No-Reference Stereoimage Quality Assessment for Multimedia Analysis Towards Internet-of-Things

One of the most important challenges of volunteered geographic information (VGI) is the quality assessment. Existing methods of VGI quality assessment, either assess the quality by comparing a reference map with the VGI map or deriving the quality from the metadata. The first approach does not work for a real-time scenario and the latter delivers approximate values of the quality. Internet of Things (IoT) networks provide real-time observations for environment monitoring. Moreover, they publish more precise information than VGI. This paper introduces a method to assess the quality of VGI in real-time using IoT observations. The proposed method filters sensor observation outliers in the first step. Then it matches sensors and volunteers’ relationships in terms of location, time, and measurement type similarity using a hypergraph model. Then the quality of matched data is assessed by calculating positional and attribute accuracy. To evaluate the method, VGI data of the water level and quality in Tarashk–Bakhtegan–Maharlou water basin is studied. A VGI quality map of the data is assessed by a referenced authoritative map. The output of this step is a VGI quality map, which was used as a reference to check the proposed method quality. Then this reference VGI quality map and the proposed method VGI quality map are compared to assess positional and attribute accuracy. Results demonstrated that 76% of the method results have less than 20 m positional error (i.e., difference with the reference VGI quality map). Additionally, more than 92% of the proposed method VGI data have higher than 90% attribute accuracy in terms of similarity with the reference VGI quality map. These findings support the notion that the proposed method can be used to assess VGI quality in real-time.

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Development and Implementation of Water Quality Assessment Monitoring (WQAM) System using the Internet of Things (IoT) in Water Environment

Journal of Electronic Voltage and Application ◽

10.30880/jeva.2021.02.02.001 ◽

2021 ◽

Vol 2 (2) ◽

Author(s):

Muhammad Farhan Johan ◽

◽

Samihah Abdullah ◽

Nor Shahanim Mohamad Hadis ◽

Saodah Omar ◽

...

Keyword(s):

Water Quality ◽

Internet Of Things ◽

Quality Assessment ◽

Real Time ◽

Technology Implementation ◽

Water Quality Assessment ◽

Water Quality Monitoring ◽

Time Data ◽

Cloud Server ◽

Real Time Data

Water quality monitoring (WQM) system is widely being explored as it is needed to prevent the problem of water contamination worldwide. Nowadays, there are various studies on WQM system that are being integrated with Internet of Things (IoT) concept for Wireless Sensor Network (WSN) technology implementation to get real time data measurement. Traditional ways of collecting the data are more time consuming and they lack real time changes in the quality of water. This paper presents the development and implementation of Water Quality Assessment and Monitoring (WQAM) system. The system development used WiFi enabled microcontroller to connect with the IoT environment and store the data in the IoT cloud server. The microcontroller used is Arduino UNO that interacts with three types of sensor probes which are pH, turbidity and temperature probe. All the data measurements is transferred using a WiFi module which is ESP8266. The IoT cloud used to utilize the data frame is ThingSpeak. This system was implemented on Bandar Perda Lake and Derhaka River in Pulau Pinang with two systems implemented at each location. The sensors were placed on the water surface for more accurate measurements. This system continuously measures the readings of pH, turbidity dan temperature on the lake/river for every 1 hour. Twenty readings were taken for every 1 hour within the first 20 minutes with 1 minute interval and the readings were stored in the IoT cloud server. The readings are accessible via ThingSpeak GUI. In conclusion, this system would benefit the authorities to take advantage of using the WQAM system with the aid of the IoT that is less time consuming, less cost and more reliable in real time data reading.

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Image quality assessment model based on multi-feature fusion of energy Internet of Things

Future Generation Computer Systems ◽

10.1016/j.future.2020.05.037 ◽

2020 ◽

Vol 112 ◽

pp. 501-506

Author(s):

Hongpeng Zhu

Keyword(s):

Image Quality ◽

Internet Of Things ◽

Quality Assessment ◽

Image Quality Assessment ◽

Feature Fusion ◽

Assessment Model ◽

Energy Internet ◽

Model Based

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An Internet of Things Evaluation Algorithm for Quality Assessment of Computer-Based Teaching

Mobile Information Systems ◽

10.1155/2021/9919399 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Jing Huang

Keyword(s):

Internet Of Things ◽

Quality Assessment ◽

Teaching Quality ◽

Assessment Model ◽

Evaluation Index ◽

Qualitative Assessment ◽

Index Method ◽

Evaluation Algorithm ◽

Computer Based ◽

Fuzzy Assessment

Internet of Things has the ability to revolutionize computer-based teaching and assess the quality of teaching at the same time. The assessment of teaching efficiency is hampered by two issues: the evaluation index method (EIS) is insufficient, and the assessment framework is incapable of dealing with complicated fuzzy indexes. To address these issues, the theory of fuzzy stratification from IoT perspective is presented for the first time in this paper. This theory is based on the related theory of fuzzy assessment by measuring the teaching quality evaluation index. Initially, theoretical analysis and model measurement were merged to build a better multiangle EIS for teaching quality. To manage fuzzy indexes, a teaching quality assessment model was developed using both quantitative and qualitative studies. The suggested EIS and fuzzy assessment model can effectively evaluate the standard of teaching in schools, colleges, universities, and institutes, according to implementation results. This qualitative assessment approach is empirical and rational, and it strongly promotes the quality enhancement of educational effectiveness, based on our experimental and simulation results.

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