scholarly journals Implementation SEMAR-IoT-Platform for Vehicle as a Mobile Sensor Network

2020 ◽  
Vol 4 (4) ◽  
pp. 201
Author(s):  
Yohanes Yohanie Fridelin Panduman ◽  
Sritrusta Sukaridhoto ◽  
Anang Tjahjono ◽  
Rizqi Putri Nourma Budiarti
Keyword(s):  
Data Processing ◽  
Sensor Network ◽  
Delay Time ◽  
Mobile Sensor ◽  
Average Delay ◽  
Mobile Sensor Network ◽  
Average Delay Time ◽  
Iot Platform ◽  
Control Devices ◽  
And Control

With the rapid development of IoT technology in various fields such as smart cities and industry 4.0, the need for wireless sensor network-based systems has increased, one of which is the concept of using a vehicle as a mobile sensor network or known as VaaMSN. Many developers use the IoT platform as a cloud computing service in developing the VaaMSN system. However, not all IoT platform service providers provide monitoring features on every device and provide information such as device location, purpose, condition. Therefore, this research aims to develop an IoT Platform that can receive data and provide information on each device, making it easier to process data and control devices.  Therefore, this research aims to develop an IoT platform called the SEMAR-IoT-Platform that able to received data and provide information on each device for easier data processing and control devices.  The SEMAR-IoT-Platform integrates Big Data, Data Analytics, Machine Learning, using the principles of Extract, Transfer, and Load (ETL) for data processing and provides communication services using HTTP-POST, MQTT, and NATS.  The test results show that the system has been successfully implemented to complement a simple IoT system with an average delay time of HTTP, NATS, and MQTT communications of less than 150ms for the data storage process, and for the data visualization process has an average delay time of less than 300ms.

Matrix-based scheduling and control of a mobile sensor network

2008 ◽  
Vol 41 (2) ◽  
pp. 10415-10420 ◽  
Author(s):  
V. Schiraldi ◽  
V. Giordano ◽  
D. Naso ◽  
B. Turchiano ◽  
F.L. Lewis
Keyword(s):  
Sensor Network ◽  
Mobile Sensor ◽  
Mobile Sensor Network ◽  
And Control

Street-level Air Quality Inference Based on Geographically Context-aware Random Forest Using Opportunistic Mobile Sensor Network

2021 ◽  
Author(s):  
Xuening Qin ◽  
Tien Huu Do ◽  
Jelle Hofman ◽  
Esther Rodrigo ◽  
Valerio La Manna Panzica ◽  
...  
Keyword(s):  
Air Quality ◽  
Random Forest ◽  
Sensor Network ◽  
Context Aware ◽  
Mobile Sensor ◽  
Mobile Sensor Network ◽  
Street Level

scholarly journals An Optimization Model of Multi-Intersection Signal Control for Trunk Road under Collaborative Information

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xun Li ◽  
Zhengfan Zhao ◽  
Li Liu ◽  
Yao Liu ◽  
Pengfei Li
Keyword(s):  
Optimization Model ◽  
Delay Time ◽  
Traffic Simulation ◽  
Simulation Software ◽  
Transmission Model ◽  
Signal Control ◽  
Cell Transmission Model ◽  
Average Delay ◽  
Main Trunk ◽  
Average Delay Time

We proposed a signal control optimization model for urban main trunk line intersections. Four-phase intersection was analyzed and modeled based on the Cell Transmission Model (CTM). CTM and signal control model in our study had both been improved for multi-intersections by three-phase theory and information-exchanging. To achieve a real-time application, an improved genetic algorithm (GA) was proposed finally, the DISCO traffic simulation software was used for numerical simulation experiment, and comparisons with the standard GA and CTM were reported in this paper. Experimental results indicate that our searching time is less than that of SGA by 38%, and our method needs only 1/3 iteration time of SGA. According to our DISCO traffic simulation processing, compared with SGA, if the input traffic flow is changed from free phase to synchronized phase, for example, less than 900 vel/h, the delay time can reduce to 87.99% by our method, and the minimum delay time is 77.76% of existing method. Furthermore, if input traffic volume is increased to 1200 vel/h or more at the synchronized phase, the summary and minimum values of average delay time are reduced to 81.16% and 75.83%, respectively, and the average delay time is reduced to 17.72 seconds.

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