Gyu Myoung Lee

Nguyen Truong, Gyu Myoung Lee, Kai Sun et al.

Blockchain technology has been envisaged to commence an era of decentralised applications and services (DApps) without the need for a trusted intermediary. Such DApps open a marketplace in which services are delivered to end-users by contributors which are then incentivised by cryptocurrencies in an automated, peer-to-peer, and trustless fashion. However, blockchain, consolidated by smart contracts, only ensures on-chain data security, autonomy and integrity of the business logic execution defined in smart contracts. It cannot guarantee the quality of service of DApps, which entirely depends on the services' performance. Thus, there is a critical need for a trust system to reduce the risk of dealing with fraudulent counterparts in a blockchain network. These reasons motivate us to develop a fully decentralised trust framework deployed on top of a blockchain platform, operating along with DApps in the marketplace to demoralise deceptive entities while encouraging trustworthy ones. The trust system works as an underlying decentralised service providing a feedback mechanism for end-users and maintaining trust relationships among them in the ecosystem accordingly. We believe this research fortifies the DApps ecosystem by introducing an universal trust middleware for DApps as well as shedding light on the implementation of a decentralised trust system.

Yuanfang Chen, Mohsen Guizani, Yan Zhang et al.

Traffic flow prediction is an important research issue for solving the traffic congestion problem in an Intelligent Transportation System (ITS). Traffic congestion is one of the most serious problems in a city, which can be predicted in advance by analyzing traffic flow patterns. Such prediction is possible by analyzing the real-time transportation data from correlative roads and vehicles. This article first gives a brief introduction to the transportation data, and surveys the state-of-the-art prediction methods. Then, we verify whether or not the prediction performance is able to be improved by fitting actual data to optimize the parameters of the prediction model which is used to predict the traffic flow. Such verification is conducted by comparing the optimized time series prediction model with the normal time series prediction model. This means that in the era of big data, accurate use of the data becomes the focus of studying the traffic flow prediction to solve the congestion problem. Finally, experimental results of a case study are provided to verify the existence of such performance improvement, while the research challenges of this data-analytics-based prediction are presented and discussed.