Peel-and-Stick Sensors Powered by Directed Radio-Frequency Energy

2018 ◽  
Vol 140 (2) ◽  
Author(s):  
David Eric Schwartz ◽  
Clinton J. Smith ◽  
Joseph Lee ◽  
Shakthi Priya Gowri ◽  
George Daniel ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Wireless Sensors ◽  
High Performance ◽  
Energy Savings ◽  
Low Cost ◽  
Sensor Nodes ◽  
Sensor Localization ◽  
Radio Frequency Energy ◽  
Commission Regulation ◽  
Rf Energy

PARC, a Xerox Company, is developing a low-cost system of peel-and-stick wireless sensors that will enable widespread building environmental sensor deployment with the potential to deliver up to 30% energy savings. The system is embodied by a set of radio-frequency (RF) hubs that provide power to automatically located sensor nodes and relay data wirelessly to the building management system (BMS). The sensor nodes are flexible electronic labels powered by rectified RF energy transmitted by the RF hub and can contain multiple printed and conventional sensors. The system design overcomes limitations in wireless sensors related to power delivery, lifetime, and cost by eliminating batteries and photovoltaic devices. Sensor localization is performed automatically by the inclusion of a programmable multidirectional antenna array in the RF hub. Comparison of signal strengths as the RF beam is swept allows for sensor localization, reducing installation effort and enabling automatic recommissioning of sensors that have been relocated. PARC has already demonstrated wireless power and temperature data transmission up to a distance of 20 m with 71 s between measurements, using power levels well within the Federal Communications Commission regulation limits in the 902–928 MHz industrial, medical and scientific (ISM) band. The sensor's RF energy harvesting antenna achieves high performance with dimensions of 5 cm × 9.5 cm.

scholarly journals Peel-and-Stick Sensors Powered by Directed RF Energy

2017 ◽  
Author(s):  
Christopher Lalau-Keraly ◽  
George Daniel ◽  
Joseph Lee ◽  
David Schwartz
Keyword(s):  
Wireless Sensors ◽  
High Performance ◽  
Energy Savings ◽  
Low Cost ◽  
Power Delivery ◽  
Sensor Nodes ◽  
Sensor Localization ◽  
Building Management ◽  
Building Management System ◽  
Rf Energy

PARC, a Xerox Company, is developing a low-cost system of peel-and-stick wireless sensors that will enable widespread building environment sensor deployment with the potential to deliver up to 30% energy savings. The system is embodied by a set of RF hubs that provide power to automatically located sensor nodes, and relay data wirelessly to the building management system (BMS). The sensor nodes are flexible electronic labels powered by rectified RF energy transmitted by an RF hub and can contain multiple printed and conventional sensors. The system design overcomes limitations in wireless sensors related to power delivery, lifetime, and cost by eliminating batteries and photovoltaic devices. Sensor localization is performed automatically by the inclusion of a programmable multidirectional antenna array in the RF hub. Comparison of signal strengths while the RF beam is swept allows for sensor localization, reducing installation effort and enabling automatic recommissioning of sensors that have been relocated, overcoming a significant challenge in building operations. PARC has already demonstrated wireless power and temperature data transmission up to a distance of 20m with less than one minute between measurements, using power levels well within the FCC regulation limits in the 902–928 MHz ISM band. The sensor’s RF energy harvesting antenna achieves high performance with dimensions below 5cm × 9cm.

scholarly journals Enabling a Battery-Less Sensor Node Using Dedicated Radio Frequency Energy Harvesting for Complete Off-Grid Applications

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5402
Author(s):  
Timothy Miller ◽  
Stephen S. Oyewobi ◽  
Adnan M. Abu-Mahfouz ◽  
Gerhard P. Hancke
Keyword(s):  
Energy Harvesting ◽  
Radio Frequency ◽  
Sensor Node ◽  
Lithium Ion ◽  
Base Station ◽  
Sensor Nodes ◽  
Solar Pv ◽  
Radio Frequency Energy ◽  
Yagi Antenna ◽  
Rf Energy

The large-scale deployment of sensor nodes in difficult-to-reach locations makes powering of sensor nodes via batteries impractical. Besides, battery-powered WSNs require the periodic replacement of batteries. Wireless, battery-less sensor nodes represent a less maintenance-intensive, more environmentally friendly and compact alternative to battery powered sensor nodes. Moreover, such nodes are powered through wireless energy harvesting. In this research, we propose a novel battery-less wireless sensor node which is powered by a dedicated 4 W EIRP 920 MHz radio frequency (RF) energy device. The system is designed to provide complete off-grid Internet of Things (IoT) applications. To this end we have designed a power base station which derives its power from solar PV panels to radiate the RF energy used to power the sensor node. We use a PIC32MX220F32 microcontroller to implement a CC-CV battery charging algorithm to control the step-down DC-DC converter which charges lithium-ion batteries that power the RF transmitter and amplifier, respectively. A 12 element Yagi antenna was designed and optimized using the FEKO electromagnetic software. We design a step-up converter to step the voltage output from a single stage fully cross-coupled RF-DC converter circuit up to 3.3 V. Finally, we use the power requirements of the sensor node to size the storage capacity of the capacitor of the energy harvesting circuit. The results obtained from the experiments performed showed that enough RF energy was harvested over a distance of 15 m to allow the sensor node complete one sense-transmit operation for a duration of 156 min. The Yagi antenna achieved a gain of 12.62 dBi and a return loss of −14.11 dB at 920 MHz, while the battery was correctly charged according to the CC-CV algorithm through the control of the DC-DC converter.

Sitsen: Passive sitting posture sensing based on wireless devices

2021 ◽  
Vol 17 (7) ◽  
pp. 155014772110248
Author(s):  
Miaoyu Li ◽  
Zhuohan Jiang ◽  
Yutong Liu ◽  
Shuheng Chen ◽  
Marcin Wozniak ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
High Performance ◽  
Learning Algorithm ◽  
Low Cost ◽  
Recognition System ◽  
Sitting Posture ◽  
Average Accuracy ◽  
Phase Variations ◽  
Window Approach

Physical health diseases caused by wrong sitting postures are becoming increasingly serious and widespread, especially for sedentary students and workers. Existing video-based approaches and sensor-based approaches can achieve high accuracy, while they have limitations like breaching privacy and relying on specific sensor devices. In this work, we propose Sitsen, a non-contact wireless-based sitting posture recognition system, just using radio frequency signals alone, which neither compromises the privacy nor requires using various specific sensors. We demonstrate that Sitsen can successfully recognize five habitual sitting postures with just one lightweight and low-cost radio frequency identification tag. The intuition is that different postures induce different phase variations. Due to the received phase readings are corrupted by the environmental noise and hardware imperfection, we employ series of signal processing schemes to obtain clean phase readings. Using the sliding window approach to extract effective features of the measured phase sequences and employing an appropriate machine learning algorithm, Sitsen can achieve robust and high performance. Extensive experiments are conducted in an office with 10 volunteers. The result shows that our system can recognize different sitting postures with an average accuracy of 97.02%.

Building Sensor Grid Architecture for Large-Scale Air Pollution Data Management

2013 ◽  
Vol 831 ◽  
pp. 276-281
Author(s):  
Ya Jie Ma ◽  
Zhi Jian Mei ◽  
Xiang Chuan Tian
Keyword(s):  
Air Pollution ◽  
Data Management ◽  
High Performance ◽  
Large Scale ◽  
Low Cost ◽  
Physical World ◽  
Sensor Nodes ◽  
Grid Architecture ◽  
Autonomous Sensor ◽  
Efficient Data

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.

Application of WeChat Mini-Program and Wi-Fi SoC in Agricultural IoT: A Low-Cost Greenhouse Monitoring System

2020 ◽  
Vol 63 (2) ◽  
pp. 325-337
Author(s):  
Lei Zhou ◽  
Zhengjun Qiu ◽  
Yong He
Keyword(s):  
Internet Of Things ◽  
Monitoring System ◽  
High Performance ◽  
Low Cost ◽  
Rapid Development ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Smart Devices ◽  
Increasing Demand ◽  
On Chip

HighlightsA quick solution for developing and deploying custom agricultural IoT systems is proposed.Low-cost and high-performance devices are used for the design of sensor nodes.A mobile application based on WeChat Mini-Program is developed for device and data management.The proposed system brings convenience to both users and developers.Abstract. Increasing demand for automatic management of agricultural production and real-time remote monitoring has increased the need for smart devices, wireless technologies, and sensors. The internet of things (IoT) has emerged as a common technology for the management of multiple devices by multiple users. Some professional solutions are relatively difficult to implement for researchers who are interested in agricultural IoT but do not have requisite skills in computers and electronics. The unfriendliness of the user software limits the practical application of agricultural IoT in China. This article presents a simple solution based on an SoC (system-on-chip) and WeChat mini-program that focuses on low-cost hardware, rapid development, user-friendly application design, and helping developers get a quick start in building a DIY monitoring system. The ESP8266, a high-performance SoC, is used as the microcontroller and Wi-Fi module to transfer the sensor data to a remote server. A WeChat mini-program provides the graphical user interface, enabling users to manage devices and access data by clicking. Users can log into the system using their WeChat accounts and bind devices by scanning QR codes on the devices. Thus, the complex management and device binding in conventional systems can be overcome. The system is easy to be expand and has great potential for greenhouse environmental monitoring in China. Keywords: Greenhouse ambient monitoring, Internet of things, WeChat mini-program, Wi-Fi SoC.

scholarly journals RF Energy Harvesting IoT System for Museum Ambience Control with Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4465 ◽  
Author(s):  
Nermeen A. Eltresy ◽  
Osama M. Dardeer ◽  
Awab Al-Habal ◽  
Esraa Elhariri ◽  
Ali H. Hassan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Energy Harvesting ◽  
Internet Of Things ◽  
Radio Frequency ◽  
Electric Power ◽  
Sensor Nodes ◽  
Monitoring And Control ◽  
Rf Energy Harvesting ◽  
Rf Energy ◽  
And Control

Museum contents are vulnerable to bad ambience conditions and human vandalization. Preserving the contents of museums is a duty towards humanity. In this paper, we develop an Internet of Things (IoT)-based system for museum monitoring and control. The developed system does not only autonomously set the museum ambience to levels that preserve the health of the artifacts and provide alarms upon intended or unintended vandalization attempts, but also allows for remote ambience control through authorized Internet-enabled devices. A key differentiating aspect of the proposed system is the use of always-on and power-hungry sensors for comprehensive and precise museum monitoring, while being powered by harvesting the Radio Frequency (RF) energy freely available within the museum. This contrasts with technologies proposed in the literature, which use RF energy harvesting to power simple IoT sensing devices. We use rectenna arrays that collect RF energy and convert it to electric power to prolong the lifetime of the sensor nodes. Another important feature of the proposed system is the use of deep learning to find daily trends in the collected environment data. Accordingly, the museum ambience is further optimized, and the system becomes more resilient to faults in the sensed data.

scholarly journals Simplified Approach to Detect Dielectric Constant Using a Low-Cost Microfluidic Quarter Mode Substrate-Integrated Waveguide

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4985
Author(s):  
Ahmed Salim ◽  
Muhammad Usman Memon ◽  
Heijun Jeong ◽  
Sungjoon Lim
Keyword(s):  
Dielectric Constant ◽  
Radio Frequency ◽  
High Performance ◽  
Low Cost ◽  
Cost Effective ◽  
Microfluidic Channels ◽  
Substrate Integrated Waveguide ◽  
Simplified Approach ◽  
Sensor Module ◽  
Radio Frequency Sensor

Liquid materials’ characterization using commercial probes and radio frequency techniques is expensive and complex. This study proposes a compact and cost-effective radio frequency sensor system to measure the dielectric constant using a three-material calibration. The simplified approach measures reflection coefficient magnitudes for all four materials rather than the complex values in conventional permittivity detection systems. We employ a sensor module based on a circular substrate-integrated waveguide with measured unloaded quality factor = 910 to ensure measurement reliability. Miniaturized quarter-mode substrate-integrated waveguide resonators are integrated with four microfluidic channels containing three known materials and one unknown analyte. Step-wise measurement and linearity ensures maximum 4% error for the dielectric constant compared with results obtained using a high-performance commercial product.

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