Mobile Information Systems

Published by Hindawi

Online ISSN: 1875-905X

Disciplines: Mobile & Wireless Communications

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Autonomous vehicles’ safety and security.
Autonomous vehicles’ safety and security.
IoT ecosystem for AI-enabled autonomous vehicles.
AI-enabled autonomous vehicles.
Tools for autonomous vehicle.


Autonomous Vehicles and Intelligent Automation: Applications, Challenges, and Opportunities

June 2022


2,627 Reads






Shakila Basheer

Aims and scope

Recent advances in wireless technologies and battery performance have enabled increasingly more powerful mobile computing devices, that are themselves more widely available than ever before. The emergence of these devices has led to a proliferation of users engaging in data communication and processing on a massive scale. To report advances in this area, this journal publishes studies that present the theory and/or application of new ideas and concepts in the field of mobile information systems, and welcomes submissions from researchers, practitioners, and professionals across academia, government, and industry.

As well as original research, Mobile Information Systems also publishes focused review articles that examine the state of the art, identify emerging trends, and suggest future directions for developing fields.

Recent articles

Retracted: Interbank Offered Interest Rate Risk Measurement Based on Embedded Sensor Network
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December 2023


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

December 2023


10 Reads

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.

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

November 2023


29 Reads

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.

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

November 2023


46 Reads

Cloud computing provides on-demand access to a shared puddle of computing resources, containing applications, storage, services, and servers above the internet. Tis allows organizations to scale their IT infrastructure up or down as needed, reduce costs, and improve efciency and fexibility. 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 efectively 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 signifcant 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. Te proposed model has been compared with previous work, and the consequences show the signifcance of the proposed work. Our model secures a 20% improvement in scalability services. Te DCL algorithm ofers signifcant advantages over genetic and min-max algorithms in terms of training time and efectiveness. Trough 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. Te training time can be reduced by 10% to 45%, while efectiveness is enhanced by 30% to 55%. Tese improvements make the DCL algorithm a promising option for enhancing training time and efectiveness 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.