Mobile Internet Research Articles

Connectivity in crisis: the contrasting roles of mobile and non-mobile Internet on subjective well-being during the COVID-19 pandemic

As the COVID-19 pandemic has accelerated the shift towards mobile Internet while decreasing traditional, non-mobile Internet usage, understanding the implications of this trend on individuals’ subjective well-being has become particularly crucial. While the distinction in connectivity is evident, the specific ways in which each modality influences subjective well-being are not well documented. This study, grounded in the uses and gratifications theory, asserts that while both Internet types fulfil some similar gratifications, their unique gratifications lead to varying impacts on subjective well-being in normal and socially disruptive times. Utilising a representative longitudinal dataset from China comprising 46,803 observations from 15,601 individuals, our findings indicate that traditional Internet generally exerts a more positive influence on subjective well-being than mobile Internet. However, the role of mobile Internet has become significantly more pronounced during the COVID-19 pandemic. We further reveal the substitution effect between mobile and non-mobile Internet, which diminished during the COVID-19 pandemic. This study offers novel insights into the comparative effects of mobile and traditional Internet on subjective well-being, especially during social disturbances. This research contributes to a deeper understanding of technology’s role in enhancing subjective well-being, as well as in mitigating the impacts of crises.

Machine learning solutions for mobile internet of things security: A literature review and research agenda

AbstractIn recent years, the advancements in wireless technologies and sensor networks have promoted the Mobile Internet of Things (MIoT) paradigm. However, the unique characteristics of MIoT networks expose them to significant security vulnerabilities and threats, necessitating robust cybersecurity measures, including effective attack detection and mitigation techniques. Among these strategies, Artificial Intelligence (AI), and particularly Machine Learning‐ (ML) based approaches, emerge as a pivotal method for bolstering MIoT security. In this paper, we present a comprehensive literature survey regarding the utilization of ML for enhancing security in MIoT. Through an exhaustive review of existing research articles, we analyze the diverse array of ML‐based approaches employed to safeguard MIoT ecosystems and provide a holistic understanding of the current landscape, elucidating the strengths and limitations of prevailing methodologies. We propose a structured taxonomy to categorize recent works in this domain, by distinguishing approaches based on Shallow Supervised Learning (SSL), Shallow Unsupervised Learning (SUL), Deep Learning (DL), and Reinforcement Learning (RL). By delineating existing challenges and potential future directions for cybersecurity in MIoT, we aim to stimulate discourse and inspire novel approaches towards more resilient and secure MIoT ecosystems.

Research on the influence mechanism of college students' mainstream consciousness leadership under the background of mobile Internet

In recent years, the rapid innovation and popularization of mobile communication and new media technologies have brought about profound changes in the mode of social information dissemination. For college students whose ideological values are still in the critical period of development and change, how to enhance their mainstream ideological leadership in the mobile Internet era and guide the ideological and behavioral development of college students with socialist ideology guided by Marxism has become a common focus of attention in society and academia. At present, the research on the mainstream ideological leadership of college students in the academic community mainly focuses on the negative impact of modern communication technologies such as new media on the ideological work of colleges and universities and the corresponding countermeasures, while the theoretical core and internal composition of the mainstream ideological leadership, as well as the deep impact logic, positive and negative impacts and systematic improvement paths of the technological and social changes in the mobile Internet era on the mainstream ideological leadership of college students are still insufficient. Therefore, this study aims to go beyond the research perspective of traditional college ideological education management, deeply explore the basic connotation and force composition of the mainstream ideological leadership, systematically analyze the deep impact logic of the mobile Internet era on the mainstream ideological leadership of college students, sort out its improvement opportunities, and explore the structural dilemmas in it, and propose a systematic path for improving the mainstream ideological leadership of college students.

The Development of Short Plays in China

Short plays have rapidly risen in popularity due to their novel format and flexible business models, yet they face challenges such as content homogeneity, vulgar material, and profitability issues. This paper analyzes the current development, challenges, and solutions for short plays in China. It proposes measures including content innovation, copyright protection, technology enhancement, policy support, and business model innovation to promote sustainable development in the short play industry. In the future, with the widespread adoption of mobile internet and technological advancements, short plays are expected to expand their market share, improve production quality, and enhance viewing experiences, becoming a vital component of the entertainment industry.

Mobility-aware federated self-supervised learning in vehicular network

The development of the Internet of Things has led to a significant increase in the number of devices, consequently generating a vast amount of data and resulting in an influx of unlabeled data. Collecting these data enables the training of robust models to support a broader range of applications. However, labeling these data can be costly, and the models dependent on labeled data are often unsuitable for rapidly evolving fields like vehicular networks and mobile Internet of Things, where new data continuously emerge. To address this challenge, Self-Supervised Learning (SSL) offers a way to train models without the need for labels. Nevertheless, the data stored locally in vehicles are considered private, and vehicles are reluctant to share data with others. Federated Learning (FL) is an advanced distributed machine learning approach that protects each vehicle’s privacy by allowing models to be trained locally and the model parameters to be exchanged across multiple devices simultaneously. Additionally, vehicles capture images while driving through cameras mounted on their rooftops. If a vehicle’s velocity is too high, the captured images, donated as local data, may be blurred. Simple aggregation of such data can negatively impact the accuracy of the aggregated model and slow down the convergence speed of FL. This paper proposes a FL algorithm for aggregation based on image blur levels, which is called FLSimCo. This algorithm does not require labels and serves as a pre-training stage for SSL in vehicular networks. Simulation results demonstrate that the proposed algorithm achieves fast and stable convergence.

How demographic and appearance cues of a potential social engineer influence trust perception and risk-taking among targets?

Purpose The aim of this study is to investigate how the demographics and appearance cues of potential social engineers influence the likelihood that targets will trust them and accept security risk. Design/methodology/approach Data were collected through an online survey of 635 participants, including 322 participants from Arab countries and 313 participants from the UK. The survey presented scenarios with 16 personas who offered participants the use of their mobile internet hotspot. These personas were characterized by combinations of age (young vs aged), gender (male vs female), ethnicity (Arab vs UK) and look formality (casual vs formal). The study measured both participants’ offer acceptance and trust in the persona. Findings Results indicated a higher likelihood of offer acceptance from female and aged personas, as well as a greater trust in these groups. Arab participants showed a preference for personas with Arabian ethnic features. In both samples, trust and acceptance were influenced by the persona’s appearance, which was found to be gender-dependent; with female personas in casual attire and male personas in formal attire being trusted more in comparison to female with formal attire and male with informal, respectively. Practical implications Findings highlight the importance of incorporating awareness of appearance-based biases in cybersecurity training, suggesting the need for culturally sensitive training programs to enhance defense against social engineering. Originality/value This study distinguishes itself by elucidating the influence of social engineers’ demographic and appearance cues on the likelihood of individuals to take security risks, thus addressing a significant gap in the literature which has traditionally emphasized the profiles of targets.

FedGST: An Efficient Federated Graph Neural Network for Spatio-temporal PoI Recommendation

With the proliferation of sensor networks in urban areas, vast amounts of data from location-based social network platforms are now available, thus enabling the stakeholders to develop location-aware services for the end users. PoI (Point of Interests) recommendation is one of the most trending services. PoI recommendation models are developed by considering geographical information and historical records. More recently, with the demand for achieving PoI recommendations on mobile devices, embedded devices, or Internet of Things (IoT) devices, the challenges posed by the limited computational resources, malicious clients, and data silos, the model performance gets immensely affected. To address the above challenges and achieve efficient PoI recommendations, we propose an efficient federated graph learning-based model for mining complex spatiotemporal features to generate recommendations. We implement a GRU-based encoder-decoder to learn the temporal hidden state embeddings. Simultaneously, a 2-layer graph network is used to understand spatial embedding vectors. We also introduce an efficient contribution evaluation method to speed up the training process and improve the recommendation performance. Experiments on the PoI recommendation task based on real-life check-in data validate the effectiveness of our proposed model, and the results indicate that our recommendation model can achieve competitive results with lower computational costs.

A comprehensive review of model compression techniques in machine learning

This paper critically examines model compression techniques within the machine learning (ML) domain, emphasizing their role in enhancing model efficiency for deployment in resource-constrained environments, such as mobile devices, edge computing, and Internet of Things (IoT) systems. By systematically exploring compression techniques and lightweight design architectures, it is provided a comprehensive understanding of their operational contexts and effectiveness. The synthesis of these strategies reveals a dynamic interplay between model performance and computational demand, highlighting the balance required for optimal application. As machine learning (ML) models grow increasingly complex and data-intensive, the demand for computational resources and memory has surged accordingly. This escalation presents significant challenges for the deployment of artificial intelligence (AI) systems in real-world applications, particularly where hardware capabilities are limited. Therefore, model compression techniques are not merely advantageous but essential for ensuring that these models can be utilized across various domains, maintaining high performance without prohibitive resource requirements. Furthermore, this review underscores the importance of model compression in sustainable artificial intelligence (AI) development. The introduction of hybrid methods, which combine multiple compression techniques, promises to deliver superior performance and efficiency. Additionally, the development of intelligent frameworks capable of selecting the most appropriate compression strategy based on specific application needs is crucial for advancing the field. The practical examples and engineering applications discussed demonstrate the real-world impact of these techniques. By optimizing the balance between model complexity and computational efficiency, model compression ensures that the advancements in AI technology remain sustainable and widely applicable. This comprehensive review thus contributes to the academic discourse and guides innovative solutions for efficient and responsible machine learning practices, paving the way for future advancements in the field.Graphical abstract

On Development of a SIM-Based Prototype for Enhancing the Usability of Mobile Value-Added Services

The advent of Mobile Value-Added Services (VAS) has witnessed widespread adoption in developing regions, notably within East Africa. This surge is attributed to advancements in information and communication technology, 3G/4G proliferation, growth of mobile internet accessibility, and rising smartphone penetration. Despite this progress, a significant segment of the population continues to rely on basic phones. Studies have shown that the text-based applications used on these types of mobile phones still pose significant obstacles in delivering a user-friendly VAS experience. This paper explores the potential for improving the usability of mobile value-added services on basic phones. We employed an action research approach, utilizing quantitative and qualitative data collection methods. Questionnaires gathered feedback on end-user testing, while in-depth interviews with five industry experts provided insights into potential solutions. This multi-faceted approach ensures a comprehensive understanding of user feedback and expert insights, culminating in the design of an intuitive SIM-based user interface prototype. This highly customised prototype prioritises services and functionalities simplify the user experience through improved navigability and accessibility. The effectiveness of this prototype was quantitatively analysed using descriptive statistics, focusing on ease of use, efficiency, effectiveness, and user satisfaction. With an average ease of use score of 85.8%, a geometric mean time on task of 29.43 seconds, and a user satisfaction score of 79.5%, the prototype demonstrates impressive functionality and garners acclaim for improved navigation and information discovery from users and experts. Further, comparative analysis from expert interviews reveald a more nuanced understanding of technology preferences for basic phone usability, contrary to previous studies. Building upon these demonstrably positive results, the paper concludes by advocating for user interface standardisation and recommending user-generated content for a more inclusive VAS ecosystem.

Assessing the substitutability of mobile and fixed internet: The impact of 5G services on consumer valuation and price elasticity

In this study, we explore the dynamics of consumer choices in the Polish telecommunications market, focusing on preferences and valuations for home fixed, home mobile, and purely mobile Internet connections. Key attributes such as speed, latency, data limits, and cost are examined. Central to our research is the investigation of how the integration of 5G technology might influence demand elasticity. Using a detailed discrete choice experiment, we apply a mixed logit model with random parameters to analyze stated choice data, enabling us to unravel the complexities of demand elasticity, especially in terms of own and cross-price elasticities. This approach facilitates an assessment of the degree of substitutability between fixed and mobile Internet services.Our findings indicate a moderate substitution effect between fixed and mobile Internet services. Results from a Small but Significant and Non-transitory Increase in Price (SSNIP) test suggest that these markets should continue to be regulated separately, mirroring the distinct regulation observed in fixed and mobile telephony. Furthermore, simulations provide insights into potential future market shifts with the advent of 5G services. This paper contributes significantly to the discourse on fixed-mobile Internet substitution and offers vital insights for defining markets in antitrust discussions, competitive agreements, and potential mergers within the telecom sector.

Enhancing 5G energy efficiency through digital twin networks: A comprehensive review

AbstractIn the era of 5G, seamless mobility handovers are critical in densely populated regions like Malaysia to mitigate disruptions and inefficiencies. 5G networks offer unprecedented data speeds and reliability, essential for advancing mobile communication and Internet of Things applications. However, ensuring continuous connectivity and service during mobility remains challenging, especially in urban settings. Digital twin technology presents a promising solution to enhance 5G handover mechanisms. A digital twin network (DTN) mirroring Malaysia's 5G infrastructure is proposed, utilising real‐time data and user behaviour insights to optimise energy consumption during handovers. The focus is on energy‐efficient protocols and algorithms, reviewed through a systematic literature review. The DTN aims to enhance mobility handover efficiency through predictive handovers and adaptive resource allocation, bolstered by sustainable practices such as edge computing. The potential of DTNs to optimise 5G handover processes is explored, starting with the foundational concepts of 5G mobility and digital twins, highlighting the need for improved strategies in high‐mobility scenarios. Methodologies leveraging digital twins to predict network conditions, simulate handover scenarios, and proactively manage decisions are examined, reducing latency and packet loss. Case studies demonstrate how digital twins adapt dynamically to network changes and user mobility, thereby improving quality of service and user experience. Malaysia's specific 5G mobility challenges are addressed with a tailored DTN emphasising energy efficiency, evaluated through practical applications. Evaluation criteria assess effectiveness with in‐depth analysis of methods, performance metrics, limitations, and recommendations for future research. Challenges and future directions including scalability, security, and real‐time data processing, are discussed, aiming to integrate digital twin technology with 5G networks for enhanced connectivity. This abstract provides a roadmap for leveraging digital twins to optimise 5G network performance sustainably, guiding future research and implementation strategies.

Distributed DDPG-Based Resource Allocation for Age of Information Minimization in Mobile Wireless-Powered Internet of Things

Distributed DDPG-Based Resource Allocation for Age of Information Minimization in Mobile Wireless-Powered Internet of Things

Stratospheric airship trajectory planning in wind field using deep reinforcement learning

Stratospheric airships, with their long endurance, high flight altitude, and large payload capacity, show promise in earth observation and mobile internet applications. However, challenges arise due to their low flight speed, limited maneuverability and energy constraints when planning trajectories in dynamic wind fields. This paper proposes a deep reinforcement learning-based method for trajectory planning of stratospheric airships. The model considers the motion characteristics of stratospheric airships and environmental factors like wind fields and solar radiation. The soft actor-critic (SAC) algorithm is utilized to assess the effectiveness of the method in various scenarios. A comparison between time-optimized and energy-optimized trajectories reveals that time-optimized trajectories are smoother with a higher speed, while energy-optimized trajectories can save up to 10% energy by utilizing wind fields and solar energy absorption. Overall, the deep reinforcement learning approach proves effective in trajectory planning for stratospheric airships in deterministic and dynamic wind fields, offering valuable insights for flight design and optimization.

Aspects of mobile phone use and behavioural intentions for learning in rural South African schools

This paper focuses on a study conducted in two historically disadvantaged schools (deep-rural and semi-urban) in South Africa where the rising access to mobile technology implies that the long-standing promise of mobile learning is becoming accessible despite the lack of recent research. This research aims to investigate the relationship between the type of school and various aspects of mobile phone use as well as differences in behavioural intention constructs to use mobile phones for learning. After a literature review, several hypotheses were developed. Using non-probability sampling techniques and the survey method, data was obtained from 128 participants, 79 from the deep-rural school and 49 from the semi-urban school. The semi-urban school exhibited slightly higher percentages in all aspects of cell phone use. Outside of mobile phone ownership and Internet access, all associations were significant. Significant differences were reported in favour of the semi-urban school for all constructs regarding the behavioural intention to use mobile phones for learning. However, the future of m-learning appears to be more promising than reported in earlier research by reporting positive behaviour from students in both schools. Critical stakeholders in education should take note and move from the goals of improving broad access to explicit objectives to implement m-learning in rural schools.

Research on UI Interface Design of NAIS Small Program--Research on APP Design based on KANO Model

With the popularization of the mobile Internet and the wide application of smartphones, the importance of the UI interface design of the small program, as an important window for the interaction between the brand and the user, has become more and more prominent. This paper adopts the KANO model to analyze the UI interface of the NAIS applet deeply and puts forward targeted improvement suggestions. Through the KANO model to analyze the basic needs, expectation needs and excitement needs of the target users, this paper aims to match the young aesthetics and shape a unique interface style through the three aspects of design style and color matching, interface layout and function setting, interaction optimization and kinetic effect design; optimize the user experience and enhance the operation convenience; strengthen the brand identity and improve the competitiveness of the market.

Lightweight ring-neighbor-based user authentication and group-key agreement for internet of drones

As mobile internet and Internet of Things technologies continue to advance, the application scenarios of peer-to-peer Internet of Drones (IoD) are becoming increasingly diverse. However, the development of IoD also faces significant challenges, such as security, privacy protection, and limited computing power, which require technological innovation to overcome. For group secure communication, it is necessary to provide two basic services, user authentication and group key agreement. Due to the limited storage of IoD devices, group key negotiation requires lightweight calculations, and conventional schemes cannot satisfy the requirements of group communication in the IoD. To this end, a new lightweight communication scheme based on ring neighbors is presented in this paper for IoD, which not only realizes the identity verification of user and group key negotiation, but also improves computational efficiency on each group member side. A detailed security analysis substantiates that the designed scheme is capable of withstanding attacks from both internal and external adversaries while satisfying all defined security requirements. More importantly, in our proposal, the computational cost on the user side remains unaffected by the variability of the number of members participating in group communication, as members communicate in a non-interactive manner through broadcasting. As a result, the protocol proposed in this article demonstrates lower computational and communication costs in comparison to other cryptographic schemes. Hence, this proposal presents a more appealing approach to lightweight group key agreement protocol with user authentication for application in the IoD.

Spatio-Temporal Big Data Collaborative Storage Mechanism Based on Incremental Aggregation Subvector Commitment in On-Chain and Off-Chain Systems

As mobile internet and Internet of Things technologies rapidly advance, the amount of spatio-temporal big data have surged, and efficient and secure management solutions are urgently needed. Although cloud storage provides convenience, it also brings significant data security challenges. Blockchain technology is an ideal choice for processing large-scale spatio-temporal big data due to its unique security features, but its storage scalability is limited because the data need to be replicated throughout the network. To solve this problem, a common approach is to combine blockchain with off-chain storage to form a hybrid storage blockchain. However, these solutions cannot guarantee the authenticity, integrity, and consistency of on-chain and off-chain data storage, and preprocessing is required in the setup phase to generate public parameters proportional to the data length, which increases the computational burden and reduces transmission efficiency. Therefore, this paper proposes a collaborative storage mechanism for spatio-temporal big data based on incremental aggregation sub-vector commitments, which uses vector commitment binding technology to ensure the secure storage of on-chain and off-chain data. By generating public parameters of fixed length, the computational complexity is reduced and the communication efficiency is improved while improving the security of the system. In addition, we design an aggregation proof protocol that integrates aggregation algorithms and smart contracts to improve the efficiency of data query and verification and ensure the consistency and integrity of spatio-temporal big data storage. Finally, simulation experiments verify the correctness and security of the proposed protocol, providing a solid foundation for the blockchain-based spatio-temporal big data storage system.

Link between Wi-Fi, cordless devices, mobile phone usage patterns, and behavioral problems among Japanese children: A prospective cohort study

BackgroundWith the recent advent of technology, it is important to confirm the health and safety of the youth. This study aimed to prospectively evaluate the relationship between Wi-Fi, cordless phones, and mobile phone usage patterns and behavioral problems. MethodsThis study involved 2465 children aged 8–17 years from the Hokkaido Study on Environment and Children’s Health from October 2020 to January 2021, with a follow-up from September 2021 to March 2022. The mother–child dyad provided information on the presence of residential Wi-Fi and cordless phones, cordless phone call duration, and mobile phone usage pattern (duration of calls using mobile network and internet, online audio streaming, online video streaming, and playing online games) via a baseline questionnaire. Based on the scores on Strength and Difficulties Questionnaire at baseline and follow-up, the children were categorized into four groups: normal, persistent, improved, and concurrent. ResultsNo significant association was found between Wi-Fi, mobile phone calls via mobile networks, and behavioral problems. Cordless phone at home had higher odds for improvement in total difficulty scores, and cordless phone for calling more than 4 min per week had lower odds of persistent problematic prosocial behavior. Longer duration of mobile phone calling via the internet (>40 min/week) had higher odds of concurrent total difficulties. Mobile phone calling via mobile network for <5 min per week had higher odds for improved total difficulty scores. Audio streaming via mobile phones for 60–120 min had lower odds of persistent total difficulties. ConclusionOur results showed sporadic findings between residential RF-EMF indoor sources and mobile phone usage pattern. These observed findings could be affected by residual confounding and chance findings. Ongoing follow-up studies are necessary to further explore this association through detailed exposure assessment and addressing the potential limitations of our study.

Deep reinforcement learning based resource provisioning for federated edge learning

With the rapid development of mobile internet technology and increasing concerns over data privacy, Federated Learning (FL) has emerged as a significant framework for training machine learning models. Given the advancements in technology, User Equipment (UE) can now process multiple computing tasks simultaneously, and since UEs can have multiple data sources that are suitable for various FL tasks, multiple tasks FL could be a promising way to respond to different application requests at the same time. However, running multiple FL tasks simultaneously could lead to a strain on the device’s computation resource and excessive energy consumption, especially the issue of energy consumption challenge. Due to factors such as limited battery capacity and device heterogeneity, UE may fail to efficiently complete the local training task, and some of them may become stragglers with high-quality data. Aiming at alleviating the energy consumption challenge in a multi-task FL environment, we design an automatic Multi-Task FL Deployment (MFLD) algorithm to reach the local balancing and energy consumption goals. The MFLD algorithm leverages Deep Reinforcement Learning (DRL) techniques to automatically select UEs and allocate the computation resources according to the task requirement. Extensive experiments validate our proposed approach and showed significant improvements in task deployment success rate and energy consumption cost.

Short Video Platform Advertising Marketing Ideas and Logic--Taking KANS as an Example

With the rapid rise of mobile Internet, short video platforms have captured massive user attention. In 2023, short video users exceeded 750 million, shifting advertising revenue from traditional to new media. Short video marketing, with its rich content and immersive experiences, has become crucial for brand promotion. Young users' creativity is pivotal, and short video ads blend seamlessly with content to enhance brand recognition. However, challenges still exist. Content homogenization, low user participation may impact brand loyalty. Ad effectiveness should be evaluated properly. This paper examines KANS's short video marketing, highlighting strategies like "short drama + advertising," and addressing issues through creative planning and interactive engagement. KANS's success, aided by precise audience targeting and innovative tactics, demonstrates the potential of short video advertising. However, continuous improvement in content uniqueness, user engagement, and data-driven evaluation is necessary for sustained effectiveness. The study provides suggestions for brands to optimize their short video marketing strategies, ensuring alignment with user needs and market dynamics.