IEEE Technology Policy and Ethics Articles Published in 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 the 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 

Read Part 1.

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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Towards Zero-Trust Database Security – Part 1

By Walid Rjaibi and Mohammad Hammoudeh

The rise of external threats, internal threats and data breaches is driving enterprises to implement zero-trust security to better protect their IT assets and reduce risk. While zero-trust security for networks and identity management systems have received a great deal of focus, very little attention has been devoted to zero-trust security for database systems. This is a major issue as database systems are the custodian of enterprises most critical data and are often the primary target of both external and internal attacks. After all, databases contain valuable data such attackers want to steal. In Part One of this series, we explore both 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. In Part Two, we outline a set of 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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Artificial Intelligence Powered EEG-EMG Electrodes for Assisting the Paralyzed

Sunil Jacob, Center for Robotics, Varun G Menon, Department of Computer Science and Engineering and Saira Joseph, Department of Electronics and Communication Engineering, SCMS School of Engineering and Technology, India

Paralysis is the loss of muscle function in any part of a person’s body. This occurs when the passage of messages between the brain and muscles is hindered. Over the past few years, it is seen that paralysis is more widespread than ever before; a recent survey shows that around the world, 1 out of 111 people are affected by paralysis [1-2]. This raises the need for developing an interface to aid people who suffer from this unfortunate condition [3-4]. Paralysis limits a person from completing even basic chores in life without any assistance. At times, the paralyzed parts become so stiff, it is difficult for the caregivers to provide assistance. Physiotherapy is also not the best option because of its various limitations such as the need for multiple weekly appointments, long sessions and high cost. Most of our previous work had focused on designing of efficient exoskeletons for rehabilitation. But exoskeletons [5-6] introduced additional burdens to patients with their heaviness and complexity to the caregivers. In this article we discuss an efficient and better way by which friends and caregivers can assist the paralyzed to improve their lifestyle. The primary objective of this article is to discuss the working of Artificial Intelligence based Electroencephalograph (EEG) – Electromyogram (EMG) electrodes for Paralyzed (AI-EEP). This technique has been recently investigated by our team at the Center for Robotics laboratory at SCMS School of Engineering and Technology, India. This device will help paralyzed people to move independently by using the recorded movements of a normal person as a reference.

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Automated Machine Learning for Future Networks Including 5G 

By Shagufta Henna and Alan Davy

It is possible to select a set of potential candidate machine learning (ML) models based on 5G use-case requirements and characteristics of the ML model, however, it is extremely difficult to predict the best model right at the start. This work proposes an automated ML  framework called automated 5G machine learning (Auto5GML), which can be integrated with the unified ML architecture, by the International Telecommunication Union (ITU). Based on the potential search space of ML models, the Auto5GML framework selects the best model to be used for a 5G use-case. It evaluates the potential models by inserting data and running them in parallel by using data parallelism or model parallelism. The proposed framework can optimize the learning performance based on the strict use-case requirements.

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Adaption of Autonomous Vehicles (via APIs) in Society

by Manish Rathore, Inderjit Rai, and Salah Sharieh, Ph.D

Autonomous Vehicles (AVs) were considered an interesting topic in science fiction movies such as Knight Rider, however, AVs are becoming a reality and will change the way we get around. Not only the major automakers but software companies such as Google, Uber, and Apple are all developing autonomous vehicle capabilities. Experts have categorized AVs into five different levels. In this work we will primarily focus on Level 5, which is defined as vehicles that “can do all the driving in all circumstances”.  The human occupants are just passengers and need never be involved in driving. There has been quite a bit of discussion around the benefits of Level 5 autonomy; we would like to cover whether people are ready to embrace the autonomous vehicle future, or are all these companies wasting their time and won’t see returns for the foreseeable future. 

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Clinical Decision Support Systems Leverage Machine Learning for Predictive Analytics – Part 1

By Tahir Hameed

Medical practice has always remained at the forefront of data-driven decision-making. For instance, primary care physicians have commonly used several types of risk scores and diagnostic data to predict morbidity and mortality in their patients. However, with Terabyte (TB) size structured and unstructured datasets abound, a massive shift is underway in the clinical decision support scene. The costs, efficiency, and effectiveness of decision-making for care planning, diagnosis, treatment, adherence monitoring and management of patient health outcomes have been improving at unprecedented rates in the last two decades. 

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Offload Computation in Mobile Edge Computing with Wide-Band Support of Channel Bonding

By Ali Raza, Syed Hashim Raza Bukhari, and Farhan Aadil

Extensive enhancement in technology enables the use of mobile and Internet of Things (IoT) devices in the high demanding applications and services. The users’ demands for quality of service (QoS) and computation intensive applications such as augmented reality, online gaming, e-health, smart home and environmental monitoring etc. are also increasing exponentially. Although, mobile and IoT devices have become much more powerful in the last decades and can perform a variety of tasks in an efficient manner, these are still incapable for computation intensive and time critical applications. With the low processing capacity, IoT devices cannot produce results within a given time constraint. Even with high CPU power, executing a computational expensive task depletes their battery energy and shortens the operational lifetime. Mobile cloud computing (MCC) is one of the most promising solutions in the last decade. It empowers the resource constrained mobile and IoT devices with elastic computing power and large storage capacity. MCC enables the realization of centralized computing and allows offload computation for CPU hungry and time sensitive tasks at the cloud server. However, intrinsically the MCC has certain serious concerns such as long transmission latency, privacy/security of user data, immense increase in Internet traffic due to billions of IoT devices in the near future. These issues motivate the emerging a new paradigm known as mobile edge computing (MEC). MEC decouples the cloud resources into edge servers which are located near the end user, usually alongside access point (AP).

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Fake News Remastered: The Impact of Technology

Renato Opice Blum and Camila Rioja

Fake news is the buzzword of the moment. Specifically, in Brazil, the theme was one of the most relevant topics in the elections held this past October. With flashy titles and loaded with content aimed at triggering emotions, fake news is a powerful weapon in today’s digital reality. Brazil has more than one functioning smartphone per inhabitant (approximately 220 million currently and counting), as estimated by a study conducted by a prestigious Brazilian university in early 20181.  Worldwide, a projection made by the Statista portal2 shows that smartphone users will reach over 2.87 billion by 2020. Thus, is somewhat intuitive how fast fake news can spread through social media and/or communication apps such as WhatsApp and Telegram.

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Current State of API Security and Machine Learning

Fatima Hussain, Brett Noye, and Salah Sharieh, Royal Bank of Canada, Toronto

The adaptation of application program interface (API)s in every enterprise is the emerging business trend, and at the same time, it diversifies the threat domain for businesses. APIs are becoming the new and most important infrastructure layer on the Internet and are the most vulnerable point of attack in modern systems. Each API adds new dimensions to security threats and attack vectors to corporate data and applications, therefore critically forfeiting the business systems. Traditional security features for API protection are provided through API gateways, and it had been nothing more than API keys and username/password combinations (HTTP authentication). On the other hand, intruders and hackers are getting smarter. Combining the proliferation of social engineering platforms with recent technological advancements, the ability to gain access to confidential data has become both easier and common. APIs funnel data among applications, a multitude of various API users, and cloud infrastructure, therefore sensitive or confidential information might get exposed to unauthorized users if API security is not carefully crafted. Using a holistic approach to securing APIs not only addresses the vulnerability issues but offers protection for all of the infrastructure, networks, and information.

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Information Assurance and Security Issues in Telemedicine – Future Directions

By Ankur Chattopadhyay and Robert Ruska Jr., University of Wisconsin, Green Bay

One of the challenges in healthcare is to provide equitable access to services, given that the provider and the patient are traditionally expected to be physically present in the same place [6, 8, 9]. Technological advancements have been made to overcome obstacles to equitable healthcare services and enable convenient access to quality healthcare for consumers throughout the world. The field of digital healthcare, which is known as telemedicine [4, 7, 8, 9], is rapidly making its way globally across the healthcare services domain. It allows the transfer of images and video through telecommunication technology, giving physicians the ability to evaluate, diagnose, and in certain cases, treat patients remotely [10]. Patients can visit providers over live video without traveling for immediate care and for follow-up treatment. Not only does it give patients the ability to schedule appointments with local physicians via live video communication without having to leave home, but it also allows them to consult with distant healthcare professionals and avail their services remotely.

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Caveats for the Emergence of Virtual Wallets

By Najee Searcy and Syed Hassan Ahmed

As smartphones have become integrated in the daily lives of citizens of developed nations, common monetary transactions that at one point in time required the consumer to search their cars, couches, and coat pockets for spare change can now be completed with the tap of a finger on their smartphone. Examples of these situations may include paying a toll or purchasing a beverage from a vending machine. One major factor considered to be a catalyst for the shift to virtual wallets is the idea that cashless transactions are more convenient[1]. Opposing ideas to this shift in commerce trends include concerns about security, and the criticisms can be translated to the fear of putting all your eggs in one basket[2]. By analyzing the pros and cons of the strengthening relationship between the smartphone and commerce, a clearer understanding of its limitations can be achieved.

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Are Smart Cities Smart?

By H. Kieu, L. Borrello, KC Samiran, J. Martin, K. Watts, S. Jones

It is estimated that by 2030, 70% of the global population will live in cities. Cities today need to accommodate more people, as well as create a sustainable environment with efficient resources. Therefore, the concept of smart cities, which entails utilizing technological innovations, has become an important priority on many cities’ agendas [1]. We will attempt to answer the question: “Are smart cities smart?” by looking at the five pillars associated with smart cities – Smart Grid, E-Governance, Infrastructure and Transportation, Crime Prevention, and Information and Communication Technology (ICT) applications.

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