Mobile Information Systems
 Journal metrics
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Acceptance rate5%
Submission to final decision187 days
Acceptance to publication137 days
CiteScore1.400
Journal Citation Indicator-
Impact Factor-

The Review and Comparison between Centralized and Decentralized Digital Identity Systems

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 Journal profile

Mobile Information Systems publishes original research articles as well as review articles that report the theory and/or application of new ideas and concepts in the field of mobile information systems.

 Editor spotlight

Chief Editor Dr Alessandro Bazzi is based at the University of Bologna, Italy. His current research is focused on wireless technologies applied to automated and connected vehicles.

 Special Issues

Do you think there is an emerging area of research that really needs to be highlighted? Or an existing research area that has been overlooked or would benefit from deeper investigation? Raise the profile of a research area by leading a Special Issue.

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Research Article

Exploring the Privacy Paradox in Social Network Users: A Double-Entry Mental Accounting Theory Perspective

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.

Research Article

Challenges and Possible Solutions of Implementing 5G Mobile Networks in Bangladesh

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.

Research Article

Blockchain-Based Authentication Scheme with an Adaptive Multi-Factor Authentication Strategy

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.

Research Article

Dynamic Q-Learning-Based Optimized Load Balancing Technique in Cloud

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.

Research Article

Optimizing QoS Metrics for Software-Defined Networking in Federated Learning

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.

Research Article

The Effect of Consumer Resistance and Trust on the Intention to Accept Fully Autonomous Vehicles

Fully autonomous vehicles are a new technology that is expected to be widely accepted by consumers because of their various advantages. This study examined consumers’ intention to accept fully autonomous vehicles based on trust and resistance. To this end, consumer data were analyzed by integrating the innovation resistance model and the technology trust model. The subjects of the survey were 400 drivers between the ages of twenty and sixty-nine. As a result of the study, variables related to the “technical characteristics” of fully autonomous vehicles affected the resistance. On the other hand, “experiential characteristics” were confirmed to affect trust. Second, consumers with a high innovation propensity are more likely to accept fully autonomous vehicles when they are commercialized in the future. Third, it was found that resistance had a negative effect and trust had a positive effect on consumers’ intention to accept fully autonomous vehicles. Therefore, for consumers to accept these, technology should be developed in the direction of removing factors affecting resistance and providing factors increasing trust. As such, consumers have anxiety and concerns as well as expectations, even though they have not yet experienced a fully autonomous vehicle. In particular, since fully autonomous vehicles completely change the existing driving paradigm, more careful consideration is required in the diffusion of this technology.

Mobile Information Systems
 Journal metrics
See full report
Acceptance rate5%
Submission to final decision187 days
Acceptance to publication137 days
CiteScore1.400
Journal Citation Indicator-
Impact Factor-
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