Research Goal, Methodology, and Contributions

The research goal is to understand, identify, and analyze potential threats and vulnerabilities in the Internet of Medical Things (IoMT) devices, and provide an analysis of solutions to the issues identified from a cybersecurity perspective. Through the investigation of the Mobile Cardiac Telemetry 3 Lead and the remote patient monitoring kit from Vivify Health, a critical analysis of security issues and potential solutions is presented to reach the research goal.

IoMT is a growing application of IoT technology. It directly improves the quality of life of patients and eases the burden on healthcare providers. IoMT consists of IoT devices specifically developed to meet the needs of patients and healthcare facilities. IoMT is developed for remote patient monitoring, intensive care, and context awareness, which gathers information about a patient’s environment. IoMT can also be utilized beyond direct patient treatment, such as medical equipment and medication management.

I have conducted investigative analysis of two IoMT devices, the Mobile Cardiac Telemetry 3 Lead (MCT3L) and the remote patient monitoring kit from Vivify Health. I have formulated a series of questions to meet the research goal. These questions serve as the basis for the analysis of the IoMT environment. I met with a local hospital that provided the MCT3L User Guide, which contained the specifications for the MCT3L. I also met with Wellstar Home Health for a demonstration of the remote patient monitoring kit from Vivify Health. The information gathered from these sources provided me with the information to conduct analyses to fulfill the research goal.

My contributions to the field include the analysis of real IoMT devices, their threats and vulnerabilities, and solutions to further protect them. Furthermore, the work in this thesis illustrates the lacking security countermeasures found in IoMT devices currently deployed. This work contributes to the IoMT literature by conducting a thorough investigation of specific devices, the MCT3L, and the remote patient monitoring kit. The IoMT security literature is lacking in thorough technical examination of devices that are currently deployed. This work provides details of IoMT devices and the security risks they impose, along with a comprehensive analysis of possible solutions. These solutions consist of using secure communication protocols, using multi-factor authentication for user equipment, understanding the device manufacturer’s role in security, implementing robust security policies and procedures organization wide, utilizing network hardening techniques and network segmentation, providing IoMT security training and education, using lightweight security protocols, and using blockchain and cloud computing technologies.

Abstract

Study 1

Blockchain technology (BT) in the supply chain/logistics has drawn much attention. Several benefits associated with the technology include cost-saving, sustainability enhancement, and economic viability. However, there are limited BT performance measurement models. The topic of the BT performance in supply chain management (SCM) is scattered across multiple disciplines. This study identifies different dimensions of BT performance and supports SCM managers to understand the systematic and holistic assessment of BT performance.

Study 2

The specific features of blockchain provide promise in managing supply chain risks. Given the growing research scrutiny in blockchain-based supply risk management, the development of a reliable and valid instrument to measure supply chain risk management is imperative. Nonetheless, no systematic research has been done to develop such an instrument. This study employs a comprehensive and rigorous procedure to develop a multifaceted measurement scale through an empirical analysis. We defined and operationalized the concept of blockchain-based supply chain risk management followed by validation and item measurement development.

Study 3

The supply chain management field is experimenting with the integration of blockchain, a cutting-edge and highly disruptive technology. However, blockchain research in supply chain risk is still nascent, especially the relationship between blockchain adoption and its impact on both risk management performance and supply chain competency. We aim to investigate the potential influence of BT-based security management in mediating the effects of blockchain adoption on both risk management performance and firm performance. We plan to administer a survey to review the opinions and views of supply chain practitioners.

Abstract

Customer’s behavior varies from person to person based on their segmentations, while understanding these differences is one of the key elements of success in food and beverage sector. By understanding customer’s behaviors, restaurant’s owners will be able to identify their targeted customers and will give a clear insight on their menu products. Additionally, it will allow them to target their marketing campaigns, increase the revenue and optimize the cost. Artificial intelligence applications in this field have a huge positive impact in operations of food and beverage sector and depending on of this technology will change the way of restaurant’s management. Data mining prediction model is a tool that can be used by business’s stakeholders to determine and predict the most attributes that can affect their customer behavior. Therefore, the current research finds better solutions to enhance business decision-making by the use of AI and data analytics which will help in understanding the consumer’s behaviors.

Introduction

One of the most noteworthy arcs of the 21st century has been the explosive growth seen in the usage and deployment of technology. More specifically, the advancement of information and communications technology (ICT) has been the foundation for many of the innovations seen over this century thus far. The wide-spanning adoption of the Internet for social media, e-commerce, news consumption, and more has redefined much of our everyday lives and industries across the world. Due to the advancements of ICT, there are many exciting opportunities for what is yet to come. One longstanding opportunity for the future is the vision of the smart city.

There are countless different ideas and definitions for what exactly qualifies as a smart city. A more general definition, provided by Bakıcı et al. in [1], defines smart cities as being high-tech cities that serve to connect people, information, and city-wide infrastructure and resources using advances in technology to improve sustainability, innovation, and overall quality-of-life. The extent to which how pervasive (or intrusive) technologies are incorporated to accomplish such goals is often a point of contention in the literature [2]. However, the many different visions for what define smart cities all generally involve heavy utilization of technology to improve the maintenance of cities to further improve and/or optimize city-specific operations for different stakeholders (e.g., citizens, the environment).

Realizing the ambitious vision of smart cities requires seamlessly making decisions for a host of different applications and domains with minimal sacrifice with respect to performance and resource costs. Some applications of interest for smart cities include, but are not limited to, smart traffic light control, smart information spread (or advertisement), smart energy distribution, smart waste management, etc. Ideally, data generated in urban environments can be collected in smart cities using CT-powered infrastructure to train and inform models designed for these applications to make realtime decisions. For instance, insistent collection of traffic congestion data from roadside units(RSU) and other sources can be sent to ICT infrastructure to train machine learning models how to make optimal decisions related to traffic lights to reduce congestion. This approach is becoming more within reach given the advances in vehicular ad hoc networks (VANET) [3, 4, 5] and other Internetof-Vehicles (IoV) systems [6, 7, 8].

Making smart decisions in a timely fashion will require a powerful pipeline ready to support intense amounts of data collection, analytics, and adaptive decision-making. For instance, video feeds at road intersections — to empower models for smart traffic light control — are large in size (in terms of bytes) and will require automated processes to make meaningful decisions in real-time. However, video cameras that provide such video feeds will be unable, on their own, to perform such analytics and decision-making. Such tasks would require more computational resources than would be available at video cameras and other similar sensors that can be found in urban environments. Thus, for such applications, smart cities will need to be able to provide heavy computational power on-demand, even for devices that do not have such resources locally. Applications (e.g., big data analytics, video classification) all require large amounts of compute resources in order to adequately process. Because Internet-of-Things (IoT) devices like smartphones are often ill-equipped to meet the needs of such processes [9, 10], a standard approach for providing these services on these devices is to rely on remote resources.