Journal articles: 'Sensor Management'

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

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As many sensor networks have been deployed in industry monitoring area, the focus on sensor data quality has also increased. Sensor networks provide us with process details which we can utilize to help making decisions on process monitoring.In order to make meaningful decisions, the quality of the data produced by sensors must be validated. As we evaluate the status of a specific sensor, we may also regard the status of the related sensors. If a sensor’s data show some abnormal, but the sensors related to it didn’t, we may have much more confidence to believe that the sensor is malfunction. In our early study, the sensors grouping strategy is manual. In this paper, we proposed a sensor grouping algorithm, which combines both PCA decouple method and the K-means cluster method. Finally, a test has been made with real data from an oilfield.

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Palmer, Allan G. "Impact of Innovative Pulse Oximeter Sensor Management Strategy." Biomedical Instrumentation & Technology 55, no. 2 (May 1, 2021): 59–62. http://dx.doi.org/10.2345/0890-8205-55.1.59.

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Abstract Background: Following a merger of two children's hospitals, leadership discovered a considerable utilization volume of single-use sensors that was associated with declining hospital reimbursements. This discovery resulted in the establishment of a new sensor management strategy, the goal of which was to decrease costs and waste associated with disposable pulse oximetry sensors. Implementation: The sensor management strategy involved using replacement tapes with single-patient-use pulse oximeter sensors instead of the current practice of reprobing with a new sensor. A 60% utilization goal was set, with the focus shifted from sensors used per patient to replacement tapes per sensor. Results: The implementation of a new sensor management strategy between the years 2006 and 2019 in a hospital system decreased sensor volume by more than 780,000 sensors and realized a cost avoidance of more than 7 million dollars. Conclusion: A sensor management strategy can substantially reduce the cost and medical waste commonly associated with the use of disposable, single-patient pulse oximetry sensors.

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Palmer, Allan G. "Impact of Innovative Pulse Oximeter Sensor Management Strategy." Biomedical Instrumentation & Technology 55, no. 2 (May 1, 2021): 59–62. http://dx.doi.org/10.2345/0890-8205-55.2.59.

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Abstract Background: Following a merger of two children's hospitals, leadership discovered a considerable utilization volume of single-use sensors that was associated with declining hospital reimbursements. This discovery resulted in the establishment of a new sensor management strategy, the goal of which was to decrease costs and waste associated with disposable pulse oximetry sensors. Implementation: The sensor management strategy involved using replacement tapes with single-patient-use pulse oximeter sensors instead of the current practice of reprobing with a new sensor. A 60% utilization goal was set, with the focus shifted from sensors used per patient to replacement tapes per sensor. Results: The implementation of a new sensor management strategy between the years 2006 and 2019 in a hospital system decreased sensor volume by more than 780,000 sensors and realized a cost avoidance of more than 7 million dollars. Conclusion: A sensor management strategy can substantially reduce the cost and medical waste commonly associated with the use of disposable, single-patient pulse oximetry sensors.

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Rao, Dr Tavanam Venkata. "Manhole Management System." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4262–65. http://dx.doi.org/10.22214/ijraset.2021.35668.

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

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Zhang, Yuan, Yue Liu, and Zhong Tian Jia. "A Sensor Data Management Scheme for Wireless Sensor Networks." Key Engineering Materials 467-469 (February 2011): 709–12. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.709.

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One of the major difficulties of wireless sensor network (WSN) applications is how to efficiently manage the large amount of data produced by sensors. The differences from standard database source pose challenges of sensor data management. In this paper, we propose a novel sensor data management architecture based on our extensive discussion on existing works. The hierarchical system model consists of sensor network layer and proxy network layer. Sensor network layer performs limited computation and communication while being managed intelligently by the proxy network. The proxy network receives sensor data, manages sensor data and processes queries in a distributed manner. We also provide insight into possible research directions in this area.

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Gupta, Anju, and R. K. Bathla. "Energy Efficient Opportunistic Sensing Management in Fog Cloud Environment." International Journal of Computer Science and Mobile Computing 10, no. 10 (October 30, 2021): 20–26. http://dx.doi.org/10.47760/ijcsmc.2021.v10i10.004.

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

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Crain, Jared, Ivan Ortiz-Monasterio, and Bill Raun. "Evaluation of a Reduced Cost Active NDVI Sensor for Crop Nutrient Management." Journal of Sensors 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/582028.

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There are methods to increase fertilizer nitrogen use efficiency through optical sensor-based nitrogen application; however, the sensors are expensive and cost prohibitive to farmers in the developing world. This study evaluated a novel, reduced cost, prototype, and optical sensor to determine if it could be used with the same level of accuracy as a commercial sensor. The stability of the prototype sensor (pocket sensor) to maintain an accurate calibration over time, the effect of operator on sensor readings, and sensor performance in maize and wheat were assessed. Evaluation of the sensor performance was conducted in existing wheat and maize trials, as well as turf grass canopies at the International Maize and Wheat Improvement Center, Ciudad Obregon, Mexico.The prototype sensors were highly correlated to the commercial GreenSeeker NDVI sensor in turf grass, wheat, and maize canopies (r2>0.97,r2>0.95, andr2>0.91, resp.). The Pocket Sensors lacked some precision in comparison to the commercial sensor; however, even with the reduced precision, the cost of the sensor and robustness ofNfertilizer algorithms compensate for this apparent weakness. The pocket sensor is a new and viable tool to assess wheat and maize nitrogen status and make nitrogen recommendations based upon the data collected with this sensor.

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Liang, Shuang, Yun Zhu, Hao Li, and Junkun Yan. "Evolutionary Computational Intelligence-Based Multi-Objective Sensor Management for Multi-Target Tracking." Remote Sensing 14, no. 15 (July 28, 2022): 3624. http://dx.doi.org/10.3390/rs14153624.

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In multi-sensor systems (MSSs), sensor selection is a critical technique for obtaining high-quality sensing data. However, when the number of sensors to be selected is unknown in advance, sensor selection is essentially non-deterministic polynomial-hard (NP-hard), and finding the optimal solution is computationally unacceptable. To alleviate these issues, we propose a novel sensor selection approach based on evolutionary computational intelligence for tracking multiple targets in the MSSs. The sensor selection problem is formulated in a partially observed Markov decision process framework by modeling multi-target states as labeled multi-Bernoulli random finite sets. Two conflicting task-driven objectives are considered: minimization of the uncertainty in posterior cardinality estimates and minimization of the number of selected sensors. By modeling sensor selection as a multi-objective optimization problem, we develop a binary constrained evolutionary multi-objective algorithm based on non-dominating sorting and dynamically select a subset of sensors at each time step. Numerical studies are used to evaluate the performance of the proposed approach, where the MSS tracks multiple moving targets with nonlinear/linear dynamic models and nonlinear measurements. The results show that our method not only significantly reduces the number of selected sensors but also provides superior tracking accuracy compared to generic sensor selection methods.

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Toliupa, Sergey, Yuriy Kravchenko, and Aleksander Trush. "ORGANIZATION OF IMPLEMENTATION OF UBIQUITOUS SENSOR NETWORKS." Informatics Control Measurement in Economy and Environment Protection 8, no. 1 (February 28, 2018): 36–39. http://dx.doi.org/10.5604/01.3001.0010.8643.

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The article deals with the implementation of one of the most promising technologies of the 21st century – the permeable sensor networks of the USN. The features, architecture, organization and routing algorithms of sensory networks are described. It is determined that further improvement of the work of such networks requires standardization of the development process and implementation process. USN's Vertical Sensor Networks is one of the most promising technologies of the 21st century. Cheap and "smart" sensors, in large quantities combined into a wireless network connected to the public communications network, today provide an unprecedentedly wide range of control and management services for buildings, businesses, cars, and so forth. USN networks, depending on the type of sensors, can be deployed on the ground, in the air, under and over water, in buildings and, finally, on the skin and inside living organisms, including humans. They are also widely used in such important areas as military affairs, crisis and emergency management, and the fight against terrorism.

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Hang, Lei, Wenquan Jin, HyeonSik Yoon, Yong Hong, and Do Kim. "Design and Implementation of a Sensor-Cloud Platform for Physical Sensor Management on CoT Environments." Electronics 7, no. 8 (August 7, 2018): 140. http://dx.doi.org/10.3390/electronics7080140.

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

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Kim, Jong Won, Youn Kwae Jeong, and Il Woo Lee. "Automatic Sensor and Meter Arrangement System for Building Energy Management." Applied Mechanics and Materials 253-255 (December 2012): 746–50. http://dx.doi.org/10.4028/www.scientific.net/amm.253-255.746.

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Building Energy Management System (BEMS) can save energy and minimize the impact on the environment due to energy efficiency technologies and systems. The most important things of Building Energy Management are the monitoring of indoor environment in a building by various sensors and the measurement of energy consumption in a building by various meters. We need to arrange sensors and meters automatically, and develop an automatic sensor and meter arrangement system being usable in any kinds of wired or wireless network. In this paper, we propose an automatic sensor and meter arrangement system for building energy management and explain each component operation of an automatic sensor and meter arrangement system for building energy management, a sensor arrangement simulator and a meter arrangement simulator.

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Kamel, Khaled, and Dr Smys s. "SUSTAINABLE LOW POWER SENSOR NETWORKS FOR DISASTER MANAGEMENT." IRO Journal on Sustainable Wireless Systems 1, no. 04 (December 23, 2019): 247–55. http://dx.doi.org/10.36548/jsws.2019.4.005.

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The wireless sensor networks that framed using the tiny sensors that are very efficient in monitoring and conveying the information’s that are sensed are almost engaged in a wide range of applications with the from environment monitoring to health monitoring. The conventional wireless sensor in spite of its sensing capabilities requires lot of power to operate which in return reduces the life time of the sensors. This paves a way for the low power wireless sensor networks that brings downs the energy consumption, by setting limit on the modes of the device operation. The paper uses the low power wireless devices for the management of a manmade disaster situation such as the air pollution created by the vehicle movement in the cities. The system acts as an early warning for metro cities, with heavy vehicle flow, the industrial outcomes and the other causes that emits the poisonous gas polluting the environment. The proposed system with the low power wireless sensor networks is tested over a busy are to evince the system in terms of the battery lifetime of the system and its monitoring capability.

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Ma, Ya Jie, Zhi Jian Mei, and Xiang Chuan Tian. "Building Sensor Grid Architecture for Large-Scale Air Pollution Data Management." Advanced Materials Research 831 (December 2013): 276–81. http://dx.doi.org/10.4028/www.scientific.net/amr.831.276.

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Large-scale sensor networks are systems that a large number of high-throughput autonomous sensor nodes are distributed over wide areas. Much attention has paid to provide efficient data management in such systems. Sensor grid provides low cost and high performance computing to physical world data perceived through sensors. This article analyses the real-time sensor grid challenges on large-scale air pollution data management. A sensor grid architecture for pollution data management is proposed. The processing of the service-oriented grid management is described in psuedocode. A simulation experiment investigates the performance of the data management for such a system.

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Marković, Monika, Goran Krizmanić, Andrija Brkić, Atilgan Atilgan, Božica Japundžić-Palenkić, Davor Petrović, and Željko Barač. "Sustainable Management of Water Resources in Supplementary Irrigation Management." Applied Sciences 11, no. 6 (March 10, 2021): 2451. http://dx.doi.org/10.3390/app11062451.

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Watermark, Tensiometer and Time Domain Reflectometry (TDR) are commonly used soil water sensors in irrigation practice whose performance depends on soil type, depth and growing conditions. Here, the results of sensor performance evaluation in different soil depths as well as the field and laboratory testing in silty clay loamy soil are presented. Gravimetric soil moisture samples were taken from sensor installation depths (10, 20, 30 and 45 cm) and used as reference Soil Water Content (SWC). The measurements varied significantly (p < 0.05) across the monitoring depths. On average across the soil depths, there was a strong negative linear relationship between Watermark (r = −0.91) and TDR (r = 0.94), and a moderate negative (r = −0.75) linear relationship between SWC and Tensiometer. In general, Watermark and Tensiometer measured SWC with great accuracy in the range of readily available water, generated larger Mean Difference (MD) than TDR and overestimated SWC, while TDR underestimated SWC. Overall, laboratory testing reduced the root mean square error (RMSE, Watermark = 1.2, Tensiometer = 2.6, TDR = 1.9) and Mean Average Error (MAE, Watermark = 0.9, Tensiometer = 2.04. TDR = 1.04) for all tested sensors.

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Song, Yang, Mu Qing Wu, and Kai Jia. "Design of Wireless Sensor Management Platform Based on Embedded ARM." Applied Mechanics and Materials 148-149 (December 2011): 106–11. http://dx.doi.org/10.4028/www.scientific.net/amm.148-149.106.

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Wireless Sensor Network is receiving more widely appreciate because of its great prospect. But a variety of sensors need a unified mobility management platform. This paper considers this problem, proposes and implements a wireless sensor management platform which can control a variety of sensors and transfer the collected data by using wireless technology. Then a testing wireless network is built to test the performance of this mobile platform. The test result proves that this platform has the ability of manage sensors, and has the advantages of mobility and portability.

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Kapitsaki, Georgia M., Achilleas P. Achilleos, Philippos Aziz, and Athina C. Paphitou. "SensoMan: Social Management of Context Sensors and Actuators for IoT." Journal of Sensor and Actuator Networks 10, no. 4 (November 27, 2021): 68. http://dx.doi.org/10.3390/jsan10040068.

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Sensor networks that collect data from the environment can be utilized in the development of context-aware applications, bringing into sight the need for data collection, management, and distribution. Boards with microcontrollers, such as Arduino and Raspberry Pi, have gained wide acceptance and are used mainly for educational and research purposes. Utilizing the information available via sensors connected to these platforms requires extended technical knowledge. In this work, we present a sensor management framework, SensoMan, that manages a collection of sensors spread in the environment connected to microcontroller boards. We present the framework’s architecture, a method for sensor data management, and a prototype system. Sensor data can also trigger the execution of actions on actuators. Thus, we further propose a rule engine as well as social connectivity following a scheme where sensors and their data can be shared among users. Our work shows that the creation of such a system is feasible and can use simple equipment (e.g., sensors, controller plugs) that can be replicated in other environments. The use of SensoMan is demonstrated via two scenarios that show its potential in combining simple tools that do not require an extended learning curve. A small-scale user study was also performed.

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V, Kanchana Devi, and Ganesan R. "A GENERIC TRUST MANAGEMENT FRAMEWORK FOR HETEROGENEOUS SENSORS IN CYBER PHYSICAL SYSTEMS." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (April 1, 2017): 176. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19631.

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Objective: “Wireless Technology” is the magic word in today’s era. In which, Cyber Physical Systems (CPS) is the booming world which binds the physical world and cyber world together. The CPS is also called as Safety Critical System because of the human life involvement. In this emerging technology, lots of heterogeneous sensors are involved and each sensor will play an important role. If something goes wrong with sensor or sensor data. It will definitely affect the human life involved in it.Methods: In this paper, we proposed a generic trust management framework for heterogeneous sensors which will detect the sensor data falsification (Data Integrity), faulty sensor reading, and packet dropping nodes (Selfish Nodes) through rules and rating concept.Results: The efficiency of the proposed framework is evaluated with the help of Network Simulator 2 (NS-2.35). The maximum numbers of untrusted nodes are identified in point 0.40 than Multi-Level Trust Framework for Wireless Sensor Network (MTF-WSN) and Framework for Packet-Droppers Mitigation (FPDM). It is also evident that Trust Management Framework for Cyber Physical Systems (TRMF-CPS) identifies maximum number of untrusted nodes in the detection range of 0.35 and 0.45. Therefore, 0.35 and 0.45 are considered as maximum and minimum threshold points for effective untrusted nodes. Conclusion:The experimentation results and comparative study shows that, our trust management framework will easily detected sensors which misbehave.

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Li, Guo Xin, Fei Lao, Bing Quan Huo, and Long Cheng. "Design of Fire Alarm System in Laboratory." Advanced Materials Research 912-914 (April 2014): 1232–35. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1232.

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Fire warning is a matter of concern in laboratory, this paper presents the design of distributed sensors fire warning system. The core idea is that a number of the bottom sensor collection terminals that consists of temperature and smoking sensors are evenly distributed in the laboratory; when there is a fire to happen, the bottom sensor collection terminals will send an alarming signal by the way of wireless communication to the middle layer alarming signal collection terminal, which starts a GPRS module to capture the scene and send the image to the senior monitoring management center. The senior manager receives an alarming image information, analysis and process, start the appropriate fire alarm system. After a year of testing and running, the system achieves the originally designed aim, and is reliable and stable.

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Pang, Ce, Ganlin Shan, Xiusheng Duan, and Gongguo Xu. "A Multi-Mode Sensor Management Approach in the Missions of Target Detecting and Tracking." Electronics 8, no. 1 (January 8, 2019): 71. http://dx.doi.org/10.3390/electronics8010071.

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In this paper, sensor management is divided into two processes: sensor deployment and sensor scheduling, after which a multi-mode sensor management approach based on risk theory is proposed. Firstly, the definition of risk is provided, on the basis of which the target detecting risk and the target tracking risk are separately presented, along with their computing methods. Secondly, when deploying sensors, the objective is to obtain the minimum target detecting risk. Similarly, when scheduling sensors, the objective is to obtain the minimal sum of target detecting risk and target tracking risk. Furthermore, to obtain sensor management schemes according to the objective functions, the improved bee colony algorithm based on double-probability and in combination with the particle swarm optimization algorithm is proposed. Finally, simulations are conducted, which indicate that the models and the algorithm in the paper possess some advantages over existing ones.

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O'Shaughnessy, Susan. "Sensor-Based Water Management." CSA News 62, no. 9 (September 2017): 30–31. http://dx.doi.org/10.2134/csa2017.62.0923.

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Cortez, R., Xanthi Papageorgiou, Herbert Tanner, Alexei Klimenko, Konstantin Borozdin, Ron Lumia, and William Priedhorsky. "Smart radiation sensor management." IEEE Robotics & Automation Magazine 15, no. 3 (September 2008): 85–93. http://dx.doi.org/10.1109/mra.2008.928590.

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Mullen, T., V. Avasarala, and D. L. Hall. "Customer-Driven Sensor Management." IEEE Intelligent Systems 21, no. 2 (March 2006): 41–49. http://dx.doi.org/10.1109/mis.2006.23.

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Watanabe, Yasuo. "Intelligent Sensor Management System." JAPAN TAPPI JOURNAL 61, no. 8 (2007): 946–49. http://dx.doi.org/10.2524/jtappij.61.946.

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Asadzadeh, Amin, Mehrdad Arashpour, Heng Li, Tuan Ngo, Alireza Bab-Hadiashar, and Ali Rashidi. "Sensor-based safety management." Automation in Construction 113 (May 2020): 103128. http://dx.doi.org/10.1016/j.autcon.2020.103128.

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Bodenbenner, Matthias, Benjamin Montavon, and Robert H. Schmitt. "FAIR sensor services - Towards sustainable sensor data management." Measurement: Sensors 18 (December 2021): 100206. http://dx.doi.org/10.1016/j.measen.2021.100206.

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Liu, Yong, Baohua Liang, and Jiabao Jiang. "Information Processing and Data Management Technology in Wireless Sensor Networks." International Journal of Online Engineering (iJOE) 14, no. 09 (September 30, 2018): 66. http://dx.doi.org/10.3991/ijoe.v14i09.8270.

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<p>The wireless sensor network is essentially a data-centric network that processes the continuous stream of data, which is collected by different sensors. Therefore, the existing data management technologies regard the wireless sensor network, which is named WSN as a distributed database, and it is composed of continuous data streams from the physical world. Wireless sensor networks are emerging next-generation sensor networks, but their transmission of information is highly dependent. The wireless sensor network processes the continuous stream of data collected by the sensor. Based on the features of wireless sensor networks, this paper presents a topology-dependent model of cluster evolution with fault tolerance. Through the limited data management, resources have reasonably configured, while also saving energy. The model is based on the energy-aware routing protocol in its network layer protocols. The key point is the energy routing principle. According to its own local view, the cluster head node builds the inter-cluster topology to achieve fault-tolerant and energy-saving goals. Simulation results show that the model has good fault tolerance and energy efficiency.</p>

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Clutter, Melissa, and Kendall DeJonge. "Optimizing Soil Moisture Sensor Depth for Irrigation Management Using Universal Multiple Linear Regression." Journal of the ASABE 65, no. 4 (2022): 739–49. http://dx.doi.org/10.13031/ja.15044.

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HighlightsUniversal multiple linear regression (uMLR) was used to optimize soil moisture sensor depth.Eight years of soil moisture data from various water treatments were tested, then validated.The best single observation depth for irrigation management is at 30 cm below soil surface.A single calibrated soil moisture sensor may be used to aggregate soil water deficit in the root zone.Abstract. Soil moisture sensors are valuable tools in irrigation management, but due to soil spatial variability it is challenging for point-based sensors to characterize the entire soil water profile. Universal multiple linear regression (uMLR) is a simple machine learning tool that analyzes the relationship between all possible subsets of observations and the prediction of interest. This study used uMLR and volumetric water content data (VWC) taken from maize field experiments at the Limited Irrigation Research Farm near Greeley, Colorado, to optimize the number and placement of soil moisture sensors to characterize the entire soil water deficit (SWD) in the root zone. The uMLR analysis quantified the accuracy of using one or two VWC measurements to represent SWD. We evaluated seven field seasons, with 12 treatments and four replicates of each, where volumetric VWC was typically measured one to three times weekly. Results indicate that the best single sensor depth is at 30 cm, and the best combination of two sensors is at 0 to 15 cm and 60 cm. To validate the experiment, a continuous VWC sensor was used at 30 cm to predict SWD results with acceptable accuracy (RMSE = 4.60 mm). This study suggests that in the sandy loam site evaluated and with varying irrigation levels, soil moisture sensor placement near the soil surface along with knowledge of soil texture can be used to characterize SWD. This technique may be transferable to other locations and soils and shows potential for optimizing irrigation management via soil moisture sensor networks with continuous SWD data. Keywords: Deficit irrigation, Machine learning, Soil moisture sensor, Soil water deficit, Universal multiple linear regression, Volumetric water content.

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Kim, Changsu, Hongyoul Kim, and Hoekyung Jung. "Sensor data identification based reagent cabinet management system." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (December 1, 2019): 5304. http://dx.doi.org/10.11591/ijece.v9i6.pp5304-5311.

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Recently, a reagent cabinet is used in a laboratory or a laboratory is required to have a system capable of identifying a dangerous situation through sensor data as various sensors are utilized. The existing system identifies the dangerous situation through various sensor data, but there is a problem that the server performs all the operations and the operation of the device is performed manually. In order to solve this problem, this paper proposes a system that can identify the dangerous situation and automatically operate the equipment through the internal environment data of the reagent cabinet. Identification of the hazardous situation is done through the master node used in the reagent cabinet, not the server. The server can continuously update the sensor data through the master node and monitor the real-time status of the reagent cabinet through the application. In this way, it is expected that the risk situation will be promptly addressed by identifying the dangerous situation in the reagent cabinet and operating the device.

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Yang, Wen Xue, Zhe Chen, and Feng Yang. "A Survey of Sensor Technologies for Prognostics and Health Management of Electronic Systems." Applied Mechanics and Materials 602-605 (August 2014): 2229–32. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2229.

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Recently, the field of Prognostics and Health Management (PHM) for electronic products and systems has received increasing attention due to the potentialities to provide early warning of system failures, reduce life cycle costs, and forecast maintenance as needed. This paper introduces the sensors and their sensor technologies. The required attributes of sensors for the development for PHM of electronics are discussed. Finally, their trends in sensor systems are presented.

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Villaverde, Jocelyn Flores, Wen-Yaw Chung, and Shih-Lun Chen. "Compressive Sensing Algorithm for Wireless Sensor Network Power Management." International Journal of Computer and Electrical Engineering 7, no. 3 (2015): 199–205. http://dx.doi.org/10.17706/ijcee.2015.7.3.199-205.

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Rizvi, S. S. H., M. Z. Ahmad, J. Ahmed, and A. Karim. "Active Node Based Sensor Wireless Network for Energy Management." International Journal of Engineering and Technology 4, no. 5 (2012): 590–93. http://dx.doi.org/10.7763/ijet.2012.v4.439.

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Tang, Yongchuan, Deyun Zhou, Zichang He, and Shuai Xu. "An improved belief entropy–based uncertainty management approach for sensor data fusion." International Journal of Distributed Sensor Networks 13, no. 7 (July 2017): 155014771771849. http://dx.doi.org/10.1177/1550147717718497.

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In real applications, sensors may work in complicated environments; thus, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. To address this issue, an improved belief entropy–based uncertainty management approach for sensor data fusion is proposed in this article. First, the sensor report is modeled as the body of evidence in Dempster–Shafer framework. Then, the uncertainty measure of each body of evidence is based on the subjective uncertainty represented as the evidence sufficiency and evidence importance, and the objective uncertainty measure is expressed as the improved belief entropy. Evidence modification of conflict sensor data is based on the proposed uncertainty management approach before evidence fusion with Dempster’s rule of combination. Finally, the fusion result can be applied in real applications. A case study on sensor data fusion for fault diagnosis is presented to show the rationality of the proposed method.

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Diop, Abdoulaye, Yue Qi, and Qin Wang. "A Novel Key Management Scheme for Cluster Based Wireless Sensors Networks." Advanced Materials Research 846-847 (November 2013): 864–68. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.864.

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WSN comprise of large number of sensor nodes with different hardware abilities and functions. Due to the limited memory resources and energy constraints, complex security algorithms cannot be used in sensor networks. Therefore, it is necessary to well balance between the security level and the associated energy consumption overhead to mitigate the security risks. In this paper, we present A Novel Key Management Scheme for Cluster Based Wireless Sensors Networks (NKMS). The security analysis demonstrates that NKMS is an effective solution to the key management of cluster based wireless sensor networks.

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Ala-Laurinaho, Riku, Juuso Autiosalo, and Kari Tammi. "Open Sensor Manager for IIoT." Journal of Sensor and Actuator Networks 9, no. 2 (June 19, 2020): 30. http://dx.doi.org/10.3390/jsan9020030.

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Data collection in an industrial environment enables several benefits: processes and machinery can be monitored; the performance can be optimized; and the machinery can be proactively maintained. To collect data from machines or production lines, numerous sensors are required, which necessitates a management system. The management of constrained IoT devices such as sensor nodes is extensively studied. However, the previous studies focused only on the remote software updating or configuration of sensor nodes. This paper presents a holistic Open Sensor Manager (OSEMA), which addresses also generating software for different sensor models based on the configuration. In addition, it offers a user-friendly web interface, as well as a REST API (Representational State Transfer Application Programming Interface) for the management. The manager is built with the Django web framework, and sensor nodes rely on ESP32-based microcontrollers. OSEMA enables secure remote software updates of sensor nodes via encryption and hash-based message authentication code. The collected data can be transmitted using the Hypertext Transfer Protocol (HTTP) and Message Queuing Telemetry Transport (MQTT). The use of OSEMA is demonstrated in an industrial domain with applications estimating the usage roughness of an overhead crane and tracking its location. OSEMA enables retrofitting different sensors to existing machinery and processes, allowing additional data collection.

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Ganesan, T., and Pothuraju Rajarajeswari. "Hybrid Genetic Algorithm With Haar Wavelet for Maximum Target Coverage Node Deployment in Wireless Sensor Networks." Journal of Cases on Information Technology 23, no. 3 (July 2021): 78–95. http://dx.doi.org/10.4018/jcit.20210701.oa6.

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Wireless sensor networks (WSNs) are used in industrial applications and focused on target coverage and node connectivity based WSNs. The set of sensors and targets is placed in optimal position the target coverage and node connectivity achieving maximum with limited senor nodes. To resolve this problem, the proposed hybrid genetic algorithm combined with lifting wavelet multi-resolution principles for recognizing optimal position for sensors to cover entire targets present in the fields. The hybrid genetic algorithm randomly identifies each sensor position and 2D Haar lifting wavelet transform to improve the quality of target coverage by adjusting node position. The 2D Haar lifting decomposes the population matrix into the optimal position of sensors. Experimental results show the performance of the proposed hybrid genetic algorithm and fast local search method compared with available algorithms improves the target coverage and the number of nodes with varying and fixed sensing ranges with a different region.

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Albakri, Ashwag, Lein Harn, and Sejun Song. "Hierarchical Key Management Scheme with Probabilistic Security in a Wireless Sensor Network (WSN)." Security and Communication Networks 2019 (July 14, 2019): 1–11. http://dx.doi.org/10.1155/2019/3950129.

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Securing data transferred over a WSN is required to protect data from being compromised by attackers. Sensors in the WSN must share keys that are utilized to protect data transmitted between sensor nodes. There are several approaches introduced in the literature for key establishment in WSNs. Designing a key distribution/establishment scheme in WSNs is a challenging task due to the limited resources of sensor nodes. Polynomial-based key distribution schemes have been proposed in WSNs to provide a lightweight solution for resource-constraint devices. More importantly, polynomial-based schemes guarantee that a pairwise key exists between two sensors in the WSNs. However, one problem associated with all polynomial-based approaches in WSNs is that they are vulnerable to sensor capture attacks. Specifically, the attacker can compromise the security of the entire network by capturing a fixed number of sensors. In this paper, we propose a novel polynomial-based scheme with a probabilistic security feature that effectively reduces the security risk of sensor-captured attacks and requires minimal memory and computation overhead. Furthermore, our design can be extended to provide hierarchical key management to support data aggregation in WSNs.

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Kodukula, Venkatesh, Saad Katrawala, Britton Jones, Carole-Jean Wu, and Robert LiKamWa. "Dynamic Temperature Management of Near-Sensor Processing for Energy-Efficient High-Fidelity Imaging." Sensors 21, no. 3 (January 30, 2021): 926. http://dx.doi.org/10.3390/s21030926.

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Vision processing on traditional architectures is inefficient due to energy-expensive off-chip data movement. Many researchers advocate pushing processing close to the sensor to substantially reduce data movement. However, continuous near-sensor processing raises sensor temperature, impairing imaging/vision fidelity. We characterize the thermal implications of using 3D stacked image sensors with near-sensor vision processing units. Our characterization reveals that near-sensor processing reduces system power but degrades image quality. For reasonable image fidelity, the sensor temperature needs to stay below a threshold, situationally determined by application needs. Fortunately, our characterization also identifies opportunities—unique to the needs of near-sensor processing—to regulate temperature based on dynamic visual task requirements and rapidly increase capture quality on demand. Based on our characterization, we propose and investigate two thermal management strategies—stop-capture-go and seasonal migration—for imaging-aware thermal management. For our evaluated tasks, our policies save up to 53% of system power with negligible performance impact and sustained image fidelity.

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Tran, Manh-Trung, Abhishek Kumar, Abhishek Sachan, Mickaël Castro, Willy Allegre, and Jean-François Feller. "Emerging Strategies Based on Sensors for Chronic Wound Monitoring and Management." Chemosensors 10, no. 8 (August 5, 2022): 311. http://dx.doi.org/10.3390/chemosensors10080311.

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Pressure ulcers (PUs) are a serious global health challenge, affecting a large section of the population and putting immense pressure on healthcare systems. Sensor-based diagnostic tools and monitoring systems have emerged as a potential non-invasive solution to reduce the occurrence of new cases of PUs and promise a significant reduction in treatment expenditure and time. In this endeavour, the present manuscript reviews the advancements made in the last decade in the development and commercial adoption of different sensor systems for PU-associated chronic wound management. Different types of smart sensor systems have been developed in which pressure, chemical, and optical sensors have witnessed a lot of interest and significant advancement among research communities and industries alike. These sensors utilize a host of nanomaterial-based sensing materials, flexible support, diverse transducing modes, and different device designs to achieve high sensitivity and selectivity for skin pressure, temperature, humidity, and biomarkers released from the wound. Some of these sensor’s array-based electronic skin (e-skin) has reached the stage of commercialization and is being used in commercial products, such as smart bandages, shoes, watches, and mattress among others. Nonetheless, further innovations are necessary in the direction of associating multiple types of sensor arrays, particularly pressure and chemical sensor-based e-skins in a microsystem for performing real-time assessment of all the critical wound parameters.

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Miptahudin, Apip, Titiek Suryani, and Wirawan Wirawan. "Wireless Sensor Network Based Monitoring System: Implementation, Constraints, and Solution." JOIV : International Journal on Informatics Visualization 6, no. 4 (December 31, 2022): 778. http://dx.doi.org/10.30630/joiv.6.4.1530.

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Wireless Sensor Network (WSN) is a collection of sensors communicating at close range by forming a wireless-based network (wireless). Since 2015 research related to the use of WSN in various health, agriculture, security industry, and other fields has continued to grow. One interesting research case is the use of WSN for the monitoring process by collecting data using sensors placed and distributed in locations based on a wireless system. Sensors with low power, multifunction, supported by a combination of wireless network, microcontroller, memory, operating system, radio communication, and energy source in the form of an integrated battery enable a monitoring process of the monitoring area to run properly. The implementation of the wireless sensor network includes five main parts, namely sender, receiver, wireless transmission media, data/information, network architecture/configuration, and network management. Network management itself includes network configuration management, network performance management, network failure management, network security management, and network financing management. The main obstacles in implementing a wireless sensor network include three things: an effective and efficient data sending/receiving process, limited and easily depleted sensor energy/power, network security, and data security that is vulnerable to eavesdropping and destruction. This paper presents a taxonomy related to the constraints in implementing Wireless Sensor Networks. This paper also presents solutions from existing studies related to the constraints of implementing the WSN. Furthermore, from the results of the taxonomy mapping of these constraints, new gaps were identified related to developing existing research to produce better solutions.

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Chaczko, Zenon, Christopher Chiu, Shahrzad Aslanzadeh, and Toby Dune. "Sensor-Actor Network Solution for Scalable Ad-hoc Sensor Networks." International Journal of Electronics and Telecommunications 58, no. 1 (March 1, 2012): 55–62. http://dx.doi.org/10.2478/v10177-012-0008-4.

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Sensor-Actor Network Solution for Scalable Ad-hoc Sensor NetworksArchitects of ad-hoc wireless Sensor-Actor Networks (SANETS) face various problems and challenges. The main limitations relate to aspects such as the number of sensor nodes involved, low bandwidth, management of resources and issues related to energy management. In order for these networks to be functionally proficient, the underlying software system must be able to effectively handle unreliable and dynamic distributed communication, power constraints of wireless devices, failure of hardware devices in hostile environments and the remote allocation of distributed processing tasks throughout the wireless network. The solution must be solved in a highly scalable manner. This paper provides the requirements analysis and presents the design of a software system middleware that provides a scalable solution for ad-hoc sensor network infrastructure made of both stationary and mobile sensors and actuators.

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Gull, Shazmina, Imran Sarwar Bajwa, Waheed Anwar, and Rubina Rashid. "Smart eNose Food Waste Management System." Journal of Sensors 2021 (July 22, 2021): 1–13. http://dx.doi.org/10.1155/2021/9931228.

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The modern age is an era of fast-growing technology, all thanks to the Internet of Things. The IoT becomes a prime factor of human life. As in this running world, no one cares about the wastage of food. However, this causes environment pollution as well as loss of many lives. A lot of researchers help in this era by introducing some great and beneficial projects. Our work is introducing a new approach by utilizing some low-cost sensors. In this work, Arduino UNO is used as a microcontroller. We use the eNose system that comprises MQ4 and MQ135 to detect gas emission from different food items, i.e., meat, rice, rice and meat, and bread. We collect our data from these food items. The MQ4 sensor detects the CH4 gas while the MQ135 sensor detects CO2 and NH3 in this system. We use a 5 kg strain gauge load cell sensor and HX711 A/D converter as a weight sensor to measure the weight of food being wasted. To ensure the accuracy and efficiency of our system, we first calibrate our sensors as per recommendations to run in the environment with the flow. We collect our data using cooked, uncooked, and rotten food items. To make this system a smart system, we use a machine learning algorithm to predict the food items on the basis of gas emission. The decision tree algorithm was used for training and testing purposes. We use 70 instances of each food item in the dataset. On the rule set, we implement this system working to measure the weight of food wastage and to predict the food item. The Arduino UNO board fetches the sensor data and sends it to the computer system for interpretation and analysis. Then, the machine learning algorithm works to predict the food item. At the end, we get our data of which food item is wasted in what amount in one day. We found 92.65% accuracy in our system. This system helps in reducing the amount of food wastage at home and restaurants as well by the daily report of food wastage in their computer system.

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Cui, Dong Chu, and Yue Yu. "The Optimization Layout Method of Intelligent Roadside Sensor System in Traffic Management and Control." Advanced Materials Research 591-593 (November 2012): 1251–55. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1251.

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The vehicle sensor information and road side sensor information will be collaborative used in traffic management and control. In order to improve the comprehensiveness and economy of the traffic and road conditions’ information collection, we focus on the intelligent roadside system in this paper. Firstly, we analyse the functions of the intelligent roadside system. Through the analysis of the detection range, detection accuracy, price and applicable conditions of similar sensor, we delineate the selection range of the intelligent roadside sensor. Then we determine the layout scheme of the testing equipment sensors for different functions according to different types of network structure. Finally, we apply similarity analysis to optimize the configuration density to reduce system cost by selecting the sensor layout-intensive sections.

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Gaynor, Mark, and G. Shankaranarayanan. "Implications of sensors and sensor-networks for data quality management." International Journal of Information Quality 2, no. 1 (2008): 75. http://dx.doi.org/10.1504/ijiq.2008.019564.

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Alippi, C., G. Anastasi, M. Di Francesco, and M. Roveri. "Energy management in wireless sensor networks with energy-hungry sensors." IEEE Instrumentation & Measurement Magazine 12, no. 2 (April 2009): 16–23. http://dx.doi.org/10.1109/mim.2009.4811133.

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Hetherington, Amy Lee, Renee F. Brown, Adrienne George, and Sandra R. Villamizar. "FRESHWATER ADVANCED AQUATIC SENSOR WORKSHOP: SENSORS, PLATFORMS, AND DATA MANAGEMENT." Limnology and Oceanography Bulletin 20, no. 4 (December 2011): 82. http://dx.doi.org/10.1002/lob.201120482.

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Rayri, Yassine, Hatim Kharraz Aroussi, and Abdelaziz Mouloudi. "Improved Energy Management Performance for Hierarchical Routing in WSNs." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 6, no. 2 (August 29, 2018): 61. http://dx.doi.org/10.3991/ijes.v6i2.8818.

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The rapidly evolving of technology in the area of communication and information, allowed the creation of a new device called wireless sensor. This small device allows measuring physical phenomenon quantity in environment such as light, pressure, temperature and transforming it autonomously into a digital value, In order to send to one or several points of collections, which is going to be processed. However, a wireless sensor network (WSN) is a network formed by a large number of sensor nodes, which are able to acquire, aggregate compress and to transmit data towards a base station. However, several constraints complicate the deployment and management of wireless sensor networks. The economy of energy is among the major issues of these networks, and it is difficult even impossible to replace the sensors or their battery because of the location of deployment that is often inaccessible. Among Scientifics researches, we find the improvement the energy management in WSNs. In this paper, we propose a new routing protocol based on clustering. The purpose is to have an equal distribution of the energy throughout the network

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Ankamma rao, B., M. Harika Devi, and Krishna Veni Kommuri. "Development of cognitive vehicle assistance and driver safety control management." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 930. http://dx.doi.org/10.14419/ijet.v7i2.7.11426.

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In this paper we present new approach for automobiles safety driving system involving the multiple sensor monitoring an area around vehicle and conveying alert the driver. There is some distance between two vehicles if apply break we can lose the control our vehicle. The proposed system works like that it has the four Ultrasonic sensors are fixed four sides of car they will detect the cars and obstacles within in the monitored area based on how much distance they are near to car and alert the driver via leds.an alcohol sensor fixed in the steering if he driver drink alcohol it will detect and switch off the vehicle ignition. The IR sensor monitors the eye blinks the driver. If the driver sleeps it makes ring the buzzer.

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Zhang, Sen, Wendong Xiao, Jun Gong, and Yixin Yin. "Mobile Sensing and Simultaneously Node Localization in Wireless Sensor Networks for Human Motion Tracking." Applied Bionics and Biomechanics 9, no. 4 (2012): 367–74. http://dx.doi.org/10.1155/2012/513046.

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This paper exploits optimal position of the mobile sensor to improve the target tracking performance of wireless sensor networks and simultaneously localize both of the static sensor nodes and mobile sensor nodes when tracking the human motion. In our approach, mobile sensors collaborate with static sensors and move optimally to achieve the required detection performance. The accuracy of final tracking result is then improved as the measurements of mobile sensors have higher signal-to-noise ratios after the movement. Specifically, we can simultaneously localize the mobile sensor and static sensors position when localizing the human’s position based on augmented extended Kalman filters (EKF). In the algorithm, we develop a sensor movement optimization algorithm that achieves near-optimal system tracking performance. We also presented an sensor nodes management scheme in order to deduce the computation complexity when localizing the static sensor nodes. The effectiveness of our approach is validated by extensive simulations using the simulations.

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Aringo, M. Q., C. G. Martinez, O. G. Martinez, and V. B. Ella. "Development of Low-cost Soil Moisture Monitoring System for Efficient Irrigation Water Management of Upland Crops." IOP Conference Series: Earth and Environmental Science 1038, no. 1 (June 1, 2022): 012029. http://dx.doi.org/10.1088/1755-1315/1038/1/012029.

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Abstract With the advancement of information and communication technology, various types of soil moisture sensors have been developed. Coupled with data loggers, these sensors could prove useful in monitoring soil moisture in upland crop production areas which in turn could be used for efficient irrigation water management. However, most of these sensors are costly and unaffordable to most farmers in developing countries. Hence, a low-cost soil moisture monitoring system intended to facilitate irrigation water management in upland crop production systems was developed in this study. The device was built with a capacitive soil moisture sensor, an ESP8266 Wi-Fi mini board, and a datalogging shield with RTC. Soil moisture measurements are transmitted via ESP-NOW to a server which also uses an ESP8266 Wi-Fi mini board. The low-cost soil moisture monitoring system was evaluated based on its measurement of volumetric water content and transmission of data via ESP-NOW. The performance of the capacitive soil moisture sensor was compared with the ICT International MP306 soil moisture sensor. Statistical analyses showed that volumetric water contents measured by the capacitive soil moisture sensor are comparable to those of the MP306 soil moisture sensor, thus conceived as a low-cost alternative to the high-end sensor. Moreover, test results on the range of ESP-NOW showed that data can be successfully transmitted over long distances. With regards to this, the low-cost soil moisture monitoring system may be integrated with other technologies to enable irrigation scheduling and automation for efficient irrigation water management in upland crop production systems.

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Kweon, Dae-Gon, and Sin-Hyeong Choi. "Sensor Data Management using Database." Journal of the Korea Academia-Industrial cooperation Society 10, no. 7 (July 31, 2009): 1608–13. http://dx.doi.org/10.5762/kais.2009.10.7.1608.

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Journal articles on the topic 'Sensor Management'

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