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Добірка наукової літератури з теми "EMBEDDED SENSOR NETWORKS"

Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "EMBEDDED SENSOR NETWORKS"

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Kusuma, S. M., K. N. Veena, B. P. Vijaya Kumar, and B. V. Varun. "Performance Modeling of Energy Efficiency for Sensors Deployment in Embedded Wireless Sensor Networks." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4515–24. http://dx.doi.org/10.1166/jctn.2020.9107.

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Present trend of Internet of Things (IoT) and sensors deployment increased in every sectors enormously from last one decade. But the deployment challenges of sensors and their networks with respect to their contextual dynamics and system performance is not much investigated. Hence there is a need to investigate the deployment challenges of sensors supporting the computing system that exactly imitates the phenomenon by understanding the context and other influencing parameters, i.e., to sense the environmental parameter values accurately and precisely from the respective embedded sensor system. In this paper, a methodology is proposed to analyze the performance of embedded Wireless Sensor Networks (eWSNs) with respect to energy efficiency based on sensors deployment. The method involves in clustering the sensor nodes based on distance from the phenomenon and its physical location. Sensors and sensor network lifetime energy consumption for data acquisition is analyzed using Markovian model. Simulation platform for random deployment of sensor nodes along with Self Organizing map neural network for clustering with various cases of sensors deployment, network dynamics and environment are studied to understand the performance of the embedded WSN system for energy efficiency.
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2

Burger, Alwyn, Gregor Schiele, and David W. King. "Reconfigurable Embedded Devices Using Reinforcement Learning to Develop Action Policies." ACM Transactions on Autonomous and Adaptive Systems 15, no. 4 (December 31, 2020): 1–25. http://dx.doi.org/10.1145/3487920.

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The size of sensor networks supporting smart cities is ever increasing. Sensor network resiliency becomes vital for critical networks such as emergency response and waste water treatment. One approach is to engineer “self-aware” sensors that can proactively change their component composition in response to changes in work load when critical devices fail. By extension, these devices could anticipate their own termination, such as battery depletion, and offload current tasks onto connected devices. These neighboring devices can then reconfigure themselves to process these tasks, thus avoiding catastrophic network failure. In this article, we compare and contrast two types of self-aware sensors. One set uses Q-learning to develop a policy that guides device reaction to various environmental stimuli, whereas the others use a set of shallow neural networks to select an appropriate reaction. The novelty lies in the use of field programmable gate arrays embedded on the sensors that take into account internal system state, configuration, and learned state-action pairs, which guide device decisions to meet system demands. Experiments show that even relatively simple reward functions develop both Q-learning policies and shallow neural networks that yield positive device behaviors in dynamic environments.
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Qu, Ming Zhe. "Research on the Applications and Characteristics of the Wireless Sensor Network." Applied Mechanics and Materials 538 (April 2014): 498–501. http://dx.doi.org/10.4028/www.scientific.net/amm.538.498.

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A wireless sensor network (WSN) consists of spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, pressure, etc. and to cooperatively pass their data through the network to a main location. The more modern networks are bi-directional, also enabling control of sensor activity. The development of wireless sensor networks was motivated by military applications such as battlefield surveillance; today such networks are used in many industrial and consumer applications, such as industrial process monitoring and control, machine health monitoring, and so on. The WSN is built of "nodes" – from a few to several hundreds or even thousands, where each node is connected to one (or sometimes several) sensors. Each such sensor network node has typically several parts: a radio transceiver with an internal antenna or connection to an external antenna, a microcontroller, an electronic circuit for interfacing with the sensors and an energy source, usually a battery or an embedded form of energy harvesting.
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4

Kumble, Lithin, and Kiran Kumari Patil. "Data Transmission in Wearable Sensor Network for Human Activity Monitoring Using Embedded Classifier Technique." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1986–93. http://dx.doi.org/10.22214/ijraset.2022.41234.

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Abstract: The recent development of wireless wearable sensor networks has opened up a slew of new possibilities in industries as diverse as healthcare, medicine, activity monitoring, sports, safety, human-machine interface, and more. The battery-powered sensor nodes' longevity is critical to the technology's success. This research proposes a new strategy for increasing the lifetime of wearable sensor networks by eliminating redundant data transmissions. The proposed solution is based on embedded classifiers that allow sensor nodes to determine whether current sensor readings should be sent to the cluster head. A strategy was developed to train the classifiers, which takes into account the impact of data selection on the accuracy of a recognition system. This method was used to create a wearable sensor network prototype for human monitoring of activity Experiments were carried out in the real world to assess the novel method in terms of network lifetime, energy usage, and human activity recognition accuracy. The proposed strategy allows for a large increase in network lifetime while maintaining excellent activity detection accuracy, according to the results of the experimental evaluation. Experiments have also demonstrated that the technology has advantages over state-of-the-art data transmission reduction strategies. Keywords: wireless sensor network; wearable sensors; activity recognition; lifetime; energy con- sumption; transmission suppression; embedded machine learning
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Kumble, Lithin, and Kiran Kumari Patil. "Data Transmission in Wearable Sensor Network for Human Activity Monitoring Using Embedded Classifier Technique." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1986–93. http://dx.doi.org/10.22214/ijraset.2022.41234.

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Анотація:
Abstract: The recent development of wireless wearable sensor networks has opened up a slew of new possibilities in industries as diverse as healthcare, medicine, activity monitoring, sports, safety, human-machine interface, and more. The battery-powered sensor nodes' longevity is critical to the technology's success. This research proposes a new strategy for increasing the lifetime of wearable sensor networks by eliminating redundant data transmissions. The proposed solution is based on embedded classifiers that allow sensor nodes to determine whether current sensor readings should be sent to the cluster head. A strategy was developed to train the classifiers, which takes into account the impact of data selection on the accuracy of a recognition system. This method was used to create a wearable sensor network prototype for human monitoring of activity Experiments were carried out in the real world to assess the novel method in terms of network lifetime, energy usage, and human activity recognition accuracy. The proposed strategy allows for a large increase in network lifetime while maintaining excellent activity detection accuracy, according to the results of the experimental evaluation. Experiments have also demonstrated that the technology has advantages over state-of-the-art data transmission reduction strategies. Keywords: wireless sensor network; wearable sensors; activity recognition; lifetime; energy con- sumption; transmission suppression; embedded machine learning
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Grobelna, Iwona, Michał Grobelny, Agnieszka Węgrzyn, and Marek Węgrzyn. "Embedded WWW Server in Wireless Sensor Networks." IFAC Proceedings Volumes 42, no. 21 (2009): 220–25. http://dx.doi.org/10.3182/20091006-3-es-4010.00041.

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Slijepcevic, Sasha, Seapahn Megerian, and Miodrag Potkonjak. "Location errors in wireless embedded sensor networks." ACM SIGMOBILE Mobile Computing and Communications Review 6, no. 3 (June 2002): 67–78. http://dx.doi.org/10.1145/581291.581301.

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Bundalo, Zlatko V. "Energy efficient embedded systems and their application in wireless sensor networks." IOP Conference Series: Materials Science and Engineering 1208, no. 1 (November 1, 2021): 012002. http://dx.doi.org/10.1088/1757-899x/1208/1/012002.

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Abstract Embedded systems are specialised electronic systems that perform limited number of fixed operations and are used in many application areas. Such systems are based on using microprocessors for their implementation. Embedded systems are usually part of other systems where they are embedded into some embedding systems. They have to be efficient in electrical energy consumption, size of program code, time of operation, weight and cost. Embedded systems are inexpensive and are used in almost every electronic product or other electronic systems. Many embedded systems are mobile systems supplied by batteries and the available electrical energy must be used efficiently as much as possible. Application areas where embedded systems are used and where minimal consumption of energy is required are battery powered wireless sensor networks. The methods for reduction of energy consumption and for power management in embedded systems are considered and described in this paper. The accent is given on design and application of energy efficient embedded systems in wireless sensor networks and on possibilities to reduce energy consumption in such systems. The methods for energy harvesting, that are very attractive and very useful in wireless sensor networks applications, are also considered and described. One practically implemented battery supplied wireless sensor network for application for environmental data acquisition and monitoring in agriculture is described in the paper.
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Doraipandian, Manivannan, and Periasamy Neelamegam. "Wireless Sensor Network Using ARM Processors." International Journal of Embedded and Real-Time Communication Systems 4, no. 4 (October 2013): 48–59. http://dx.doi.org/10.4018/ijertcs.2013100103.

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Анотація:
The hardware design of Wireless Sensor Networks (WSN) is the crux of its effective deployment. Nowadays these networks are used in microscopic, secure and high-end embedded products. WSN's potentiality in terms of efficient data sensing and distributed data processing has led to its usage in applications for measurement and tracking. WSN comprises of small number of embedded devices known as sensor nodes, gateways and base stations. Sensor nodes consist of sensors, processors and transceivers. The property of embedded sensor devices, also called motes, is to determine the strength of WSN. Thus processor selection for the motes plays a critical role in determining a WSN's competency. In this article, the absolute and obvious hardware characteristics of available and proposed sensor nodes are discussed. The objective of this work was to increase the efficiency and provision of sensor nodes by evaluating their processing and transceiver units. During this work, a sensor node was developed with ARM processor and XBee series 2 Unit. LPC 2148, LPC 2378 ARM processors were posed as processing unit and XBee series 2 acted as communication unit. Results of this experimental setup were recorded. Also a comparative study of the various available sensor nodes and proposed sensor nodes was done extensively.
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Liu, Yan Ju, and Xin Hua Li. "Study on Application of Wireless Sensor Networking in Environmental Monitoring." Applied Mechanics and Materials 157-158 (February 2012): 1297–300. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.1297.

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A novel wireless sensor networks is designed with integrating sensors, embedded operating systems and wireless networking technology. The temperature, humidity, light strength and pressure around the sensor could be measured accurately. The collected data by sensor networks are analysed and treated in PC computer via USB interface. LEACH communication protocol was introduced to ZigBee networks in this paper. The node programs were exploited based on IAR System platform to accomplish data collection.
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Дисертації з теми "EMBEDDED SENSOR NETWORKS"

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Singh, Vaneet. "Design of energy efficient embedded controlled sensor networks." Thesis, IIT Delhi, 2015. http://localhost:8080/xmlui/handle/12345678/6917.

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Tewatia, Rohit. "Security in Distributed Embedded Systems." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1379.

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Communication in a sensor network needs guaranteed reception of data without fail and providing security to it. The authenticity and confidentiality of the data has to be ensured as sensors have limited hardware resources as well as the bandwidth. This thesis addresses the security aspects in wireless sensor networks. The main task of the project is to identify the critical security parameters for these distributed embedded systems. The sensors have extremely limited resources: small amount of memory, low computation capability and poor bandwidth. For example, a sensor platform can have 8KB of flash memory, a 4MHz 8-bit Atmel processor, and a 900MHz radio interface. Various security threats posed to these small wireless sensor networks has been made and solutions proposed. Secure communication between these communicating partners is to be achieved using cryptography.

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Persson, Erik. "Energy Harvesting in Wireless Sensor Networks." Thesis, Uppsala universitet, Signaler och System, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388006.

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Анотація:
Over the past few years, the interest of remote wireless sensor networks has increased with the growth of Internet of Things technology. The wireless sensor network applications vary from tracking animal movement to controlling small electrical devices. Wireless sensors deployed in remote areas where the grid is unavailable are normally powered by batteries, inducing a limited lifespan for the sensor. This thesis work presents a solution to implement solar energy harvesting to a wireless sensor network. By gathering energy from the environment and using it in conjunction with an energy storage, the lifetime of a sensor node can be extended while at the same time reducing maintenance costs. To make sensor nodes in a network energy efficient, an adaptive controller of the nodes energy consumption can be used. A network consisting of a client node and a server node was created. The client node was powered by a small solar cell in conjunction with a capacitor. A linear-quadratic tracking algorithm was implemented to adaptively change the transmission rate for a node based on its current and previous battery level and the energy harvesting model. The implementation was done using only integers. To evaluate the system for extended run-times, the battery level was simulated using MATLAB. The system was simulated for different weather conditions. The simulation results show that the system is viable for both cloudy and sunny weather conditions. The integer linear-quadratic algorithm responds to change very abruptly in comparison to a floating point-version.
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Dang, Thanh Xuan. "Scalable and Efficient Tasking for Dynamic Sensor Networks." PDXScholar, 2011. https://pdxscholar.library.pdx.edu/open_access_etds/269.

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Sensor networks including opportunistic networks of sensor-equipped smartphones as well as networks of embedded sensors can enable a wide range of applications including environmental monitoring, smart grids, intelligent transportation, and healthcare. In most real-world applications, to meet end-user requirements, the network operator needs to define and update the sensors' tasks dynamically, such as updating the parameters for sensor data collection or updating the sensors' code. Tasking sensor networks is necessary to reduce the effort in programming sensor networks. However, it is challenging due to dynamics and scale in terms of number of nodes, number of tasks, and sensing regions of the networks. In addition, tasking sensor networks must also be efficient in terms of bandwidth, latency, energy consumption, and memory usage. This dissertation identifies and addresses the problems of scalability and efficiency in tasking sensor networks. The first challenge in tasking sensor networks is to define a mechanism that represents multiple tasks and sensor groups efficiently taking into account the heterogeneity and mobility of sensors deployed over a large geographical region. Another challenge in tasking sensor networks in general, and embedded sensor networks in particular, is to design protocols that can not only efficiently disseminate tasks but also maintain a consistent view of the task to be performed among inherently unreliable and resource-limited sensors. We believe that a scalable and efficient tasking framework can greatly benefit the development and deployment of sensor network applications. Our thesis is that decoupling the task specification from task implementation using a spatial two-dimensional (2D) representation of a tasking region such as maps enables scalable, efficient, and resource-adaptive tasking over heterogeneous mobile sensor networks. In addition, reducing overhead in detecting inconsistencies across nodes enables scalable and efficient task dissemination and maintenance. We present the design, implementation, and evaluation of Zoom, a multiresolution tasking framework that efficiently encapsulates multiple tasks and sensor groups for sensor networks deployed in a large geographical region. The key ideas in Zoom are (i) decoupling task specification and task implementation to support heterogeneity, (ii) using maps for representing spatial sensor groups and tasks to scale with the number of sensor groups and sensing regions, and (iii) using image encoding techniques to reduce the map size and provide adaptation to sensor platforms with different resource capabilities. We present the design, implementation, and evaluation of our protocol, DHV, which efficiently disseminates task content and ensures that all nodes have up-to-date task content in sensor networks. It achieves this by minimizing both the redundant information in each message and the number of transmitted messages in the networks. DHV has been included in the official distribution of TinyOS, a popular operating system for embedded sensor networks. As sensor networks continue to develop, they will evolve from dedicated and single-purpose systems to open and multi-purpose large scale systems. Nodes in the network will be retasked frequently to support multiple applications and multiple users. We believe that this work is an important step in enabling seamless interaction between users and sensor networks and to make sensor networks more widely adopted.
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Darr, Matthew J. "Advanced embedded systems and sensor networks for animal environment monitoring." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1196199349.

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Sorenson, Carl E., Stanton K. Yarbrough, Lawrence C. Freudinger, and Philip T. Gonia. "RESEARCH ENVIRONMENT FOR VEHICLE EMBEDDED ANALYSIS ON LINUX." International Foundation for Telemetering, 2003. http://hdl.handle.net/10150/605588.

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Анотація:
International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada
This paper overviews the Research Environment for Vehicle-Embedded Analysis on Linux (REVEAL), which is an open standards framework for the creation and deployment of realtime embedded and network distributed data systems. REVEAL is an ongoing project at NASA Dryden to evaluate the feasibility and benefits of using Linux in a modern generic web-enabled data system for measurement and telemetry network research, by actually building such a system. Novel features are described, such as XML based self-configuring, self-verifying and self-documenting software, and automatic XML metadata generation. The REVEAL architecture is described, including the core server and scheduler, and the management of system and user job processing. Performance, timing, determinism, and security issues are discussed, as well as the advantages and limitations of Linux.
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Sim, Zhi Wei. "Radio frequency energy harvesting for embedded sensor networks in the natural environment." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/radio-frequency-energy-harvesting-for-embedded-sensor-networks-in-the-natural-environment(b0f3db83-8a82-4376-841b-d79bcd0d16ae).html.

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The agricultural sector is an emerging application area for Wireless Sensor Networks (WSNs). This requires sensor nodes to be deployed in the outdoor environment so as to monitor pertinent natural features, such as soil condition or pest infestation. Limited energy supply and subsequent battery replacement are common issues for these agricultural sensor nodes. One possible solution is to use energy harvesting, where the ambient energy is extracted and converted into usable electrical form to energise the wireless sensors. The work presented in this thesis investigates the feasibility of using Radio Frequency (RF) energy harvesting for a specific application; that is powering a generic class of wireless ground-level, agricultural sensor networks operating in an outdoor environment. The investigation was primarily undertaken through a literature study of the subject. The first part of the thesis examines several energy harvesting/ wireless energy transfer techniques, which may be applicable to power the targeted agricultural WSN nodes. The key advantages and limitations of each technique are identified, and the rationale is being given for selecting far-field RF energy harvesting as the investigated technique. It is then followed by a theoretical-based system analysis, which seeks to identify all relevant design parameters, and to quantify their impact on the system performance. An RF link budget analysis was also included to examine the feasibility of using RF energy harvesting to power an exemplar WSN node - Zyrox2 Bait Station. The second part of the thesis focuses on the design of two energy harvesting antennas. The first design is an air-substrate-based folded shorted patch antenna (FSPA) with a solid ground plane, while the second design is a similar FSPA structure with four pairs of slot embedded into its ground plane. Both antennas were simulated, fabricated and tested inside an anechoic chamber, and in their actual operating environment - an outdoor field. In addition, a power harvester circuit, built using the commercially available off-the-shelf components, was tested in the laboratory using an RF signal generator source. The results from both the laboratory and field trial were analysed. The measurement techniques used were reviewed, along with some comments on how to improve them. Further work on the RF energy harvester, particularly on the improvement of the antenna design must be carried out before the feasibility and viable implementations for this application can be definitively ascertained.
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Koch, John R. "A hybrid sensor network for watershed monitoring." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2008. http://scholarsmine.mst.edu/thesis/pdf/jrk4y8_09007dcc804f8fe6.pdf.

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Thesis (M.S.)--Missouri University of Science and Technology, 2008.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed May 27, 2008) Includes bibliographical references (p. 84-86).
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Huang, Ya-Lin. "Ad hoc distributed simulation: a method for embedded online simulations." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49060.

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The continual growth of computing power in small devices has motivated the development of novel approaches to optimizing operational systems efficiently and effectively. These optimization problems are often so complex that solving them analytically may be difficult, if not prohibited. One method for solving such problems is to use online simulation. However, challenges in using online simulation include the issues of responsiveness (e.g., because of communication delays), scalability, and failure resistance. To tackle these issues, this study proposes embedding online simulations into a network of sensors that monitors the system under investigation. This thesis explores an approach termed “ad hoc distributed simulation,” which is based on embedding online simulations into a sensor network and adding communication and synchronization among simulators to model operational systems. This approach offers several potential advantages over existing approaches: (1) it can provide rapid response to system dynamics as well as efficiency since data exchange is local to the sensor network, (2) it can achieve better scalability to incorporate more sensors, and (3) it can provide better robustness to failures because portions of the system are still under local control. This research addresses several statistical issues in this ad hoc approach: (1) rapid and effective estimation of the input processes at model boundaries, (2) estimation of system-wide performance measures from individual simulator outputs, and (3) correction mechanisms responding to unexpected events or inaccuracies within the model. This thesis examines ad hoc distributed simulation analytically and experimentally, mainly focusing on the accuracy of predicting the performance of open queueing networks. First, the analytical part formalizes the ad hoc approach and evaluates its accuracy at modeling certain class of open queueing networks with regard to the steady-state system performance measures. This work concerning steady-state metrics is extended to a broader class of networks by an empirical study, which presents evidence to show that the ad hoc approach can generate predictions comparable to those from sequential simulations. Furthermore, a “buffered-area” mechanism is proposed to substantially reduce prediction bias with a moderate increase in execution time. In addition to those steady-state studies, another empirical study targets the prediction accuracy of the ad hoc approach at open queueing networks with short-term system-state transients. This study demonstrates that, with slight modification to the prior design of the ad hoc queueing simulation method for those steady-state studies, system dynamics can be well modeled. The results, again, support the conclusion that the ad hoc approach is competitive to the sequential simulation method in terms of prediction accuracy.
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Waterman, Jason. "Coordinated Resource Management in Networked Embedded Systems." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10651.

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This dissertation shows that with simple programming abstractions, network-wide resource coordination is efficient and useful for programming embedded sensor networks. Existing systems have focused primarily on managing resources for individual nodes, but a sensor network is not merely a collection of nodes operating independently: it must coordinate behavior across multiple nodes to achieve high efficiency. We need tools that can enable system-wide coordination at a higher level of abstraction than what exists today. We present three core contributions. The first is a service called IDEA that enables networkwide energy management for sensor networks. It unites energy monitoring, load modeling, and distributed state sharing into a single service that facilitates distributed decision making. Using simulation and testbed results, we show that IDEA enables improvements in network lifetime of up to 35% over approaches that do not consider energy distribution. Our second contribution is Karma, a system for coordinating insect-sized robotic microaerial vehicle (MAV) swarms, an emerging class of mobile sensor networks. Karmas system architecture simplifies the functionality of an individual MAV to a sequence of sensing and actuation commands called behaviors. Each behavior has an associated progress function, a measure of how much of that behavior has been completed. Programming is done by composing behaviors which are coordinated using input from the progress functions. Through simulation and testbed experiments, we demonstrate Karma applications can run on limited resources, are robust to individual MAV failure, and adapt to changes in the environment. Our final contribution is Simbeeotic, a testbed for MAV coordination algorithms. MAV sensors must be codesigned with the software and coordination algorithms that depend on them. This requires a testbed capable of simulating sensors to evaluate them before actual hardware is available and the ability to test with real flight dynamics for accurate control evaluation. In addition, simulation should be able to scale to hundreds or thousands of MAVs at a reduced level of fidelity in order to test at scale. We demonstrate that Simbeeotic provides the appropriate level of fidelity to evaluate prototype systems while maintaining the ability to test at scale.
Engineering and Applied Sciences
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Книги з теми "EMBEDDED SENSOR NETWORKS"

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Banâtre, Michel, Pedro José Marrón, Anibal Ollero, and Adam Wolisz, eds. Cooperating Embedded Systems and Wireless Sensor Networks. London, UK: ISTE, 2008. http://dx.doi.org/10.1002/9780470610817.

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1950-, Banâtre Michel, ed. Cooperating embedded systems and wireless sensor networks. Hoboken, NJ: ISTE/John Wiley, 2008.

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3

Hendrik, Spaanenburg, ed. Cloud connectivity and embedded sensory systems. New York: Springer, 2010.

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4

Machinery, Association for Computing, and Sigarch, eds. SenSys '05: Proceedings of the Third International Conference on Embedded Networked Sensor Systems : November 2-4, 2005, San Diego, California, USA. New York, N.Y: ACM Press, 2005.

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Fuat, Akyildiz Ian, Estrin Deborah, Association for Computing Machinery, and Association for Computing Machinery. Special Interest Group on Data Communications., eds. SenSys '03: Proceedings of the First International Conference on Embedded Networked Sensor Systems : November 5-7, 2003, Los Angeles, California, USA. New York, N.Y: Association for Computing Machinery, 2003.

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International Conference on Embedded Networked Sensor Systems (1st 2003 Los Angeles, Calif.). SenSys'03: Proceedings of the First International Conference on Embedded Networked Sensor Systems : November 5-7, Los Angeles, California, USA. New York, NY: Association for Computing Machinery, 2003.

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Embedded systems and wireless technology. Enfield, NH: Science Publishers, 2012.

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8

G, Heiden Charles, Holden William T, and U.S. Army Research Institute for the Behavioral and Social Sciences., eds. Battle Command Visualization 101: Prototype embedded training on networked sensors. Alexandria, Va: U.S. Army Research Institute for the Behavioral and Social Sciences, 2004.

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Ricardo, Carmona-Galán, Rodríguez-Vázquez Angel, and SpringerLink (Online service), eds. Low-Power Smart Imagers for Vision-Enabled Sensor Networks. New York, NY: Springer New York, 2012.

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Mauri, Kuorilehto, ed. Ultra-low energy wireless sensor networks in practice: Theory, realization and deployment. Chichester, England: John Wiley & Sons, 2007.

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Частини книг з теми "EMBEDDED SENSOR NETWORKS"

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Heidemann, John, and Ramesh Govindan. "Embedded Sensor Networks." In Handbook of Networked and Embedded Control Systems, 721–38. Boston, MA: Birkhäuser Boston, 2005. http://dx.doi.org/10.1007/0-8176-4404-0_31.

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Iyengar, Sitharama S. "Embedded Sensor Networks." In Information Systems, Technology and Management, 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00405-6_1.

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Agrawal, Dharma Prakash. "Applications of Sensor Networks." In Embedded Sensor Systems, 35–63. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3038-3_2.

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Agrawal, Dharma Prakash. "Personal/Body Area Networks and Healthcare Applications." In Embedded Sensor Systems, 353–90. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3038-3_16.

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Shucker, Brian, Jeff Rose, Anmol Sheth, James Carlson, Shah Bhatti, Hui Dai, Jing Deng, and Richard Han. "Embedded Operating Systems for Wireless Microsensor Nodes." In Handbook of Sensor Networks, 173–97. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/047174414x.ch6.

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Xiaoling, Wu, Shu Lei, Yang Jie, Xu Hui, Jinsung Cho, and Sungyoung Lee. "Swarm Based Sensor Deployment Optimization in Ad Hoc Sensor Networks." In Embedded Software and Systems, 533–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11599555_51.

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Lu, Jun, Lichun Bao, and Tatsuya Suda. "Coverage-Aware Sensor Engagement in Dense Sensor Networks." In Embedded and Ubiquitous Computing – EUC 2005, 639–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596356_64.

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Kulathumani, Vinod, Srikanth Parupati, Arun Ross, and Raghavender Jillela. "Collaborative Face Recognition Using a Network of Embedded Cameras." In Distributed Video Sensor Networks, 373–87. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-127-1_25.

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Yang, Wenlu, Chongqing Zhang, and Minglu Li. "LBN: Load-Balancing Network for Data Gathering Wireless Sensor Networks." In Embedded and Ubiquitous Computing, 204–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11802167_22.

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Moubarak, Mohamed, and Mohamed K. Watfa. "Embedded Operating Systems in Wireless Sensor Networks." In Computer Communications and Networks, 323–46. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-218-4_13.

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Тези доповідей конференцій з теми "EMBEDDED SENSOR NETWORKS"

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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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Mottola, Luca. "Real-world Drone Sensor Networks." In SenSys '15: The 13th ACM Conference on Embedded Network Sensor Systems. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2820990.2820991.

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Singhvi, Vipul, Michael W. Bigrigg, H. Scott Matthews, and James H. Garrett, Jr. "Continuous Commissioning Using Embedded Sensor Networks." In International Conference on Computing in Civil Engineering 2005. Reston, VA: American Society of Civil Engineers, 2005. http://dx.doi.org/10.1061/40794(179)47.

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Alankus, Gazihan, Nuzhet Atay, Chenyang Lu, and O. Burchan Bayazit. "Adaptive Embedded Roadmaps For Sensor Networks." In 2007 IEEE International Conference on Robotics and Automation. IEEE, 2007. http://dx.doi.org/10.1109/robot.2007.364037.

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5

Elkateeb, Ali, and Aiyappa Mandepanda. "Embedded Soft Processor for Sensor Networks." In 2009 International Conference on Network-Based Information Systems (NBIS). IEEE, 2009. http://dx.doi.org/10.1109/nbis.2009.36.

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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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Avci, Besim, Bing Zhang, Muhammed Mas-ud Hussain, and Goce Trajcevski. "Evolving shapes in wireless sensor networks." In SenSys '14: The 12th ACM Conference on Embedded Network Sensor Systems. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2668332.2668376.

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Sen, Sougata, Sunghoon Ivan Lee, Robert Jackson, Rui Wang, Nabil Alshurafa, Josiah Hester, and Jeremy Gummeson. "Towards Battery-Free Body Sensor Networks." In SenSys '20: The 18th ACM Conference on Embedded Networked Sensor Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3417308.3430275.

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Qin, Zhou, Yikun Xian, and Desheng Zhang. "A neural networks based caching scheme for mobile edge networks." In SenSys '19: The 17th ACM Conference on Embedded Networked Sensor Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3356250.3361961.

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Al Nahas, Beshr, Simon Duquennoy, and Olaf Landsiedel. "Network-wide Consensus Utilizing the Capture Effect in Low-power Wireless Networks." In SenSys '17: The 15th ACM Conference on Embedded Network Sensor Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3131672.3131685.

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Звіти організацій з теми "EMBEDDED SENSOR NETWORKS"

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Lanzara, Giulia, Lunwei Zhang, and Fu-Kuo Chang. Design of CNT Embedded Adhesive Film for Sensing, Control, and Reinforcement of PZT Actuator/Sensor Networks in Multifunctional Composites During Cure. Fort Belvoir, VA: Defense Technical Information Center, December 2010. http://dx.doi.org/10.21236/ada563591.

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Dafflon, Baptiste, S. Wielandt, S. Uhlemann, Haruko Wainwright, K. Bennett, Jitendra Kumar, Sebastien Biraud, Susan Hubbard, and Stan Wullschleger. Revolutionizing observations and predictability of Arctic system dynamics through next-generation dense, heterogeneous and intelligent wireless sensor networks with embedded AI. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769774.

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Lickteig, Carl W., Charles G. Heiden, William T. Holden, and Jr. Battle Command Visualization 101: Prototype Embedded Training on Networked Sensors. Fort Belvoir, VA: Defense Technical Information Center, December 2004. http://dx.doi.org/10.21236/ada429188.

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4

Raghavan, Ajay. TRANSENSOR: Transformer Real-time Assessment INtelligent System with Embedded Network of Sensors and Optical Readout. Final Report. Office of Scientific and Technical Information (OSTI), April 2020. http://dx.doi.org/10.2172/1615666.

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5

Yang, Kyoung. Embedded Electro-Optic Sensor Network for the On-Site Calibration and Real-Time Performance Monitoring of Large-Scale Phased Arrays. Fort Belvoir, VA: Defense Technical Information Center, July 2005. http://dx.doi.org/10.21236/ada438489.

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Taiber, Joachim. Unsettled Topics Concerning the Impact of Quantum Technologies on Automotive Cybersecurity. SAE International, December 2020. http://dx.doi.org/10.4271/epr2020026.

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Анотація:
Quantum computing is considered the “next big thing” when it comes to solving computational problems impossible to tackle using conventional computers. However, a major concern is that quantum computers could be used to crack current cryptographic schemes designed to withstand traditional cyberattacks. This threat also impacts future automated vehicles as they become embedded in a vehicle-to-everything (V2X) ecosystem. In this scenario, encrypted data is transmitted between a complex network of cloud-based data servers, vehicle-based data servers, and vehicle sensors and controllers. While the vehicle hardware ages, the software enabling V2X interactions will be updated multiple times. It is essential to make the V2X ecosystem quantum-safe through use of “post-quantum cryptography” as well other applicable quantum technologies. This SAE EDGE™ Research Report considers the following three areas to be unsettled questions in the V2X ecosystem: How soon will quantum computing pose a threat to connected and automated vehicle technologies? What steps and measures are needed to make a V2X ecosystem “quantum-safe?” What standardization is needed to ensure that quantum technologies do not pose an unacceptable risk from an automotive cybersecurity perspective?
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Wu, Yingjie, Selim Gunay, and Khalid Mosalam. Hybrid Simulations for the Seismic Evaluation of Resilient Highway Bridge Systems. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, November 2020. http://dx.doi.org/10.55461/ytgv8834.

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Bridges often serve as key links in local and national transportation networks. Bridge closures can result in severe costs, not only in the form of repair or replacement, but also in the form of economic losses related to medium- and long-term interruption of businesses and disruption to surrounding communities. In addition, continuous functionality of bridges is very important after any seismic event for emergency response and recovery purposes. Considering the importance of these structures, the associated structural design philosophy is shifting from collapse prevention to maintaining functionality in the aftermath of moderate to strong earthquakes, referred to as “resiliency” in earthquake engineering research. Moreover, the associated construction philosophy is being modernized with the utilization of accelerated bridge construction (ABC) techniques, which strive to reduce the impact of construction on traffic, society, economy and on-site safety. This report presents two bridge systems that target the aforementioned issues. A study that combined numerical and experimental research was undertaken to characterize the seismic performance of these bridge systems. The first part of the study focuses on the structural system-level response of highway bridges that incorporate a class of innovative connecting devices called the “V-connector,”, which can be used to connect two components in a structural system, e.g., the column and the bridge deck, or the column and its foundation. This device, designed by ACII, Inc., results in an isolation surface at the connection plane via a connector rod placed in a V-shaped tube that is embedded into the concrete. Energy dissipation is provided by friction between a special washer located around the V-shaped tube and a top plate. Because of the period elongation due to the isolation layer and the limited amount of force transferred by the relatively flexible connector rod, bridge columns are protected from experiencing damage, thus leading to improved seismic behavior. The V-connector system also facilitates the ABC by allowing on-site assembly of prefabricated structural parts including those of the V-connector. A single-column, two-span highway bridge located in Northern California was used for the proof-of-concept of the proposed V-connector protective system. The V-connector was designed to result in an elastic bridge response based on nonlinear dynamic analyses of the bridge model with the V-connector. Accordingly, a one-third scale V-connector was fabricated based on a set of selected design parameters. A quasi-static cyclic test was first conducted to characterize the force-displacement relationship of the V-connector, followed by a hybrid simulation (HS) test in the longitudinal direction of the bridge to verify the intended linear elastic response of the bridge system. In the HS test, all bridge components were analytically modeled except for the V-connector, which was simulated as the experimental substructure in a specially designed and constructed test setup. Linear elastic bridge response was confirmed according to the HS results. The response of the bridge with the V-connector was compared against that of the as-built bridge without the V-connector, which experienced significant column damage. These results justified the effectiveness of this innovative device. The second part of the study presents the HS test conducted on a one-third scale two-column bridge bent with self-centering columns (broadly defined as “resilient columns” in this study) to reduce (or ultimately eliminate) any residual drifts. The comparison of the HS test with a previously conducted shaking table test on an identical bridge bent is one of the highlights of this study. The concept of resiliency was incorporated in the design of the bridge bent columns characterized by a well-balanced combination of self-centering, rocking, and energy-dissipating mechanisms. This combination is expected to lead to minimum damage and low levels of residual drifts. The ABC is achieved by utilizing precast columns and end members (cap beam and foundation) through an innovative socket connection. In order to conduct the HS test, a new hybrid simulation system (HSS) was developed, utilizing commonly available software and hardware components in most structural laboratories including: a computational platform using Matlab/Simulink [MathWorks 2015], an interface hardware/software platform dSPACE [2017], and MTS controllers and data acquisition (DAQ) system for the utilized actuators and sensors. Proper operation of the HSS was verified using a trial run without the test specimen before the actual HS test. In the conducted HS test, the two-column bridge bent was simulated as the experimental substructure while modeling the horizontal and vertical inertia masses and corresponding mass proportional damping in the computer. The same ground motions from the shaking table test, consisting of one horizontal component and the vertical component, were applied as input excitations to the equations of motion in the HS. Good matching was obtained between the shaking table and the HS test results, demonstrating the appropriateness of the defined governing equations of motion and the employed damping model, in addition to the reliability of the developed HSS with minimum simulation errors. The small residual drifts and the minimum level of structural damage at large peak drift levels demonstrated the superior seismic response of the innovative design of the bridge bent with self-centering columns. The reliability of the developed HS approach motivated performing a follow-up HS study focusing on the transverse direction of the bridge, where the entire two-span bridge deck and its abutments represented the computational substructure, while the two-column bridge bent was the physical substructure. This investigation was effective in shedding light on the system-level performance of the entire bridge system that incorporated innovative bridge bent design beyond what can be achieved via shaking table tests, which are usually limited by large-scale bridge system testing capacities.
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