The Interplay Between Problematic Internet Use, Anxiety, Depression and Functional Impairment in Front-Line Healthcare Workers During the COVID-19 Pandemic.

OBJECTIVE: We aimed to assess the interplay between functional impairment and anxiety, depression, and problematic Internet use levels in front-line healthcare workers who work in inpatient clinics of coronavirus disease-2019 (COVID-19) during the COVID-19 pandemic. METHODS: Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Internet Addiction Test (IAT), and Sheehan Disability Scale (SDS) were administered to assess the depression, anxiety, problematic Internet use, and functional impairment levels of the participants. RESULTS: Two hundred thirteen participants were enrolled in the present study. Medical doctors showed significantly higher scores of IAT than the nurses and other medical staff (Kruskal-Wallis=6.519, p=0.038). Levels of SDS total are significantly correlated with scores of IAT (r=0.257, p<0.001), BDI (r=0.383, p<0.001), and BAI (r=0.308, p<0.001). All subdomain scores of SDS (social, family, work) and total scores of SDS were significantly and positively correlated with BAI, BDI, and IAT scores (p<0.05). In the separation mediation analysis, problematic Internet use partially mediated the relationship between anxiety-depression and global functional impairment. CONCLUSION: Health politicians should produce policies to develop strategies for coping with consequences of anxiety and depression in healthcare professionals during any health crisis. In addition, we should raise healthcare professionals' awareness that problematic Internet use is not suitable for dealing with anxiety and depression and may even lead to increase of functional loss.

Different Characteristics of Psychological and Sleep Symptoms Across Social Media Addiction and Internet Gaming Disorder in Chinese Adolescents- A Network Analysis.

OBJECTIVE: Previous research has explored a variety of mental disorders associated with Internet Gaming Disoder (IGD) and Social Media Addiction (SMA). To date, few studies focused on the network characteristics and investigated mood and sleep symptoms across SMA and IGD of adolescence at a group-specific level. This study aims to identify different characteristics of IGD and SMA and further determine the group-specific psychopathology process among adolescents. METHODS: We conducted a cross-sectional study to recruit a cohort of 7,246 adolescents who were scored passing the cutoff point of Internet Gaming Disorder Scale-Short Form and Bergen Social Media Addiction Scale, as grouped in IGD and SMA, or otherwise into the control group. Patient Health Questionnaire-9, Generalized Anxiety Disorder 7-item, and Pittsburgh Sleep Quality Index were assessed for the current study, and all assessed items were investigated using network analysis. RESULTS: Based on the analytical procedure, the participants were divided into three groups, the IGD group (n=789), SMA group (n=713) and control group (n=5,744). The edge weight bootstrapping analysis shows that different groups of networks reach certain accuracy, and the network structures of the three groups are statistically different (pcontrol-IGD=0.004, pcontrol-SMA<0.001, pIGD-SMA<0.001). The core symptom of SMA is "feeling down, depressed, or hopeless", while IGD is "feeling tired or having little energy". CONCLUSION: Although IGD and SMA are both subtypes of internet addiction, the psychopathology processes of IGD and SMA are different. When dealing with IGD and SMA, different symptoms should be addressed.

Decreased Serum Cocaine- and Amphetamine-Regulated Transcript Level in Internet Gaming Disorder.

OBJECTIVE: Vulnerability to internet gaming disorder (IGD) has increased as internet gaming continues to grow. Cocaine- and amphetamine-regulated transcript (CART) is a hormone that plays a role in reward, anxiety, and stress. The purpose of this study was to identify the role of CART in the pathophysiology of IGD. METHODS: The serum CART levels were measured by enzyme-linked immunosorbent assay, and the associations of the serum CART level with psychological variables were analyzed in patients with IGD (n=31) and healthy controls (HC) (n=42). RESULTS: The serum CART level was significantly lower in the IGD than HC group. The IGD group scored significantly higher than the HC group on the psychological domains of depression, anxiety, the reward response in the Behavioral Activation System and Behavioral Inhibition System. There were no significant correlations between serum CART level and other psychological variables in the IGD group. CONCLUSION: Our results indicate that a decrease in the expression of the serum CART level is associated with the vulnerability of developing IGD. This study supports the possibility that CART is a biomarker in the pathophysiology of IGD.

The analysis of educational informatization management learning model under the internet of things and artificial intelligence.

This study explores the influence of the Internet of Things (IoT) and Artificial Intelligence (AI)-enhanced learning models on student management in educational informatization management. A game-theoretic enhanced learning model is proposed to achieve this objective, incorporating resource scheduling strategies under fog computing and a student management system that integrates IoT and AI technologies. This model's performance and the student management system are then tested. The results indicate that the fog computing-based hierarchical Q-learning (Q) model proposed in this study achieves faster convergence than a single Q model, reaching convergence after 80 training rounds, ten rounds earlier than the comparative algorithm. The model exhibits a lower average workload delay of 0.5 ms and fog node delay below 1 ms, showcasing significant advantages in terms of overall cost-effectiveness, thus minimizing service costs. The student management system has 3000 concurrent user connections, static page request times ranging from 0 to 25 s, login response time predominantly at 60 s, and a capacity to process up to 20 parallel tasks per second with zero errors. The system functionalities are fully realized, meeting usage demands effectively and achieving the highest average functional score of 9.03 for online interaction functionality. This study demonstrates the efficacy of the game-theoretic enhanced learning model in a fog computing environment and the positive impact of IoT and AI technologies on student management. The proposed student management system better caters to individual student needs, enhancing learning outcomes and experiences. The study's innovation lies in the integration of IoT technology with AI-enhanced learning models, coupled with the introduction of game-theoretic resource scheduling strategies, enabling the student management system to intelligently identify student requirements, allocate learning resources, and dynamically optimize the educational process, ultimately improving learning outcomes. This holds significant implications for enhancing education quality and promoting personalized student development.

Continence App® Impact on Puerperal Women's Knowledge, Attitude and Practice: Randomized Clinical Trial.

INTRODUCTION AND HYPOTHESIS: Enhancing women's knowledge, attitude, and practice (KAP) concerning urinary incontinence (UI) through diverse educational strategies has been a focal point for professionals in recent years. This study was aimed at assessing the impact of the educational application Continence App® on the KAP of postpartum women experiencing UI. We hypothesized that access to the app would lead to improved KAP among these women. METHODS: Postpartum women who had undergone vaginal birth, aged 18 years or above, literate, admitted in a maternity ward, delivered a full-term or large-for-gestational-age infant, and possessed a smartphone or compatible device for app usage were included. Changes in KAP were evaluated using a survey specifically designed for this purpose. The Mann-Whitney U test was employed to compare KAP scores between control and intervention groups, as well as between baseline and post-intervention assessments. RESULTS: Among the 542 women screened for eligibility, 349 were enrolled in the study, with 138 completing post-intervention assessments. The mean (standard deviation [SD]) age of participants was 25.9 (5.8) years. Post-intervention scores for knowledge and practice demonstrated a decline among non-app users, whereas a significant increase was observed among those in the intervention group. Attitudinal changes remained insignificant. CONCLUSIONS: The findings highlight the effectiveness of an app-based educational intervention in enhancing the knowledge and practice related to UI among postpartum women.

Skill enactment and knowledge acquisition among community users of digital mental health interventions: qualitative study with thematic analysis.

BACKGROUND: The acquisition of knowledge and use of skills from digital mental health interventions (DMHIs) are considered important for effectiveness. However, our understanding of user experiences implementing skills learned from these interventions is limited, particularly outside of research trials. This qualitative study aimed to investigate how community users learn and apply knowledge and skills from DMHIs based on cognitive behavioural therapy (CBT) in daily life. The study also examined factors influencing the selection and use of skills and explored perceived changes in mental health resulting from the intervention. METHODS: Thirteen adults aged 26 to 66 years (10 females) were recruited using social media advertising and participated in semi-structured interviews by telephone or videoconference. All participants were living in Australia and had used a digital CBT program within the past 3 months. Interviews lasted on average 45 min. Transcripts were analysed using theoretical thematic analysis. RESULTS: Participants demonstrated high levels of program engagement. Findings were organised into three topics with six major themes. Participants reported that their chosen intervention reinforced existing knowledge and fostered new skills and insights (Topic 1, Theme 1: knowledge consolidation). Most described actively applying skills (Topic 1, Theme 2: active approach to skill enactment), although the extent of learning and range of skills enacted varied across participants. Influences on skill selection included the perceived relevance of intervention strategies to the user's needs and personal characteristics (Topic 2, Theme 1: relevance of intervention strategies), as well as the perceived or experienced effectiveness of those strategies (Topic 2, Theme 2: perceived and experienced benefit). Challenges to ongoing skill enactment included time scarcity, prioritisation difficulties, and lack of motivation (Topic 2, Theme 3: navigating time constraints and low motivation). Improvements in mental health were generally modest and attributed mainly to participants' proactive efforts (Topic 3, Theme 1: perceived changes). CONCLUSIONS: DMHIs may reinforce existing understanding of psychotherapeutic strategies, offer new knowledge, and encourage the application of skills in everyday life among community users who actively engage with these interventions. Future research should prioritise personalising DMHIs and investigating methods to optimise the acquisition, retention, and sustained application of knowledge and skills.

Rates and psychological stress predictors of problematic internet use (PIU) during the COVID-19 pandemic in a racially diverse sample of young adults.

BACKGROUND: Problematic internet use (PIU), which includes social media misuse (SMM) and gaming misuse (GM), is uncontrollable and associated with significant psychological impairment. PIU is a coping behavior for COVID-19-related stress. We explored distress-related predictors of PIU in a young adult racially diverse sample during the pandemic. METHODS: Analyses used cross-sectional survey data (N = 1956). Psychological diagnoses, financial distress, COVID-19-related emotions, psychological distress, distress tolerance, social support, loneliness, SMM and GM were measured. Hierarchical multiple regressions identified predictors of PIU. Race-stratified exploratory analyses sought to understand if predictors held true across racial groups. RESULTS: Low distress tolerance was associated with SMM and GM, as were depression symptoms, with racial differences observed. SMM was associated with younger age, and GM was associated with male gender. PTSD symptoms predicted more GM. SMM and GM rates varied between racial groups. COVID-19-related adjustment challenges and stress predicted SMM and GM respectively, with racial differences observed. CONCLUSION: Individual psychological distress and low distress tolerance markedly increased PIU risk. Clinicians should screen for stress-related PIU risk factors and bolster distress tolerance in vulnerable patients. Comparing PIU to different forms of coping in a larger sample would further clarify groups differences in stress coping behaviors.

Orthodontic YouTube™ videos made by patients for patients: What are they about and are they accurate?

OBJECTIVES: To evaluate the characteristics and content of YouTube™ videos created by patients undergoing orthodontic fixed appliance treatment and to assess the content accuracy of these videos. DESIGN: A mixed-methods quantitative and qualitative study. DATA SOURCE: YouTube™ webpage. METHODS: The term 'braces' was used to search for relevant videos on the YouTube™ webpage between 18 August and 30 August 2020, with no limits imposed regarding how long the video had been available on YouTube™. Videos were included if they were made by patients and were predominantly about patients' experiences during treatment with labial fixed appliances. The main themes/subthemes of the included videos were identified. A checklist was then developed to assess accuracy of the video content for two of the main themes and the videos were assessed against the checklist. RESULTS: The video search identified 350 videos, of which 64 were selected as potentially eligible; 41 were subsequently excluded as they related primarily to the bond up/debond experience or had minimal information about orthodontics. This meant that 23 videos were ultimately included for analysis. Six main themes were identified in the videos: problems with fixed appliances, effects of fixed appliances, oral hygiene maintenance, dietary advice, treatment duration/appointment frequency and auxiliaries used with fixed appliances. From the 23 videos, 20 were assessed against the checklist for content accuracy related to two selected themes: oral hygiene maintenance and dietary advice. The majority of videos had low content accuracy scores, indicating that important and relevant content was generally missing. CONCLUSION: Several included videos focused on oral hygiene maintenance and dietary advice associated with fixed appliances; however, the content was incomplete and not always accurate. This is concerning to the profession, and it is therefore recommended that clinicians consider collaborating with patients to produce videos that are patient-centred and that also contain accurate information.

Positive and negative factors of the influence of new media and the digital environment on ideal and belief education among university students in China.

The authors identity the relationship between the positive and negative aspects of social media and the ideal belief learning and behavior of university students. The cluster sampling method was adopted in the paper, including Guangdong, Shandong, Henan, Sichuan, and Jiangsu provinces. A total of 1014 questionnaires were distributed to a purposive sample of university students between the ages of 16 and 35. The authors applied the uses and gratifications theory to study students' social media behavior. This study identified 18 positive and negative effects of social media. Noteworthy positive outcomes attributed to social media in fostering ideals and beliefs encompass heightened awareness, enhanced communication facilitation, convenient connectivity, reduced expenses on educational materials, improved social and communication proficiencies, as well as diminished stress levels. The negative effects of new media and the Internet include a lack of critical thinking, a waste of time, dysgraphia, disrupted connection to learning, students' laziness, and health risks.

Sports and exercise-related smartphone use is antagonistic to hedonic use in regular exercisers: A cross-sectional study examining the roles of exercise frequency and duration.

Background: Hedonic smartphone use has been associated with dependence and addiction studied under the umbrella term Problematic Smartphone Use (PSU). Research usually explores total screen time as an index of PSU. A few studies suggest that exercise is inversely related to smartphone use time. However, it is unknown which primary characteristics of exercise behavior are related to more moderate smarthone use. Furthermore, the purpose of smartphone use, such as hedonic use associated with PSU versus utilitarian use, was not tested in the sports and exercise contexts. Hedonic use generally means playing with the smartphone for joy, distraction, and satisfaction. Utilitarian use implies practical and valuable use. There is a conjecture that sports involvement may foster utilitarian use through increased involvement in sports-related information-seeking, goal-setting, and self-monitoring. Methods: Therefore, we examined whether weekly exercise frequency, workout duration, and perceived exercise intensity relate to total daily smarthone and hedonic use and whether this relationship is mediated by sports-related utilitarian device use. We tested regularly exercising adults (n = 360, 132 males, M age = 39.0 ± 9.8, M weekly exercise = 5.8 ± 1.9) who volunteered for this study and provided demographic information about their exercise habits and smartphone use. Results: The results revealed that all exercise parameters mediated the total daily smartphone use, with perceived exercise intensity being a negative predictor. Further, exercise frequency and duration (but not intensity) positively predicted sports-related smartphone use, which inversely predicted hedonic use. Conclusion: These results suggest that exercise parameters directly relate to daily smartphone use, which completely mediates hedonic use. These findings may partially account for the frequently reported inverse relationship between regular exercise and PSU by suggesting that the connection is mediated via sports-related smartphone use.

Quality of Chronic Obstructive Pulmonary Disease Information on the Chinese Internet: Website Evaluation Study.

BACKGROUND: The development of internet technology has greatly increased the ability of patients with chronic obstructive pulmonary disease (COPD) to obtain health information, giving patients more initiative in the patient-physician decision-making process. However, concerns about the quality of website health information will affect the enthusiasm of patients' website search behavior. Therefore, it is necessary to evaluate the current situation of Chinese internet information on COPD. OBJECTIVE: This study aims to evaluate the quality of COPD treatment information on the Chinese internet. METHODS: Using the standard disease name "" ("chronic obstructive pulmonary disease" in Chinese) and the commonly used public search terms "" ("COPD") and "" ("emphysema") combined with the keyword "" ("treatment"), we searched the PC client web page of Baidu, Sogou, and 360 search engines and screened the first 50 links of the website from July to August 2021. The language was restricted to Chinese for all the websites. The DISCERN tool was used to evaluate the websites. RESULTS: A total of 96 websites were included and analyzed. The mean overall DISCERN score for all websites was 30.4 (SD 10.3; range 17.3-58.7; low quality), no website reached the maximum DISCERN score of 75, and the mean score for each item was 2.0 (SD 0.7; range 1.2-3.9). There were significant differences in mean DISCERN scores between terms, with "chronic obstructive pulmonary disease" having the highest mean score. CONCLUSIONS: The quality of COPD information on the Chinese internet is poor, which is mainly reflected in the low reliability and relevance of COPD treatment information, which can easily lead consumers to make inappropriate treatment choices. The term "chronic obstructive pulmonary disease" has the highest DISCERN score among commonly used disease search terms. It is recommended that consumers use standard disease names when searching for website information, as the information obtained is relatively reliable.

Harnessing the power of nature exposure to mitigate adolescents' Internet addiction: A chain mediation model.

Previous studies have focused more on the facilitating effect of nature exposure on positive behavioural consequences. However, less attention has been paid to whether nature exposure can inhibit internalized problem behaviours, such as Internet addiction. Within the framework of the stimuli-organism-response theory, the present study examined the relationship between nature exposure and Internet addiction and investigated the mediating roles of anthropomorphism of nature and awe. In China, we recruited 1469 adolescents (mean age = 13.90 years old, SD = 0.59, 53.2% girls). Mediation analyses indicated that awe partially mediated the relationship between nature exposure and adolescents' Internet addiction. The anthropomorphism of nature and awe served as sequential mediating roles in the relationship between nature exposure and adolescents' Internet addiction. This study provides a nature-based perspective on the prevention and intervention of adolescents' Internet addiction.

The relationship between internet addiction, sleep quality, and psycho-social problems among secondary school students in Erbil, 2023-2024.

OBJECTIVE: This study aimed to show the association between internet addiction (IA), sleep quality, and psycho-social problems among secondary school students DESIGN: A cross-sectional, descriptive, and correlational study design was used. SAMPLE: A total of 557 students from four secondary schools in Erbil were selected using multistage cluster sampling MEASUREMENTS: The questionnaires of this research contained socio-demographic data, Internet Addiction Test (IAT), Pittsburgh Sleep Quality Index (PSQI), and Pediatric Symptom Checklist-Y (PSC-Y) questionnaire. RESULTS: Findings indicated that students displayed a mild IA, averaging a score of 42.9 ± 19.18. Furthermore, the average sleep quality (PSQI) score was 8.95 ± 2.75, indicating moderate sleep disturbance, and the average score for psycho-social problems was 27.78 ± 13.29. Importantly, there was a strong and positive association between IA and psycho-social issues, as shown by a correlation coefficient of 0.31 (p < .001). Sleep quality was correlated with IA and psychosocial issues (p < .001, correlation values: .23 and .27, respectively) CONCLUSIONS: The study highlights the urgent need for health policymakers and nursing managers in Erbil to develop targeted interventions, such as awareness campaigns and digital well-being programs in school curricula, to mitigate the interlinked issues of IA, sleep quality, and psycho-social problems among students.

Smart Partitioned Blockchain.

Blockchain is a developing technology that promises advancements when it is applied to other fields. Applying blockchain to other systems requires a customized blockchain model to satisfy the requirements of different application fields. One important area is to integrate blockchain with smart spaces and the Internet of Things to process, manage, and store data. Actually, smart spaces and Internet of Things systems include various types of transactions in terms of sensitivity. The sensitivity can be considered as correctness sensitivity, time sensitivity, and specialization sensitivity. Correctness sensitivity means that the systems should accept precise or approximated data in some cases, whereas time sensitivity means that there are time bounds for each type of transaction, and specialization sensitivity applies when some transactions are processed only by specialized people. Therefore, this work introduces the smart partitioned blockchain model, where we use machine learning and deep learning models to classify transactions into different pools according to their sensitivity levels. Then, each pool is mapped to a specific part of the smart partitioned blockchain model. The parts can be permissioned or permissionless. The permissioned parts can have different sub-parts if needed. Consequently, the smart partitioned blockchain can be customized to meet application-field requirements. In the experimental results, we use bank and medical datasets with a predefined sensitivity threshold for classification accuracy in each system. The bank transactions are critical, whereas the classification of the medical dataset is speculative and less critical. The Random Forest model is used for bank-dataset classification, and its accuracy reaches 100%, whereas Sequential Deep Learning is used for the medical dataset, which reaches 91%. This means that all bank transactions are correctly mapped to the corresponding parts of the blockchain, whereas accuracy is lower for the medical dataset. However, acceptability is determined based on the predefined sensitivity threshold.

A Deep Learning-Based Method for Preventing Data Leakage in Electric Power Industrial Internet of Things Business Data Interactions.

In the development of the Power Industry Internet of Things, the security of data interaction has always been an important challenge. In the power-based blockchain Industrial Internet of Things, node data interaction involves a large amount of sensitive data. In the current anti-leakage strategy for power business data interaction, regular expressions are used to identify sensitive data for matching. This approach is only suitable for simple structured data. For the processing of unstructured data, there is a lack of practical matching strategies. Therefore, this paper proposes a deep learning-based anti-leakage method for power business data interaction, aiming to ensure the security of power business data interaction between the State Grid business platform and third-party platforms. This method combines named entity recognition technologies and comprehensively uses regular expressions and the DeBERTa (Decoding-enhanced BERT with disentangled attention)-BiLSTM (Bidirectional Long Short-Term Memory)-CRF (Conditional Random Field) model. This method is based on the DeBERTa (Decoding-enhanced BERT with disentangled attention) model for pre-training feature extraction. It extracts sequence context semantic features through the BiLSTM, and finally obtains the global optimal through the CRF layer tag sequence. Sensitive data matching is performed on interactive structured and unstructured data to identify privacy-sensitive information in the power business. The experimental results show that the F1 score of the proposed method in this paper for identifying sensitive data entities using the CLUENER 2020 dataset reaches 81.26%, which can effectively prevent the risk of power business data leakage and provide innovative solutions for the power industry to ensure data security.

Multi-Objective Distributed Client Selection in Federated Learning-Assisted Internet of Vehicles.

Federated learning is an emerging distributed machine learning framework in the Internet of Vehicles (IoV). In IoV, millions of vehicles are willing to train the model to share their knowledge. Maintaining an active state means the participants must update their state to the FL server in a fixed interval and participate in the next round. However, the cost of maintaining an active state is very large when there are a huge number of participating vehicles. In this paper, we propose a distributed client selection scheme to reduce the cost of maintaining the active state for all participants. The clients with the highest evaluation are elected among the neighbors. In the evaluator, four variables are considered, including the sample quantity, available throughput, computational capability, and the quality of the local dataset. We adopt fuzzy logic as the evaluator since the closed-form solution over four variables does not exist. Extensive simulation results show that our proposal approximates the centralized client selection in terms of accuracy and can significantly reduce the communication overhead.

Federated Learning-Oriented Edge Computing Framework for the IIoT.

With the maturity of artificial intelligence (AI) technology, applications of AI in edge computing will greatly promote the development of industrial technology. However, the existing studies on the edge computing framework for the Industrial Internet of Things (IIoT) still face several challenges, such as deep hardware and software coupling, diverse protocols, difficult deployment of AI models, insufficient computing capabilities of edge devices, and sensitivity to delay and energy consumption. To solve the above problems, this paper proposes a software-defined AI-oriented three-layer IIoT edge computing framework and presents the design and implementation of an AI-oriented edge computing system, aiming to support device access, enable the acceptance and deployment of AI models from the cloud, and allow the whole process from data acquisition to model training to be completed at the edge. In addition, this paper proposes a time series-based method for device selection and computation offloading in the federated learning process, which selectively offloads the tasks of inefficient nodes to the edge computing center to reduce the training delay and energy consumption. Finally, experiments carried out to verify the feasibility and effectiveness of the proposed method are reported. The model training time with the proposed method is generally 30% to 50% less than that with the random device selection method, and the training energy consumption under the proposed method is generally 35% to 55% less.

Post-Quantum Secure Identity-Based Signature Scheme with Lattice Assumption for Internet of Things Networks.

The Internet of Things (IoT) plays an essential role in people's daily lives, such as healthcare, home, traffic, industry, and so on. With the increase in IoT devices, there emerge many security issues of data loss, privacy leakage, and information temper in IoT network applications. Even with the development of quantum computing, most current information systems are weak to quantum attacks with traditional cryptographic algorithms. This paper first establishes a general security model for these IoT network applications, which comprises the blockchain and a post-quantum secure identity-based signature (PQ-IDS) scheme. This model divides these IoT networks into three layers: perceptual, network, and application, which can protect data security and user privacy in the whole data-sharing process. The proposed PQ-IDS scheme is based on lattice cryptography. Bimodal Gaussian distribution and the discrete Gaussian sample algorithm are applied to construct the fundamental difficulty problem of lattice assumption. This assumption can help resist the quantum attack for information exchange among IoT devices. Meanwhile, the signature mechanism with IoT devices' identity can guarantee non-repudiation of information signatures. Then, the security proof shows that the proposed PQ-IDS can obtain the security properties of unforgeability, non-repudiation, and non-transferability. The efficiency comparisons and performance evaluations show that the proposed PQ-IDS has good efficiency and practice in IoT network applications.

Dual-Slope Path Loss Model for Integrating Vehicular Sensing Applications in Urban and Suburban Environments.

The development of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs), and autonomous driving (AD) has progressed rapidly in recent years, driven by artificial intelligence (AI), the internet of things (IoT), and their integration with dedicated short-range communications (DSRC) systems and fifth-generation (5G) networks. This has led to improved mobility conditions in different road propagation environments: urban, suburban, rural, and highway. The use of these communication technologies has enabled drivers and pedestrians to be more aware of the need to improve their behavior and decision making in adverse traffic conditions by sharing information from cameras, radars, and sensors widely deployed in vehicles and road infrastructure. However, wireless data transmission in VANETs is affected by the specific conditions of the propagation environment, weather, terrain, traffic density, and frequency bands used. In this paper, we characterize the path loss based on the extensive measurement campaign carrier out in vehicular environments at 700 MHz and 5.9 GHz under realistic road traffic conditions. From a linear dual-slope path loss propagation model, the results of the path loss exponents and the standard deviations of the shadowing are reported. This study focused on three different environments, i.e., urban with high traffic density (U-HD), urban with moderate/low traffic density (U-LD), and suburban (SU). The results presented here can be easily incorporated into VANET simulators to develop, evaluate, and validate new protocols and system architecture configurations under more realistic propagation conditions.

Dynamic Cooperative Communications with Mutual Information Accumulation for Mobile Robots in Industrial Internet of Things.

Mobile robots play an important role in the industrial Internet of Things (IIoT); they need effective mutual communication between the cloud and themselves when they move in a factory. By using the sensor nodes existing in the IIoT environment as relays, mobile robots and the cloud can communicate through multiple hops. However, the mobility and delay sensitivity of mobile robots bring new challenges. In this paper, we propose a dynamic cooperative transmission algorithm with mutual information accumulation to cope with these two challenges. By using rateless coding, nodes can reduce the delay caused by retransmission under poor channel conditions. With the help of mutual information accumulation, nodes can accumulate information faster and reduce delay. We propose a two-step dynamic algorithm, which can obtain the current routing path with low time complexity. The simulation results show that our algorithm is better than the existing heuristic algorithm in terms of delay.