A Novel Framework for Software Defined Wireless Body Area Network

Software Defined Networking (SDN) has gained huge popularity in replacing traditional network by offering flexible and dynamic network management. It has drawn significant attention of the researchers from both academia and industries. Particularly, incorporating SDN in Wireless Body Area Network (WBAN) applications indicates promising benefits in terms of dealing with challenges like traffic management, authentication, energy efficiency etc. while enhancing administrative control. This paper presents a novel framework for Software Defined WBAN (SDWBAN), which brings the concept of SDN technology into WBAN applications. By decoupling the control plane from data plane and having more programmatic control would assist to overcome the current lacking and challenges of WBAN. Therefore, we provide a conceptual framework for SDWBAN with packet flow model and a future direction of research pertaining to SDWBAN.Comment: Presented on 8th International Conference on Intelligent Systems, Modelling and Simulatio

Blockchain of Things: Benefits, Challenges and Future Directions

As Internet of Things (IoT) technologies become increasingly integrated into our daily lives through a multitude of Internet-enabled devices, the efficient, secure, and cost-effective management of the vast amount of data generated by these devices poses a significant challenge. Blockchain has recently emerged as a promising technique to address this challenge by providing a means to establish trust without relying on a trusted third party. The convergence of blockchain and IoT presents a transformative opportunity to establish a secure and robust mechanism for managing the data generated by IoT devices. It is recognized as the essential missing link for enabling IoT devices to fully harness their benefits. This Special Issue delves into a diverse range of IoT-enabled blockchain-driven solutions that leverage the integration of IoT and blockchain technologies, aiming to explore and advance the intersection of these two innovative technologies.For this Special Issue, we received 19 papers in total, and 11 of them were accepted and published. The authors presented some novel ideas, frameworks, and smart contract vulnerability detection methods to solve many real-world problems. These advanced models not only offer tailored solutions but also contribute significantly to increased efficiency, heightened security, and improved efficiency, highlighting the transformative potential of the integration of IoT and blockchain technology. We extend our heartfelt gratitude to all authors for their valuable contributions to this field

Immutable Autobiography of Smart Cars Leveraging Blockchain Technology

The popularity of smart cars is increasing around the world as they offer a wide range of services and conveniences. These smart cars are equipped with a variety of sensors generating a large amount of data, many of which are critical. Besides, there are multiple parties involved in the lifespan of a smart car, such as manufacturers, car owners, government agencies, and third-party service providers who also generate data about the vehicle. In addition to managing and sharing data amongst these entities in a secure and privacy-friendly way which is a great challenge itself, there exists a trust deficit about some types of data as they remain under the custody of the car owner (e.g. satellite navigation and mileage data) and can easily be manipulated. In this paper, we propose a blockchain assisted architecture enabling the owner of a smart car to create an immutable record of every data, called the autobiography of a car, generated within its lifespan. We also explain how the trust about this record is guaranteed by the immutability characteristic of the blockchain. Furthermore, the paper describes how the proposed architecture enables a secure and privacy-preserving mechanism for sharing of smart car data among different parties

A Comparative Analysis of Distributed Ledger Technology Platforms

Distributed Ledger Technology (DLT) has emerged as one of the most disruptive technologies in the last decade. It promises to change the way people do their business, track their products, and manage their personal data. Though the concept of DLT was first implemented in 2009 as Bitcoin, it has gained significant attention only in the past few years. During this time, different DLT enthusiasts and commercial companies have proposed and developed several DLT platforms. These platforms are usually categorized as public vs private, general-purpose vs application-specific and so on. As a growing number of people are interested to build DLT applications, it is important to understand their underlying architecture and capabilities in order to determine which DLT platform should be leveraged for a specific DLT application. In addition, the platforms need to be evaluated and critically analyzed to assess their applicability, resiliency and sustainability in the long run. In this paper, we have surveyed several leading DLT platforms and evaluated their capabilities based on a number of quantitative and qualitative criteria. The comparative analysis presented in this paper will help the DLT developers and architects to choose the best platform as per their requirement(s)

COVID-19 Contact Tracing: Challenges and Future Directions.

Contact tracing has become a vital tool for public health officials to effectively combat the spread of new diseases, such as the novel coronavirus disease COVID-19. Contact tracing is not new to epidemiologist rather, it used manual or semi-manual approaches that are incredibly time-consuming, costly and inefficient. It mostly relies on human memory while scalability is a significant challenge in tackling pandemics. The unprecedented health and socio-economic impacts led researchers and practitioners around the world to search for technology-based approaches for providing scalable and timely answers. Smartphones and associated digital technologies have the potential to provide a better approach due to their high level of penetration, coupled with mobility. While data-driven solutions are extremely powerful, the fear among citizens is that information like location or proximity associated with other personal data can be weaponised by the states to enforce surveillance. Low adoption rate of such apps due to the lack of trust questioned the efficacy and demanded researchers to find innovative solution for building digital-trust, and appropriately balancing privacy and accuracy of data. In this paper, we have critically reviewed such protocols and apps to identify the strength and weakness of each approach. Finally, we have penned down our recommendations to make the future contact tracing mechanisms more universally inter-operable and privacy-preserving

Quantitative research design to evaluate learning platforms and learning methods for cyber-security courses

CONTEXT Teaching security courses is a challenging task in computer science program since it requires careful integration of theoretical concepts with their practical applications. In this paper, a quantitative approach is used to evaluate effective learning platforms and different learning styles for cyber-security courses. The outcomes of the study show that practice-based learning is the most effective learning method for cyber-security courses and student performance can further be enhanced significantly through social learning instead of solitary learning. PURPOSE The main goal of this research is to understand the effects of learning styles and platforms for successful adaptation of different pedagogical practices. The following research questions are designed to achieve the expected outcomes. For cyber-security courses, does the performance of a student match with his/her selfspecified learning performance? How learning platforms affect a student's performance in cyber-security courses? What factors play significant roles to successfully run a cyber-security course? Which type of learning mechanism is the most effective for cyber-security courses? Is learning in a group better than individual learning? APPROACH Quantitative research is defined as a scientific method which follows a number of procedures such as generation of models, identifying theories and hypotheses, development of instrumentals and methods for measurement, experimental control and manipulation of variables, collection of empirical data, modelling and analysis of data and evaluation of results. This research follows experimental modes of inquiry which follows a standard form namely, participants, materials, procedures and measures. RESULTS The results show that there is no single platform that includes all features to successfully run a cyber-security course. However, this problem can be solved by integrating those features with existing platforms. The study also suggests that learning performance can further be enhanced by choosing appropriate learning style. CONCLUSIONS This paper investigates the impacts of learning platforms and learning strategies for cyber-security courses. Similar experiments from different aspects will be interesting to test their validity. The outcome can be used for further decision making e.g., the correlation of learning style difference could help to determine whether customized learning styles would be more effective for teaching cyber-security courses