Technology Policy & Ethics: December 2019

Blockchain Based Connected Vehicles for Smart Green City Environment 

Prateeti Mukherjee, ReSENSE Lab Intern, Department of Computer Science, Institute of Engineering and Management, India, and Dhananjay Singh, ReSENSE Lab Director, Department of Electronics Engineering, Hankuk University of Foreign Studies, Korea

Air pollution is perceived as a modern-day curse, a fatal by-product of increasing urbanization and rapid industrialization. The phenomenon has a plethora of negative impacts, including human health issues, damage to ecosystems, decreasing quality of food crops, and abatement of environmental standards. Passenger vehicles are a major contributor to air pollution, producing significant amounts of nitrogen oxides and carbon monoxide, among other pollutants. It is therefore necessary to enforce greener practices among citizens and introduce a reliable mechanism that encourages responsible behavior to support the environmental cause. The proposed work aims to circumvent the aforementioned problem with an IoT-enabled solution powered by blockchain technology to foster environment friendly practices in registered vehicle owners.  The real essence of this project is to leverage technological innovations to solve pollution issues that befoul the urban atmosphere. We seek to achieve this goal with a financial reward-penalty scheme that drives the urban populace to practice green living, while monitoring pollution levels using an IoT device. The ability of blockchain networks to provide a verifiable record of actions and violations [1], coupled with the capabilities of Internet of Things (IoT) paradigms to detect pollution levels present in vehicular exhaust, is exploited in this project.

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Clinical Decision Support Systems – Part 2

By Tahir Hameed, Girard School of Business, Merrimack College

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Part one of this article published in July 2019 focused on two major application areas of Machine Learning (ML) in Clinical Decision Support Systems (CDSS) i.e., 1) Computer Aided Diagnosis (CAD) and prognosis (progression) and 2) Risk stratification and preventive healthcare. In this second part of the article, we continue to discuss more cutting-edge ML applications in CDSS including clinical pathways optimization, diagnosis and personalized medicine development based on genomics and related data.

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Towards Zero-Trust Database Security – Part 2

By Walid Rjaibi,  Department of Computing and Mathematics, Manchester Metropolitan University and the IBM Canada Lab, and Mohammad Hammoudeh, Department of Computing and Mathematics, Manchester Metropolitan University 

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In Part One of this article, we have explored the direct and indirect means through which the same data in a database system can be accessed and the challenges they pose to adhering to the basic tenets of zero-trust security. Here, we outline the solutions that are most suitable to address these challenges and enable enterprises to implement zero-trust database security without negatively impacting core database tenets such as query performance.

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