Challenges Of Internet Research Articles

Advancements and Security Challenges in the Industrial Internet of Things (IIoT)

This paper explores the significant advancements in IIoT, focusing on the increased efficiency, productivity, and innovation brought about by interconnected industrial systems. Despite the numerous benefits, the paper also delves into the critical security challenges inherent in IIoT deployments. These include vulnerabilities in network infrastructure, potential cyber- attacks targeting industrial control systems, and data privacy concerns stemming from the vast amounts of sensitive information generated. The analysis highlights the necessity for robust cybersecurity measures, including advanced encryption, intrusion detection systems, and secure communication protocols. This report offers a thorough assessment of the current status of IIoT technology by looking at both the opportunities and threats related to it. It offers insights into best practices and emerging solutions aimed at mitigating security threats, ensuring a secure and resilient industrial ecosystem. The paper concludes by discussing future directions for research and development, emphasizing the importance of a multidisciplinary approach to addressing the complex challenges and unlocking the full potential of IIoT.

Confronting Fake News Through Collective Intelligence in Adolescents

Adolescents are vulnerable to the exponential increase in fake news spread. Collective judgment on the quality of news has been an effective intervention. Collective Learning platform makes it possible to apply collective intelligence through simultaneous interaction in a large group, similar to social networks. The objective of this article is to analyze whether this platform effectively improves how adolescents confront fake news. 360 Spanish students aged 15–16 years old participated in the experiment confronting a fictitious case of fake news. The quantitative analysis using ANOVA demonstrated that there were significant positive changes for addressing fake news, both overall as well as in each of the participants. We discuss how these results confirm that collective intelligence improves digital literacy in the Internet challenges.

A joint vehicular device scheduling and uncertain resource management scheme for Federated Learning in Internet of Vehicles

Federated learning (FL) offers an effective framework for the efficient process in vehicular edge computing. However, FL encompasses the process of distributing and uploading model parameters, which are inevitably transmitted in a wireless network environment. Some challenges in FL-assisted Internet of Vehicles (IoV) sceneries gradually emerging, such as data heterogeneity, concerned device resources, and unstable communication environment, which necessitate intelligent vehicle selection schemes that accelerate training efficiency. Based on these, we consider a new scenario, specifically an FL-assisted IoV system under uncertain communication conditions, and develop an interval many-objective vehicle selection and bandwidth allocation (IMoVSBA) joint optimization scheme. This scheme takes into account computation latency, energy consumption, server utilization, and data quality, while meeting multi-criteria resource optimization requirements. Among these, server utilization is a new objective designed specifically for this joint optimization problem. For the proposed problem, a novel interval many-objective evolutionary algorithm with individual comprehensive indicator to control the evolution direction (IMaOEACI) is designed. Simulation results demonstrate that this method outperforms other schemes in terms of accuracy, training cost, and server utilization, effectively improving training efficiency in wireless channel environments and reasonably utilizing bandwidth resources. It provides significant scientific value and application potential in the field of the IoVs.

The Prospects and Challenges of Internet of Things (IoT)-based Public Healthcare System in Bangladesh

Purpose: The purpose of this study is to discern the prospects and challenges of integrating Internet of Things (IoT) into the public healthcare sector of Bangladesh. This is timely because the present public healthcare status of the country needs dire improvement. Hence, this study explores the future scope of IoT-based public healthcare systems to be introduced to the Directorate General of Health Services (DGHS) of Bangladesh henceforth recommends how the country can integrate the implementation of this technology to deliver better public healthcare services to its population. Methodology: The authors reviewed extensively literature from several reputed sources to identify the current public healthcare conditions, as well as the benefits and impediments of IoT-based public healthcare system in Bangladesh. Based on the searches pertaining to the data base, this study then outlines appropriate future scopes of IoT in the public healthcare of Bangladesh. Findings: This research identified major impediments such as lack of infrastructure, low motivation, awareness and acceptance as well as poor healthcare startups prevalent in the public health services of Bangladesh. To surmount the inefficiencies of the current services, the authors deliberated how IoT based public healthcare system could foster better monitoring; management and rehabilitation thereby improve the overall Bangladesh’s public healthcare system. Practical Implications: IoT healthcare organizations can also use technology to improve patient outcomes and increase efficiencies, which will lower the cost of treatment, reduce death rates and make it possible to give public healthcare to people who live in rural areas. Originality/Value: The researchers of this study are confident that the findings of this study will benefit other researchers, policymakers, healthcare service providers/designers and professionals to realize the potentials of IoT-based public healthcare system that could overcome the challenges faced and finally could deliver sound public healthcare facilities and services for Bangladeshis in the near future. Limitations: In the future, we plan to develop the Central Repository of patient database system using blockchain technology.

Ideas of Collaboration and Convergence in Education: to the Essence of Concepts

Artificial intelligence, distance and hybrid formats, Internet challenges that create the opportunity to search for original ideas in the educational space, actualize such resource contents as collaboration and convergence, which create opportunities for improving education as a complex phenomenon of challenges and opportunities, meanings and values in a changing world. The purpose of the article. To methodologically substantiate and theoretically develop a model of education based on the ideas of two innovative paradigms – collaboration and convergence, to reveal their essential resource characteristics in relation to the phenomenon of “education”. Methodology. The basis of the methodology of the model developed by us is the ideas of the scientific school of L. I. Novikova, N. L. Selivanova, P. V. Stepanov, T. A. Romm, M. V. Shakurova. The systemic and interdisciplinary approaches formed the basis for the selection of the content and types of educational activities in the organization and implementation of the educational process. “Collaboration” and “convergence” as pedagogical phenomena of the interdisciplinary approach constitute the methodological integral core of the model, which is characterized by organization, openness, internal activity, dynamism and integration, and promising cooperation. Results. The analysis of sources allowed us to reveal the essence of the concepts: “collaboration” and “convergence”. In the course of the study, we established two facts: on the one hand, collaboration and convergence are tools and mechanisms for improving the organization of education; on the other hand, the synthesis of ideas of collaboration and convergence – create conditions for the value and semantic space in improving and enriching the phenomenon of modern education. Conclusion. The author’s contribution consists in the theoretical and methodological substantiation and development of a model based on the ideas of collaboration and convergence in education, its essential characteristics are given. The presentation and meaning of the organization of the education process based on the ideas of convergence and collaboration are expanded, 10 characteristics are developed that combine these two phenomena in the education process for the effective achievement of the goal.

An adaptive asynchronous federated learning framework for heterogeneous Internet of things

Federated learning (FL) is a distributed machine learning framework that enables the training of shared models without the need to share local data. However, FL faces challenges in heterogeneous Internet of Things (IoT) environments, including communication bottleneck, staleness, and non- independent and identically distributed (non-IID) data. To tackle these challenges, we present ASAFL, an Asynchronous Federated Learning framework with an Adaptive Scheduling Strategy. Firstly, we quantify the potential contribution of client models relative to the server model. Secondly, we design a client upload strategy to reduce the uploading of redundant models with low contribution. Finally, we propose a server model update method based on the contributions to address model divergence caused by staleness and non-IID data. Furthermore, our theoretical analysis guarantees ASAFL's convergence, and experiments show it reduces communication overhead by over 70% compared to traditional asynchronous FL.

Guest Editorial Special Issue on Current Research Trends and Open Challenges for Industrial Internet of Things

Guest Editorial Special Issue on Current Research Trends and Open Challenges for Industrial Internet of Things

Optimization of Reinforcement Learning Algorithms for Addressing the Linear Function Fitting Challenge in the Electric Power Internet of Things

Optimization of Reinforcement Learning Algorithms for Addressing the Linear Function Fitting Challenge in the Electric Power Internet of Things

Media Competition and News Diets

Technological innovations like broadcast television and the internet challenge local newspapers’ business model of bundling their local content with third-party content, such as wire national news. We examine how the entry of television affected newspapers and news diets in the United States. We construct a dataset of newspapers’ economic performance and content choices from 1944 to 1964 and exploit quasi-random variation in the rollout of television to show its negative impact in the readership and advertising markets. Newspapers responded by reducing content, particularly local news. We tie this change to increased party vote share congruence between congressional and presidential elections. (JEL D72, L25, L82, M37, N42, N72)

Advances in Architecture, Protocols, and Challenges in Internet of Things: From Technologies to Applications

With the rapid development and widespread application of Internet of Things (IoT) technology, we are in an era of digital transformation, where the integration between the physical and digital worlds continues to deepen [...]

The Inadequacy of the Copyright System in Addressing Digitalization and Internet Challenges

At the international as well as the local level, several different legal frameworks have been developed to affirm and protect fundamental rights. Copyright law has developed in reaction to technological advancements; since its inception. The most recent breakthroughs in digital technology; such as digital communications networks like the Internet and personal computers; are generating headlines throughout the world. This study attempts to add to the current discussion on copyright law; change by doing extensive research. This research seeks to provide light on the confluence between existing intellectual property laws; and the growing digital ecosystem. Overall, the stud aims to explore all the related issues and laws associated with intellectual property in the Pakistan which is relatively in the early phases.

The Factors Affecting Electronic Government Procurement (EGP) Implementation in Zambia’s City Councils: A case of the Lusaka City Council

The Lusaka city council was used as a case in the research to identify the factors that hinder or facilitate the successful implementation of E-GP. A non-probability sampling technique, namely purposive sampling, was used, using a sample of 80 personnel and suppliers of the Lusaka city council. The research used a quantitative and qualitative approach with a descriptive research design. Data was gathered via a questionnaire and interview, and descriptive and inferential statistics were used to assess it. The results show that the benefits were confirmed by both the suppliers and the end users. They all stated that E-GP has reduced the time and cost of doing business for both bidders and procurement entities (PEs). It has also allowed equal opportunity to all bidders because it involves the free download of bidding documents, and bidders are only charged at bid submission. In the past, there was too much corruption, but the automation of the publication of tenders and contract awards has led to greater transparency in the procurement system. The results also show that personnel working in departments related to E-GP are not receiving the required support from superiors in using the e-government procurement system. The study further indicates there is limited reliable, affordable, and fast internet, which in turn is affecting e-government procurement at Lusaka City Council. Implementing e-procurement is faced with the most prominent challenge of poor internet at the institution. This study recommends, among other things, enhancement of the system and improvement of organizational culture to allow others to be part of the process. In addition, LCC should invest heavily in the system if they want to reap the benefits of its existence in the future.

An Emerging Ophthalmology Challenge: A Narrative Review of TikTok Trends Impacting Eye Health Among Children and Adolescents.

In today's digital age, children and teenagers are deeply entrenched in the world of personal electronic devices including laptops, tablets, and smartphones. These serve as gateways to captivate online content. With children as young as 4years old having access to these gadgets, the potential benefits of technology coexist with many possible risks. One such risk is the growing global issue of myopia. TikTok, a mobile application that gained immense popularity since its inception in 2016, has garnered 1.7billion active monthly users in 2023 and is expected to reach two billion in 2024. A significant portion of TikTok's user base consists of adolescents, with approximately 41% falling in the 16-24 age group and a third aged 14 or younger. This social media platform has given rise to the phenomenon of "internet challenges," where users complete and share various tasks through videos. While most of these challenges are harmless, some pose serious risks to teenagers who eagerly seek validation and attention from their peers. This manuscript sheds light on a specific subset of TikTok challenges, namely those related to ophthalmological risks. These challenges include "rubbing castor oil trend," "bleach/bright eye challenge," "mucus fishing challenge," "eggsplosions", "beezin challenge", "Orbeez challenge", "blow-drying eyelashes", "sun gazing," and "popping styes". The manuscript emphasizes the importance of monitoring and regulating potentially dangerous content on social media platforms like TikTok. While these platforms offer educational opportunities, they can also promote life-threatening actions, necessitating efforts to protect vulnerable young users and promote safe online engagement.

Towards an AI-Driven Data Reduction Framework for Smart City Applications.

The accelerated development of technologies within the Internet of Things landscape has led to an exponential boost in the volume of heterogeneous data generated by interconnected sensors, particularly in scenarios with multiple data sources as in smart cities. Transferring, processing, and storing a vast amount of sensed data poses significant challenges for Internet of Things systems. In this sense, data reduction techniques based on artificial intelligence have emerged as promising solutions to address these challenges, alleviating the burden on the required storage, bandwidth, and computational resources. This article proposes a framework that exploits the concept of data reduction to decrease the amount of heterogeneous data in certain applications. A machine learning model that predicts a distortion rate and its corresponding reduction rate of the imputed data is also proposed, which uses the predicted values to select, among many reduction techniques, the most suitable approach. To support such a decision, the model also considers the context of the data producer that dictates the class of reduction algorithm that is allowed to be applied to the input stream. The achieved results indicate that the Huffman algorithm performed better considering the reduction of time-series data, with significant potential applications for smart city scenarios.

Academic Resilience in the Face of Internet Challenges: A Narrative Analysis of Tertiary Students’ Experiences

This qualitative research explored internet connectivity's influence on tertiary students' academic lives in Palimbang, Sultan Kudarat. It highlighted the challenges posed by unstable internet connections, students' resilience in adapting to them, the coping mechanisms employed by the students in accessing unstable internet connections, and the emotional toll it took on them. This paper used narrative analysis to interpret the data set from the participants. Through in-depth interviews, ten (10) participants shared their experiences of grappling with sluggish and unstable internet connections, hindering access to educational resources, and disrupting collaborative learning endeavours. Despite these challenges, students demonstrated resilience, employing strategies like accessing the Internet during off-peak hours and utilizing offline resources. However, these adaptations came with their own set of drawbacks, including sleep deprivation and financial strain. Emotionally, frustration with unreliable internet connectivity was palpable, yet students recognized the Internet's transformative role in their learning journey. The paper underscored the vital importance of reliable Internet infrastructure in rural education, advocating for concerted efforts to bridge the digital divide and ensure equitable access to quality Internet services. Ultimately, it highlighted the resilience of rural students while emphasizing the urgent need to improve the digital infrastructure to support their academic endeavors in the digital age.

Revolutionizing Connectivity: Advancing Equitable Traffic Access through Advanced Scalability and Load Balancing Network Architecture via Adaptive Equalization Algorithms

The contemporary internet landscape grapples with reliability issues, primarily stemming from internet traffic challenges that lead to slowdowns. A significant contributor to this predicament is the multitude of devices and users connected to the internet, straining the available bandwidth. This research introduces a system designed to address this problem by implementing bandwidth balancing and limitations on individual users. To initiate this study, specific requirements, both hardware and software, need consideration. Given the study's involvement with networks, basic network adapters become essential. The bandwidth limiter, a key feature of the system, regulates the internet bandwidth accessible to users. Regardless of download/upload activities, the limiter ensures users stay within preset bandwidth limits. This proactive measure aids in preventing or minimizing average internet traffic issues within a network. The system's implementation results in a more balanced distribution of internet bandwidth among users connected to the same network, ultimately enhancing internet connectivity. This research addresses contemporary internet challenges by introducing a system that employs adaptive algorithms for equitable traffic access. The proposed system, encompassing bandwidth balancing and limitations, aligns with the overarching goal of achieving advanced scalability and load balancing in network architecture. By proactively managing internet traffic through the use of a bandwidth limiter, the system aims to enhance the equitable distribution of internet bandwidth among connected users, contributing to a more efficient and reliable network infrastructure

Burn Injuries From TikTok Challenges: A Brief Report.

Social media (e.g., TikTok) challenge is a relatively new phenomenon wherein a user creates and posts videos performing an often-dangerous task. The ease of access and availability of social media in recent times make teens and young adults susceptible to these viral Internet challenges and accidental injury. The severity, morbidity, and mortality of burn injuries from social media challenges have not readily been documented in the medical literature. In this brief report, we present three cases of accidental burns after attempting social media challenges involving boiling water or flame. The injuries ranged from superficial partial thickness burns to 24% total body surface area (TBSA) full thickness burns. Online challenges show the potential for severe injury and disability and underlie the importance of awareness and education of the public, further research into the usage of TikTok and other media platforms, and early referral to the American Burn Association recognized center.

Recent Advances and Challenges in Internet of Things (IoT)-Based Smartphone Biosensors for COVID-19 and Zika Viruses Detection: A Review

Recent Advances and Challenges in Internet of Things (IoT)-Based Smartphone Biosensors for COVID-19 and Zika Viruses Detection: A Review

A Grant-Based Random Access Scheme With Low Latency for mMTC in IoT Networks

The design of transmission schemes and receiver techniques with high reliability and low latency for the massive machine-type communications (mMTC) is an important challenge for the future Internet of Things (IoT) systems in the sixth-generation (6G) wireless communication networks. In this paper, we propose a new physical-layer transceiver scheme for mMTC, where users transmit their data based on a pre-designed sparse Tanner graph, and the base station (BS) employs blind channel estimation and message-passing decoding to decode user data. In particular, low latency is achieved by the construction of the transmission Tanner graph, as well as a hybrid modulation scheme that consists of BPSK symbols to facilitate blind channel estimation, and general M-ary modulation to reduce the transmission delay. Moreover, high reliability is achieved by the proposed message-passing decoder that fully exploits the diversity of the transmitted signal and offers soft demodulation and decoding capabilities. We provide both performance analyses based on density evolution, and simulation results, to demonstrate the superior performance of the proposed physical-layer transceiver solution for mMTC.

PCNNCEC: Efficient and Privacy-Preserving Convolutional Neural Network Inference Based on Cloud-Edge-Client Collaboration

Deploying convolutional neural network (CNN) inference on resource-constrained devices remains a remarkable challenge for industrial Internet of Things (IIoT). Although the cloud computing shows great promise in machine learning training and prediction, outsourcing data to remote cloud always incurs privacy risk and high latency. Therefore, we design a new framework for efficient and privacy-preserving CNN inference based on cloud-edge-client collaboration <formula><tex>$(\rm{named} \, \text{PCNN}_{\text{CEC}})$</tex></formula>. In <formula><tex>$ \text{PCNN}_{\text{CEC}} $</tex></formula>, the model of cloud and the data of client in IIoT are split into two shares and sent to two non-colluded edge servers. By applying the arithmetic secret sharing and pre-computation of beaver's triplets, the two edge servers can jointly calculate the predicting results without learning anything about the model and data. To speed up the pre-computation of offline phase and not sacrifice security, the task of triplets generation is delegated to the cloud, so that the edge servers do not require frequent interactions to generate triplets themselves or introducing additional trusted party. The experimental results show the proposed private comparison protocol achieves a better tradeoff between low latency and high throughput, when it is compared with garbled circuit based protocols and other secret sharing based protocols. Additionally, the benchmarks conducted on realistic MNIST and CIFAR-10 datasets demonstrate that <formula><tex>$ \text{PCNN}_{\text{CEC}} $</tex></formula> costs less communication and runtime than two recently related schemes under the same security level.