Trusted Data Collection Gateway for BlockChain Traceability Applications and Edge Computing

2021 ◽  
pp. 104088
Author(s):  
Minhui Yang
Keyword(s):  
Data Collection ◽  
Edge Computing ◽  
Trusted Data

Study on Learning Analytics Data Collection Model using Edge Computing

2021 ◽  
Vol 69 (4) ◽  
pp. 142-145
Author(s):  
Myung-Suk Lee ◽  
Joo-Hwa Lee ◽  
Ju-Geon Pak
Keyword(s):  
Data Collection ◽  
Learning Analytics ◽  
Edge Computing

A Distributed and Anonymous Data Collection Framework Based on Multilevel Edge Computing Architecture

2020 ◽  
Vol 16 (9) ◽  
pp. 6114-6123
Author(s):  
Muhammad Usman ◽  
Mian Ahmad Jan ◽  
Alireza Jolfaei ◽  
Min Xu ◽  
Xiangjian He ◽  
...  
Keyword(s):  
Data Collection ◽  
Edge Computing ◽  
Computing Architecture

Edge-Computing-Based Trustworthy Data Collection Model in the Internet of Things

2020 ◽  
Vol 7 (5) ◽  
pp. 4218-4227 ◽  
Author(s):  
Tian Wang ◽  
Lei Qiu ◽  
Arun Kumar Sangaiah ◽  
Anfeng Liu ◽  
Md Zakirul Alam Bhuiyan ◽  
...  
Keyword(s):  
Data Collection ◽  
Internet Of Things ◽  
Edge Computing ◽  
The Internet ◽  
The Internet Of Things

scholarly journals Platform for distributed execution of agents for trusted data collection

2010 ◽  
Vol 1 (1) ◽  
pp. 2023-2032 ◽  
Author(s):  
Emil Gatial ◽  
Zoltán Balogh ◽  
Ladislav Hluchý
Keyword(s):  
Data Collection ◽  
Distributed Execution ◽  
Trusted Data

scholarly journals Bandwidth-Aware Traffic Sensing in Vehicular Networks with Mobile Edge Computing

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3547 ◽  
Author(s):  
Kong Ye ◽  
Penglin Dai ◽  
Xiao Wu ◽  
Yan Ding ◽  
Huanlai Xing ◽  
...  
Keyword(s):  
Data Collection ◽  
Vehicular Networks ◽  
Estimation Error ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Traffic Data ◽  
Norm Minimization ◽  
Traffic Data Collection ◽  
Dynamic Mobility ◽  
Traffic Systems

Traffic sensing is one of the promising applications to guarantee safe and efficient traffic systems in vehicular networks. However, due to the unique characteristics of vehicular networks, such as limited wireless bandwidth and dynamic mobility of vehicles, traffic sensing always faces high estimation error based on collected traffic data with missing elements and over-high communication cost between terminal users and central server. Hence, this paper investigates the traffic sensing system in vehicular networks with mobile edge computing (MEC), where each MEC server enables traffic data collection and recovery in its local server. On this basis, we formulate the bandwidth-constrained traffic sensing (BCTS) problem, aiming at minimizing the estimation error based on the collected traffic data. To tackle the BCTS problem, we first propose the bandwidth-aware data collection (BDC) algorithm to select the optimal uploaded traffic data by evaluating the priority of each road segment covered by the MEC server. Then, we propose the convex-based data recovery (CDR) algorithm to minimize estimation error by transforming the BCTS into an l 2 -norm minimization problem. Last but not the least, we implement the simulation model and conduct performance evaluation. The comprehensive simulation results verify the superiority of the proposed algorithm.

scholarly journals Edge Computing Approach for Vessel Monitoring System

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3087 ◽  
Author(s):  
Joao C. Ferreira ◽  
Ana Lucia Martins
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Monitoring System ◽  
Communication Systems ◽  
Satellite Communications ◽  
Edge Computing ◽  
Abnormal Behavior ◽  
Collection Process ◽  
Data Collection Process ◽  
Computing Approach

A vessel monitoring system (VMS) is responsible for real-time vessel movement tracking. At sea, most of the tracking systems use satellite communications, which have high associated costs. This leads to a less frequent transmission of data, which reduces the reliability of the vessel location. Our research work involves the creation of an edge computing approach on a local VMS, creating an intelligent process that decides whether the collected data needs to be transmitted or not. Only relevant data that can indicate abnormal behavior is transmitted. The remaining data is stored and transmitted only at ports when communication systems are available at lower prices. In this research, we apply this approach to a fishing control process increasing the data collection process from once every 10 min to once every 30 s, simultaneously decreasing the satellite communication costs, as only relevant data is transmitted in real-time to the competent central authorities. Findings show substantial communication savings from 70% to 90% as only abnormal vessel behavior is transmitted. Even with a data collection process of once every 30 s, findings also show that the use of more stable fishing techniques and fishing areas result in higher savings. The proposed approach is assessed as well in terms of the environmental impact of fishing and potential fraud detection and reduction.

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