scholarly journals Cloud Server and Internet of Things Assisted System for Stress Monitoring

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3133
Author(s):  
Rajesh Singh ◽  
Anita Gehlot ◽  
Mamoon Rashid ◽  
Ritika Saxena ◽  
Shaik Vaseem Akram ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Threshold Value ◽  
Sensor Data ◽  
Stress Monitoring ◽  
Sensory Data ◽  
Time Stress ◽  
Rehabilitation Centers ◽  
Cloud Server ◽  
Remote Stress

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.

scholarly journals AdaGUM: An Adaptive Graph Updating Model-Based Anomaly Detection Method for Edge Computing Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiang Yu ◽  
Chun Shan ◽  
Jilong Bian ◽  
Xianfei Yang ◽  
Ying Chen ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Anomaly Detection ◽  
Real Time ◽  
Detection Method ◽  
Edge Computing ◽  
Sensor Data ◽  
Computing Environment ◽  
The Real ◽  
Edge Node ◽  
Model Based

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.

Smart Healthcare

2018 ◽  
pp. 21-33
Author(s):  
R. Rajkumar
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Cost Effective ◽  
Reliable System ◽  
Smart Healthcare ◽  
Cloud Server ◽  
Android App ◽  
The Cost ◽  
Very High ◽  
At Home

Internet of things is a revolutionary domain, when we use it for the wellness of people in a smart way. As of now, the cost to implement IoT-enabled services is very high. So, this chapter introduces a cost effective and a reliable system to monitor patients at home and in hospitals with the help of IoT. The monitored details of a person can be drawn at any time with the help of an android app, which can produce output at real-time. The processed data are stored in the UBIDOTS cloud server, and the patients' needs can be met in time as well lives saved during critical cases with the help of the system proposed in this chapter.

A Discussion about FBG Sensors Application of Crane SHM System

2013 ◽  
Vol 336-338 ◽  
pp. 185-191
Author(s):  
Xiao Peng Xie ◽  
Dong Hui Wang ◽  
Guo Jian Huang ◽  
Xin Hua Wang
Keyword(s):  
Finite Element ◽  
Real Time ◽  
Failure Modes ◽  
Safety Evaluation ◽  
Structural Condition ◽  
Sensor Data ◽  
Gantry Crane ◽  
The Real ◽  
Time Stress ◽  
Fbg Sensors

The arrangement positions and the quantities are different for different types of cranes. In order to make suitable decision, much investigate and survey was done at preliminary stage, and we know that the flange connected gate legs and turntables, the connections between load-bearing beam and rotary column under the engine room and the connections between jib and turntable are easy to lose efficient, and their mainly failure modes are cracks. By the method of finite element, 32 sensors (including 21 welding strain FBG sensors and 11 temperature FBG sensors) were used after doing much investigate and survey and finite element modeling analysis, which are arranged in different places of a gantry crane of MQ2533, for real-time structure health monitoring. This method makes the sensor data obtained more realistically reflects the crane structural condition, which provides reliable data support for crane safety monitoring and safety evaluation. Then a software platform is developed to monitor the real-time stress. If the real-time stress exceeds the allowable stress, it issues an alarm signal to the operator.

SmartEmoDetect: An Internet of Things Based Emotion Monitoring Wearable Technology for Drivers

2019 ◽  
Vol 16 (9) ◽  
pp. 3969-3973
Author(s):  
Jasleen Kaur ◽  
Neera Batra ◽  
Sonali Goyal
Keyword(s):  
Threshold Value ◽  
Emotional Reactions ◽  
Sensor Data ◽  
Stress Monitoring ◽  
The Road ◽  
Severe Accidents ◽  
Warning Message ◽  
Sensor Signals ◽  
The Government ◽  
On The Road

Negative emotional reactions are the major source of severe accidents on the road. In this paper, an IoT based wearable device is proposed that will estimate the four negative emotions (stress, anger, terror, sad) in the driver and hence would be helpful to prevent roadway disasters. An intelligent stress monitoring control system at the cloud to analyze the sensor signals and to make the decision based upon the variation received in the signals is proposed. This system can also be effective for the government bus drivers. An auditory output response is from buzzer and a warning message is displayed on the screen inside the vehicle. The continuously monitored real time sensor data in the form of graphs is displayed on the PC screen considered as a central server. When any of the sensor value exceeds predefined threshold value, the driver is considered to be in subconscious state and the break system will be implemented to stop the DC motor.

scholarly journals IOTFlood: hardware and software platform using internet of things to monitor floods in real time

2021 ◽  
Vol 16 (4) ◽  
pp. 1
Author(s):  
Francisco Vital Da Silva Júnior ◽  
Mônica Ximenes Carneiro Da Cunha ◽  
Marcílio Ferreira De Souza Júnior
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Monitoring System ◽  
Research Design ◽  
Open Source Software ◽  
Low Cost ◽  
Sensor Data ◽  
Software Platform ◽  
Web Interface ◽  
Iot Platform

Floods are responsible for a high number of human and material losses every year. Monitoring of river levels is usually performed with radar and pre-configured sensors. However, a major flood can occur quickly. This justifies the implementation of a real-time monitoring system. This work presents a hardware and software platform that uses Internet of Things (IoTFlood) to generate flood alerts to agencies responsible for monitoring by sending automatic messages about the situation of rivers. Research design involved laboratory and field scenarios, simulating floods using mockups, and later tested on the Mundaú River, state of Alagoas, Brazil, where flooding episodes have already occurred. As a result, a low-cost, modular and scalable IoT platform was achieved, where sensor data can be accessed through a web interface or smartphone, without the need for existing infrastructure at the site where the IOTFlood solution was installed using affordable hardware, open source software and free online services for the viewing of collected data.

scholarly journals Hybrid Architectural Network Implementation to Realize a Fire Evacuation Path with 2.4 GHz Zigbee and LoRa

2021 ◽  
Vol 13 (23) ◽  
pp. 13238
Author(s):  
Rajesh Singh ◽  
Gajanand S. Birajdar ◽  
Mamoon Rashid ◽  
Anita Gehlot ◽  
Shaik Vaseem Akram ◽  
...  
Keyword(s):  
Real Time ◽  
Fire Detection ◽  
Sensor Data ◽  
Delivery Ratio ◽  
Hybrid Architecture ◽  
Medium Access ◽  
Fire Event ◽  
Sensory Data ◽  
Link Budget ◽  
Safety Operation

The Internet of Things (IoT) is playing a significant role in realizing real monitoring. In fire safety and evacuation, early fire event detection using IoT-enabled sensors may help to control and minimize further consequences of the fire accident. In this study, we propose a hybrid architecture based on 2.4 GHz Zigbee and long-range (LoRa) for real-time fire detection, monitoring, and assisting in the safe evacuation of the building. The architecture comprises five different components, namely: end device, evacuation path display controller, safety operation controller, vision node, and gateway. The end device and vision node provide real-time sensory data and visuals that provide details of fire occurrence. The evacuation path display controller and the safety operation controller based on the 2.4 GHz Zigbee receive data from the end device and make the decision accordingly. In addition, a Zigbee simulation is performed on the OPNET simulator to analyze the network parameters such as throughput, retransmission attempts, medium access (MAC) queue size and queue delay, and packet delivery ratio (PDR). The evaluation metrics of link budget and ToA of LoRa are also calculated by varying the code rate and spreading factor. To realize the proposed architecture, customization of hardware is carried out with the development of hardware prototypes. Dijkstra’s shortest path algorithm is implemented in the evacuation path display controller to provide the shortest evacuation path during a fire incident. The hardware of the system is implemented in real-time, and the system provides real-time sensor data along with the evacuation path.

scholarly journals A Secure Internet of Things based Smart Monitoring System

2019 ◽  
Vol 8 (9) ◽  
pp. 1416-1422
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Sensor Network ◽  
Secure Communication ◽  
Fire Detection ◽  
Raspberry Pi ◽  
Cloud Server ◽  
Result Show ◽  
Local Cloud ◽  
Level Monitoring

Internet of Things (IoT) is a connection of interconnected any things deployed in different applications. The IoT is emerging as a technique for real-time monitoring the environment like Earthquake early detection. Snow level monitoring, forest fire detection, Gas level monitoring in a Smart Kitchen. IoT based sensor network need secure communication, processing, analytics and transient storage for better monitoring of smart environments. In this article, the authors proposed a secure framework for data collection and further processing from the sensors network to the core of the network using Fog-Cloud based architecture. The paper implemented the proposed framework taking different sensors deploy in an IoT based smart kitchen environment. For experimental purpose, Raspberry Pi used as a fog node and local cloud server to monitor the environment in real-time, SQLite is used as buffer storage. The experimental setup and result show that the proposed framework is secure to monitoring in a sensor network.

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