Relevant bibliographies by topics / Sensor-cloud

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

Academic literature on the topic 'Sensor-cloud'

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

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Journal articles on the topic "Sensor-cloud"

1

Fung, V., J. L. Bosch, S. W. Roberts, and J. Kleissl. "Cloud speed sensor." Atmospheric Measurement Techniques Discussions 6, no. 5 (2013): 9037–59. http://dx.doi.org/10.5194/amtd-6-9037-2013.

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Abstract. Changing cloud cover is a major source of solar radiation variability and poses challenges for the integration of solar energy. A compact and economical system that measures cloud motion vectors to estimate power plant ramp rates and provide short term solar irradiance forecasts is presented. The Cloud Speed Sensor (CSS) is constructed using an array of luminance sensors and high-speed data acquisition to resolve the progression of cloud passages across the sensor footprint. An embedded microcontroller acquires the sensor data and uses a cross-correlation algorithm to determine cloud motion vectors. The CSS was validated against an artificial shading test apparatus, an alternative method of cloud motion detection from ground measured irradiance (Linear Cloud Edge, LCE), and a UC San Diego Sky Imager (USI). The CSS detected artificial shadow directions and speeds to within 15 and 6% accuracy, respectively. The CSS detected (real) cloud directions and speeds without average bias and with average weighted root mean square difference of 22° and 1.9 m s−1 when compared to USI and 33° and 1.5 m s−1 when compared to LCE results.
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Kumar, Vimal, Amartya Sen, and Sanjay Madria. "Secure Sensor Cloud." Synthesis Lectures on Algorithms and Software in Engineering 9, no. 2 (2018): 1–140. http://dx.doi.org/10.2200/s00886ed1v01y201811ase018.

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Ketema, Zemenu, Baba Ahmad Mala, Gradwell Dzikanyanga, Romário Tomo, and Jamilu Ibrahim Argungu. "Sensor-cloud Architecture: a Security Taxonomy in Cloud-assisted Sensor Networks." International Journal of Advanced Engineering and Management Research 09, no. 02 (2024): 33–50. http://dx.doi.org/10.51505/ijaemr.2024.9204.

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The integration of cloud computing with wireless sensor networks (WSN), known as SensorCloud, has garnered significant attention for its application in fields such as healthcare, habitat monitoring, military surveillance, and disaster management. This fusion aims to overcome the inherent processing and storage limitations of sensor networks by leveraging the cloud's flexibility, scalability, and enhanced capacities. Despite these advantages, Sensor-Cloud systems face challenges including latency, dependability, load balancing, bandwidth constraints, resource optimization, and security vulnerabilities. Security concerns are paramount, as the architecture's integrity is threatened by potential attacks on sensor nodes, communication channels, and the cloud infrastructure. Although existing literature extensively explores these issues, a comprehensive analysis of security threats specific to Sensor-Cloud remains essential. This paper presents an in-depth examination of security challenges within Sensor-Cloud environments, proposing innovative solutions and developing taxonomies of security attacks from an architectural perspective. Through this analysis, the paper aims to fortify Sensor-Cloud architecture against diverse security threats, ensuring its robustness and reliability across various applications.
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Madria, Sanjay, Vimal Kumar, and Rashmi Dalvi. "Sensor Cloud: A Cloud of Virtual Sensors." IEEE Software 31, no. 2 (2014): 70–77. http://dx.doi.org/10.1109/ms.2013.141.

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Fung, V., J. L. Bosch, S. W. Roberts, and J. Kleissl. "Cloud shadow speed sensor." Atmospheric Measurement Techniques 7, no. 6 (2014): 1693–700. http://dx.doi.org/10.5194/amt-7-1693-2014.

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Abstract. Changing cloud cover is a major source of solar radiation variability and poses challenges for the integration of solar energy. A compact and economical system is presented that measures cloud shadow motion vectors to estimate power plant ramp rates and provide short-term solar irradiance forecasts. The cloud shadow speed sensor (CSS) is constructed using an array of luminance sensors and a high-speed data acquisition system to resolve the progression of cloud passages across the sensor footprint. An embedded microcontroller acquires the sensor data and uses a cross-correlation algorithm to determine cloud shadow motion vectors. The CSS was validated against an artificial shading test apparatus, an alternative method of cloud motion detection from ground-measured irradiance (linear cloud edge, LCE), and a UC San Diego sky imager (USI). The CSS detected artificial shadow directions and speeds to within 15° and 6% accuracy, respectively. The CSS detected (real) cloud shadow directions and speeds with average weighted root-mean-square difference of 22° and 1.9 m s−1 when compared to USI and 33° and 1.5 m s−1 when compared to LCE results.
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Alturki, Ryan, Hasan Jumaili Alyamani, Mohammed Abdulaziz Ikram, et al. "Sensor-Cloud Architecture: A Taxonomy of Security Issues in Cloud-Assisted Sensor Networks." IEEE Access 9 (2021): 89344–59. http://dx.doi.org/10.1109/access.2021.3088225.

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Henze, Martin, René Hummen, Roman Matzutt, Daniel Catrein, and Klaus Wehrle. "Maintaining User Control While Storing and Processing Sensor Data in the Cloud." International Journal of Grid and High Performance Computing 5, no. 4 (2013): 97–112. http://dx.doi.org/10.4018/ijghpc.2013100107.

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Clouds provide a platform for efficiently and flexibly aggregating, storing, and processing large amounts of data. Eventually, sensor networks will automatically collect such data. A particular challenge regarding sensor data in Clouds is the inherent sensitive nature of sensed information. For current Cloud platforms, the data owner loses control over her sensor data once it enters the Cloud. This imposes a major adoption barrier for bridging Cloud computing and sensor networks, which we address henceforth. After analyzing threats to sensor data in Clouds, the authors propose a Cloud architecture that enables end-to-end control over sensitive sensor data by the data owner. The authors introduce a well-defined entry point from the sensor network into the Cloud, which enforces end-to-end data protection, applies encryption and integrity protection, and grants data access. Additionally, the authors enforce strict isolation of services. The authors show the feasibility and scalability of their Cloud architecture using a prototype and measurements.
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Lim, Yujin, and Jaesung Park. "Sensor Resource Sharing Approaches in Sensor-Cloud Infrastructure." International Journal of Distributed Sensor Networks 10, no. 4 (2014): 476090. http://dx.doi.org/10.1155/2014/476090.

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Dr. Soundara Rajan, Pradeep K. G. M. ,. Dr S. VENKATESAN,. "A NEURAL NETWORK BASED SMART BUILDING MONITORING SYSTEM USING WIRELESS SENSOR NETWORK." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (2021): 232–39. http://dx.doi.org/10.17762/itii.v9i1.123.

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A Smart building monitoring System uses Sensors like temperature Sensor, humidity sensor, motion / occupancy Sensor, contact sensor, Gas/air quality Sensor, Strain Sensor, electrical current monitoring Sensor etc for monitoring an environmental factors and to control devices such as air conditioner, ventilator, alarm, Security grand system etc using sensor information. Our proposed system uses effective neural network to gather information from different Sensor and consolidate it by Information Isolation Node (IIN) and send to sink node The Sink node sends information. to cloud where remote monitoring is done and it also receives Control information from cloud and operates different equipments of building. For efficient implementation only needed information are sent to cloud and necessary action are taken by remote system based on consolidated information sent by Information Isolation Node (IN) through Sink node The Sink node sends information to cloud where remote monitoring is done and it also receives Control information from cloud and operates different equipments of building.
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Subramanian, Murali, Manikandan Narayanan, B. Bhasker, S. Gnanavel, Md Habibur Rahman, and C. H. Pradeep Reddy. "Hybrid Electro Search with Ant Colony Optimization Algorithm for Task Scheduling in a Sensor Cloud Environment for Agriculture Irrigation Control System." Complexity 2022 (October 4, 2022): 1–15. http://dx.doi.org/10.1155/2022/4525220.

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Integrating cloud computing with wireless sensor networks creates a sensor cloud (WSN). Some real-time applications, such as agricultural irrigation control systems, use a sensor cloud. The sensor battery life in sensor clouds is constrained. The data center’s computers consume a lot of energy to offer storage in the cloud. The emerging sensor cloud technology-enabled virtualization. Using a virtual environment has many advantages. However, different resource requirements and task execution cause substantial performance and parameter optimization issues in cloud computing. In this study, we proposed the hybrid electro search with ant colony optimization (HES-ACO) technique to enhance the behavior of task scheduling, for those considering parameters such as total execution time, cost of the execution, makespan time, the cloud data center energy consumption like throughput, response time, resource utilization task rejection ratio, and deadline constraint of the multicloud. Electro search and the ant colony optimization algorithm are combined in the proposed method. Compared to HESGA, HPSOGA, AC-PSO, and PSO-COGENT algorithms, the created HES-ACO algorithm was simulated at CloudSim and found to optimize all parameters.
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Dissertations / Theses on the topic "Sensor-cloud"

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Moilanen, T. (Tuomo). "Scalable cloud database solution for sensor networks." Master's thesis, University of Oulu, 2013. http://urn.fi/URN:NBN:fi:oulu-201306061571.

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This Master’s thesis presents the theory, design, and implementation of a scalable cloud database solution for a sensor network utilizing a cloud-based database-as-a-service platform. The sensor network is a part of the project called Internet of Things, a project studying on networking physical objects and visualizing of sensor data in a 3D environment. The challenge comes from being able to handle the massive data set expected to be produced by the sensor network. This is an example of the concept of Big Data, a data set so large that it becomes difficult and time consuming to be processed with conventional relational database methods. The solution can be found in NoSQL architectures. However, the distributed NoSQL architectures trade off several advantages of the relational database model to achieve database scaling, query performance, and partition tolerance. In this work, the database infrastructure is acquired as-a-service from one of the major cloud database service providers. Several decision criteria have been used to choose the database infrastructure service, such as scalability, absence of administrative duties, query functions, and service pricing. The work presents the rationale behind the database service selection and the design and implementation of the database and client software. Amazon’s DynamoDB is chosen as a target system due to its high scalability and performance, which offset its restrictions to query versatility. The resulting database design offering scalability beyond the traditional database management systems is presented with additional discussion on how to further improve the system and to implement new features<br>Tämä diplomityö käsittelee sensoriverkon datan tallennukseen hyödynnettävän skaalautuvan pilvitietokantaratkaisun teoriaa, suunnittelua ja toteutusta käyttäen olemassaolevan pilvipalvelutarjoajan tietokanta-alustaa. Sensoriverkko on osa Internet of Things -projektia, joka tutkii fyysisten objektien verkottumista ja sensoridatan visualisointia 3D-ympäristössä. Sensoriverkon tuottama valtava datamäärä on haasteellista hallita. Tästä ilmiöstä käytetään nimitystä Big Data, jolla tarkoitetaan niin suurta datan määrää, että sitä on vaikeata ja hidasta käsitellä perinteisillä tietokantasovelluksilla. Ratkaisu on hajautetut NoSQL tietokanta-arkkitehtuurit. NoSQL-arkkitehtuureissa joudutaan kuitenkin tekemään myönnytyksiä, jotta saavutaan tietokannan skaalautuvuus, hakujen suorituskyky ja partitioiden sieto. Tässä työssä tietokantainfrastruktuuri hankitaan palveluna suurelta pilvitietokantapalveluiden tarjoajalta. Tietokantapalvelun valintaan vaikuttavat useat kriteerit kuten skaalautuvuus, hallinnoinnin helppous, hakufunktiot sekä palvelun kustannukset. Työssä esitetään tietokantapalvelun valintaperusteet sekä tietokannan ja asiakasohjelman suunnittelu ja toteutus. Tietokantapalveluksi valitaan Amazon DynamoDB, jonka suuri skaalautuvuus ja suorituskyky kompensoivat tietokantahakujen rajoituksia. Työssä kehitetään tietokantasuunnitelma, joka tarjoaa perinteisiä relaatiotietokantoja suuremman skaalautuvuuden, sekä käsitellään menetelmiä uusien ominaisuuksien lisäämiseksi ja järjestelmän parantamiseksi
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Doddapaneni, Purna, Quincy Wofford, and Nicole Maneth. "MULTI-SENSOR HEALTH PLATFORM WITH CLOUD ANALYSIS." International Foundation for Telemetering, 2016. http://hdl.handle.net/10150/624186.

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What could we learn from monitoring our body processes with various portable sensors and an unconstrained analysis platform? Physiological processes in the human body produce observable biosignals. These signals contain a wealth of information about the condition of the body, and its reaction to environmental factors. Our study harnesses 9 unique sensors, integrated by the eHealthSensor platform for Arduino, to transmit data to an Android device. The Android device contains a local PostgreSQL database, which synchronizes with the cloud. Using this platform, researchers can monitor a subjects biosignals as they ride a roller coaster or participate in exercise activities. Nurses can monitor the vitals of multiple patients remotely. Analytic, cloud based services, managed by healthcare providers, could ultimately enable automated diagnosis of medical conditions.
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Pasika, Hugh Joseph Christopher. "Neural network sensor fusion : creation of a virtual sensor for cloud-base height estimation /." *McMaster only, 1999.

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Pasika, Hugh Joseph Christopher. "Neural network sensor fusion, creation of a virtual sensor for cloud-base height estimation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0031/NQ66287.pdf.

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Romanazzi, Stefano. "Water Supply Network Management: Sensor Analysis using Google Cloud Dataflow." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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The growing field of IoT increases the amount of time series data produced every day. With such information overload it is necessary to promptly clean and process those information extracting meaningful knowledge and avoiding raw data storage. Nowadays cloud infrastructures allow to adopt this processing demand by providing new models for defining data-parallel processing pipelines, such as the Apache Beam unified model which evolved from Google Cloud Dataflow and MapReduce paradigm. The projects of this thesis have been implemented during a three-month internship at Injenia srl, and face this exact trail, by processing external IoT-acquired data, going through a cleansing and a processing phase in order to obtain neural networks ready-to-feed data. The sewerage project acquires signals from IoT sensors of a sewerage infrastructure and aims at predicting signals' trends over close future periods. The aqueduct project acquires the same information type from aqueduct plants and aims to reduce the false alarm rate of the telecontrol system. Given the good results of both projects it can be concluded that the data processing phase has produced high-quality information which is the main objective of this thesis.
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Grankvist, Ola. "Recognition and Registration of 3D Models in Depth Sensor Data." Thesis, Linköpings universitet, Datorseende, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-131452.

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Object Recognition is the art of localizing predefined objects in image sensor data. In this thesis a depth sensor was used which has the benefit that the 3D pose of the object can be estimated. This has applications in e.g. automatic manufacturing, where a robot picks up parts or tools with a robot arm. This master thesis presents an implementation and an evaluation of a system for object recognition of 3D models in depth sensor data. The system uses several depth images rendered from a 3D model and describes their characteristics using so-called feature descriptors. These are then matched with the descriptors of a scene depth image to find the 3D pose of the model in the scene. The pose estimate is then refined iteratively using a registration method. Different descriptors and registration methods are investigated. One of the main contributions of this thesis is that it compares two different types of descriptors, local and global, which has seen little attention in research. This is done for two different scene scenarios, and for different types of objects and depth sensors. The evaluation shows that global descriptors are fast and robust for objects with a smooth visible surface whereas the local descriptors perform better for larger objects in clutter and occlusion. This thesis also presents a novel global descriptor, the CESF, which is observed to be more robust than other global descriptors. As for the registration methods, the ICP is shown to perform most accurately and ICP point-to-plane more robust.
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Zhu, Chunsheng. "Improving sensor-cloud : energy efficiency, security, sensory data transmission, and quality of service." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58352.

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Recently, induced by incorporating 1) the ubiquitous data gathering capabilities of wireless sensor networks (WSNs) as well as 2) the powerful data storage and data processing abilities of cloud computing (CC), Sensor-Cloud is attracting growing attention from both academia and industry. However, Sensor-Cloud is still in its infancy and a lot of research efforts are expected to emerge in this area. Improving Sensor-Cloud, this thesis first presents the important research issues that are yet to be widely investigated by other researchers regarding the energy efficiency, security, sensory data transmission and quality of service (QoS) of Sensor-Cloud, respectively. Further, our accomplished work regarding solving the identified research issues is described. Particularly, two collaborative location-based sleep scheduling (CLSS) schemes are designed. Based on the locations of mobile users, CLSS dynamically determines the awake or asleep status of each sensor node to reduce energy consumption of the WSN integrated with mobile cloud. An authenticated trust and reputation calculation and management (ATRCM) system is introduced. ATRCM considers i) the authenticity of cloud service provider (CSP) and sensor network provider (SNP); ii) the attribute requirement of cloud service user (CSU) and CSP; iii) the cost, trust, and reputation of the service of CSP and SNP. A mechanism named TPSS is shown. TPSS consists of two main parts: 1) time and priority-based selective data transmission (TPSDT) for WSN gateway to selectively transmit sensory data to the cloud and 2) priority-based sleep scheduling (PSS) algorithm for WSN to save energy consumption. Trust-Assisted Sensor-Cloud (TASC) is exhibited. In TASC, the sensory data is gathered and transmitted to cloud, by the trusted sensors (i.e., sensors which own trust values surpassing a threshold) in WSN. The sensory data is stored, processed and on demand delivered to users, by the trusted data centers (i.e., data centers which own trust values surpassing a threshold) in cloud. The analytical and experimental results conducted in our work show that the proposed approaches can effectively alleviate the corresponding research issues, respectively. We hope our work can attract more research into Sensor-Cloud to make it develop faster and better.<br>Applied Science, Faculty of<br>Electrical and Computer Engineering, Department of<br>Graduate
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Khan, Imran. "Cloud-based cost-efficient application and service provisioning in virtualized wireless sensor networks." Thesis, Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0019/document.

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Des Réseaux de Capteurs Sans Fil (RdCSF) deviennent omniprésents et sont utilisés dans diverses applications domaines. Ils sont les pierres angulaires de l'émergence de l'Internet des Objets (IdO) paradigme. Déploiements traditionnels de réseaux de capteurs sont spécifiques à un domaine, avec des applications généralement incrustés dans le RdCSF, excluant la ré-utilisation de l'infrastructure par d'autres applications. Maintenant, avec l'avènement de l'IdO, cette approche est de moins en moins viable. Une solution possible réside dans le partage d'une même RdCSF par de plusieurs applications et services, y compris même les applications et services qui ne sont pas envisagées lors du déploiement de RdCSF. Deux principaux développements majeurs ont conduit à cette solution potentielle. Premièrement, comme les nœuds de RdCSF sont de plus en plus puissants, il devient de plus en plus pertinent de rechercher comment pourrait plusieurs applications partager les mêmes déploiements WSN. La deuxième évolution est le Cloud Computing paradigme qui promeut des ressources et de la rentabilité en appliquant le concept de virtualisation les ressources physiques disponibles. Grâce à ces développements de cette thèse fait les contributions suivantes. Tout d'abord, un vaste état de la revue d'art est présenté qui présente les principes de base de RdCSF la virtualisation et sa pertinence avec précaution motive les scénarios sélectionnés. Les travaux existants sont présentés en détail et évaluées de manière critique en utilisant un ensemble d'exigences provenant du scénario. Cette contribution améliore sensiblement les critiques actuelles sur l'état de l'art en termes de portée, de la motivation, de détails, et les questions de recherche futures. La deuxième contribution se compose de deux parties: la première partie est une nouvelle architecture de virtualization RdCSF multicouche permet l'approvisionnement de plusieurs applications et services au cours du même déploiement de RdCSF. Il est mis en œuvre et évaluée en utilisant un prototype basé sur un scénario de preuve de concept en utilisant le kit Java SunSpot. La deuxième partie de cette contribution est l'architecture étendue qui permet à l’infrastructure virtualisée RdCSF d'interagir avec un RdCSF Platform-as-a-Service (PaaS) à un niveau d'abstraction plus élevé. Grâce à ces améliorations RdCSF PaaS peut provisionner des applications et des services RdCSF aux utilisateurs finaux que Software-as-a-Service (SaaS). Les premiers résultats sont présentés sur la base de l'implantation de l'architecture améliorée en utilisant le kit Java SunSpot. La troisième contribution est une nouvelle architecture d'annotation de données pour les applications sémantiques dans les environnements virtualisés les RdCSF. Il permet en réseau annotation de données et utilise des superpositions étant la pierre angulaire. Nous utilisons la base ontologie de domaine indépendant d'annoter les données du capteur. Un prototype de preuve de concept, basé sur un scénario, est développé et mis en œuvre en utilisant Java SunSpot, Kits AdvanticSys et Google App Engine. La quatrième et dernière contribution est l'amélioration à l'annotation de données proposée l'architecture sur deux fronts. L'un est l'extension à l'architecture proposée pour soutenir la création d'ontologie, de la distribution et la gestion. Le deuxième front est une heuristique génétique basée algorithme utilisé pour la sélection de noeuds capables de stocker l'ontologie de base. L'extension de la gestion d'ontologie est mise en oeuvre et évaluée à l'aide d'un prototype de validation de principe à l'aide de Java kit SunSpot, tandis que les résultats de la simulation de l'algorithme sont présentés<br>Wireless Sensor Networks (WSNs) are becoming ubiquitous and are used in diverse applications domains. Traditional deployments of WSNs are domain-specific, with applications usually embedded in the WSN, precluding the re-use of the infrastructure by other applications. This can lead to redundant deployments. Now with the advent of IoT, this approach is less and less viable. A potential solution lies in the sharing of a same WSN by multiple applications and services, to allow resource- and cost-efficiency. In this dissertation, three architectural solutions are proposed for this purpose. The first solution consists of two parts: the first part is a novel multilayer WSN virtualization architecture that allows the provisioning of multiple applications and services over the same WSN deployment. The second part of this contribution is the extended architecture that allows virtualized WSN infrastructure to interact with a WSN Platform-as-a-Service (PaaS) at a higher level of abstraction. Both these solutions are implemented and evaluated using two scenario-based proof-of-concept prototypes using Java SunSpot kit. The second architectural solution is a novel data annotation architecture for the provisioning of semantic applications in virtualized WSNs. It is capable of providing in-network, distributed, real-time annotation of raw sensor data and uses overlays as the cornerstone. This architecture is implemented and evaluated using Java SunSpot, AdvanticSys kits and Google App Engine. The third architectural solution is the enhancement to the data annotation architecture on two fronts. One is a heuristic-based genetic algorithm used for the selection of capable nodes for storing the base ontology. The second front is the extension to the proposed architecture to support ontology creation, distribution and management. The simulation results of the algorithm are presented and the ontology management extension is implemented and evaluated using a proof-of-concept prototype using Java SunSpot kit. As another contribution, an extensive state-of-the-art review is presented that introduces the basics of WSN virtualization and motivates its pertinence with carefully selected scenarios. This contribution substantially improves current state-of-the-art reviews in terms of the scope, motivation, details, and future research issues
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9

Khan, Imran. "Cloud-based cost-efficient application and service provisioning in virtualized wireless sensor networks." Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0019.

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Des Réseaux de Capteurs Sans Fil (RdCSF) deviennent omniprésents et sont utilisés dans diverses applications domaines. Ils sont les pierres angulaires de l'émergence de l'Internet des Objets (IdO) paradigme. Déploiements traditionnels de réseaux de capteurs sont spécifiques à un domaine, avec des applications généralement incrustés dans le RdCSF, excluant la ré-utilisation de l'infrastructure par d'autres applications. Maintenant, avec l'avènement de l'IdO, cette approche est de moins en moins viable. Une solution possible réside dans le partage d'une même RdCSF par de plusieurs applications et services, y compris même les applications et services qui ne sont pas envisagées lors du déploiement de RdCSF. Deux principaux développements majeurs ont conduit à cette solution potentielle. Premièrement, comme les nœuds de RdCSF sont de plus en plus puissants, il devient de plus en plus pertinent de rechercher comment pourrait plusieurs applications partager les mêmes déploiements WSN. La deuxième évolution est le Cloud Computing paradigme qui promeut des ressources et de la rentabilité en appliquant le concept de virtualisation les ressources physiques disponibles. Grâce à ces développements de cette thèse fait les contributions suivantes. Tout d'abord, un vaste état de la revue d'art est présenté qui présente les principes de base de RdCSF la virtualisation et sa pertinence avec précaution motive les scénarios sélectionnés. Les travaux existants sont présentés en détail et évaluées de manière critique en utilisant un ensemble d'exigences provenant du scénario. Cette contribution améliore sensiblement les critiques actuelles sur l'état de l'art en termes de portée, de la motivation, de détails, et les questions de recherche futures. La deuxième contribution se compose de deux parties: la première partie est une nouvelle architecture de virtualization RdCSF multicouche permet l'approvisionnement de plusieurs applications et services au cours du même déploiement de RdCSF. Il est mis en œuvre et évaluée en utilisant un prototype basé sur un scénario de preuve de concept en utilisant le kit Java SunSpot. La deuxième partie de cette contribution est l'architecture étendue qui permet à l’infrastructure virtualisée RdCSF d'interagir avec un RdCSF Platform-as-a-Service (PaaS) à un niveau d'abstraction plus élevé. Grâce à ces améliorations RdCSF PaaS peut provisionner des applications et des services RdCSF aux utilisateurs finaux que Software-as-a-Service (SaaS). Les premiers résultats sont présentés sur la base de l'implantation de l'architecture améliorée en utilisant le kit Java SunSpot. La troisième contribution est une nouvelle architecture d'annotation de données pour les applications sémantiques dans les environnements virtualisés les RdCSF. Il permet en réseau annotation de données et utilise des superpositions étant la pierre angulaire. Nous utilisons la base ontologie de domaine indépendant d'annoter les données du capteur. Un prototype de preuve de concept, basé sur un scénario, est développé et mis en œuvre en utilisant Java SunSpot, Kits AdvanticSys et Google App Engine. La quatrième et dernière contribution est l'amélioration à l'annotation de données proposée l'architecture sur deux fronts. L'un est l'extension à l'architecture proposée pour soutenir la création d'ontologie, de la distribution et la gestion. Le deuxième front est une heuristique génétique basée algorithme utilisé pour la sélection de noeuds capables de stocker l'ontologie de base. L'extension de la gestion d'ontologie est mise en oeuvre et évaluée à l'aide d'un prototype de validation de principe à l'aide de Java kit SunSpot, tandis que les résultats de la simulation de l'algorithme sont présentés<br>Wireless Sensor Networks (WSNs) are becoming ubiquitous and are used in diverse applications domains. Traditional deployments of WSNs are domain-specific, with applications usually embedded in the WSN, precluding the re-use of the infrastructure by other applications. This can lead to redundant deployments. Now with the advent of IoT, this approach is less and less viable. A potential solution lies in the sharing of a same WSN by multiple applications and services, to allow resource- and cost-efficiency. In this dissertation, three architectural solutions are proposed for this purpose. The first solution consists of two parts: the first part is a novel multilayer WSN virtualization architecture that allows the provisioning of multiple applications and services over the same WSN deployment. The second part of this contribution is the extended architecture that allows virtualized WSN infrastructure to interact with a WSN Platform-as-a-Service (PaaS) at a higher level of abstraction. Both these solutions are implemented and evaluated using two scenario-based proof-of-concept prototypes using Java SunSpot kit. The second architectural solution is a novel data annotation architecture for the provisioning of semantic applications in virtualized WSNs. It is capable of providing in-network, distributed, real-time annotation of raw sensor data and uses overlays as the cornerstone. This architecture is implemented and evaluated using Java SunSpot, AdvanticSys kits and Google App Engine. The third architectural solution is the enhancement to the data annotation architecture on two fronts. One is a heuristic-based genetic algorithm used for the selection of capable nodes for storing the base ontology. The second front is the extension to the proposed architecture to support ontology creation, distribution and management. The simulation results of the algorithm are presented and the ontology management extension is implemented and evaluated using a proof-of-concept prototype using Java SunSpot kit. As another contribution, an extensive state-of-the-art review is presented that introduces the basics of WSN virtualization and motivates its pertinence with carefully selected scenarios. This contribution substantially improves current state-of-the-art reviews in terms of the scope, motivation, details, and future research issues
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Adaldo, Antonio. "Event-triggered control of multi-agent systems: pinning control, cloud coordination, and sensor coverage." Licentiate thesis, KTH, Reglerteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192342.

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A multi-agent system is composed of interconnected subsystems, or agents. In control of multi-agent systems, the aim is to obtain a coordinated behavior of the overall system through local interactions among the agents. Communication among the agents often occurs over a wireless medium with finite capacity. In this thesis, we investigate multiagent control systems where inter-agent communication is modelled by discrete events triggered by state conditions. In the first part, we consider event-triggered pinning control for a network of agents with nonlinear dynamics and time-varying topologies. Pinning control is a strategy to steer the behavior of a multi-agent system in a desired manner by controlling only a small fraction of the agents. We express the controllability of the network in terms of an average value of the network connectivity over time, and we show that all the agents can be driven to a desired reference trajectory. In the second part, we propose a control algorithm for multi-agent systems where inter-agent communication is substituted with a shared remote repository hosted on a cloud. Communication between each agent and the cloud is modelled as a sequence of events scheduled recursively by the agent. We quantify the connectivity of the network and we show that it is possible to synchronize the multi-agent system to the same state trajectory, while guaranteeing that two consecutive cloud accesses by the same agent are separated by a finite time interval. In the third part, we propose a family of distributed algorithms for coverage and inspection tasks for a network of mobile sensors with asymmetric footprints. We develop an abstract model of the environment under inspection and define a measure of the coverage attained by the sensor network. We show that the sensor network attains nondecreasing coverage, and we characterize the equilibrium configurations. The results presented in the thesis are corroborated by simulations or experiments.<br><p>QC 20160909</p>
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Books on the topic "Sensor-cloud"

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Kumar, Vimal, Amartya Sen, and Sanjay Madria. Secure Sensor Cloud. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-01527-4.

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Ghari Neiat, Azadeh, and Athman Bouguettaya. Crowdsourcing of Sensor Cloud Services. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91536-4.

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Wang, Guojun, Md Zakirul Alam Bhuiyan, Sabrina De Capitani di Vimercati, and Yizhi Ren, eds. Dependability in Sensor, Cloud, and Big Data Systems and Applications. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1304-6.

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Gerald, Schimak, Denzer Ralf, and SpringerLink (Online service), eds. Environmental Software Systems. Frameworks of eEnvironment: 9th IFIP WG 5.11 International Symposium, ISESS 2011, Brno, Czech Republic, June 27-29, 2011. Proceedings. IFIP International Federation for Information Processing, 2011.

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Sen, Amartya Kumar. Secure Sensor Cloud. Morgan & Claypool Publishers, 2018.

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Sen, Amartya Kumar. Secure Sensor Cloud. Morgan & Claypool Publishers, 2018.

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Sen, Amartya Kumar. Secure Sensor Cloud. Springer International Publishing AG, 2018.

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Sen, Amartya Kumar. Secure Sensor Cloud. Morgan & Claypool Publishers, 2018.

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Bouguettaya, Athman, and Azadeh Ghari Neiat. Crowdsourcing of Sensor Cloud Services. Springer, 2018.

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Bouguettaya, Athman, and Azadeh Ghari Neiat. Crowdsourcing of Sensor Cloud Services. Springer, 2019.

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Book chapters on the topic "Sensor-cloud"

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Kumar, Vimal, Amartya Sen, and Sanjay Madria. "Introduction." In Secure Sensor Cloud. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-01527-4_1.

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Kumar, Vimal, Amartya Sen, and Sanjay Madria. "Sensor Cloud Architecture and Implementation." In Secure Sensor Cloud. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-01527-4_3.

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Dash, Sanjit Kumar, Jyoti Prakash Sahoo, Subasish Mohapatra, and Sarada Prasanna Pati. "Sensor-Cloud: Assimilation of Wireless Sensor Network and the Cloud." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27299-8_48.

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Büchel, Gregor, Henning Budde, Maria Bunina, et al. "Testbed for the Sensor Cloud." In Trusted Cloud Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12718-7_14.

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Ghari Neiat, Azadeh, and Athman Bouguettaya. "Introduction." In Crowdsourcing of Sensor Cloud Services. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91536-4_1.

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Ghari Neiat, Azadeh, and Athman Bouguettaya. "Background." In Crowdsourcing of Sensor Cloud Services. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91536-4_2.

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Ghari Neiat, Azadeh, and Athman Bouguettaya. "Spatio-Temporal Linear Composition of Sensor Cloud Services." In Crowdsourcing of Sensor Cloud Services. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91536-4_3.

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Ghari Neiat, Azadeh, and Athman Bouguettaya. "Crowdsourced Coverage as a Service: Two-Level Composition of Sensor Cloud Services." In Crowdsourcing of Sensor Cloud Services. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91536-4_4.

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Ghari Neiat, Azadeh, and Athman Bouguettaya. "Incentive-Based Crowdsourcing of Hotspot Services." In Crowdsourcing of Sensor Cloud Services. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91536-4_5.

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Ghari Neiat, Azadeh, and Athman Bouguettaya. "Conclusion." In Crowdsourcing of Sensor Cloud Services. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91536-4_6.

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Conference papers on the topic "Sensor-cloud"

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Madria, Sanjay K. "Sensor Cloud: A Cloud of Sensor Networks." In 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2017. http://dx.doi.org/10.1109/icdcs.2017.276.

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Madria, Sanjay Kumar. "Sensor Cloud: A Cloud of Sensor Networks." In 2016 IEEE International Conference on Cloud Engineering Workshop (IC2EW). IEEE, 2016. http://dx.doi.org/10.1109/ic2ew.2016.55.

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Bhunia, Suman Sankar. "Sensor-Cloud." In MobiSys'15: The 13th Annual International Conference on Mobile Systems, Applications, and Services. ACM, 2015. http://dx.doi.org/10.1145/2752746.2752787.

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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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Dwivedi, Rajendra Kumar, and Rakesh Kumar. "Sensor Cloud: Integrating Wireless Sensor Networks with Cloud Computing." In 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). IEEE, 2018. http://dx.doi.org/10.1109/upcon.2018.8597008.

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Kaikkonen, Ville A., Eero O. Molkoselkä, Harri J. Juttula, and Anssi J. Mäkynen. "UAV Cloud Particle Sensor." In 2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2024. http://dx.doi.org/10.1109/i2mtc60896.2024.10560651.

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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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Bose, Sunanda, Atrayee Gupta, Sriyanjana Adhikary, and Nandini Mukherjee. "Towards a Sensor-Cloud Infrastructure with Sensor Virtualization." In the Second Workshop. ACM Press, 2015. http://dx.doi.org/10.1145/2757743.2757748.

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Bose, Sunanda, and Nandini Mukherjee. "SensIaas: A Sensor-Cloud Infrastructure with Sensor Virtualization." In 2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud). IEEE, 2016. http://dx.doi.org/10.1109/cscloud.2016.28.

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Todorov, Milen Hrabarov, Boyanka Marinova Nikolova, and Georgi Todorov Nikolov. "Cloud-managed ZigBee sensor networks." In 2016 XXV International Scientific Conference Electronics (ET). IEEE, 2016. http://dx.doi.org/10.1109/et.2016.7753518.

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Reports on the topic "Sensor-cloud"

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Jones, S. Field Validation of Cloud Properties Sensor Field Campaign Report. Office of Scientific and Technical Information (OSTI), 2024. https://doi.org/10.2172/2482466.

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Jones, Stephen. Field Validation of Cloud Properties Sensor – SAIL Field Campaign Report. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2274903.

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Jones, Stephen. Field Validation of Cloud Properties Sensor – SAIL Field Campaign Report. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2280540.

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Sassen, K. Cloud and aerosol characterization for the ARM central facility: Multiple remote sensor techniques development. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/6955485.

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Mishra, Aditi. Cloud infrastructure for multi-sensor remote data acquisition system for precision agriculture (CSR-DAQ). Iowa State University, 2020. http://dx.doi.org/10.31274/cc-20240624-369.

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Sassen, K. Cloud and aerosol characterization for the ARM central facility: Multiple remote sensor techniques development. Final technical report. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10105834.

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Sassen, K. Cloud and aerosol characterization for the ARM central facility: Multiple remote sensor techniques development. Technical progress report. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10183917.

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Ennasr, Osama, Michael Paquette, and Garry Glaspell. UGV SLAM payload for low-visibility environments. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47589.

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Herein, we explore using a low size, weight, power, and cost unmanned ground vehicle payload designed specifically for low-visibility environments. The proposed payload simultaneously localizes and maps in GPS-denied environments via waypoint navigation. This solution utilizes a diverse sensor payload that includes wheel encoders, inertial measurement unit, 3D lidar, 3D ultrasonic sensors, and thermal cameras. Furthermore, the resulting 3D point cloud was compared against a survey-grade lidar.
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Niple, N., J. Conant, S. Jones, H. Scott, and F. Lannarilli. Field Evaluation of Real-time Cloud OD Sensor TWST during the DOE ARM TCAP Campaign 2013 Final Campaign Report. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1242828.

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Niple, E., J. Conant, S. Jones, H. Scott, and F. Iannarilli. Field Evaluation of Real-time Cloud OD Sensor TWST during the DOE ARM TCAP Campaign 2013 Final Campaign Report. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1245973.

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