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Journal articles on the topic 'Sensor-cloud'

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Published: 4 June 2025

Last updated: 15 July 2025

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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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Zhu, Chunsheng, Xiuhua Li, Victor C. M. Leung, Laurence T. Yang, Edith C. H. Ngai, and Lei Shu. "Towards Pricing for Sensor-Cloud." IEEE Transactions on Cloud Computing 8, no. 4 (2020): 1018–29. http://dx.doi.org/10.1109/tcc.2017.2649525.

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Liu, Zhou-zhou, and Shi-ning Li. "Sensor-cloud data acquisition based on fog computation and adaptive block compressed sensing." International Journal of Distributed Sensor Networks 14, no. 9 (2018): 155014771880225. http://dx.doi.org/10.1177/1550147718802259.

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The emergence of sensor-cloud system has completely changed the one-to-one service mode of traditional wireless sensor networks, and it greatly expands the application field of wireless sensor networks. As the high delay of large-scale data processing tasks in sensor-cloud, a sensor-cloud data acquisition scheme based on fog computing and adaptive block compressive sensing is proposed. First, the sensor-cloud framework based on fog computing is constructed, and the fog computing layer includes many wireless mobile nodes, which helps to realize the implementation of information transfer management between lower wireless sensor networks layer and upper cloud computing layer. Second, in order to further reduce network traffic and improve data processing efficiency, an adaptive block compressed sensing data acquisition strategy is proposed in the lower wireless sensor networks layer. By dynamically adjusting the size of the network block and building block measurement matrix, the implementation of sensor compressed sensing data acquisition is achieved; in order to further balance the lower wireless sensor networks’ node energy consumption, reduce the time delay of data processing task in fog computing layer, the mobile node data acquisition path planning strategy and multi-mobile nodes collaborative computing system are proposed. Through the introduction of the fitness value constraint transformation processing technique and parallel discrete elastic collision optimization algorithm, the efficient processing of the fog computing layer data is realized. Finally, the simulation results show that the sensor-cloud data acquisition scheme can effectively achieve large-scale sensor data efficient processing. Moreover, compared with cloud computing, the network traffic is reduced by 20% and network task delay is reduced by 12.8%–20.1%.

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Trisnawan, Primantara Hari, Fariz Andri Bakhtiar, and Eko Sakti Pramukantoro. "Developing Actor-Based Middleware as Collector System for Sensor Data in Internet of Things (IoT)." Journal of Information Technology and Computer Science 5, no. 1 (2020): 1. http://dx.doi.org/10.25126/jitecs.202051101.

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The use of Internet of Things (IoT) plays an important role in supporting wireless communication for middleware in collecting data sensors. An actor-based middleware is designed to bridge protocol differences between cloud and sensor nodes. This middleware also acts as an initiator in accessing data from several sensor nodes, and then sending data that has been collected to the cloud. Incorporating the differences of communication protocols and data formats between sensor nodes and cloud is the responsibility of middleware. This Middleware acts as an actor by acting proactively accessing data from each sensor node, so that it can facilitate the completion of sending data from the sensor node to the middleware by avoiding from "signal collisions” among sensor nodes. After the data is collected in the middleware, the data is sent to the cloud using the Websocket or HTTP protocol above the TCP / IP protocol. The performance of the system is evaluated based on the success of the middleware bridging communication between sensor nodes and the cloud, as well as the readability of IoT data sensors that have been adjusted by cloud. The test results show that built-in middleware can bridge protocols between cloud and sensor nodes. In addition, the Websocket usage protocol produces a lower delay value than the MQTT and CoAP protocols.

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Das, Kalyan, and Satyabrata Das. "Energy-Efficient Cloud-Integrated Sensor Network Model Based on Data Forecasting Through ARIMA." International Journal of e-Collaboration 18, no. 1 (2022): 1–17. http://dx.doi.org/10.4018/ijec.290292.

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An energy-efficient Model for Sensor-cloud is proposed based on data forecasting through an autoregressive integrated moving average (ARIMA). Generally, all the user requests are redirected to the wireless sensor network (WSN) through the cloud. In the traditional approach, user requests are generated every fifteen minutes, so the sensor must send data to the cloud every fifteen minutes. In the current approach, the sensors within the WSN communicate with the cloud every two hours. The data forecasting technique addresses most of the user requests using the ARIMA one-step ahead forecasting model in the cloud. This results in less frequency of data communication, thereby increasing the battery life of the sensor. The ARIMA-based forecasting model provides better accuracy because of fewer temperature data changes with respect to the current temperature, for the next two hours. The proposed method for the simulation in the sensor cloud system consumes significantly less energy than the traditional approach, and the error in forecasting becomes highly negligible.

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Tsvetanov, Filip, and Martin Pandurski. "Efficiency of integration between sensor networks and clouds." International journal of electrical and computer engineering systems 13, no. 6 (2022): 427–33. http://dx.doi.org/10.32985/ijeces.13.6.2.

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Numerous wireless sensor networks (WSN) applications include monitoring and controlling various conditions in the environment, industry, healthcare, medicine, military affairs, agriculture, etc. The life of sensor nodes largely depends on the power supply type, communication ability, energy storage capacity and energy management mechanisms. The collection and transmission of sensor data streams from sensor nodes lead to the depletion of their energy. At the same time, the storage and processing of this data require significant hardware resources. Integration between clouds and sensor networks is an ideal solution to the limited computing power of sensor networks, data storage and processing. One of the main challenges facing systems engineers is to choose the appropriate protocol for integrating sensor data into the cloud structure, taking into account specific system requirements. This paper presents an experimental study on the effectiveness of integration between sensor networks and the cloud, implemented through three protocols HTTP, MQTT and MQTT-SN. A model for studying the integration of sensor network - Cloud with the communication models for integration - request-response and publish- subscribe, implemented with HTTP, MQTT and MQTT-SN. The influence of the number of transmitted data packets from physical sensors to the cloud on the transmitted data delay to the cloud, the CPU and memory load was studied. After evaluating the results of sensor network and cloud integration experiments, the MQTT protocol is the most efficient in terms of data rate and power consumption.

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Dave, Devangini, Swapnil Parikh, and Pratik Patel. "Power Reduction Sleep Scheduling Technique for Cloud Integrated Green Social Sensor Network." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 01–07. http://dx.doi.org/10.17762/ijritcc.v11i10.8414.

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The wireless sensor network is the maximum appropriate technology nowadays with such awesome applications and areas including Infrastructure tracking, environment tracking, health care tracking, etc. Cloud Computing has fantastic data collecting skills and effective data processing ability. Social Network is a group of People or organizations of human beings with similar intentions. Social Sensor Cloud is one type of expertise-sharing mechanism wherein similar types of human beings can connect. Energy Consumption is nowadays the largest challenge as far as the concern with green environment. Because the battery life of the sensor is so limited, the Social Sensor Cloud must be energy efficient. As a result, this article will concentrate on Energy-Efficient Techniques for the Social Sensor Cloud. According to our findings, findings, the majority of energy-saving measures will cope with not unusual place Parameters including Network Lifetime, Network Work rate, Throughput, Energy, Bandwidth, etc. We will Summarize current Technology and we Will Provide Our Architecture for Energy Reduction in Social Sensor Cloud.

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Wind, G., A. M. da Silva, P. M. Norris, and S. Platnick. "Equivalent sensor radiance generation and remote sensing from model parameters – Part 1: Equivalent sensor radiance formulation." Geoscientific Model Development Discussions 6, no. 3 (2013): 4105–36. http://dx.doi.org/10.5194/gmdd-6-4105-2013.

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Abstract. In this paper we describe a general procedure for calculating equivalent sensor radiances from variables output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint the algorithm takes explicit account of the model subgrid variability, in particular its description of the probability density function of total water (vapor and cloud condensate). The equivalent sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies. We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products). We focus on clouds and cloud/aerosol interactions, because they are very important to model development and improvement.

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Das, Kalyan, Satyabrata Das, Rabi Kumar Darji, and Ananya Mishra. "Survey of Energy-Efficient Techniques for the Cloud-Integrated Sensor Network." Journal of Sensors 2018 (2018): 1–17. http://dx.doi.org/10.1155/2018/1597089.

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The sensor cloud is a combination of cloud computing with a wireless sensor network (WSN) which provides an easy to scale and efficient computing infrastructure for real-time application. A sensor cloud should be energy efficient as the life of the battery in the sensor is limited and there is a huge consumption of energy in the data centre in running the servers to provide storage. In this paper, we have classified energy-efficient techniques for sensor cloud into different categories and analyzed each technology by using various parameters. Usage percentage of each parameter for every technology is calculated and for all technologies on average is also calculated. From our analysis, we found that most of the energy-efficient techniques ignore quality of service (QoS) parameters, scalability, and network lifetime. Multiparameter optimization including other QoS parameters along with energy may be the future direction of research. Our study will be helpful for researchers to get information regarding current methods used for an energy-efficient sensor cloud and also to build advanced systems in the future.

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Wang, Guang, Ben Kurtz, and Jan Kleissl. "Cloud base height from sky imager and cloud speed sensor." Solar Energy 131 (June 2016): 208–21. http://dx.doi.org/10.1016/j.solener.2016.02.027.

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Wang, Tian, Yucheng Lu, Zhihan Cao, et al. "When Sensor-Cloud Meets Mobile Edge Computing." Sensors 19, no. 23 (2019): 5324. http://dx.doi.org/10.3390/s19235324.

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Sensor-clouds are a combination of wireless sensor networks (WSNs) and cloud computing. The emergence of sensor-clouds has greatly enhanced the computing power and storage capacity of traditional WSNs via exploiting the advantages of cloud computing in resource utilization. However, there are still many problems to be solved in sensor-clouds, such as the limitations of WSNs in terms of communication and energy, the high latency, and the security and privacy issues due to applying a cloud platform as the data processing and control center. In recent years, mobile edge computing has received increasing attention from industry and academia. The core of mobile edge computing is to migrate some or all of the computing tasks of the original cloud computing center to the vicinity of the data source, which gives mobile edge computing great potential in solving the shortcomings of sensor-clouds. In this paper, the latest research status of sensor-clouds is briefly analyzed and the characteristics of the existing sensor-clouds are summarized. After that we discuss the issues of sensor-clouds and propose some applications, especially a trust evaluation mechanism and trustworthy data collection which use mobile edge computing to solve the problems in sensor-clouds. Finally, we discuss research challenges and future research directions in leveraging mobile edge computing for sensor-clouds.

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Verma, Jyotsna. "Enabling Internet of Things through Sensor Cloud: A Review." Scalable Computing: Practice and Experience 22, no. 4 (2021): 445–62. http://dx.doi.org/10.12694/scpe.v22i4.1878.

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With the inception of the Internet of Things (IoT), wireless technology found a new outlook where the physical objects can interact with each other and can sense the environment. The IoT has found its way in the real world and has connected billions of devices throughout the world. However, its limitations, such as limited processing capability, storage capability, security and privacy issues, and energy constraints prevent the IoT system to be properly utilized by the real-world applications. Hence, the integration of IoT with various emerging technologies like big data, software defined networks, machine learning, fog computing, sensor cloud, etc., will make the IoT system a more powerful technology. The sensor cloud provides an open, secure, flexible, large storage and a computational capable infrastructure which makes the ensemble architecture of IoT and sensor cloud more efficient. An extensive review of the IoT system enabled sensor cloud is presented in the paper, and with this context, the paper attempts to summarize the sensor cloud infrastructure along with its challenges. In addition, the paper presents the possible integrated architecture of the IoT and the sensor cloud which enables the network to be properly utilized. Further, the importance of integrating these two promising technologies and research challenges associated with it is also identified. Finally, the paper analyses and discusses the motivation behind the ensemble system along with future research direction.

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Yoddumnern, Anekwong, Roungsan Chaisricharoen, and Thongchai Yooyativong. "Cloud Based WiFi Multi-Sensor Network." International Journal of Online Engineering (iJOE) 14, no. 08 (2018): 35. http://dx.doi.org/10.3991/ijoe.v14i08.8536.

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A WiFi technology was the basis of the Internet of Things (IoT) and many popularity of the wireless communication in the social network. A smart device used any kinds of the detectors all of the analog and digital sensor. This study simulates on the scope of the home security system (HSS). There used three types of sensor; a temperature sensor, smoke-CO, and PIR sensor. This study aims to design a multi-sensor node. All of the sensors are connected on a microcontroller unit (MCU) with the general purpose input output (GPIO). After the connection, there got invalid multi-sensor data. This experiment tried to run over ten times. There appeared some invalid when the processor startup. First, the temperature sensor did not work. Second, the smoke-CO sensors read an invalid value there were higher than the actual. This problem can solve the situation by the sensor calibration methodology—to set the calibration time with the dynamic time follow up on the GPIO function of each sensor and self-calibrate by the finite impulse response (FIR) filter in the part of setup portion. When the system was running for a long time this should take the invalid data. There were high and low from the actual and there got the difference value suddenly a swinging value. During the system was running there had some noise and the heat collected on the device. There got the invalid value. This error is solved by the Full Scale Kalman Filter (FSKF) to fill and estimate the right value. Next, there used the OFF-Mode to save the power consumption and do not send sensor data to the Cloud all time. This method helps the device will be run as long time and work in long life. Finally, there got a high-performance WiFi multi-sensor network.

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Sangulagi, Prashant, Ashok V. Sutagundar, and Tabrunisa Abdul Rashid. "Network Lifetime Optimization in Sensor Cloud." International Journal of Advanced Networking Applications 11, no. 02 (2019): 4198–204. http://dx.doi.org/10.35444/ijana.2019.11022.

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SuryaBharat, K., and A. Naga Priyanka. "Sensor Information Management using Cloud Computing." International Journal of Computer Applications 103, no. 14 (2014): 7–13. http://dx.doi.org/10.5120/18140-9330.

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Maddux, B. C., S. A. Ackerman, and S. Platnick. "Viewing Geometry Dependencies in MODIS Cloud Products." Journal of Atmospheric and Oceanic Technology 27, no. 9 (2010): 1519–28. http://dx.doi.org/10.1175/2010jtecha1432.1.

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Abstract Characterizing the earth’s global cloud field is important for the proper assessment of the global radiation budget and hydrologic cycle. This characterization can only be achieved with satellite measurements. For complete daily coverage across the globe, polar-orbiting satellites must take observations over a wide range of sensor zenith angles. This paper uses Moderate Resolution Imaging Spectroradiometer (MODIS) Level-3 data to determine the effect that sensor zenith angle has on global cloud properties including the cloud fraction, cloud-top pressure, effective radii, and optical thickness. For example, the MODIS cloud amount increases from 57% to 71% between nadir and edge-of-scan (∼67°) observations, for clouds observed between 35°N and 35°S latitude. These increases are due to a combination of factors, including larger pixel size and longer observation pathlength at more oblique sensor zenith angles. The differences caused by sensor zenith angle bias in cloud properties are not readily apparent in monthly mean regional or global maps because the averaging of multiple satellite overpasses together “washes out” the zenith angle artifact. Furthermore, these differences are not constant globally and are dependent on the cloud type being observed.

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Wind, G., A. M. da Silva, P. M. Norris, and S. Platnick. "Multi-sensor cloud retrieval simulator and remote sensing from model parameters – Part 1: Synthetic sensor radiance formulation." Geoscientific Model Development 6, no. 6 (2013): 2049–62. http://dx.doi.org/10.5194/gmd-6-2049-2013.

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Abstract. In this paper we describe a general procedure for calculating synthetic sensor radiances from variable output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint, the algorithm takes explicit account of the model subgrid variability, in particular its description of the probability density function of total water (vapor and cloud condensate.) The simulated sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies. We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products). We focus on clouds because they are very important to model development and improvement.

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M, ArulMozhi Pradeepa, and Gomathi B. "Towards Fog Computing based Cloud Sensor Integration for Internet of Things." International Journal of Computer Science and Engineering Communications 5, no. 6 (2017): 1761–73. https://doi.org/10.5281/zenodo.1155772.

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The Internet of Things (IoT) interconnects various identifiable devices within Internet for sensing and monitoring processes. In particular, Wireless Sensor Network (WSN) is formed by connecting identifiable devices like smart sensor, embedded CPU (Central Processing Unit), low power radios, to the internet through gateway that interfaces WSN to the internet. To handle the large amount of data generated by devices in IOT environment, cloud infrastructure provides Sensing as a Service(SeaaS) which can make sensor data available in cloud infrastructure for sensing and observing the environment conditions. Today’s cloud models are not designed for the volume, variety, and velocity of data that the IoT generates. Handling the volume, variety, and velocity of IoT data requires a new computing model. In this paper, we surveyed some typical applications of Sensor Network using Cloud computing as backbone spotlighting on fog computing to overcome some of the management issues of cloud computing and to handle time-sensitive data. Since Cloud computing provides plenty of application, platforms and infrastructure over the Internet, it may combined with Sensor network and fog computing in the application areas such as environmental monitoring, weather forecasting, transportation business, healthcare, military application etc which requires to handle within a second.

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Kinoshita, Tetsuo, Yujin Lim, and Gianluigi Ferrari. "Advanced Applications of Wireless Sensor Network Using Sensor Cloud Infrastructure." International Journal of Distributed Sensor Networks 10, no. 4 (2014): 652862. http://dx.doi.org/10.1155/2014/652862.

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Misra, Sudip, Anuj Singh, Subarna Chatterjee, and Amit Kumar Mandal. "QoS-aware sensor allocation for target tracking in sensor-cloud." Ad Hoc Networks 33 (October 2015): 140–53. http://dx.doi.org/10.1016/j.adhoc.2015.04.009.

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Wang, Yonggang, and Bart Geerts. "Estimating the Evaporative Cooling Bias of an Airborne Reverse Flow Thermometer." Journal of Atmospheric and Oceanic Technology 26, no. 1 (2009): 3–21. http://dx.doi.org/10.1175/2008jtecha1127.1.

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Abstract Airborne reverse flow immersion thermometers were designed to prevent sensor wetting in cloud. Yet there is strong evidence that some wetting does occur and therefore also sensor evaporative cooling as the aircraft exits a cloud. Numerous penetrations of cumulus clouds in a broad range of environmental and cloud conditions are used to estimate the resulting negative temperature bias. This cloud exit “cold spike” can be found in all cumulus clouds, even at subfreezing temperatures, both in continental and maritime cumuli. The magnitude of the spike correlates most strongly with the dryness of the ambient air. A temperature correction based on this relationship is proposed. More important than the cloud exit cold spike, from a cumulus dynamics perspective, is the negative bias within cloud. Such bias is expected, due to evaporative cooling as well. Evaporation from the wetted sensor in cloud is surmised because air decelerates into the thermometer housing, and thus is heated and becomes subsaturated. Thus an in-cloud temperature correction is proposed, based on the composite cloud exit evaporative cooling behavior. This correction leads to higher and more realistic estimates of cumulus buoyancy and lower estimates of entrainment.

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Sato, Kaori, Hajime Okamoto, Shuichiro Katagiri, Masataka Shiobara, Masanori Yabuki, and Toshiaki Takano. "Active sensor synergy for arctic cloud microphysics." EPJ Web of Conferences 176 (2018): 08004. http://dx.doi.org/10.1051/epjconf/201817608004.

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In this study, we focus on the retrieval of liquid and ice-phase cloud microphysics from spaceborne and ground-based lidar-cloud radar synergy. As an application of the cloud retrieval algorithm developed for the EarthCARE satellite mission (JAXA-ESA) [1], the derived statistics of cloud microphysical properties in high latitudes and their relation to the Arctic climate are investigated.

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Menzel, W. Paul, Richard A. Frey, Eva E. Borbas, Bryan A. Baum, Geoff Cureton, and Nick Bearson. "Reprocessing of HIRS Satellite Measurements from 1980 to 2015: Development toward a Consistent Decadal Cloud Record." Journal of Applied Meteorology and Climatology 55, no. 11 (2016): 2397–410. http://dx.doi.org/10.1175/jamc-d-16-0129.1.

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AbstractThis paper presents the cloud-parameter data records derived from High Resolution Infrared Radiation Sounder (HIRS) measurements from 1980 through 2015 on the NOAA and MetOp polar-orbiting platforms. Over this time period, the HIRS sensor has been flown on 16 satellites from TIROS-N through NOAA-19 and MetOp-A and MetOp-B, forming a 35-yr cloud data record. Intercalibration of the Infrared Advanced Sounding Interferometer (IASI) and HIRS on MetOp-A has created confidence in the onboard calibration of this HIRS as a reference for others. A recent effort to improve the understanding of IR-channel response functions of earlier HIRS sensor radiance measurements using simultaneous nadir overpasses has produced a more consistent sensor-to-sensor calibration record. Incorporation of a cloud mask from the higher-spatial-resolution Advanced Very High Resolution Radiometer (AVHRR) improves the subpixel cloud detection within the HIRS measurements. Cloud-top pressure and effective emissivity (εf, or cloud emissivity multiplied by cloud fraction) are derived using the 15-μm spectral bands in the carbon dioxide (CO2) absorption band and implementing the CO2-slicing technique; the approach is robust for high semitransparent clouds but weak for low clouds with little thermal contrast from clear-sky radiances. This paper documents the effort to incorporate the recalibration of the HIRS sensors, notes the improvements to the cloud algorithm, and presents the HIRS cloud data record from 1980 to 2015. The reprocessed HIRS cloud data record reports clouds in 76.5% of the observations, and 36.1% of the observations find high clouds.

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Milana N., Megha, and M. Z. Kurian. "IoT Based Sensor Network for Agricultural Application." International Journal of Advance Research and Innovation 4, no. 2 (2016): 78–84. http://dx.doi.org/10.51976/ijari.421612.

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A conventional sensor network is a radio network of sensor nodes with ability to sense physical parameters, store sensed data, carry out simple processing on data and forward the data through radio interface. The objective of such network is to push the data to a sink node which can then forward the data to server ( or cloud). However many real time applications includes sensors spread over long areas. As such they are treated as independent networks. Internet of Things is a new paradigm of connecting devices like microcontroller and smart objects to cloud. Using IoT services, we can now connect sensors to internet directly. In the proposed work more comprehensive state of art cloud extension of WSN through IoT has been focused, more focus on being towards bettering each of the current state of art building blocks including but not limited to sensor network, coordinator protocol, data analysis in sensor network, cloud services, IoT protocols and so on.

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Srirampavan, J. "Smart Secured Real Time Agriculture Monitoring System." International Journal of Engineering & Technology 7, no. 3.6 (2018): 281. http://dx.doi.org/10.14419/ijet.v7i3.6.15043.

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Embedded systems in Agriculture play a vital role in unifying the work involved and improve conservations. Designing a smart as well as a cost efficient and more user-friendly system will be idealistic challenge. The following system that has been proposed is designed with those ideal constraints in mind. It consists of a Raspberry pi3 as a gateway that links the sensor networks with the cloud. To improve security an MQTT protocol is used for cloud connectivity. The communication between the sensor networks is managed by NRF24L01. The Sensor network is a separate entity that can used like a plug and play device and is built by a micro controller with a LCD display and an interfaced GPS. Multicasting is also possible between sensor networks and the gateway. The processed data from the sensor networks is sent through NRF24L01 to the gateway. The gateway further processes and encapsulates the data and through MQTT the data gets stored on the cloud. This cloud data can be accessed through computer or mobile device

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Liu, Shuang, and Zhong Zhang. "Adaptive Graph Cut Based Cloud Detection in Wireless Sensor Networks." International Journal of Distributed Sensor Networks 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/947169.

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We focus on the issue of cloud detection in wireless sensor networks (WSN) and propose a novel detection algorithm named adaptive graph cut (AGC) to tackle this issue. We first automatically label some pixels as “cloud” or “clear sky” with high confidence. Then, those labelled pixels serve as hard constraint seeds for the following graph cut algorithm. In addition, a novel transfer learning algorithm is proposed to transfer knowledge among sensor nodes, such that cloud images captured from different sensor nodes can adapt to different weather conditions. The experimental results show that the proposed algorithm not only achieves better results than other state-of-the-art cloud detection algorithms in WSN, but also achieves comparable results compared with the interactive segmentation algorithm.

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Itani, Wassim, Ayman Kayssi, and Ali Chehab. "Wireless Body Sensor Networks." International Journal of Reliable and Quality E-Healthcare 5, no. 2 (2016): 1–30. http://dx.doi.org/10.4018/ijrqeh.2016040101.

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In this paper, the authors provide a detailed overview and technical discussion and analysis of the latest research trends in securing body sensor networks. The core of this work aims at: (1) identifying the resource limitations and energy challenges of this category of wireless sensor networks, (2) considering the life-critical applications and emergency contexts that are encompassed by body sensor network services, and (3) studying the effect of these peculiarities on the design and implementation of rigorous and efficient security algorithms and protocols. The survey discusses the main advancements in the design of body sensor network cryptographic services (key generation and management, authentication, confidentiality, integrity, and privacy) and sheds the light on the prominent developments achieved in the field of securing body sensor network data in Cloud computing architectures. The elastic virtualization mechanisms employed in the Cloud, as well as the lucrative computing and storage resources available, makes the integration of body sensor network applications, and Cloud platforms a natural choice that is packed with various security and privacy challenges. The work presented in this paper focuses on Cloud privacy and integrity mechanisms that rely on tamper-proof hardware and energy-efficient cryptographic data structures that are proving to be well-suited for operation in untrusted Cloud environments. This paper also examines two crucial design patterns that lie at the crux of any successful body sensor network deployment which are represented in: (1) attaining the right balance between the degree, complexity, span, and strength of the cryptographic operations employed and the energy resources they consume. (2) Achieving a feasible tradeoff between the privacy of the human subject wearing the body sensor network and the safety of this subject. This is done by a careful analysis of the medical status of the subject and other context-related information to control the degree of disclosure of sensitive medical data. The paper concludes by presenting a practical overview of the cryptographic support in the main body sensor network development frameworks such and TinyOS and SPINE and introduces a set of generalized guideline patterns and recommendations for designing and implementing cryptographic protocols in body sensor network environments.

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Singh, Rajesh, Anita Gehlot, Mamoon Rashid, et al. "Cloud Server and Internet of Things Assisted System for Stress Monitoring." Electronics 10, no. 24 (2021): 3133. http://dx.doi.org/10.3390/electronics10243133.

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Currently, the Internet of Things (IoT) has gained attention for its capability for real-time monitoring. The advancement in sensor and wireless communication technology has led to the widespread adoption of IoT technology in distinct applications. The cloud server, in conjunction with the IoT, enables the visualization and analysis of real-time sensor data. The literature concludes that there is a lack of remote stress-monitoring devices available to assist doctors in observing the real-time stress status of patients in the hospital and in rehabilitation centers. To overcome this problem, we have proposed the use of the IoT and cloud-enabled stress devices to detect stress in a real-time environment. The IoT-enabled stress device establishes piconet communication with the master node to allow visualization of the sensory data on the cloud server. The threshold value (volt) for real-time stress detection by the stress device is identified by experimental analysis using MATLAB based on the results obtained from the performance of three different physical-stress generating tasks. In addition, the stress device is interfaced with the cloud server, and the sensor data are recorded on the cloud server. The sensor data logged into the cloud server can be utilized for future analysis.

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Hidayat, Andreas Setiawan, and Resmana Lim. "Aplikasi Mobile Untuk Pemantauan Simulasi Ketinggian Permukaan Air Berbasis Internet of Things (IoT)." Jurnal Teknik Elektro 12, no. 2 (2020): 69–76. http://dx.doi.org/10.9744/jte.12.2.69-76.

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Pada paper ini, dibuat sistem pemantauan ketinggian permukaan air berbasis Internet of Things. Sistem terdiri dari aplikasi mobile, Raspberry Pi, sensor ultrasonik, Pi Camera, dan Cloud. Diharapkan sistem dapat membantu untuk antisipasi banjir. Aplikasi mobile berfungsi sebagai antarmuka pengguna dengan sistem. Raspberry Pi dan sensor ultrasonik berfungsi untuk mengambil dan mengelola data ketinggian permukaan air. Pi Camera berfungsi untuk melihat keadaan perairan secara langsung. Cloud berfungsi untuk menyimpan data ketinggian permukaan air. Dari pengujian, didapatkan bahwa aplikasi mobile dapat melakukan komunikasi dengan Cloud untuk mengambil dan mengirim data. Raspberry Pi dapat melakukan komunikasi dengan Cloud untuk mengirim data. Pi Camera dapat diakses oleh aplikasi. Pembacaan dengan sensor ultrasonik menggunakan 3 filter: tanpa filter, filter median, moving average filter. Pengujian menunjukkan hasil pembacaan terbaik dihasilkan filter median. Ratarata kesalahan pembacaan ketinggian permukaan air menggunakan sensor ultrasonik dengan filter median dibandingkan dengan pembacaan menggunakan meter ukur tanpa gangguan sebesar 6,2%, dan dengan gangguan sebesar 6,3%.

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Alexan, Alexandru, Anca Alexan, Oniga Ștefan, and Alin Tisan. "SoC as IoT sensor network hub." Carpathian Journal of Electronic and Computer Engineering 12, no. 1 (2019): 42–45. http://dx.doi.org/10.2478/cjece-2019-0008.

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Abstract Nowadays SoC’s miniaturization provide smaller yet more powerful devices that are perfect to be used as local hubs for small to medium sensor networks. Although sensors can now be easily connected directly to the cloud, a hub can simplify the process of bringing sensor to the IoT cloud. One of the most popular SoC board, Raspberry PI, is perfect for the hub role due to its small form factor, price, processing power and connectivity. Our proposed system consists in a SoC based low cost raspberry pi hub that connects two Bluetooth sensortag CC2650 modules to a mongoDB cloud database.

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Kiruthikaa R, Ms. "Privacy and Security of IOT Devices for Monitoring Vulnerable People." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49036.

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ABSTRACT This project presents a real-time IoT-based health monitoring system developed using the ESP32 Uno microcontroller. The system integrates multiple sensors—including a DHT11 sensor (for temperature and humidity), a pulse sensor (for heart rate), and an MQ-3 alcohol sensor—to monitor essential health parameters. Sensor data is displayed locally on an LCD screen and transmitted remotely via a GSM module. To provide seamless data visualization and access, the system uses cloud platforms such as ThingSpeak and Blynk for real-time monitoring through mobile and web interfaces. A Java-based website has also been developed to allow users to securely view their health data, with a user-friendly dashboard for live updates. Additionally, all health reports are stored in the AWS cloud, enabling long-term data storage, report generation, and remote access for healthcare providers. This system offers a scalable and accessible solution for continuous health monitoring, suitable for both personal and clinical use. By combining IoT, cloud services, and Java web development, it ensures real-time feedback, remote monitoring, and early detection of health anomalies. This ESP32-powered system is a low-cost, scalable, and efficient solution for personal and clinical health monitoring, ensuring early detection of anomalies and continuous patient care. Keywords— IoT-based Health Monitoring,ESP32 Uno,DHT11 Sensor,Pulse Sensor,MQ-3 Alcohol Sensor,GSM Module,ThingSpeak,Blynk,Java Web Dashboard,AWS Cloud Storage

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Arshad, Hashmi, Tabrez Nafis Md, and Rahman Nafisur. "A Descriptive Study on Wireless Sensor Networks (WSNs) using Cloud Computing (CC)." Recent Innovations in Wireless Network Security 6, no. 1 (2023): 6–14. https://doi.org/10.5281/zenodo.10147676.

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This paper presents a descriptive study on Wireless Sensor Networks (WSNs) using Cloud Computing (CC). WSNs are widely used in various applications, including environmental monitoring, industrial automation, and healthcare. However, WSNs face several challenges, such as limited storage capacity, processing power, and energy constraints. Cloud Computing (CC) provides a viable solution to overcome these challenges by providing a scalable, cost-effective, and on-demand computing platform for WSNs.The paper examines the benefits and challenges of using Cloud Computing (CC) in WSNs. Moreover, the study analyzes the current trends and future directions of cloud-based WSNs, including the use of edge computing, machine learning, and artificial intelligence. The paper also discusses the security and privacy concerns associated with cloud-based WSNs and examines the different security solutions and best practices to ensure the security and privacy of WSNs.Overall, this descriptive study provides valuable insights into the integration of Cloud Computing (CC) with Wireless Sensor Networks (WSNs) and highlights the potential of cloud-based WSNs to transform various industries and domains. The study serves as a useful resource for researchers, practitioners, and organizations interested in leveraging the power of Cloud Computing (CC) for Wireless Sensor Networks (WSNs).

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SAI, Oleksandr, and Kyrylo KRASNIKOV. "FUZZY MODEL OF ELECTRICITY CONTROL WITH WIRELESS INFORMATION PROCESSED ON GPU." Computer systems and information technologies, no. 3 (September 26, 2024): 44–50. http://dx.doi.org/10.31891/csit-2024-3-6.

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Methods of processing information transmitted through wireless networks with software development are investigated in the work. Innovative methods of data transmission such as optical technologies, quantum data transmission and wireless data transmission technologies are disclosed. It is noted that in the modern understanding, the concept of distributed computing defines the process of convergence (convergence) of distributed processing methods, such as GRID, cloud and fog computing, with the combination of virtual cluster systems (grid clusters, cloud clusters and fog clusters) into a single information communication and computing system . It is emphasized that, unlike cellular modems, ZigBee technology nodes have microcontrollers with a pre-installed operating system and flash memory, which allows solving simple computational tasks in real time before sending data. It is advisable to solve such tasks within the framework of a multi-agent approach, which will increase the efficiency of the use of sensor nodes and the entire sensor network. The advantages of the multi-agent technology of fog computing based on sensor nodes of the wireless network of the ZigBee standard are revealed. The method of multi-agent processing of sensory information and its main components are described. The architecture of the system of distributed sensor data processing is outlined, which includes 4 hardware and software levels: Terminal sensor nodes and controllers of measuring devices and automation devices that implement fuzzy calculations; Coordinators, sensor segment routers and cellular modems that collect, protect and transmit sensor data to the processing center; A data processing center that includes a cluster of servers for GRID calculations and a cloud data storage server; Client devices to access cloud storage, computing cluster servers, and distributed fog computing terminals. It is emphasized that indicators and forecast results can be stored on distributed sensor nodes or transmitted for accumulation in cloud storage for further extraction and intelligent processing in the GRID cluster of the data center.

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Maity, Koustab. "An Application of IoT to revolutionize Agro Sector." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (2022): 31–38. http://dx.doi.org/10.22214/ijraset.2022.39709.

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Abstract: In this paper, an IoT based automated water irrigation system is proposed. This system is used to increase the production rate of agriculture based on the internet of things (IoT) and cloud computing. Sensor technology has been developed and various kinds of sensor such as humidity, temperature, soil moisture sensor, and pH sensors are used to collect information about the condition of the soil. By using the advanced technologies, the farmers get benefitted for better production in agriculture. Keywords: Sprinkling, Smart Sensor Pouch, MSP 430, RS 485 Port, IoT, Cloud Computing

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Sai Prasad, Dogga Shyam, Aaditya Taluja, and Vikas Sharma. "Feedback System using ESP8266." MR International Journal of Engineering and Technology 10, no. 1 (2023): 8–11. http://dx.doi.org/10.58864/mrijet.2023.10.1.2.

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The Internet of Things (IoT) has brought about a revolution in the field of technology by connecting billions of devices every day to the internet and allowing them to communicate with each other facilitating remote data collection and analysis. In this paper, we present a system that collects feedback from a touch sensor, sends the data to the cloud, and displays it on a mobile phone in real-time. The system consists of an ESP8266 microcontroller, a touch sensor, and a cloud server. The touch sensor is used to gather user input, which is then transmitted to the cloud server through the ESP8266 microcontroller. The cloud server processes the data and makes it accessible to the user through a mobile application. The system has been tested and found to be reliable, fast, and easy to use.

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Eko, Sakti Pramukantoro, Luckies Maxi, and Andri Bakhtiar Fariz. "Bridging IoT infrastructure and cloud application using cellular-based internet gateway device." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 3 (2019): 1439–46. https://doi.org/10.12928/TELKOMNIKA.v17i3.12229.

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An Internet of Things (IoT) middleware can solve interoperability problem among “things” in IoT infrastructure by collecting data. However, the sensor nodes’ data that is collected by the middleware cannot be directly delivered to cloud applications since the sensor nodes and the middleware are located in intranet. A solution to this problem is an Internet Gateway Device (IGD) that retrieves data from the middleware in intranet then forwards them to cloud applications in the internet. In this study, an IGD based on cellular network is proposed to provide wide-coverage internet connectivity. Two test scenarios were conducted to measure delay and throughput between the IGD and the cloud application; using data from DHT22 sensor and image sensor respectively. The results of the first test scenario using DHT22 sensor show that the average delay is under 5 seconds and the maximum throughput is 120 bps, while the second one using image sensor concludes that the average delay is 595 seconds and the maximum throughput is 909 bps.

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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 (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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Sivakumar, Sivaramakrishnan, and Adnan Al-Anbuky. "Dense Clustered Multi-Channel Wireless Sensor Cloud." Journal of Sensor and Actuator Networks 4, no. 3 (2015): 208–25. http://dx.doi.org/10.3390/jsan4030208.

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Nicoll, K. A., and R. G. Harrison. "A lightweight balloon-carried cloud charge sensor." Review of Scientific Instruments 80, no. 1 (2009): 014501. http://dx.doi.org/10.1063/1.3065090.

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Corbellini, S., E. Di Francia, S. Grassini, L. Iannucci, L. Lombardo, and M. Parvis. "Cloud based sensor network for environmental monitoring." Measurement 118 (March 2018): 354–61. http://dx.doi.org/10.1016/j.measurement.2017.09.049.

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De, Debashis, Anwesha Mukherjee, Anindita Ray, Deepsubhra Guha Roy, and Suchismita Mukherjee. "Architecture of green sensor mobile cloud computing." IET Wireless Sensor Systems 6, no. 4 (2016): 109–20. http://dx.doi.org/10.1049/iet-wss.2015.0050.

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