Mobile Information Systems

The growing capability of the digital world empowers physical objects to possess much more digital assets than they ever had, and the concepts of the Internet of Things and the Industrial Internet are coming out continually. Digital identity, a unique mark of two-way mapping, dynamic interaction between physical and virtual objects, is one of the fundamental elements among these novel concepts because it is the only way to distinguish each other from hundreds of millions of digital objects. In order to create a world with everything connected on the Internet, plenty of digital identity-orientated systems were raised, and new frameworks are emerging iteratively. To the best of our knowledge, this review lists some of the most applicable digital identity-based solutions by giving mechanism, digital identity solution, feature, and comparison between these models.

Social networking service (SNS) users often express great concern for their personal privacy, yet continue to disclose personal information on these platforms. This privacy paradox between privacy concerns and disclosure behavior has drawn widespread academic attention. In this study, we use the double-entry mental accounting theory to construct a theoretical model and conduct an in-depth analysis of the privacy paradox phenomenon and its causes through empirical verification. Our research shows a significant positive correlation between perceived benefits and users’ intention to disclose privacy, while perceived risks and users’ intention to disclose privacy are significantly negatively correlated. The double-entry mental accounting theory plays a crucial role in mediating the relationship between perceived values and users’ intention to disclose privacy. Furthermore, we found that information sensitivity negatively regulates the relationship between perceived risks, the pleasure attenuation coefficient α, the pain buffering coefficient β, and the intention to disclose privacy. Our study provides theoretical and empirical information on the reasons for the privacy paradox and offers insights for social networking service providers to optimize their services.

Recently, fifth-generation (5G) mobile connectivity has been launched in Bangladesh on a trial-run basis. 5G is a super-speed mobile network that is much faster than the existing fourth-generation (4G) technology. It is excruciatingly hard to deploy a fully functioning 5G in any country regardless of its available resources and technological advancements because of some apparent technological complexity and limitations. In addition, when deploying this technology in developing countries such as Bangladesh, the costs come into play. To cope with the world’s advancement in science and technology, Bangladesh is planning to implement 5G covering the whole country. In this paper, we present the major challenges in implementing a wide area 5G network in Bangladesh and find some possible solutions. This research work has also tried to get a clear picture of the service quality of the existing 4G cellular communication by analyzing some of the mobile operators’ download speeds over 24 hours. In addition, this paper presents the current comparison of Internet facilities in Bangladesh with those of other countries across the globe. To the best of our knowledge, there is no publicly available study that has focused on the deployment of the 5G network in Bangladesh after assessing the current state of the cellular network. Therefore, this study could serve as a guiding resource, providing valuable information for decision-making.

Authentication is of paramount significance to cybersecurity. However, most of conventional authentication schemes are implemented in a centralized mode, in which potential problems that could arise include single-point failure, the exposure of personal information, and the risk of identity theft. Additionally, static single-factor authentication schemes are unsuitable for dynamic environments like mobile applications. In order to tackle these difficulties, we propose a blockchain-based authentication scheme with an adaptive multi-factor authentication strategy. Our scheme features a blockchain-based authentication framework that prevents unauthorized information alteration and system corruption. Additionally, we design an adaptive multi-factor authentication strategy model to ensure trustworthy multi-factor authentication in dynamic scenarios. Last, we construct a Raft-based consensus model to select an authoritative leading node for rapid authentication. The security analysis demonstrates the effectiveness of the proposed scheme in effectively countering various forms of cyberattacks targeted at authentication systems, and experiments demonstrate its superior effectiveness and efficiency compared to existing studies.

Cloud computing provides on-demand access to a shared puddle of computing resources, containing applications, storage, services, and servers above the internet. This allows organizations to scale their IT infrastructure up or down as needed, reduce costs, and improve efficiency and flexibility. Improving professional guidelines for social media interactions is crucial to address the wide range of complex issues that arise in today’s digital age. It is imperative to enhance and update professional guidelines regarding social media interactions in order to effectively tackle the multitude of intricate issues that emerge. In this paper, we propose a reinforcement learning (RL) method for handling dynamic resource allocation (DRA) and load balancing (LB) activity in a cloud environment and achieve good scalability and a significant improvement in performance. To address this matter, we propose a dynamic load balancing technique based on Q-learning, a reinforcement learning algorithm. Our technique leverages Q-learning to acquire an optimal policy for resource allocation in real-time based on existing workload, resource accessibility, and user preferences. We introduce a reward function that takes into account performance metrics such as response time and resource consumption, as well as cost considerations. We evaluate our technique through simulations and show that it outperforms traditional load balancing techniques in expressions of response time and resource utilization while also reducing overall costs. The proposed model has been compared with previous work, and the consequences show the significance of the proposed work. Our model secures a 20% improvement in scalability services. The DCL algorithm offers significant advantages over genetic and min-max algorithms in terms of training time and effectiveness. Through simulations and analysis on various datasets from the machine learning dataset repository, it has been observed that the proposed DCL algorithm outperforms both genetic and min-max algorithms. The training time can be reduced by 10% to 45%, while effectiveness is enhanced by 30% to 55%. These improvements make the DCL algorithm a promising option for enhancing training time and effectiveness in machine learning applications. Further research can be conducted to investigate the potential of combining the DCL algorithm with a supervised training algorithm, which could potentially further improve its performance and apply in real-world application.

In the modern and complex realm of networking, the pursuit of ideal QoS metrics is a fundamental objective aimed at maximizing network efficiency and user experiences. Nonetheless, the accomplishment of this task is hindered by the diversity of networks, the unpredictability of network conditions, and the rapid growth of multimedia traffic. This manuscript presents an innovative method for enhancing the QoS in SDN by combining the load-balancing capabilities of FL and genetic algorithms. The proposed solution aims to improve the dispersed aggregation of multimedia traffic by prioritizing data privacy and ensuring secure network load distribution. By using federated learning, multiple clients can collectively participate in the training process of a global model without compromising the privacy of their sensitive information. This method safeguards user privacy while facilitating the aggregation of distributed multimedia traffic. In addition, genetic algorithms are used to optimize network load balancing, thereby ensuring the efficient use of network resources and mitigating the risk of individual node overload. As a result of extensive testing, this research has demonstrated significant improvements in QoS measurements compared to traditional methods. Our proposed technique outperforms existing techniques such as RR, weighted RR, server load, LBBSRT, and dynamic server approaches in terms of CPU and memory utilization, as well as server requests across three testing servers. This novel methodology has applications in multiple industries, including telecommunications, multimedia streaming, and cloud computing. The proposed method presents an innovative strategy for addressing the optimization of QoS metrics in SDN environments, while preserving data privacy and optimizing network resource usage.