Relevant bibliographies by topics / Sensor Management

Contents

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

Academic literature on the topic 'Sensor Management'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Sensor Management"

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Zhang, Chang Jie, and Yu Liu. "A Sensor Grouping Method for Industrial Sensor Health Management." Applied Mechanics and Materials 621 (August 2014): 271–76. http://dx.doi.org/10.4028/www.scientific.net/amm.621.271.

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As many sensor networks have been deployed in industry monitoring area, the focus on sensor data quality has also increased. Sensor networks provide us with process details which we can utilize to help making decisions on process monitoring.In order to make meaningful decisions, the quality of the data produced by sensors must be validated. As we evaluate the status of a specific sensor, we may also regard the status of the related sensors. If a sensor’s data show some abnormal, but the sensors related to it didn’t, we may have much more confidence to believe that the sensor is malfunction. In our early study, the sensors grouping strategy is manual. In this paper, we proposed a sensor grouping algorithm, which combines both PCA decouple method and the K-means cluster method. Finally, a test has been made with real data from an oilfield.
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Palmer, Allan G. "Impact of Innovative Pulse Oximeter Sensor Management Strategy." Biomedical Instrumentation & Technology 55, no. 2 (May 1, 2021): 59–62. http://dx.doi.org/10.2345/0890-8205-55.1.59.

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Abstract Background: Following a merger of two children's hospitals, leadership discovered a considerable utilization volume of single-use sensors that was associated with declining hospital reimbursements. This discovery resulted in the establishment of a new sensor management strategy, the goal of which was to decrease costs and waste associated with disposable pulse oximetry sensors. Implementation: The sensor management strategy involved using replacement tapes with single-patient-use pulse oximeter sensors instead of the current practice of reprobing with a new sensor. A 60% utilization goal was set, with the focus shifted from sensors used per patient to replacement tapes per sensor. Results: The implementation of a new sensor management strategy between the years 2006 and 2019 in a hospital system decreased sensor volume by more than 780,000 sensors and realized a cost avoidance of more than 7 million dollars. Conclusion: A sensor management strategy can substantially reduce the cost and medical waste commonly associated with the use of disposable, single-patient pulse oximetry sensors.
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Palmer, Allan G. "Impact of Innovative Pulse Oximeter Sensor Management Strategy." Biomedical Instrumentation & Technology 55, no. 2 (May 1, 2021): 59–62. http://dx.doi.org/10.2345/0890-8205-55.2.59.

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Abstract Background: Following a merger of two children's hospitals, leadership discovered a considerable utilization volume of single-use sensors that was associated with declining hospital reimbursements. This discovery resulted in the establishment of a new sensor management strategy, the goal of which was to decrease costs and waste associated with disposable pulse oximetry sensors. Implementation: The sensor management strategy involved using replacement tapes with single-patient-use pulse oximeter sensors instead of the current practice of reprobing with a new sensor. A 60% utilization goal was set, with the focus shifted from sensors used per patient to replacement tapes per sensor. Results: The implementation of a new sensor management strategy between the years 2006 and 2019 in a hospital system decreased sensor volume by more than 780,000 sensors and realized a cost avoidance of more than 7 million dollars. Conclusion: A sensor management strategy can substantially reduce the cost and medical waste commonly associated with the use of disposable, single-patient pulse oximetry sensors.
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Rao, Dr Tavanam Venkata. "Manhole Management System." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4262–65. http://dx.doi.org/10.22214/ijraset.2021.35668.

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A smart city is the future goal of providing cleaner and better services to society. Smart underground infrastructure is an important feature to consider when implementing a smart city. Monitoring the drainage system plays a vital role in keeping the city clean and healthy. The supervision is incompetent, this results in slow treatment of drainage problems and takes more time to resolve. In order to alleviate all of these problems, the system is being developed with a wireless sensor network consisting of sensor nodes. The project aims to design a well management system using IOT that will notify the management station via email when a well exceeds its thresholds, and the system will also monitor the data from the sensors on the LCD module, the system will reduce the risk of death for manual collectors that clean the underground drain and also benefits the public. The project uses a water level sensor, a water flow sensor, an SR04 ultrasonic sensor, a temperature sensor, a gas sensor, an Arduino UNO atmega328 microcontroller and a Raspberrypi3 processor. This project "MANHOLE MANAGEMENT SYSTEM" helps to send signals from sensors. This project consists of Raspberry Pi3 and Arduino Uno, the two microcontrollers that are connected to each other.The signals received by the Arduino from the sensors are converted from analog signals into digital signals with the help of the ADC on the Arduino board and then processed and sent to the Raspberry Pi, it takes this as input data and the Raspberry Pi is sent to an E. -Mail to the appropriate authority. The status of the project is displayed on the LCD module.
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Zhang, Yuan, Yue Liu, and Zhong Tian Jia. "A Sensor Data Management Scheme for Wireless Sensor Networks." Key Engineering Materials 467-469 (February 2011): 709–12. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.709.

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One of the major difficulties of wireless sensor network (WSN) applications is how to efficiently manage the large amount of data produced by sensors. The differences from standard database source pose challenges of sensor data management. In this paper, we propose a novel sensor data management architecture based on our extensive discussion on existing works. The hierarchical system model consists of sensor network layer and proxy network layer. Sensor network layer performs limited computation and communication while being managed intelligently by the proxy network. The proxy network receives sensor data, manages sensor data and processes queries in a distributed manner. We also provide insight into possible research directions in this area.
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Gupta, Anju, and R. K. Bathla. "Energy Efficient Opportunistic Sensing Management in Fog Cloud Environment." International Journal of Computer Science and Mobile Computing 10, no. 10 (October 30, 2021): 20–26. http://dx.doi.org/10.47760/ijcsmc.2021.v10i10.004.

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With so many people now wearing mobile devices with sensors (such as smartphones), utilizing the immense capabilities of these business mobility goods has become a prospective skill to significant behavioural and ecological sensors. A potential challenge for pervasive context assessment is opportunistic sensing, has been effectively used to a wide range of applications. The sensor cloud combines cloud technology with a wireless sensor, resulting in a scalable and cost-effective computing platform for real-time applications. Because the sensor's battery power is limited and the data centre’s servers consume a significant amount of energy to supply storage, a sensor cloud must be energy efficient. This study provides a Fog-based semantic for enabling these kinds of technologies quickly and successfully. The suggested structure is comprised of fundamental algorithms to help set up and coordinate the fog sensing jobs. It creates effective multihop routes for coordinating relevant devices and transporting acquired sensory data to fog sinks. It was claimed that energy-efficient sensor cloud approaches were categorized into different groups and that each technology was examined using numerous characteristics. The outcomes of a series of thorough test simulation in NS3 to define the practicality of the created console, as well as the proportion of each parameter utilized for each technology, are computed.
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Crain, Jared, Ivan Ortiz-Monasterio, and Bill Raun. "Evaluation of a Reduced Cost Active NDVI Sensor for Crop Nutrient Management." Journal of Sensors 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/582028.

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There are methods to increase fertilizer nitrogen use efficiency through optical sensor-based nitrogen application; however, the sensors are expensive and cost prohibitive to farmers in the developing world. This study evaluated a novel, reduced cost, prototype, and optical sensor to determine if it could be used with the same level of accuracy as a commercial sensor. The stability of the prototype sensor (pocket sensor) to maintain an accurate calibration over time, the effect of operator on sensor readings, and sensor performance in maize and wheat were assessed. Evaluation of the sensor performance was conducted in existing wheat and maize trials, as well as turf grass canopies at the International Maize and Wheat Improvement Center, Ciudad Obregon, Mexico.The prototype sensors were highly correlated to the commercial GreenSeeker NDVI sensor in turf grass, wheat, and maize canopies (r2>0.97,r2>0.95, andr2>0.91, resp.). The Pocket Sensors lacked some precision in comparison to the commercial sensor; however, even with the reduced precision, the cost of the sensor and robustness ofNfertilizer algorithms compensate for this apparent weakness. The pocket sensor is a new and viable tool to assess wheat and maize nitrogen status and make nitrogen recommendations based upon the data collected with this sensor.
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Liang, Shuang, Yun Zhu, Hao Li, and Junkun Yan. "Evolutionary Computational Intelligence-Based Multi-Objective Sensor Management for Multi-Target Tracking." Remote Sensing 14, no. 15 (July 28, 2022): 3624. http://dx.doi.org/10.3390/rs14153624.

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In multi-sensor systems (MSSs), sensor selection is a critical technique for obtaining high-quality sensing data. However, when the number of sensors to be selected is unknown in advance, sensor selection is essentially non-deterministic polynomial-hard (NP-hard), and finding the optimal solution is computationally unacceptable. To alleviate these issues, we propose a novel sensor selection approach based on evolutionary computational intelligence for tracking multiple targets in the MSSs. The sensor selection problem is formulated in a partially observed Markov decision process framework by modeling multi-target states as labeled multi-Bernoulli random finite sets. Two conflicting task-driven objectives are considered: minimization of the uncertainty in posterior cardinality estimates and minimization of the number of selected sensors. By modeling sensor selection as a multi-objective optimization problem, we develop a binary constrained evolutionary multi-objective algorithm based on non-dominating sorting and dynamically select a subset of sensors at each time step. Numerical studies are used to evaluate the performance of the proposed approach, where the MSS tracks multiple moving targets with nonlinear/linear dynamic models and nonlinear measurements. The results show that our method not only significantly reduces the number of selected sensors but also provides superior tracking accuracy compared to generic sensor selection methods.
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Toliupa, Sergey, Yuriy Kravchenko, and Aleksander Trush. "ORGANIZATION OF IMPLEMENTATION OF UBIQUITOUS SENSOR NETWORKS." Informatics Control Measurement in Economy and Environment Protection 8, no. 1 (February 28, 2018): 36–39. http://dx.doi.org/10.5604/01.3001.0010.8643.

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The article deals with the implementation of one of the most promising technologies of the 21st century – the permeable sensor networks of the USN. The features, architecture, organization and routing algorithms of sensory networks are described. It is determined that further improvement of the work of such networks requires standardization of the development process and implementation process. USN's Vertical Sensor Networks is one of the most promising technologies of the 21st century. Cheap and "smart" sensors, in large quantities combined into a wireless network connected to the public communications network, today provide an unprecedentedly wide range of control and management services for buildings, businesses, cars, and so forth. USN networks, depending on the type of sensors, can be deployed on the ground, in the air, under and over water, in buildings and, finally, on the skin and inside living organisms, including humans. They are also widely used in such important areas as military affairs, crisis and emergency management, and the fight against terrorism.
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Hang, Lei, Wenquan Jin, HyeonSik Yoon, Yong Hong, and Do Kim. "Design and Implementation of a Sensor-Cloud Platform for Physical Sensor Management on CoT Environments." Electronics 7, no. 8 (August 7, 2018): 140. http://dx.doi.org/10.3390/electronics7080140.

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The development of the Internet of Things (IoT) has increased the ubiquity of the Internet by integrating all objects for interaction via embedded systems, leading to a highly distributed network of devices communicating with human beings as well as other devices. In recent years, cloud computing has attracted a lot of attention from specialists and experts around the world. With the increasing number of distributed sensor nodes in wireless sensor networks, new models for interacting with wireless sensors using the cloud are intended to overcome restricted resources and efficiency. In this paper, we propose a novel sensor-cloud based platform which is able to virtualize physical sensors as virtual sensors in the CoT (Cloud of Things) environment. Virtual sensors, which are the essentials of this sensor-cloud architecture, simplify the process of generating a multiuser environment over resource-constrained physical wireless sensors and can help in implementing applications across different domains. Virtual sensors are dynamically provided in a group which advantages capability of the management the designed platform. An auto-detection approach on the basis of virtual sensors is additionally proposed to identify the accessible physical sensors nodes even if the status of these sensors are offline. In order to assess the usability of the designed platform, a smart-space-based IoT case study was implemented, and a series of experiments were carried out to evaluate the proposed system performance. Furthermore, a comparison analysis was made and the results indicate that the proposed platform outperforms the existing platforms in numerous respects.
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Dissertations / Theses on the topic "Sensor Management"

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Williams, Jason L. "Information theoretic sensor management." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/38534.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 195-203).
Sensor management may be defined as those stochastic control problems in which control values are selected to influence sensing parameters in order to maximize the utility of the resulting measurements for an underlying detection or estimation problem. While problems of this type can be formulated as a dynamic program, the state space of the program is in general infinite, and traditional solution techniques are inapplicable. Despite this fact, many authors have applied simple heuristics such as greedy or myopic controllers with great success. This thesis studies sensor management problems in which information theoretic quantities such as entropy are utilized to measure detection or estimation performance. The work has two emphases: Firstly, we seek performance bounds which guarantee performance of the greedy heuristic and derivatives thereof in certain classes of problems. Secondly, we seek to extend these basic heuristic controllers to nd algorithms that provide improved performance and are applicable in larger classes of problems for which the performance bounds do not apply. The primary problem of interest is multiple object tracking and identification; application areas include sensor network management and multifunction radar control.
(cont.) Utilizing the property of submodularity, as proposed for related problems by different authors, we show that the greedy heuristic applied to sequential selection problems with information theoretic objectives is guaranteed to achieve at least half of the optimal reward. Tighter guarantees are obtained for diffusive problems and for problems involving discounted rewards. Online computable guarantees also provide tighter bounds in specific problems. The basic result applies to open loop selections, where all decisions are made before any observation values are received; we also show that the closed loop greedy heuristic, which utilizes observations received in the interim in its subsequent decisions, possesses the same guarantee relative to the open loop optimal, and that no such guarantee exists relative to the optimal closed loop performance. The same mathematical property is utilized to obtain an algorithm that exploits the structure of selection problems involving multiple independent objects. The algorithm involves a sequence of integer programs which provide progressively tighter upper bounds to the true optimal reward. An auxiliary problem provides progressively tighter lower bounds, which can be used to terminate when a near-optimal solution has been found.
(cont.) The formulation involves an abstract resource consumption model, which allows observations that expend different amounts of available time. Finally, we present a heuristic approximation for an object tracking problem in a sensor network, which permits a direct trade-o between estimation performance and energy consumption. We approach the trade-o through a constrained optimization framework, seeking to either optimize estimation performance over a rolling horizon subject to a constraint on energy consumption, or to optimize energy consumption subject to a constraint on estimation performance. Lagrangian relaxation is used alongside a series of heuristic approximations to and a tractable solution that captures the essential structure in the problem.
by Jason L. Williams.
Ph.D.
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Johansson, Marcus. "Energy-efficient sensor management : How dynamic sensor management affects energy consumption in battery-powered mobile sensor devices." Thesis, Högskolan i Skövde, Institutionen för kommunikation och information, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-6114.

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This thesis has investigated how the energy consumption can be reduced in a mobile sensor unit by using a dynamic measurement scheme. This was done by developing a scheme based on inspiration from existing works in related areas and on techniques found in literature. The developed scheme was then implemented on a mobile sensor unit and tests were conducted where the energy consumed by the scheme was measured. This was compared to a static baseline approach in order to evaluate the efficiency of the scheme. The results showed that on the platform used in this thesis the developed scheme can reduce the energy consumption in a typical scenario by 4.7% or 6.7% depending on which sensors are used. A conclusion drawn is that the platform has a major impact on how effective the scheme can be.
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Huber, Marco. "Probabilistic framework for sensor management." Karlsruhe Univ-Verl. Karlsruhe, 2009. http://d-nb.info/997573252/04.

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Hu, Xi. "Network and sensor management for mulitiple sensor emitter location system." Diss., Online access via UMI:, 2008.

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Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Electrical and Computer Engineering, 2008.
Includes bibliographical references.
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Teuber, Kristoffer. "Sensor Management in a Distributed Environment." Thesis, University of Skövde, Department of Computer Science, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-780.

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In this work an investigation of the benefits and problems of implementing a tracker using sensor management is done. The tracker is implemented in a fusion node in a distributed radar simulator provided by Ericsson Microwave. To investigate this, a literature study of sensor fusion and sensor management is first done, after which a practical study is chosen as method. The fusion method presented in this work is then tested so that tests of sensor management, which depend upon implemented sensor fusion, can be trusted. Sensor management is tested by letting the system track a specific target in the simulated environment. The system is tested to see what impact the delay in the distributed environment has on the implemented system’s capability to track an object. Two different scenarios are chosen to test the system, where a scenario in this thesis is a fly-by of two aircrafts in the terrain covered by the radar sensors. To test the actual correctness of the system, three dimensional coordinates of the objects are used and Euclidian distance between the original value and the fused value is used as an error measurement. The results are then displayed in a series of graphs and tables.

The results show that the chosen fusion algorithm works well with the unsynchronized data. The delay simulated in the system creates a great uncertainty where the object will be, but the presented prediction algorithm manages to find good estimates of the new positions of the object tracked. Loss of data however forces the system to use less information when estimating positions which leads to loss of track. Even though there is a long time delay the presented prediction algorithm can track the object for a period of time, until it looses track due to loss of data. It is also concluded that a system that manages to track an object using a narrow tracking beam is able to track more objects simultaneously using the same radar sensors.

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Yoon, Suyoung. "Power Management in Wireless Sensor Networks." NCSU, 2007. http://www.lib.ncsu.edu/theses/available/etd-01232007-222425/.

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One of the unique characteristics of wireless sensor networks (WSNs) is that sensor nodes have very constrained resources. Typical sensor nodes have lower computing power, communication bandwidth, and smaller memory than other wireless devices, and operate on limited capacity batteries. Hence power efficiency is very important in WSNs because power failure of some sensor nodes may lead to total network failure. In many cases the WSNs have to operate in harsh environments without human intervention for expended period time. Thus, much research on reducing or minimizing the power consumption, and thereby increasing the network lifetime, has been performed at each layer of the network layers. In this dissertation we approach three important issues related power management in WSNs: routing, time synchronization, and medium access control (MAC). We first discuss the effect of selecting routing protocols on the lifetime of the WSNs. The maximum and minimum bounds of the lifetime with respect to the routing protocols are derived. The routing protocols corresponding to the bounds are also presented. The simulation results show that the choice of the routing protocol has very little impact on the lifetime of the network and that simple routing protocols such as shortest path routing perform very close to the the maximum bound of the lifetime of the network. Next, we propose a simple and accurate time synchronization protocol that can be used a a fundamental component of other synchronization-based protocols in WSNs. Analytical bounds on the synchronization errors of proposed protocol are discussed. The implementation results on Mica2 and Telos motes show that proposed time synchronization protocol outperforms existing ones in terms of the precision and required resources. Finally, we model the power consumption of WSN MAC protocols. We derive analytically the power consumption of well known MAC protocols for WSNs, and analyze and compare their performance. We validate the models by measuring the power consumption on Mica 2 motes and comparing those measured power consumption with the analytical results.
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Zanelli, Paul Richard. "Structural pattern matching for sensor management." Thesis, University of York, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.503559.

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Page, Scott F. "Multiple objective sensor management and optimisation." Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/66600/.

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One of the key challenges associated with exploiting modern Autonomous Vehicle technology for military surveillance tasks is the development of Sensor Management strategies which maximise the performance of the on-board Data-Fusion systems. The focus of this thesis is the development of Sensor Management algorithms which aim to optimise target tracking processes. Three principal theoretical and analytical contributions are presented which are related to the manner in which such problems are formulated and subsequently solved. Firstly, the trade-offs between optimising target tracking and other system-level objectives relating to expected operating lifetime are explored in an autonomous ground sensor scenario. This is achieved by modelling the observer trajectory control design as a probabilistic, information--theoretic, multiple-objective optimisation problem. This novel approach explores the relationships between the changes in sensor-target geometry that are induced by tracking performance measures and those relating to power consumption. This culminates in a novel observer trajectory control algorithm based on the minimax approach. The second contribution is an analysis of the propagation of error through a limited-lookahead sensor control feedback loop. In the last decade, it has been shown that the use of such non-myopic (multiple-step) planning strategies can lead to superior performance in many Sensor Management scenarios. However, relatively little is known about the performance of strategies which use different horizon lengths. It is shown that, in the general case, planning performance is a function of the length of the horizon over which the optimisation is performed. While increasing the horizon maximises the chances of achieving global optimality, by revealing information about the substructure of the decision space, it also increases the impact of any prediction error, approximations, or unforeseen risk present within the scenario. These competing mechanisms are demonstrated using an example tracking problem. This provides the motivation for a novel sensor control methodology that employs an adaptive length optimisation horizon. A route to selecting the optimal horizon size is proposed, based on a new non-myopic risk equilibrium which identifies the point where the two competing mechanisms are balanced. The third area of contribution concerns the development of a number of novel optimisation algorithms aimed at solving the resulting sequential decision making problems. These problems are typically solved using stochastic search methods such as Genetic Algorithms or Simulated Annealing. The techniques presented in this thesis are extensions of the recently proposed Repeated Weighted Boosting Search algorithm. In its original form, it is only applicable to continuous, single-objective, optimisation problems. The extensions facilitate application to mixed search spaces and Pareto multiple-objective problems. The resulting algorithms have performance comparable with Genetic Algorithm variants, and offer a number of advantages such as ease of implementation and limited tuning requirements.
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Obenofunde, Simon. "Topology Management in wireless sensor networks." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCK025.

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La mise en réseau de capteurs sans fil s'intègre dans presque tous les domaines des activités humaines. Les moteurs de cette technologie comprennent ses domaines d'application et les améliorations des techniques de fabrication microélectroniques. Le réseau est constitué de plusieurs nœuds de capteurs de petite taille déployés dans la zone à détecter. Les nœuds ont des capacités de traitement, de communication et de détection qui leur permettent d'exécuter leur fonction de manière collaborative. Ils détectent les événements et transmettent les informations à un puits directement ou via des nœuds intermédiaires servant de relais.Des progrès considérables ont été réalisés sur cette technologie au cours des dernières années, cependant la gestion de l’énergie n’a pas connu la même évolution. Ceci est principalement dû au fait que la batterie est la principale source d'énergie. De plus, l’environnement du réseau peut empêcher les batteries d'être rechargées ou changées après le déploiement.Une solution classique à ce problème d'efficacité énergétique réside dans la gestion des cycles d’activation. Il s'agit d’alterner, de façon périodique ou non, les états actif et inactif des nœuds. Cela introduit des problèmes de performances réseaux en termes de disponibilité, de latence et de taux d’acheminement des paquets, car les nœuds inactifs ne participent pas aux communications. Il est donc important de trouver des solutions permettant d’utiliser les cycles d’activation tout en garantissant la disponibilité et en réduisant la latence et le taux de perte de paquets.Dans cette thèse, nous utilisons le cycle d’activation en combinaison avec la gestion de la topologie pour prolonger la durée de vie du réseau. Nous proposons cinq algorithmes pour construire différentes topologies que nous divisons en deux classes. La première classe organise les nœuds en ensembles de manière répétitive et entrelacée. C'est-à-dire que les nœuds appartenant à différents ensembles sont intercalés de manière à assurer la continuité des communications. La seconde classe d'algorithmes organise les nœuds en ensembles successifs en couronne. Nous avons montré expérimentalement la construction des différents ensembles.En utilisant la construction successive d’ensembles, nous proposons deux algorithmes qui construisent des réseaux dorsaux (backbones) virtuels disjoints pouvant être activés alternativement. Une évaluation des algorithmes fait ressortir leur efficacité, avec notamment un facteur d’approximation faible (de l’ordre de 3.5) en comparaison avec ceux des travaux de la littérature.Nous proposons ensuite un protocole basé sur les mécanismes de sommeil et relais sur ces topologies. Les périodes d’activité/inactivité sont définies par ensemble. Les résultats expérimentaux montrent que ce protocole permet une économie d’énergie sans dégrader les critères de performance tels que la latence et le taux d’acheminement des paquets
Wireless sensor networking is ingratiating itself into almost every area of human endeavors. Its drivers include its usages, improvements in microelectronics and manufacturing techniques. The network is made up of multiple tiny sensor nodes deployed in the area to be sensed, with nodes having processing, communicating, and sensing capabilities that enable them to perform their function collaboratively. Nodes sense events and transmit their data to the sink directly or through intermediate nodes acting as relay.Despite all the tremendous advances that have been made on this technology over the past few years, energy has not kept pace. This is based mostly on the fact that battery is its main source of energy. Furthermore, some applications of the network may preclude batteries from either being recharged or changed after deployment.A renowned solution to energy efficiency is duty cycling. This is the periodic or aperiodic placing of a node in an active and an inactive state. This introduces network performance issues of availability, latency, and packet delivery ratio, all linked to the fact that once a node is inactive or off, it is unavailable to communicate. It is therefore important to look for means of still applying duty cycling yet not losing out in availability, latency, and packet delivery ratio.In this dissertation we employ duty cycle on topology management to extend the network lifetime. We propose five algorithms to build various topologies that we divide into two classes. The first class enables nodes to arrange themselves into repetitive and interleaving sets. That is, nodes in the same set repeat themselves on the ground such that a set spans the entire area to be sensed. The second class of algorithms arranges nodes in continuous successive sets with members of a set covering a transmission range. We demonstrate the set formation experimentally.Building on the continuous set formation we propose two algorithms that build disjoint virtual backbone networks, with the disjointedness used for activity scheduling. We then measure the performances of the algorithms notably the approximation ratio and find it quite low (in the order of 3.5) compared to what is obtained in the literature.Finally, we propose a sleep and relay protocol that works on these topologies. Nodes sleep in sets and the activeness is relayed between sets. We evaluate the performance of this protocol and confirm that it actually leads to increase energy savings while not deteriorating other network performance metrics, like latency and packet delivery ratio
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Setty, Rahul Sridhar. "Sensor-less Smart Waste Management System." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-393301.

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In order to improve the municipal solid waste management efficiency, smart management approaches have been proposed such as wireless sensor network architecture solution which includes the use of sensors to detect the garbage bin fill levels and vehicle route optimization techniques. Experimental results show that we can save up to 35% of the operational cost by improving the efficiency of solid waste management. In this thesis, a new low-cost architecture solution is proposed for improving the efficiency of municipal solid waste management without the use of sensors. Instead, a messaging application is used to ask the customers for pick up of garbage. Based on their reply, the prototype architecture uses a cluster-first route-second method that implements a clustering algorithm with truck capacity as the constraint and solves a travelling salesman algorithm in each cluster. The prototype architecture consists of a back-end server that implements sweep clustering algorithm for clustering the customers by their location and solves travelling salesman problem with dynamic programming method in each cluster, firebase realtime database and front-end using android application for the mobile. The experimental results show that the prototype system can adapt to the change in dataset size and truck capacity constraints. We have observed that with an increase in truck capacity constraint, the number of clusters formed for the data set decreases. Forward and backward sweep clustering methods have been compared where there is no significant difference in the results produced. The dataset has been generated manually due to unavailability of real data from various sources. As a future work, we need to test the prototype with the real data in order to produce more accurate results.
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Books on the topic "Sensor Management"

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Hero, Alfred O., David A. Castañón, Douglas Cochran, and Keith Kastella, eds. Foundations and Applications of Sensor Management. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-49819-5.

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Mallick, Mahendra, Vikram Krishnamurthy, and Ba-Ngu Vo, eds. Integrated Tracking, Classification, and Sensor Management. Hoboken, New Jersey: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118450550.

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Ragab, Khaled, Noor Zaman, and Azween Bin Abdullah. Wireless sensor networks and energy efficiency: Protocols, routing, and management. Hershey PA: Information Science Reference, 2012.

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

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Montgomery, H. E. Sensor performance analysis. Greenbelt, Md: Goddard Space Flight Center, 1990.

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Montgomery, H. E. Sensor performance analysis. Washington, D.C: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.

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Montgomery, H. E. Sensor performance analysis. Washington, D.C: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.

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Ragab, Khaled, Noor Zaman, and Azween Bin Abdullah. Wireless sensor networks and energy efficiency: Protocols, routing, and management. Hershey PA: Information Science Reference, 2012.

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Ren, Ju, Ning Zhang, and Xuemin Shen. Energy-Efficient Spectrum Management for Cognitive Radio Sensor Networks. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-60318-6.

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Zhang, Deyu, Zhigang Chen, Haibo Zhou, and Xuemin Shen. Resource Management for Energy and Spectrum Harvesting Sensor Networks. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53771-9.

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Book chapters on the topic "Sensor Management"

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Mitchell, H. B. "Sensor Management." In Data Fusion: Concepts and Ideas, 323–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27222-6_15.

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Yang, Ms Yi, and Sencun Zhu. "Sensor Key Management." In Encyclopedia of Cryptography and Security, 1179–81. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-5906-5_633.

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Bonnet, Philippe, Johannes Gehrke, and Praveen Seshadri. "Towards Sensor Database Systems." In Mobile Data Management, 3–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44498-x_1.

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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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Pérez, Carlos, and Daniel Rodriguez-Martin. "Sensor Sub-System." In Parkinson's Disease Management through ICT, 91–102. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003339038-5.

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Rabinowitz, Assaf, and Dror Rawitz. "Overflow Management with Self-eliminations." In Algorithms for Sensor Systems, 124–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89240-1_9.

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Ogiela, Lidia, and Marek R. Ogiela. "Management Information Systems." In Ubiquitous Computing Application and Wireless Sensor, 449–56. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-9618-7_44.

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Li, Jinbao, Zhipeng Cai, and Jianzhong Li. "Data Management in Sensor Networks." In Wireless Sensor Networks and Applications, 287–330. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-49592-7_12.

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Zeinalipour-Yazti, Demetrios, and Panos K. Chrysanthis. "Mobile Sensor Network Data Management." In Encyclopedia of Database Systems, 1–6. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_221-2.

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Zeinalipour-Yazti, Demetrios, and Panos K. Chrysanthis. "Mobile Sensor Network Data Management." In Encyclopedia of Database Systems, 1–6. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_221-3.

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Conference papers on the topic "Sensor Management"

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Yun, Hee Cheon, Min Gyu Kim, and Jong Sin Lee. "Management of Road Pavement by Mobile Mapping System." In Sensor 2014. Science & Engineering Research Support soCiety, 2014. http://dx.doi.org/10.14257/astl.2014.62.16.

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Kim, Hyunchul, and Jungsuk Kim. "Energy-efficient resource management in Wireless Sensor Network." In 2011 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet). IEEE, 2011. http://dx.doi.org/10.1109/wisnet.2011.5725022.

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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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Liu, X., C. Leckie, and S. K. Saleem. "Power management for unattended wireless sensor networks." In 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2011. http://dx.doi.org/10.1109/issnip.2011.6146588.

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Su, Shiyan, and Chen-Khong Tham. "SensorGrid for Real-Time Traffic Management." In 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information. IEEE, 2007. http://dx.doi.org/10.1109/issnip.2007.4496884.

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Yuriyama, Madoka, and Takayuki Kushida. "Sensor-Cloud Infrastructure - Physical Sensor Management with Virtualized Sensors on Cloud Computing." In 2010 13th International Conference on Network-Based Information Systems (NBiS). IEEE, 2010. http://dx.doi.org/10.1109/nbis.2010.32.

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Schaefer, Jr., Carl G., and Kenneth J. Hintz. "Sensor management in a sensor-rich environment." In AeroSense 2000, edited by Ivan Kadar. SPIE, 2000. http://dx.doi.org/10.1117/12.395093.

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Xiao, Hui, Yaakov Bar-Shalom, and Xu Chen. "Model-Based Sparse Information Recovery by a Collaborative Sensor Management." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-9088.

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This paper considers the real-time recovery of a fast time series (e.g., updated every T seconds) by using sparsely sampled measurements from two sensors whose sampling intervals are much larger than T (e.g., MT and NT, where M and N are integers). Specifically, when the fast signal is an autoregressive process, we propose an online information recovery algorithm that reconstructs the dense underlying temporal dynamics fully, by systematically modulating the sensor speeds MT and NT, and by exploiting a model-based fusion of the sparsely collected data. We provide the collaborative sensing design, parametric analysis and optimization of the algorithm. Application to a closed-loop disturbance rejection problem reveals the feasibility to annihilate fast disturbance signals with the slow and not fully aligned sensor pair in real time, and in particular, the rejection of narrow-band disturbances whose frequencies are much higher than the Nyquist frequencies of the sensors.
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Zia, Tanveer A. "Reputation-based trust management in wireless sensor networks." In 2008 International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2008. http://dx.doi.org/10.1109/issnip.2008.4761980.

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Setser, Earl W. "Sensor management system architecture." In SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, edited by Wallace G. Fishell, Paul A. Henkel, and Alfred C. Crane, Jr. SPIE, 1994. http://dx.doi.org/10.1117/12.191905.

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Reports on the topic "Sensor Management"

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Ng, K. K. Airborne Sensor Thermal Management Solution. Office of Scientific and Technical Information (OSTI), June 2015. http://dx.doi.org/10.2172/1251091.

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Bierman, A., D. Romascanu, and K. C. Norseth. Entity Sensor Management Information Base. RFC Editor, December 2002. http://dx.doi.org/10.17487/rfc3433.

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

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Friedman, Avner, and Keith Kastella. Emerging Applications in Probability (Sensor Management). Fort Belvoir, VA: Defense Technical Information Center, February 1995. http://dx.doi.org/10.21236/ada292781.

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Hodgson, Thom J., Johnathon L. Dulin, Kristin Arney, Ben J. Lobo, Curtis M. Mears, and Reha Uzsoy. Global Sensor Management: Military Asset Allocation. Fort Belvoir, VA: Defense Technical Information Center, October 2009. http://dx.doi.org/10.21236/ada515353.

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Bellingham, James G., James W. Bales, Albert Bradley, and Michael Feezor. Extending Sensor Deployment Through Integrated Energy Management. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada634647.

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Carin, Lawrence, Nilanjan Dasgupta, and Hui Li. Optimal Sensor Management for Next-Generation EMI Systems. Fort Belvoir, VA: Defense Technical Information Center, June 2008. http://dx.doi.org/10.21236/ada495635.

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Spivey, Mark W. Completing the Sensor Grid: A Revolution in Imagery Management. Fort Belvoir, VA: Defense Technical Information Center, February 1999. http://dx.doi.org/10.21236/ada363053.

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Stoneking, Craig, Phil DiBona, and Adria Hughes. Multi-UAV Collaborative Sensor Management for UAV Team Survivability. Fort Belvoir, VA: Defense Technical Information Center, August 2006. http://dx.doi.org/10.21236/ada460418.

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Lambert, Hendrick C., and Dana Sinno. Bioinspired Resource Management for Multiple-Sensor Target Tracking Systems. Fort Belvoir, VA: Defense Technical Information Center, June 2011. http://dx.doi.org/10.21236/ada544935.

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