijeecs.v23.i1.pp438-444.

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The internet of things (IoT) protocols and regulations are being developed forvarious applications includes: habitat monitoring, machinery control, general health-care, smart-homes and more. A great part of I0T comprised of sensors nodes in connected networks (i.e. sensor networks.). A sensor network is a group of nodes with sensory module and computational elements connected through network interfaces. The most interesting type of sensor networks are wireless sensor networks. The nodes here are connected through wirless interfaces. The shared medium between these nodes, creates different challenges. Congestion in such network is ineavitable. Different models andmethods were proposed to alleviate congestion in wireless sensor networks.This paper presents a semi-cluster directive congestion method that allivatenetwork congestion forpriority-baseddata transmission. The method simprove the network performance by implementing temporary cluster forlow level priority data packets while providing a clear link between highpriority data source node and the network base station. Simulation resultsshow that. The proposed method outperformes ad hocOn-demand distance vector (AODV) reactive procotol approach and priority-based congestion control dynamic clustering (PCCDC) a cluster-based methodin network energy consumption and control packets overhead during network operation.The proposed method also shows comparative improvments in end-to-enddelays versus PCCDC.
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Petrivskyi, V. Y., V. L. Shevchenko, O. S. Bychkov, and I. P. Sinitsyn. "Information technology to ensure the survivability of sensor networks." PROBLEMS IN PROGRAMMING, no. 4 (December 2021): 062–69. http://dx.doi.org/10.15407/pp2021.04.062.

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In the modern technological world, sensors and sensor networks are widely used in the all spheres of human activity. One of the key engineering tasks in the design of sensor networks is to ensure the survivability of the network. The article presents algorithms for ensuring the survivability of sensor networks based on a preliminary assessment of this property. Estimation of survivability depends on the network topology. Increasing the survivability of the sensor network is achieved by including additional sensors in the network. An algorithm for finding the position of additional sensors in the stationary sensor network is proposed. Proposed approach takes into account the radius of coverage of the sensors and the need for the intersection of the coverage areas of the sensors to ensure the exchange of information. An iterative algorithm for ensuring the survivability of the network in the presence and consideration of dynamic sensors is presented. The results of computer simulation experiments presented in the paper confirm the effectiveness of the proposed approaches.
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S, Rakshana. "Wireless Sensor Network." International Journal of Research Publication and Reviews 4, no. 4 (April 8, 2023): 1729–31. http://dx.doi.org/10.55248/gengpi.2023.4.4.35444.

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Vervečka, Martynas. "SENSOR NETWORK DATA FUSION METHODS." Mokslas - Lietuvos ateitis 2, no. 1 (February 28, 2010): 50–53. http://dx.doi.org/10.3846/mla.2010.011.

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Sensor network data fusion is widely used in warfare, in areas such as automatic target recognition, battlefield surveillance, automatic vehicle control, multiple target surveillance, etc. Non-military use example are: medical equipment status monitoring, intelligent home. The paper describes sensor networks topologies, sensor network advantages against the isolated sensors, most common network topologies, their advantages and disadvantages.
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Bao, Xi Rong, Yue Huang, and Shi Zhang. "A Distributed Motion Algorithm for Mobile Sensor in Hybrid Wireless Sensor Networks." Applied Mechanics and Materials 719-720 (January 2015): 812–17. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.812.

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Constructing a hybrid wireless sensor networks comprising a mix of static sensors and mobile sensors can achieve a balance between improving coverage and reducing the cost of the network. In order to achieve high network coverage, mobile sensor move from a small to a big size of coverage hole in the hybrid wireless sensor networks. Due to the energy of the mobile sensor is limited, how to reduce the moving distance of the mobile sensor and reduce the energy consumption in the process of moving is a very important issue. This paper proposes a distributed minimum cost matching algorithm (DMMA) to redeploy mobile sensor, which can make the level of network coverage to meet the requirement of the environment, while effectively reducing the number of sensors. In our method, static sensors detect coverage hole by Voronoi diagrams, coverage holing sensors and mobile sensors by using DMMA to excellently heal the large coverage holes. Simulation results show that our method can effectively improve the coverage rate of the WSNs, while save the energy of mobile sensors.
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Dissertations / Theses on the topic "Sensor network"

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Karaaslan, Ibrahim. "Anti-sensor Network: Distortion-based Distributed Attack In Wireless Sensor Networks." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/3/12609276/index.pdf.

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In this thesis, a novel anti-sensor network paradigm is introduced against wireless sensor networks (WSN). Anti-sensor network (ASN) aims to destroy application reliability by adaptively and anonymously introducing adequate level of artificial distortion into the communication of the event features transported from the sensor nodes (SN) to the sink. ASN is composed of anti-sensor nodes (aSN) randomly distributed over the sensor network field. aSNs pretend to be SNs tomaintain anonymity and so improve resiliency against attack detection and prevention mechanisms. Performance evaluations via mathematical analysis and simulation experiments show that ASN can effectively reduce the application reliability of WSN.
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Tan, Hailun Computer Science &amp Engineering Faculty of Engineering UNSW. "Secure network programming in wireless sensor networks." Awarded By:University of New South Wales. Computer Science & Engineering, 2010. http://handle.unsw.edu.au/1959.4/44835.

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Network programming is one of the most important applications in Wireless Sensor Networks as It provides an efficient way to update program Images running on sensor nodes without physical access to them. Securing these updates, however, remains a challenging and important issue, given the open deployment environment of sensor nodes. Though several security schemes have been proposed to impose the authenticity and Integrity protection on network programming applications, they are either energy Inefficient as they tend to use digital signature or lacks the data confidentiality. In addition, due to the absence of secure memory management in the current sensor hardware, the attacker could inject malicious code into the program flash by exploiting buffer overflow In the memory despite the secure code dissemination. The contribution of this thesis Is to provide two software-based security protocols and one hardware-based remote attestation protocol for network programming application. Our first protocol deploys multiple one-way key chains for a multi-hop sensor network. The scheme Is shown to be lower In computational, power consumption and communication costs yet still able to secure multi??hop propagation of program images. Our second protocol utilizes an Iterative hash structure to the data packets in network programming application, ensuring the data confidentiality and authenticity. In addition, we Integrated confidentiality and DoS-attack-resistance in a multi??hop code dissemination protocol. Our final solution is a hardware-based remote attestation protocol for verification of running codes on sensor nodes. An additional piece of tamper-proof hardware, Trusted Platform Module (TPM), is imposed into the sensor nodes. It secures the sensitive information (e.g., the session key) from attackers and monitors any platform environment changes with the Internal registers. With these features of TPM, the code Injection attack could be detected and removed when the contaminated nodes are challenged in our remote attestation protocol. We implement the first two software-based protocols with Deluge as the reference network programming protocol in TinyOS, evaluate them with the extensive simulation using TOSSIM and validate the simulation results with experiments using Tmote. We implement the remote attestation protocol on Fleck, a sensor platform developed by CSIRO that Integrates an Atmel TPM chip.
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Lim, Tiong Hoo. "Dependable network protocols in wireless sensor networks." Thesis, University of York, 2013. http://etheses.whiterose.ac.uk/4903/.

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This thesis is concerned with the dependability of Wireless Sensor Networks (WSNs). We propose an approach, inspired by the immune system, that allows individual nodes to detect, diagnose and recover from different failures by switching between different protocols using a multi-modal switching mechanism. A causal link between different failures in WSN is identified. Existing fault tolerance in WSNs approaches are examined. From the survey, it is identified that various attempts have been made to improve the fault tolerance of the communication protocol especially in the routing protocols. Although tests have been performed to evaluate the communication protocols prior to deployment, failures in WSNs are still being reported when deployed in real environments. A Systematic Protocol Evaluation Technique (SPET) is proposed and applied to evaluate the dependability of the proposed multi-modal protocol and reduce the uncertainties in the experiment and to demonstrate the confidence in the measurements taken from experiments.
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Simkhada, Shailendra, Christopher Lee, David Venderwerf, Miranda Tyree, and Tyler Lacey. "Wireless Sensor Network." International Foundation for Telemetering, 2011. http://hdl.handle.net/10150/595644.

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ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada
The scope of this document is the description of design and implementation of the wireless sensor network realized as a part of our Senior Design Capstone Project. The various components and sub-systems that comprise the final product are discussed, followed by the implementation procedures and results.
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Sevgi, Cuneyt. "Network Dimensioning In Randomly Deployed Wireless Sensor Networks." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/2/12611213/index.pdf.

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In this study, we considered a heterogeneous, clustered WSN, which consists of two types of nodes (clusterheads and sensor nodes) deployed randomly over a sensing field. We investigated two cases based on how clusterheads can reach the sink: direct and multi-hop communication cases. Network dimensioning problems in randomly deployed WSNs are among the most challenging ones as the attributes of these networks are mostly non-deterministic. We focused on a number of network dimensioning problems based on the connected coverage concept, which is the degree of coverage achieved by only the connected devices. To evaluate connected coverage, we introduced the term cluster size, which is the expected value of the area covered by a clusterhead together with sensor nodes connected to it. We derived formulas for the cluster size and validated them by computer simulations. By using the cluster size formulas, we proposed a method to dimension a WSN for given targeted connected coverage. Furthermore, we formulated cost optimization problems for direct and multi-hop communication cases. These formulations utilize not only cluster size formulas but also the well-connectivity concept. We suggested some search heuristics to solve these optimization problems. Additionally, we justified that, in practical cases, node heterogeneity can provide lower cost solutions. We also investigated the lifetime of WSNs and for mulated a cost optimization problem with connected coverage and lifetime constraints. By solving this optimization problem, one can determine the number of nodes of each type and the initial energies of each type of node that leads to lowest cost solution while satisfying the minimum connected coverage and minimum lifetime requirements.
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Yuan, Fenghua. "Lightweight network management design for wireless sensor networks." Online access for everyone, 2007. http://www.dissertations.wsu.edu/Thesis/Fall2007/F_Yuan_081307.pdf.

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Damianakis, John. "Proximity sensor network for sensor-based manipulation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ29588.pdf.

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Sriporamanont, Thammakit, and Gu Liming. "Wireless Sensor Network Simulator." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-290.

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In the recent past, wireless sensor networks have been introduced to use in many applications. To

design the networks, the factors needed to be considered are the coverage area, mobility, power

consumption, communication capabilities etc. The challenging goal of our project is to create a

simulator to support the wireless sensor network simulation. The network simulator (NS-2) which

supports both wire and wireless networks is implemented to be used with the wireless sensor

network. This implementation adds the sensor network classes which are environment, sensor

agent and sensor application classes and modifies the existing classes of wireless network in NS-

2. This NS-2 based simulator is used to test routing protocols – Destination-Sequenced Distance

Vector (DSDV), and Ad-Hoc On-Demand Distance Vector (AODV) as one part of simulations.

Finally, the sensor network application models and the extension methods of this NS-2 based

simulator for simulations in specific wireless sensor network applications are proposed.

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Qu, Yipeng. "Wireless Sensor Network Deployment." FIU Digital Commons, 2013. http://digitalcommons.fiu.edu/etd/854.

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Wireless Sensor Networks (WSNs) are widely used for various civilian and military applications, and thus have attracted significant interest in recent years. This work investigates the important problem of optimal deployment of WSNs in terms of coverage and energy consumption. Five deployment algorithms are developed for maximal sensing range and minimal energy consumption in order to provide optimal sensing coverage and maximum lifetime. Also, all developed algorithms include self-healing capabilities in order to restore the operation of WSNs after a number of nodes have become inoperative. Two centralized optimization algorithms are developed, one based on Genetic Algorithms (GAs) and one based on Particle Swarm Optimization (PSO). Both optimization algorithms use powerful central nodes to calculate and obtain the global optimum outcomes. The GA is used to determine the optimal tradeoff between network coverage and overall distance travelled by fixed range sensors. The PSO algorithm is used to ensure 100% network coverage and minimize the energy consumed by mobile and range-adjustable sensors. Up to 30% - 90% energy savings can be provided in different scenarios by using the developed optimization algorithms thereby extending the lifetime of the sensor by 1.4 to 10 times. Three distributed optimization algorithms are also developed to relocate the sensors and optimize the coverage of networks with more stringent design and cost constraints. Each algorithm is cooperatively executed by all sensors to achieve better coverage. Two of our algorithms use the relative positions between sensors to optimize the coverage and energy savings. They provide 20% to 25% more energy savings than existing solutions. Our third algorithm is developed for networks without self-localization capabilities and supports the optimal deployment of such networks without requiring the use of expensive geolocation hardware or energy consuming localization algorithms. This is important for indoor monitoring applications since current localization algorithms cannot provide good accuracy for sensor relocation algorithms in such indoor environments. Also, no sensor redeployment algorithms, which can operate without self-localization systems, developed before our work.
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Gen-Kuong, Fernando, and Alex Karolys. "Smart Sensor Network System." International Foundation for Telemetering, 1997. http://hdl.handle.net/10150/607534.

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International Telemetering Conference Proceedings / October 27-30, 1997 / Riviera Hotel and Convention Center, Las Vegas, Nevada
This paper describes a Smart Sensor Network System for applications requiring sensors connected in a multidrop configuration in order to minimize interconnecting cables. The communication protocol was optimized for high speed data collection. The Smart Sensor Network System was developed with the following goals in mind: cost reduction, reliability and performance increase.
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Books on the topic "Sensor network"

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Phoha, Shashi, Thomas LaPorta, and Christopher Griffin, eds. Sensor Network Operations. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/0471784176.

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1953-, Ilyas Mohammad, and Mahgoub Imad, eds. Sensor network protocols. Boca Raton, FL: Talor & Francis, 2005.

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Wireless sensor network designs. Chichester, West Sussex, England: J. Wiley, 2003.

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Bulusu, Nirupama. Wireless sensor network systems. Boston, MA: Artech House, 2005.

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Javier, López, and Zhou Jianying, eds. Wireless sensor network security. Amsterdam: IOS Press, 2008.

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Mukhopadhyay, Subhas Chandra. Advances in Wireless Sensors and Sensor Networks. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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LTE self-organising networks (SON): Network management automation for operational efficiency. Hoboken, N.J: Wiley, 2012.

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Iyengar, S. Sitharama, Nandan Parameshwaran, Vir V. Phoha, N. Balakrishnan, and Chuka D. Okoye. Fundamentals of Sensor Network Programming. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470890158.

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Wireless sensor and ad hoc networks under diversified network scenarios. Boston, Mass: Artech House, 2012.

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Michel, Grothe, Kooijman Jan, and Nederlandse Commissie voor Geodesie, eds. Sensor web enablement. Delft: NCG, 2008.

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Book chapters on the topic "Sensor network"

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Faisal Haider, M., Amrita Kumar, Irene Li, and Fu-Kuo Chang. "Sensors, Sensor Network, and SHM." In Handbook of Nondestructive Evaluation 4.0, 1–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-48200-8_58-1.

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Faisal Haider, M., Amrita Kumar, Irene Li, and Fu-Kuo Chang. "Sensors, Sensor Network, and SHM." In Handbook of Nondestructive Evaluation 4.0, 569–602. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-73206-6_58.

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Ma, Huadong, Liang Liu, and Hong Luo. "In-Network Processing for Multimedia Sensor Networks." In Multimedia Sensor Networks, 145–201. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0107-1_4.

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Fragouli, Christina. "Network Coding for Sensor Networks." In Handbook on Array Processing and Sensor Networks, 645–67. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470487068.ch20.

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Perrig, Adrian, and J. D. Tygar. "Sensor Network Security." In Secure Broadcast Communication, 149–73. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0229-6_7.

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Erciyes, K. "Sensor Network Applications." In Computer Communications and Networks, 277–94. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5173-9_17.

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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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Kim, Hyung Won. "Wireless Sensor Network for Video Sensors." In KAIST Research Series, 339–66. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-9987-4_15.

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McGrath, Michael J., and Cliodhna Ní Scanaill. "Sensor Network Topologies and Design Considerations." In Sensor Technologies, 79–95. Berkeley, CA: Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-6014-1_4.

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D., Francina Sophiya, Swarnalatha P., Prabu Sevugan, T. D. K. Upeksha Chathurani, and R. Magesh Babu. "Smart Sensing Network for Smart Technologies." In Applications of Artificial Intelligence for Smart Technology, 177–91. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3335-2.ch012.

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Smart environments based on wireless sensor networks represent the next evolutionary development step in engineering, such as industrial automation, video surveillance, traffic monitoring, and robot control. Sensory data come from multiple networks of interconnected sensors with complex distributed locations. The recent development of communication and sensor technology results in the growth of a new attractive and challenging area: wireless sensor networks (WSNs). A wireless sensor network which consists of a large number of sensor nodes is deployed in environmental fields to serve various applications. Facilitated with the ability of wireless communication and intelligent computation, these nodes become smart sensors that do not only perceive ambient physical parameters but also are able to process information, cooperate with each other, and self-organize into the network. These new features assist the sensor nodes as well as the network to operate more efficiently in terms of both data acquisition and energy consumption.
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Conference papers on the topic "Sensor network"

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Kaur, Harminder, and Sharvan Kumar Pahuja. "MAC Protocols for Wireless Body Sensor Network." In International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.33.

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Wireless Body Area Networks, also known as the Wireless Body Sensor Networks, provides the monitoring of the health parameters in remote areas and where the medical facility is not available. Wireless Body Sensor Networks contains the body or placement of the sensors on body for measuring the medical and non-medical parameters. These networks share the wireless medium for the transmission of the data from one place to another. So the design of Medium Access Control is a challenging task for the WBSNs due to wireless media for less energy consumption and mobility. Various MAC protocols are designed to provide less energy consumption and improve the network lifetime. This paper presents the study of these existing MAC layer protocols based on different QoS parameters that define the network quality.
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Lee, Jae-Yong, and Suhada Jayasuriya. "Multi-Layered Mobile Sensor Network With Robot Team." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-81438.

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This paper presents a concept and its application of the dynamic multi-layered sensor network for gathering environment information. The architecture of the system is composed of sensor layer, physical layer, and communication layer. Sensor layer is the combination and the network of the sensor components in each sensor node. A sensor node has multiple numbers of sensors, and a number of robots for the mobile sensor network. Each mobile robot plays a role of mobile base for a sensor node, and consists of physical layer. We discuss the formation of the multi-layered sensor network, which is active, adaptive, and task oriented in the surrounding environment. As each sensor node consists of multiple numbers of heterogeneous sensors, sensor combination is not restricted to one particular objective or task. This fact allows the network to be multi-functional. Same kind of sensors can establish ad-hoc sensor network, while different kind of sensors fuse the data for improved interpretation of the environment. Experiments with mobile sensors nodes are conducted and illustrated to show the concept of the multi-layered mobile sensor network.
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ŠILJAK, HARUN, and BISWAJIT BASU. "NATURAL SYNCHRONIZATION OF WIRELESS SENSOR NETWORKS FOR STRUCTURAL HEALTH MONITORING." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36278.

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Time synchronization in communication networks is a common issue: in a sensor network it means that the order of data samples becomes uncertain, which can make it unusable. Dedicated signals and schemes for synchronization of sensor networks has hence been a well-researched topic for decades. Here we bring in an approach to synchronization which uses the sensory data. Drawing inspiration from sensor time synchronization using environmental noise, we consider synchronizing sensory nodes for structural health monitoring–if the physical quantity the sensors measure is correlated, propagating as a wave, or oscillating in regular fashion, it is intuitively clear how to put it to use. We discuss when structural health monitoring signals can aid synchronization; we also connect this synchronization scheme to the idea of using physical human-made structures as reservoirs for reservoir computing, formulating synchronization as a reservoir computing task.
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Lee, Kang B., Eugene Y. Song, and Peter S. Gu. "Integration of MTConnect and Standard-Based Sensor Networks for Manufacturing Equipment Monitoring." In ASME 2012 International Manufacturing Science and Engineering Conference collocated with the 40th North American Manufacturing Research Conference and in participation with the International Conference on Tribology Materials and Processing. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/msec2012-7398.

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MTConnect is an open and extensible protocol designed for the exchange of data between shop floor devices and software applications. MTConnect allows manufacturers to facilitate retrieval of information and data from factory devices, such as machine tools, sensors, and controllers. Currently, MTConnect users read data from sensors through proprietary sensor interfaces using adaptors. The suite of Institute of Electrical and Electronics Engineers (IEEE) 1451 standards defines a set of open, common communication interfaces for sensor networks, including both sensor interfaces and network interfaces. This paper proposes an integration architecture of MTConnect with IEEE 1451 standard-based sensor networks. In the architecture, MTConnect plays a network interface role in the IEEE 1451 standard-based sensor networks via an MTConnect Agent. An adaptor is used to provide the mapping between the MTConnect Agent and the IEEE 1451 sensor network. A prototype system integrating MTConnect with IEEE 1451.2-based sensor network has been developed. Two case studies are provided to illustrate the integration.
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Xiradakis, N., and Y. G. Li. "Gas Turbine and Sensor Fault Diagnosis With Nested Artificial Neural Networks." In ASME Turbo Expo 2004: Power for Land, Sea, and Air. ASMEDC, 2004. http://dx.doi.org/10.1115/gt2004-53570.

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Accurate gas turbine diagnosis relies on accurate measurements from sensors. Unfortunately, sensors are prone to degradation or failure during gas turbine operations. In this paper a stack of decentralised artificial neural networks are introduced and investigated as an approach to approximate the measurement of a failed sensor once it is detected. Such a system is embedded into a nested neural network system for gas turbine diagnosis. The whole neural network diagnostic system consists of a number of feedforward neural networks for engine component diagnosis, sensor fault detection and isolation; and a stack of decentralised neural networks for sensor fault recovery. The application of the decentralised neural networks for the recovery of any failed sensor has the advantage that the configuration of the nested neural network system for engine component diagnosis is relatively simple as the system does not take into account sensor failure. When a sensor fails, the biased measurement of the failed sensor is replaced with a recovered measurement approximated with the measurements of other healthy sensors. The developed approach has been applied to an engine similar to the industrial 2-shaft engine, GE LM2500+, whose performance and training samples are simulated with an aero-thermodynamic modelling tool — Cranfield University’s TURBOMATCH computer program. Analysis shows that the use of the stack of decentralised neural networks for sensor fault recovery can effectively recover the measurement of a failed sensor. Comparison between the performance of the diagnostic system with and without the decentralised neural networks shows that the sensor recovery can improve the performance of the neural network engine diagnostic system significantly when a sensor fault is present.
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Kim, Hyung-Sin, Jin-Seok Han, and Yong-Hwan Lee. "Scalable network joining mechanism in wireless sensor networks." In 2012 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet). IEEE, 2012. http://dx.doi.org/10.1109/wisnet.2012.6172139.

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Malinowski, John, Patrick Wiley, Jonathan Trent, and Emil J. Geiger. "Wireless ISFET pH Sensor Network for Offshore Microalgae Cultivation." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-89605.

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Microalgae technology continues to show tremendous promise for becoming a major source of renewable transportation fuel in the coming decades. However, for microalgae to provide a significant fraction of the current US demand for fuel, their cultivation will be required on an enormous scale. One of the many formidable challenges that must be met to achieve this scale is the development of appropriate sensor networks to provide information about the growth conditions and the algae themselves. These sensors would monitor the heterogeneity of a) environmental parameters, such as pH, oxygen, and nutrient levels and b) algal characteristics such as size, oil content, and viability. Here we present a wireless sensor network to measure the local pH in NASA OMEGA project (Offshore Membrane Enclosures for Growing Algae). The pH is measured using Ion Sensitive Field Effect Transistor (ISFET) technology, which is more robust and has a faster response than traditional glass pH electrodes. A custom circuit drives the ISFET sensor and interfaces with an ANT wireless network system. The wireless network consists of a network hub which can service up to 8 sensor nodes and a series of relays to transmit the data to a PC. The data is logged with a custom LabVIEW program. In this work, we demonstrate operation of this network using a single ISFET pH sensor, one hub, and two relay units. The performance of the pH sensor network is evaluated and compared in parallel with an existing wired glass electrode based pH monitoring system at the NASA OMEGA project.
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Chou, Yu-Cheng. "Sensor Agent Cloud: A Cloud-Based Autonomic System for Physical Sensor Nodes Management." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48732.

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An embedded sensor network is a network of sensor nodes deployed in the physical world that interacts with the environment. Each sensor node is a physically small and relatively inexpensive computer that has one or more sensors. These sensor nodes are often networked, allowing them to communicate and cooperate with each other to monitor the environment. Typically, an embedded sensor network is controlled by its own applications that can access the sensor nodes within the network. On the other hand, the sensor nodes cannot be easily accessed by applications outside of the network. Moreover, even within the same network, different applications might encounter a race condition when they are trying to access a sensor node simultaneously. The issue is related to system management. However, not much research has been done with a focus on the management of sensor nodes. In the past few years, Cloud computing has emerged as a new computing paradigm to provide reliable resources, software, and data on demand. As for resources, essentially, Cloud computing services provide users with virtual servers. Users can utilize virtual servers without concerning about their locations and specifications. With such an inspiration, this paper proposes a system, Sensor Agent Cloud, where users can access the sensor nodes without worrying about their locations and detailed specifications. Sensor Agent Cloud virtualizes a physical sensor node as a virtual “sensor agent”. Users can use and control sensor agents with standard functions. Each sensor agent operates on behalf of its user. The mandatory coordination of these sensor agents is related to the system management. Therefore, Sensor Agent Cloud must be an autonomic system that manages itself with minimum human interference. In addition, Sensor Agent Cloud supports international standard technologies regarding programming and agent communication (C and IEEE FIPA standard). Thus, it is expected that the proposed Sensor Agent Cloud can enhance the applicability and usability of embedded sensor networks in many application areas.
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Kumar, Manish, Devendra P. Garg, and Randy A. Zachery. "Intelligent Sensor Modeling and Data Fusion via Neural Network and Maximum Likelihood Estimation." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-80972.

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The major thrust of this paper is to develop a sensor model based on a probabilistic approach that could accurately provide information about individual sensor’s uncertainties and limitations. The sensor model aims to provide a most informative likelihood function that can be used to obtain a statistical and probabilistic estimate of uncertainties and errors due to some environmental parameters or parameters of any feature extraction algorithm used in estimation based on sensor’s outputs. This paper makes use of a neural network that has been trained with the help of a novel technique that obtains training signal from a maximum likelihood estimator. The proposed technique was applied to model stereo-vision sensors and Infra-Red (IR) proximity sensor, and information from these sensors were fused in a Bayesian framework to obtain a three-dimensional occupancy profile of objects in robotic workspace. The capability of the proposed technique in accurately obtaining three-dimensional occupancy profile and efficiently removing individual sensor uncertainties was demonstrated and validated via experiments carried out in the Robotics and Manufacturing Automation (RAMA) Laboratory at Duke University.
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Li, Ming, Hong Lin, John Rushing, and Sara J. Graves. "Sensor networks and network sensibility." In Defense and Security Symposium, edited by Raghuveer M. Rao, Sohail A. Dianat, and Michael D. Zoltowski. SPIE, 2007. http://dx.doi.org/10.1117/12.719980.

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Reports on the topic "Sensor network"

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Ratmanski, Kiril, and Sergey Vecherin. Resilience in distributed sensor networks. Engineer Research and Development Center (U.S.), October 2022. http://dx.doi.org/10.21079/11681/45680.

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With the advent of cheap and available sensors, there is a need for intelligent sensor selection and placement for various purposes. While previous research was focused on the most efficient sensor networks, we present a new mathematical framework for efficient and resilient sensor network installation. Specifically, in this work we formulate and solve a sensor selection and placement problem when network resilience is also a factor in the optimization problem. Our approach is based on the binary linear programming problem. The generic formulation is probabilistic and applicable to any sensor types, line-of-site and non-line-of-site, and any sensor modality. It also incorporates several realistic constraints including finite sensor supply, cost, energy consumption, as well as specified redundancy in coverage areas that require resilience. While the exact solution is computationally prohibitive, we present a fast algorithm that produces a near-optimal solution that can be used in practice. We show how such formulation works on 2D examples, applied to infrared (IR) sensor networks designed to detect and track human presence and movements in a specified coverage area. Analysis of coverage and comparison of sensor placement with and without resilience considerations is also performed.
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Stephen P. Farrington, John W. Haas, and Neal Van Wyck. LONG-TERM MONITORING SENSOR NETWORK. Office of Scientific and Technical Information (OSTI), October 2003. http://dx.doi.org/10.2172/822906.

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Litz, Marc S., Dimosthenis C. Katsis, Johnny A. Russo, James C. Brent, and James J. Carroll. Tritium-Powered Radiation Sensor Network. Fort Belvoir, VA: Defense Technical Information Center, September 2015. http://dx.doi.org/10.21236/ada621333.

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Zhang, Junshan. Networked Information Gathering in Stochastic Sensor Networks: Compressive Sensing, Adaptive Network Coding and Robustness. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada590144.

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Worth Johnson, Bonnie, and John M. Green. Naval Network-Centric Sensor Resource Management. Fort Belvoir, VA: Defense Technical Information Center, April 2002. http://dx.doi.org/10.21236/ada458080.

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Hagen Schempf. Gas Main Sensor and Communications Network System. Office of Scientific and Technical Information (OSTI), May 2006. http://dx.doi.org/10.2172/890711.

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Hagen Schempf. Gas Main Sensor and Communications Network System. Office of Scientific and Technical Information (OSTI), February 2003. http://dx.doi.org/10.2172/924030.

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Hagen Schempf. GAS MAIN SENSOR AND COMMUNICATIONS NETWORK SYSTEM. Office of Scientific and Technical Information (OSTI), September 2004. http://dx.doi.org/10.2172/836824.

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Hagen Schempf, Ph D. GAS MAIN SENSOR AND COMMUNICATIONS NETWORK SYSTEM. Office of Scientific and Technical Information (OSTI), February 2003. http://dx.doi.org/10.2172/816706.

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Alphenaar, Bruce. Wireless Sensor Network for Electric Transmission Line Monitoring. Office of Scientific and Technical Information (OSTI), June 2009. http://dx.doi.org/10.2172/1004093.

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