scholarly journals Sensor Node Easy Moving Monitoring Region Location Algorithm in Internet of Things

2015 ◽  
Vol 13 (2) ◽  
pp. 703
Author(s):  
Donghua Feng ◽  
Yahong Li
Keyword(s):  
Internet Of Things ◽  
Sensor Node ◽  
Location Algorithm

Video sensor node with distributed video summary for Internet-of-Things applications

2015 ◽  
Author(s):  
Shun-Hsing Ou ◽  
Chia-Han Lee ◽  
V. Srinivasa Somayazulu ◽  
Yen-Kuang Chen ◽  
Shao-Yi Chien
Keyword(s):  
Internet Of Things ◽  
Sensor Node ◽  
Video Summary ◽  
Video Sensor

MEGAN: Multipurpose Energy-Efficient, Adaptable, and Low-Cost Wireless Sensor Node for the Internet of Things

2020 ◽  
Vol 14 (1) ◽  
pp. 144-151 ◽  
Author(s):  
Sudip Misra ◽  
Sanku Kumar Roy ◽  
Arijit Roy ◽  
Mohammad S. Obaidat ◽  
Avantika Jha
Keyword(s):  
Internet Of Things ◽  
Energy Efficient ◽  
Sensor Node ◽  
Low Cost ◽  
Wireless Sensor ◽  
The Internet ◽  
Wireless Sensor Node ◽  
The Internet Of Things

Improved location algorithm based on DV-hop for the indoor internet of things

2018 ◽  
Vol 15 (3) ◽  
pp. 251
Author(s):  
Qian Cai ◽  
Chengtian Ouyang ◽  
Yiwang Huang
Keyword(s):  
Internet Of Things ◽  
Location Algorithm

A Reusable Device Driver Framework for the Sensor Nodes in Internet of Things

2012 ◽  
Vol 468-471 ◽  
pp. 60-63
Author(s):  
Xiao Fan Wu ◽  
Jia Jun Bu ◽  
Chun Chen
Keyword(s):  
Operating System ◽  
Internet Of Things ◽  
Embedded System ◽  
Sensor Node ◽  
Rapid Development ◽  
Source Code ◽  
Device Driver ◽  
Sensor Nodes ◽  
C Programming Language ◽  
C Programming

Due to the rapid development of Internet of Things (IoT), kinds of sensor nodes have been introduced to the different applications. Because of the variety of MCUs, sensors and radio modules, it’s challenging to reuse the device drivers between different sensor node platforms. To address this issue, a reusable device driver framework is proposed in this paper. Comparing with existed work, our framework is flexible, efficient, and easy to learn. The flexibility is achieved by layered encapsulation, which decouples the device driver with the sensor node operating system kernel. Our framework gives the reusability at the source code level, so it’s efficient. At the end, our framework is implemented in C programming language, which is the most common tool adopted by embedded system developing. This framework has applied to SenSpire OS, a micro-kernel real-time operating system for IoT sensor nodes.

scholarly journals Monitoring and Predicting Water Quality in Swimming Pools

2021 ◽  
Vol 3 (2) ◽  
pp. 119-125
Author(s):  
Lanny Sitanayah ◽  
Apriandy Angdresey ◽  
Vandri Josua Abram Sampul
Keyword(s):  
Data Mining ◽  
Water Quality ◽  
Internet Of Things ◽  
Temperature Sensor ◽  
Sensor Node ◽  
Ph Sensor ◽  
Swimming Pools ◽  
Wireless System ◽  
Wireless Monitoring ◽  
Dirty Water

Water quality in public swimming pools affects human health. While changing the water too soon is wasteful, postponing changing the dirty water is not hygiene. In this paper, we propose an Internet of Things-based wireless system to monitor and predict water quality in public swimming pools. Our system utilizes an Arduino Uno, an ESP8266 ESP-01 WiFi module, a DS18B20 temperature sensor, a pH sensor, and a turbidity sensor. We predict the water quality using a data mining prediction model, namely the decision tree Iterative Dichotomiser 3 algorithm. We show by experiment that our sensor node and the wireless monitoring system work correctly. We also show by simulation using Weka that we can get 100% accuracy with a kappa statistical value of 1 and 0% error rate.

Research on Space Location System Based on WSN

2010 ◽  
Vol 108-111 ◽  
pp. 1182-1186
Author(s):  
Xin Li ◽  
Yu Jiang ◽  
Jian Jun Song ◽  
Wei Li Cui
Keyword(s):  
Energy Cost ◽  
Random Sampling ◽  
Sensor Node ◽  
Low Energy ◽  
Arbitrary Distribution ◽  
Movement Trajectory ◽  
Location System ◽  
Location Algorithm ◽  
Simulation Results ◽  
Representation Method

This article presents a low-energy-cost space location system based on WSN technology by applying random sampling algorithm, this system can locate a person real time who holds sensor node and adopt a representation method based on random sampling, so that the location algorithm can represent arbitrary distribution. According to the comparison of the simulation results demonstrates that the movement trajectory acquired by this system is very close to the real movement trajectory, and the number of location errors does not rely on the number of sampling numbers very obviously.

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