IoT emergence has given rise to a new digital experience of payment transactions where physical objects like refrigerators, cars, and wearables will make payments. These physical objects will be storing the cardholder credentials and will directly make payments with the vendors over insecure public networks. For such payment transactions, government regulations and standards organizations require to implement PCI DSS for adapting similar set of security measures at the global level. The current version of PCI DSS is not suitable for IoT-based payment systems due to characteristics of IoT such as resource-constrained nature of devices and updating software/firmware of so many physical devices. Also, there arises an emergent need of implementing PCI DSS requirements and assessments for security of all stakeholders that store or process the user credentials in a payment. This paper is an initial effort to bring the researcher’s attention to make upcoming versions of PCI DSS suitable for IoT and thus securing the new ways of IoT-based payment systems. The paper has reviewed the traditional payment process along with considerations for IoT-based payment systems to make recommendations to modify the PCI DSS in a suitable way for IoT.

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A number of recent studies have examined the impact of advanced technologies on organizations. However, many (particularly those in developing countries) still face challenges when it comes to the adoption of mature technologies and have also continued to repeat many of the mistakes of early adopters, primarily in relation to automated workflow systems. The current paper analyses a case study of a public organization in the developing country of Saudi Arabia, with the aim of understanding its resistance to change brought about by the implementation of a mature technology, i.e., automated workflow systems. The study undertook semi-structured interviews with employees to establish the nature of this resistance, identifying their preference for familiar processes and systems, alongside their unwillingness to embrace the new system. Furthermore, the study highlighted a number of issues experienced during the implementation of automated workflow systems, including job security; changes in laws and rules; an inability to understand, and/or trust, the technology; the perceived risks and costs associated with change; and the transformation of business processes. It also cited factors related to organizational structure and power, and the discomfort involved in making difficult decisions. This study, therefore, aims to assist organizations to create a sound foundation for change prior to the adoption of more advanced technologies.

Ubiquitous mobile edge computing (MEC) using the internet of things (IoT) is a promising technology for providing low-latency and high-throughput services to end-users. Resource allocation and quality of service (QoS) optimization are critical challenges in MEC systems due to the large number of devices and applications involved. This results in poor latency with minimum throughput and energy consumption as well as a high delay rate. Therefore, this paper proposes a novel approach for resource allocation and QoS optimization in MEC using IoT by combining the hybrid kernel random Forest (HKRF) and ensemble support vector machine (ESVM) algorithms with crossover-based hunter–prey optimization (CHPO). The HKRF algorithm uses decision trees and kernel functions to capture the complex relationships between input features and output labels. The ESVM algorithm combines multiple SVM classifiers to improve the classification accuracy and robustness. The CHPO algorithm is a metaheuristic optimization algorithm that mimics the hunting behavior of predators and prey in nature. The proposed approach aims to optimize the parameters of the HKRF and ESVM algorithms and allocate resources to different applications running on the MEC network to improve the QoS metrics such as latency, throughput, and energy efficiency. The experimental results show that the proposed approach outperforms other algorithms in terms of QoS metrics and resource allocation efficiency. The throughput and the energy consumption attained by our proposed approach are 595 mbit/s and 9.4 mJ, respectively.