Relevant bibliographies by topics / Smart Sensor Network

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Smart Sensor Network'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Smart Sensor Network"

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Almogren, Ahmad S. "Developing a Powerful and Resilient Smart Body Sensor Network through Hypercube Interconnection." International Journal of Distributed Sensor Networks 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/609715.

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With recent advances in wireless sensor networks and embedded computing technologies, body sensor networks (BSNs) have become practically feasible. BSNs consist of a number of sensor nodes located and deployed over the human body. These sensors continuously gather vital sign data of the body area to be used in various intelligent systems in smart environments. This paper presents an intelligent design of the body sensor network based on virtual hypercube structure backbone termed as Smart BodyNet. The main purpose of the Smart BodyNet is to provide resilience for the BSN operation and reduce power consumption. Various experiments were carried out to show the performance of the Smart BodyNet design as compared to the state-of-the-art approaches.
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Ogbodo, Emmanuel, David Dorrell, and Adnan Abu-Mahfouz. "Energy-efficient distributed heterogeneous clustered spectrum-aware cognitive radio sensor network for guaranteed quality of service in smart grid." International Journal of Distributed Sensor Networks 17, no. 7 (2021): 155014772110283. http://dx.doi.org/10.1177/15501477211028399.

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The development of a modern electric power grid has triggered the need for large-scale monitoring and communication in smart grids for efficient grid automation. This has led to the development of smart grids, which utilize cognitive radio sensor networks, which are combinations of cognitive radios and wireless sensor networks. Cognitive radio sensor networks can overcome spectrum limitations and interference challenges. The implementation of dense cognitive radio sensor networks, based on the specific topology of smart grids, is one of the critical issues for guaranteed quality of service through a communication network. In this article, various topologies of ZigBee cognitive radio sensor networks are investigated. Suitable topologies with energy-efficient spectrum-aware algorithms of ZigBee cognitive radio sensor networks in smart grids are proposed. The performance of the proposed ZigBee cognitive radio sensor network model with its control algorithms is analyzed and compared with existing ZigBee sensor network topologies within the smart grid environment. The quality of service metrics used for evaluating the performance are the end-to-end delay, bit error rate, and energy consumption. The simulation results confirm that the proposed topology model is preferable for sensor network deployment in smart grids based on reduced bit error rate, end-to-end delay (latency), and energy consumption. Smart grid applications require prompt, reliable, and efficient communication with low latency. Hence, the proposed topology model supports heterogeneous cognitive radio sensor networks and guarantees network connectivity with spectrum-awareness. Hence, it is suitable for efficient grid automation in cognitive radio sensor network–based smart grids. The traditional model lacks these capability features.
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Alonso, Monica, Hortensia Amaris, Daniel Alcala, and Diana M. Florez R. "Smart Sensors for Smart Grid Reliability." Sensors 20, no. 8 (2020): 2187. http://dx.doi.org/10.3390/s20082187.

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Sensors for monitoring electrical parameters over an entire electricity network infrastructure play a fundamental role in protecting smart grids and improving the network’s energy efficiency. When a short circuit takes place in a smart grid it has to be sensed as soon as possible to reduce its fault duration along the network and to reduce damage to the electricity infrastructure as well as personal injuries. Existing protection devices, which are used to sense the fault, range from classic analog electro-mechanics relays to modern intelligent electronic devices (IEDs). However, both types of devices have fixed adjustment settings (offline stage) and do not provide any coordination among them under real-time operation. In this paper, a new smart sensor is developed that offers the capability to update its adjustment settings during real-time operation, in coordination with the rest of the smart sensors spread over the network. The proposed sensor and the coordinated protection scheme were tested in a standard smart grid (IEEE 34-bus test system) under different short circuit scenarios and renewable energy penetration. Results suggest that the short-circuit fault sensed by the smart sensor is improved up to 80% and up to 64% compared with analog electromechanics relays and IEDs, respectively.
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Cheung, Long Fung, King Shan Lui, Kenneth Kin Yip Wong, Wing Kin Lee, and Philip W. T. Pong. "A Laboratory-Based Smart Grid Sensor Network Testbed." Applied Mechanics and Materials 479-480 (December 2013): 747–52. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.747.

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A laboratory-based sensor network testbed for Smart Grid was developed at the Smart Grid and High Power System Laboratory of The University of Hong Kong. The setup is featured by a scaled transmission-line model, visualization of sensor measurement, optical communication network, and integration with global positioning system (GPS). The transmission-line model consists of a power cable and towers in which various types of sensors including magnetic sensors, infrared sensors, strain gauges, and accelerometers are installed to monitor the condition of the transmission line and the transmission towers. Magnetic sensors and infrared sensor are employed as advanced sensors which can provide more accurate and comprehensive information of the transmission line. The sensor data is transferred to the computer for analysis and visualization. Graphical user interface (GUI) was designed in LabVIEW to integrate the data acquisition and display of measurement results including cable position, inclination and vibration of the tower, frequency and waveform of the cable current. The host computer also forms an IP network with five remote computers, via optical fiber and optical interface card, for testing various communication protocols. The topology and connectivity of the network is graphically displayed. The sensor network is integrated with GPS and can perform synchronized measurement with the GPS timing. This sensor network testbed provides a platform for the implementation testing, experimentation, and feasibility evaluation of new sensor applications under test in Smart Grid.
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Kamal, Sahar, Rabie Ramadan, and Fawzy El-Refai. "Smart outlier detection of wireless sensor network." Facta universitatis - series: Electronics and Energetics 29, no. 3 (2016): 383–93. http://dx.doi.org/10.2298/fuee1603383k.

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Data sets collected from wireless sensor networks (WSN) are usually considered unreliable and subject to errors due to limited sensor capabilities and hard environment resulting in a subset of the sensors data called outlier data. This paper proposes a technique to detect outlier data base on spatial-temporal similarity among data collected by geographically distributed sensors. The proposed technique is able to identify an abnormal subset of data collected by sensor node as outlier data. Moreover, the proposed technique is able to classify this abnormal observation, an error data set or event affected set. Simulation result shows that high detection rate is achieved compared to conventional outlier detection techniques while preserving low positive false alarm rate.
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Himani, Rawat, and Pathak Yugal. "Smart Sensors: Analyzing Efficiency of Smart Sensors in Public Domain." International Journal of Engineering and Management Research 9, no. 5 (2019): 104–11. https://doi.org/10.31033/ijemr.9.5.15.

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The paper gives the brief idea of smart sensors, structure and its application. Smart sensor as compare to other sensors can sensor anything with the special computing devices connected with each other in sensor network. These smart sensors first convert the digital signals to analog signals and then communicate the message to the device. Now a days smart sensors are used almost everywhere around us but very few people know its working and future applications. So here is a small review on smart sensors. This paper will help you gain knowledge and its applications in daily life.
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Tuyishimire, Emmanuel, Antoine Bagula, and Adiel Ismail. "Clustered Data Muling in the Internet of Things in Motion." Sensors 19, no. 3 (2019): 484. http://dx.doi.org/10.3390/s19030484.

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This paper considers a case where an Unmanned Aerial Vehicle (UAV) is used to monitor an area of interest. The UAV is assisted by a Sensor Network (SN), which is deployed in the area such as a smart city or smart village. The area being monitored has a reasonable size and hence may contain many sensors for efficient and accurate data collection. In this case, it would be expensive for one UAV to visit all the sensors; hence the need to partition the ground network into an optimum number of clusters with the objective of having the UAV visit only cluster heads (fewer sensors). In such a setting, the sensor readings (sensor data) would be sent to cluster heads where they are collected by the UAV upon its arrival. This paper proposes a clustering scheme that optimizes not only the sensor network energy usage, but also the energy used by the UAV to cover the area of interest. The computation of the number of optimal clusters in a dense and uniformly-distributed sensor network is proposed to complement the k-means clustering algorithm when used as a network engineering technique in hybrid UAV/terrestrial networks. Furthermore, for general networks, an efficient clustering model that caters for both orphan nodes and multi-layer optimization is proposed and analyzed through simulations using the city of Cape Town in South Africa as a smart city hybrid network engineering use-case.
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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 (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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Lewandowski, Marcin, and Bartłomiej Płaczek. "An Event-Aware Cluster-Head Rotation Algorithm for Extending Lifetime of Wireless Sensor Network with Smart Nodes." Sensors 19, no. 19 (2019): 4060. http://dx.doi.org/10.3390/s19194060.

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Smart sensor nodes can process data collected from sensors, make decisions, and recognize relevant events based on the sensed information before sharing it with other nodes. In wireless sensor networks, the smart sensor nodes are usually grouped in clusters for effective cooperation. One sensor node in each cluster must act as a cluster head. The cluster head depletes its energy resources faster than the other nodes. Thus, the cluster-head role must be periodically reassigned (rotated) to different sensor nodes to achieve a long lifetime of wireless sensor network. This paper introduces a method for extending the lifetime of the wireless sensor networks with smart nodes. The proposed method combines a new algorithm for rotating the cluster-head role among sensor nodes with suppression of unnecessary data transmissions. It enables effective control of the cluster-head rotation based on expected energy consumption of sensor nodes. The energy consumption is estimated using a lightweight model, which takes into account transmission probabilities. This method was implemented in a prototype of wireless sensor network. During experimental evaluation of the new method, detailed measurements of lifetime and energy consumption were conducted for a real wireless sensor network. Results of these realistic experiments have revealed that the lifetime of the sensor network is extended when using the proposed method in comparison with state-of-the-art cluster-head rotation algorithms.
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Lieske, Tobias, Denis Shuklin, Daniel Hohnloser, et al. "Smart Sensor Framework: A Pressure Sensor for Smart Home Applications." Advanced Engineering Forum 19 (October 2016): 92–106. http://dx.doi.org/10.4028/www.scientific.net/aef.19.92.

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Smart home automation applications require a dense information network for proper func-tionality. Air-conditioning or filtration systems, for example, must detect airflows caused by openwindows and doors. An unambiguous detection of such airflows can be performed by a distributedsensor network. Current off-the-shelf sensors often lack processing and communication units, resulting in a large design assembly of discrete integrated circuits (ICs) on one printed circuit board (PCB)that requires additional power supply. Distributing such designs within a home without interferingwith the existing surroundings proves to be difficult in terms of acceptance and usability. This paperpresents a solution by offering an integrated design that includes a microelectromechanical system(MEMS) pressure sensor element along with an analog to digital converter (ADC) and a customizableand programmable processing unit. The integration leads to a smaller overall footprint and reducedpower consumption, which positively affects the acceptance rate of distributed smart sensor networksfor home automation. Clear interfaces between the components ensure an extensible and adaptablesystem design suitable for further smart sensor applications, resulting in a smart sensor framework.
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Dissertations / Theses on the topic "Smart Sensor Network"

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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<br>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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Nozik, Andrew Benjamin. "Wireless smart shipboard sensor network. /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Dec%5FNozik.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, December 2005.<br>Thesis Advisor(s): Xi ping Yun, Robert Hutchins. Includes bibliographical references (p. 67-68). Also available online.
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Nozik, Andrew B. "Wireless smart shipboard sensor network." Thesis, Monterey California. Naval Postgraduate School, 2005. http://hdl.handle.net/10945/1756.

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This thesis studies the feasibility of developing a smart shipboard sensor network. The objective of the thesis is to prove that sensors can be made smart by keeping calibration constants and other relevant data such as network information stored on the sensor and a server computer. Study will focus on the design and implementation of an Ipsil IP(micro)8930 microcontroller, which is then connected, by the standard TCP/IP implementation, to a network where the sensor information can be seen using a web page. The information to make the sensor "smart" will be stored on the Ipsil chip and server computer and can by accessed by a HTML based program. By taking pre-computed calibration constants that minimize the measurement errors and writing them through the web page stored in the Ipsil chip's EEPROM, the calibrated sensor reading can be calculated. The expected contribution from the research effort would be a reduction in manpower, increased efficiency, and a greater awareness of plant and equipment operation among naval vessels, specifically the DDX. Hardware is relatively inexpensive, reliable, and COTS (Commercial Off the Shelf) available. If implemented, a Smart Shipboard Sensor Network would allow the watch standers, CHENG, OOD, and CO, to all see the same information about the ship.s engineering plant and equipment. A prototype sensor test bed was constructed in the laboratory, which consists of an Ipsil IP(micro)8930 microcontroller, a Linksys LAN router, and a Dell Inspiron 9300 laptop. The newly developed smart sensor was successfully demonstrated.
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Sharovar, Igor. "Smart Sensor with network plug and play capabilities." Thesis, University of Ottawa (Canada), 2004. http://hdl.handle.net/10393/26771.

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This thesis proposes enhancements to the network capabilities of the IEEE 1451.1 standard. The IEEE 1451.1 standard belongs to the popularly used, innovative group of specifications called Smart Sensor. It defines a standardized way to design and develop transducers that can be easily connected to a sensor network. The IEEE 1451.1 standard defines a Common Object Model, which may be used to design the network capability of a Smart Sensor device, but does not define a standard way to connect Smart Sensor devices to different types of networks. A variety of modern technologies is applied today, allowing ease with which network devices connect to networks. One such technology is Universal Plug and Play (UPnP), which defines the mechanism and protocol for the discovery of network devices, as well as their control and event functions. The goal of this thesis is to expand the IEEE 1451.1 standard to make Smart Sensor devices capable of working as UPnP devices. Achieving this will enable the design of an UPnP Smart Sensor that will not only connect different types of transducers to a sensor network, but will also provide a standard and easy mechanism to connect these devices to different networks, including the Internet, while minimizing the effort required for their design and implementation.
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Darden, Kelvin S. "Smart Microgrid Energy Management Using a Wireless Sensor Network." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1404560/.

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Modern power generation aims to utilize renewable energy sources such as solar power and wind to supply customers with power. This approach avoids exhaustion of fossil fuels as well as provides clean energy. Microgrids have become popular over the years, as they contain multiple renewable power sources and battery storage systems to supply power to the entities within the network. These microgrids can share power with the main grid or operate islanded from the grid. During an islanded scenario, self-sustainability is crucial to ensure balance between supply and demand within the microgrid. This can be accomplished by a smart microgrid that can monitor system conditions and respond to power imbalance by shedding loads based on priority. Such a method ensures security of the most important loads in the system and manages energy by automatically disconnecting lower priority loads until system conditions have improved. This thesis introduces a prioritized load shedding algorithm for the microgrid at the University of North Texas Discovery Park and highlight how such an energy management algorithm can add reliability to an islanded microgrid.
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Xie, Wang. "Energy Consumption Modeling in Wireless Sensor Networked Smart Homes." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32071.

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Smart home automation is the dwelling bridge of smart grid technology, as it integrates the modern home appliances power consumption information over communication networks in the smart grid system. Among all the appliances, Heating, Ventilation and Cooling (HVAC) systems is one of the most primary concerns. Since a great amount of power consumption is contributed by these HVAC systems. Traditionally, HVAC systems run at a fixed schedule without automatic monitoring and control systems, which causes load variation, fluctuations in the electricity demand and inefficient utility operation. In this thesis, we propose a Finite State Machine (FSM) system to model the air condition working status to acquire the relationship between temperature changing and cooling/heating duration. Finally, we introduce the Zigbee communciation protocol into the model, the performance analysis of the impact of end-to-end delay over HVAC systems is presented.
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Prats, Vidal Oriol. "Smart Oce: Wireless Sensor Network for Energy Monitoring and User Profiling." Thesis, KTH, Reglerteknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-105139.

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In a world where there are about 7 billion people living and this number is continuously increasing, it is necessary to reduce the resource consumption to achieve a sustainable situation. In this work, a system to detect water and electricity consumption is introduced to identify resource waste and inform the consumers about it. For this purpose, many sensors are deployed in the kitchen of the department in order to gather data of the consumption. These sensors are an alternative to other ways of getting this information that are usually considered as invasive, like a camera. Even though, as far as there are many sensors deployed, the way to put all data together will be using wireless communication. Then, a treatment is performed in order to obtain some statistics about the consumption. These networks can also be accessed by people external to the department, who would be able to get information about their consumption and, consequently, their expenditures. Because all of this, a privacy problem of the enterprise appears, in this case the Electrical Engineering Automatic Control Department. Regarding the wireless communication, dierent protocols will be tested to check their reliability. Furthermore, the data from the dierent sensors makes possible to determine behaviour patterns of the people working there. By using this system, not environmentally friendly actions that lead to resource waste can be easily identied and corrected. Again, this opens a discussion about privacy, concerning the dierent people and their habits. To sum up, the system would enable the consumers to decrease their resource consumption, which would lead to an important energy saving in large scale and a reduction in costs in small scale.
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Zaker, Nima. "Fiber-wireless Sensor Broadband Access Network Integration for the Smart Grid." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/23804.

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During the last century, the significant increase in electricity demand, and its consequences, has appeared as a serious concern for the utility companies, but no essential change has been applied to the conventional power grid infrastructure till now. Recently, researchers have identified efficient control and power distribution mechanisms as the immediate challenges for conventional power grids. Hence, the next step for conventional power grid toward Smart Grid is to provide energy efficiency management along with higher reliability via smart services, in which the application of Information and Communication Technology (ICT) is inevitable. ICT introduces powerful tools to comply with the smart grid requirements. Among various ICT properties, the telecommunication network plays a key role for providing a secure infrastructure. The two-way digital communication system provides an interaction between energy suppliers and consumers for managing, controlling and optimizing energy distribution. We can also define the smart grid as a two-way flow of energy and control information, where the electricity consumers can generate energy using green energy resources. The main objective of this thesis is to select an effective communication infrastructure to support the smart grid services by considering wireless and optical communication technologies. Fiber-wireless (FiWi) networks are considered as a potential solution to provide a fast and reliable network backbone with the optical access network integration and the flexibility and mobility of the wireless network. Therefore, we adopt the integration of the wireless sensor network (WSN) to Ethernet Passive Optical Network (EPON) as a broadband access network to transmit smart meter data along with the Fiber To The Home/Building/Curb (FTTX) traffic through the shared fiber. Finally, we present and analyze the simulation results for the aforementioned infrastructure based on our adopted priority-based FTTX-WSN integration model.
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Knaian, Ara N. (Ara Nerses) 1977. "A wireless sensor network for smart roadbeds and intelligent transportation systems." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/9072.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.<br>Includes bibliographical references (leaves 36-38).<br>We have developed a wireless sensor package to instrument roadways for Intelligent Transportation Systems. The sensor package counts passing vehicles, measures the average roadway speed, and detects ice and water on the road. Clusters of sensors can transmit this information in near real-time to wired base stations for use controlling and predicting traffic, and in clearing road hazards. The sensor package draws a maximum time-averaged current of 17 tA from an internal lithium battery, allowing it to operate in the roadbed for at least 10 years without maintenance. The nodes cost well under $30 to manufacture, and can be installed without running wires under the road, facilitating wide deployment. Unlike many other types of traffic sensors, these sensors count vehicles in bumper-to-bumper traffic just as well as in widely separated traffic. The devices detect vehicles by detecting the perturbations in the Earth's magnetic field caused by the vehicles. They measure this perturbation using an anisotropic magneto-resistive magnetic field sensor. The radio transmitters in the sensor are frequency-agile, and the sensors use a randomized sparse TDMA protocol, which allows several transmit-only devices to share a channel. The sensor package includes a custom-designed, compact, broadband, inexpensive printed circuit microstrip antenna for the 915 MHz U.S. ISM band. We built a prototype sensor package, and installed it in a pothole in a city street. We used the sensor to monitor the traffic flow rate during free-flowing traffic and a traffic jam.<br>by Ara N. Knaian.<br>M.Eng.
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GIUSTO, EDOARDO. "Sensor-based ICT Systems for Smart Societies." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2925002.

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Books on the topic "Smart Sensor Network"

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1953-, Ilyas Mohammad, and Mahgoub Imad, eds. Smart dust: Sensor network applications, architecture, and design. Taylor & Francis, 2006.

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Kang, Lee, and National Institute of Standards and Technology (U.S.), eds. Multi-network access to IEEE P1451 smart sensor information using World Wide Web technology. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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Kang, Lee, and National Institute of Standards and Technology (U.S.), eds. Multi-network access to IEEE P1451 smart sensor information using World Wide Web technology. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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Kang, Lee, and National Institute of Standards and Technology (U.S.), eds. Multi-network access to IEEE P1451 smart sensor information using World Wide Web technology. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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Kang, Lee, and National Institute of Standards and Technology (U.S.), eds. Multi-network access to IEEE P1451 smart sensor information using World Wide Web technology. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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Kang, Lee, and National Institute of Standards and Technology (U.S.), eds. Multi-network access to IEEE P1451 smart sensor information using World Wide Web technology. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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Singh, Umang, Ajith Abraham, Arturas Kaklauskas, and Tzung-Pei Hong, eds. Smart Sensor Networks. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-77214-7.

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Nayak, Soumya Ranjan, Biswa Mohan Sahoo, Muthukumaran Malarvel, and Jibitesh Mishra. Smart Sensor Networks Using AI for Industry 4.0. CRC Press, 2021. http://dx.doi.org/10.1201/9781003145028.

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Ecke, Wolfgang. Smart sensor phenomena, technology, networks, and systems 2008. Edited by Society of Photo-optical Instrumentation Engineers, American Society of Mechanical Engineers, Intelligent Materials Forum (Mitō Kagaku Gijutsu Kyōkai), Jet Propulsion Laboratory (U.S.), and National Science Foundation (U.S.). SPIE, 2008.

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Fernández-Berni, Jorge, Ricardo Carmona-Galán, and Ángel Rodríguez-Vázquez. Low-Power Smart Imagers for Vision-Enabled Sensor Networks. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-2392-8.

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Book chapters on the topic "Smart Sensor Network"

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Sinha, Adwitiya, Manju, and Samayveer Singh. "Sensor Network Applications." In Metaheuristics and Reinforcement Techniques for Smart Sensor Applications. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003427780-4.

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Raju, Robinson, Melody Moh, and Teng-Sheng Moh. "Compression of Wearable Body Sensor Network Data." In Smart Data. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429507670-10.

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Wang, Ning. "Wireless Sensor Network in Agriculture." In Encyclopedia of Smart Agriculture Technologies. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-89123-7_163-2.

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Wang, Ning. "Wireless Sensor Network in Agriculture." In Encyclopedia of Smart Agriculture Technologies. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-89123-7_163-1.

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Tomaš, Boris, and Neven Vrček. "Smart City Vehicular Mobile Sensor Network." In Internet of Things. IoT Infrastructures. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19743-2_11.

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Maniscalco, Umberto, Giovanni Pilato, and Filippo Vella. "Soft Sensor Network for Environmental Monitoring." In Smart Innovation, Systems and Technologies. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39345-2_63.

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Pax, Rafael, Marlon Cárdenas Bonett, Jorge J. Gómez-Sanz, and Juan Pavón. "Virtual Development of a Presence Sensor Network Using 3D Simulations." In Smart Cities. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59513-9_16.

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Subert-Semanat, Andrés. "Poster: Wireless Sensor Network to Predict Black Sigatoka in Banana Cultivations." In Smart Industry & Smart Education. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95678-7_18.

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Vinogradov, Gennady P., and Igor A. Konyukhov. "Patterns in Smart Wireless Sensor Network Nodes." In Proceedings of the Sixth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’22). Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19620-1_8.

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Ghidini, Giacomo, Sajal K. Das, and Dirk Pesch. "Sensor Network Protocols for Greener Smart Environments." In Design Technologies for Green and Sustainable Computing Systems. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4975-1_8.

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Conference papers on the topic "Smart Sensor Network"

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Ayello, Francois, Davion Hill, Stefan Marion, and Narasi Sridhar. "Integrated Sensor Networks for Corrosion under Insulation: Monitoring, Cost Reduction, and Life Extension Strategies." In CORROSION 2011. NACE International, 2011. https://doi.org/10.5006/c2011-11281.

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Abstract System health assessment using sensor networks has been used in the past to detect the presence of corrosion under insulation (CUI) and coatings. Corrosion sensors placed under insulation offer a replacement or alternative to inspections, especially if implemented at a cost savings. If installed during the early project design stage, sensor networks can greatly reduce the need for inspection and unexpected replacement costs. This paper shows three different techniques to detect the presence of moisture and its corrosiveness. The techniques are: direct impedance measurement, the combination of galvanic couple with active RFID tags, and Wi-Fi based motes connected to 2-wire electrodes. While all techniques were able to detect CUI, only one can be used to create an intelligent wireless sensor network capable to detect CUI. This paper also demonstrates the efficiency of smart sensor networks to detect CUI and propose the architecture for an intelligent sensor network designed to detect the presence of CUI in an early stage. It also demonstrates how to create an intelligent sensor network, and how to model corrosion risks using the large amount of data generated by a sensor network.
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Jalalpour, Mohammad, Mahmoud Reda Taha, and Aly El-Osery. "Damage Tracking Using Smart Sensor Network." In ASME 2010 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2010. http://dx.doi.org/10.1115/smasis2010-3876.

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Sensors are frequently used in damage diagnosis for structural health monitoring (SHM) of aerospace structures. This process typically requires a considerably large number of sensors. By increasing the number of sensors, the amount of data collected, though useful, becomes a burden due to the required computational overhead. In this paper, a random correlation cumulative approach is used to track damage intensity and propagation. The relative correlation between any two randomly chosen sensors is recorded and compared over time. The randomness of the process leads to detecting and tracking any arbitrary crack propagation. A case study for damage detection and tracking using 40 sensors in a steel plate is presented and discussed. It is shown that the proposed method can successfully allow damage tracking while limiting the data considered in the sensor network.
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Lobachev, Ivan, and Edmond Cretu. "Smart sensor network for smart buildings." In 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE, 2016. http://dx.doi.org/10.1109/iemcon.2016.7746273.

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Patra, Jagdish C., Cedric Bornand, and Pramod K. Meher. "Laguerre neural network-based smart sensors for wireless sensor networks." In 2009 IEEE Intrumentation and Measurement Technology Conference (I2MTC). IEEE, 2009. http://dx.doi.org/10.1109/imtc.2009.5168565.

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Casciati, Fabio, Sara Casciati, Lucia Faravelli, and Roberto Rossi. "Hybrid wireless sensor network." In Smart Structures and Materials, edited by Shih-Chi Liu. SPIE, 2004. http://dx.doi.org/10.1117/12.539499.

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Senel, Numan, Gordon Elger, and Andreas Festag. "Sensor Time Synchronization in Smart Road Infrastructure." In FISITA World Congress 2021. FISITA, 2021. http://dx.doi.org/10.46720/f2020-acm-083.

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In smart cities, infrastructure-side sensors are already used to increase safety, to mitigate traffic congestion and to reduce pollution caused by vehicles. In the future, infrastructural safeguarding is expected to get a large potential due to availability of advanced infrastructure sensors (camera,radar,lidar) and vehicle-to-infrastructure (V2I) communications. In principle, installations of infrastructure-side sensor systems can be divided into two main categories as temporary and permanent. Permanent systems are typically installed for highly used part of urban environments such as city centers, whereas temporary systems are setup for events in cities (concerts,sports games,etc.) with a temporally high density of traffic participants. For both categories different networking options (wired,wireless) as well as sensor data fusion methods (centralized, decentralized data fusion) are possible in order to meet requirements of the specific use case. In this paper, we consider two main architecture options: (1) wired networking with centralized data fusion (centralize architecture) and (2) wireless networking with decentralized data fusion (decentralized architecture). In the centralized network architecture, the we assume that raw data from multiple sensors will be transmitted to a central processor unit (master). In this architecture, a single node handles the data processing and the fusion process. In the decentralized network architecture, every sensor is equipped with a computing and a communication component. Sensors use a co-located processor unit (slave) to, preprocess data and to transmit these data to a master node. In both architecture options, the master node will be responsible for combining all inputs from multiple sensors to form a common estimate for future state. A centralized architecture requires high-bandwidth connectivity between the sensors and the master, whereas the preprocessing in the decentralized approach has lower demands on the networking bandwidth. Considering the two architectures the synchronization of the sensor time strongly impacts the state estimation. Sensor nodes need to synchronize their operation and collaborate to accomplish the sensing task. For example, in order to track a vehicle, sensors need to report the location and detection time of vehicle to master node. Then, the master node combines the information to estimate the location and velocity of the vehicle. Evidently, if the sensors do not have a common timescale, the state estimation will be inaccurate. Other aspect is how old the fused data (estimation) are before it delivered to road participants. When road participants receive a current estimation at the timestamp T2, these data represent state at time T1. The time difference between T1 and T2 depends on primarily on the network delay and process time of sensor data. For performance evaluation of the network options with respect to time synchronization the data processing will be separated as an additional source of delay. In this paper, we study the two architecture options for a smart road infrastructure. The study is based on prototype system using the Robot Operating System (ROS) and cameras, whereas also other sensors (radar,LiDAR) are considered. We specifically assess the impact of sensor time synchronization on the reliability of the sensor data fusion and evaluate the latency between the acquisition of sensor data and reception of the data by the road participants.
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Bandara, H. M. A. P. K., J. D. C. Jayalath, A. R. S. P. Rodrigo, A. U. Bandaranayake, Z. Maraikar, and R. G. Ragel. "Smart campus phase one: Smart parking sensor network." In 2016 Manufacturing & Industrial Engineering Symposium (MIES). IEEE, 2016. http://dx.doi.org/10.1109/mies.2016.7780262.

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Stisen, Allan, Henrik Blunck, Sourav Bhattacharya, et al. "Smart Devices are Different." In SenSys '15: The 13th ACM Conference on Embedded Network Sensor Systems. ACM, 2015. http://dx.doi.org/10.1145/2809695.2809718.

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Kulkarni, Umesh M., Deepti V. Kulkarni, and Harish H. Kenchannavar. "Neural network based energy conservation for wireless sensor network." In 2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon). IEEE, 2017. http://dx.doi.org/10.1109/smarttechcon.2017.8358579.

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Ukil, Arijit, Soma Bandyopadhyay, and Arpan Pal. "SPA: smart meter privacy analyzer." In SenSys '14: The 12th ACM Conference on Embedded Network Sensor Systems. ACM, 2014. http://dx.doi.org/10.1145/2674061.2675029.

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Reports on the topic "Smart Sensor Network"

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Schneeman, Richard D., and Kang Lee. Multi-network access to IEEE P1451 smart sensor information using World Wide Web Technology. National Institute of Standards and Technology, 1999. http://dx.doi.org/10.6028/nist.ir.6136.

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Brown, Christopher U., Gregory W. Vogl, and Wai Cheong Tam. Measuring water flow rate for a fire hose using a wireless sensor network for smart fire fighting. National Institute of Standards and Technology, 2019. http://dx.doi.org/10.6028/nist.tn.2074.

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Brown, Christopher U. Measuring Water Flow Rate for a Fire Hose Using a Wireless Sensor Network for Smart Fire Fighting An Update using Various Hose Conditions. National Institute of Standards and Technology, 2023. http://dx.doi.org/10.6028/nist.tn.2245.

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Feng, Philip. Transforming Ordinary Buildings into Smart Buildings via Low-Cost, Self-Powering Wireless Sensors & Sensor Networks. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1372099.

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Kwiat, Paul, Eric Chitambar, Andrew Conrad, and Samantha Isaac. Autonomous Vehicle-Based Quantum Communication Network. Illinois Center for Transportation, 2022. http://dx.doi.org/10.36501/0197-9191/22-020.

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Quantum communication was demonstrated using autonomous vehicle-to-vehicle (V2V), as well as autonomous vehicle-to-infrastructure (V2I). Supporting critical subsystems including compact size, weight, and power (SWaP) quantum sources; optical systems; and pointing, acquisition, and tracking (PAT) subsystems were designed, developed, and tested. Novel quantum algorithms were created and analyzed, including quantum position verification (QPV) for mobile autonomous vehicles. The results of this research effort can be leveraged in support of future cross-platform, mobile quantum communication networks that provide improved security, more accurate autonomous sensors, and connected quantum computing nodes for next-generation, smart-infrastructure systems.
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Kwon, Heeseo Rain, HeeAh Cho, Jongbok Kim, Sang Keon Lee, and Donju Lee. International Case Studies of Smart Cities: Namyangju, Republic of Korea. Inter-American Development Bank, 2016. http://dx.doi.org/10.18235/0007014.

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This case study is one of ten international studies developed by the Korea Research Institute for Human Settlements (KRIHS), in association with the Inter-American Development Bank (IDB), for the cities of Anyang, Medellin, Namyangju, Orlando, Pangyo, Rio de Janeiro, Santander, Singapore, Songdo, and Tel Aviv. At the IDB, the Competitiveness and Innovation Division (CTI), the Fiscal and Municipal Management Division (FMM), and the Emerging and Sustainable Cities Initiative (ESCI) coordinated the study. This project was part of technical cooperation ME-T1254, financed by the Knowledge Partnership Korean Fund for Technology and Innovation of the Republic of Korea. At KRIHS, the National Infrastructure Research Division coordinated the project and the Global Development Partnership Center provided the funding. Namyangju, a city of 650,000 populations in Korea has been promoting smart city project since 2008 as a response to recent growth of population, increased share of transport and crime rate. Namyangju offers various civic services especially via smartphone such as customized real-time road CCTV images, traffic flow and incident information, as well as application for senior resident protection. Namyangju is also equipped with security system at bus stops and multifunctional "smart pole", which combines street light, CCTV, and traffic signal controller to promote efficient use of roadside facility. The city promotes local economy through online market system making use of its local organic farms and actively utilizes bus stops and roadside VMS in attracting advertisement to raise regular profit. Namyangju is in the process of installing 101km fiber-optic network and plans to complete the construction of Integrated Operation and Control Center (IOCC) by 2016. The city's current focus is on citizen interaction and further business model development.
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A technology review of smart sensors with wireless networks for applications in hazardous work environments. U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, 2007. http://dx.doi.org/10.26616/nioshpub2007114.

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