Application Service Research Articles

BCDA: A blockchain-based dynamic auditing scheme for intelligent IoT

Intelligent Internet of Things (IoT) provides services for machine learning applications by collecting real-time data, where data correctness crucially impacts the training accuracy of models. Data owners host incremental real-time data in the cloud and use blockchain as a public platform for data query, usage, and certification. Malicious attackers may manipulate data through methods like data poisoning, compromising model parameters. Blockchain-based data integrity verification (auditing) schemes can effectively detect and monitor such activities. However, managing the storage demands for massive on-chain data certificates poses significant challenges. Existing compressed certificate storage strategies, such as the Merkle-Hash tree (MHT), incur significant on-chain storage costs because the auxiliary paths and the entire tree structure must be uploaded during verification, resulting in an O(n) storage overhead. Polynomial commitments offer a promising alternative by reducing storage costs to hundreds of bytes. However, current polynomial commitment methods lack effective support for incremental data updates, which are essential for real-time applications. In this paper, we propose a novel polynomial commitment-based dynamic integrity verification scheme that implements cumulative commitment updates for the first time. Our scheme includes a new data block insertion and deletion strategy that maintains the original order of data blocks within polynomial commitments. Theoretical security analysis confirms the robustness of the proposed scheme, while simulation results demonstrate that it meets the efficiency requirements for blockchain-based distributed data integrity verification mechanisms.

Applying Educational Accommodation for ADHD Pupils: Teachers and Parents Perspectives

Attention Deficit Hyperactivity Disorder (ADHD) is one of the disorders that threaten the learning quality of the students. The study aims to identify the degree of application of educational accommodations for students with attention deficit hyperactivity disorder (ADHD) in primary schools from teachers and parents' perspectives. Data have been collected from two samples: 70 primary school teachers and 62 parents of students with ADHD in the Eastern Province and Riyadh, who were chosen randomly. The results showed that there were statistically significant differences between the teachers’ and parents’ perspectives. In the degree of application of educational adaptations for this category of students, it came in favor of teachers. The results showed that there were statistically significant differences in the degree of application of adaptations according to the gender variable, which came in favor of female teachers, and the results also showed that there were no statistically significant differences between schools in terms of supported services for application of educational adaptations for students with ADHD.

Interface Design and User Experience in Selalulaku Applications Using Lean UX Methods

Consumptive behaviour often occurs in the younger generation, where changes in appearance and lifestyle have become essential today, especially in purchasing goods online. Online shopping events in Indonesia are interesting to study further because they have become a habit or trend for the younger generation, especially since they are easy to access anytime and anywhere. User interface and user experience are essential things that are useful for making it easier for users to operate applications, obtain responses from application services, and meet and understand user needs. Therefore, this research aims to design the user interface and user experience of the mobile-based Selalulaku application using the Lean UX method. This research found that the overall average score was 1.34, with a comparative value of "Good". So, it can be said that the interface design and user experience regarding efficiency, functionality and appearance received a "Good" rating.

Unlocking the AI-powered customer experience: Personalized service, enhanced engagement, and data-driven strategies for e-commerce applications

In recent times, there has been a surge of interest in the transformative potential of artificial intelligence (AI), particularly within the realm of online advertising. This research focuses on the critical examination of AI’s role in enhancing customer experience (CX) across diverse business applications. The aim is to identify key themes, assess the impact of AI-powered CX initiatives, and highlight directions for future research. Employing a systematic and comprehensive approach, the study analyzes academic publications, industry reports, and case studies to extract theoretical frameworks, empirical findings, and practical insights. The findings underscore a significant transformation catalyzed by AI integration into Customer Relationship Management (CRM). AI enables personalized interactions, fortifies customer engagement through interactive agents, provides data-driven insights, and empowers informed decision-making throughout the customer journey. Four central themes emerge: personalized service, enhanced engagement, data-driven strategy, and intelligent decision-making. However, challenges such as data privacy concerns, ethical considerations, and potential negative experiences with poorly implemented AI persist. This article contributes significantly to the discourse on AI in CRM by synthesizing the current state, exploring key themes, and suggesting research avenues. It advocates for responsible AI implementation, emphasizing ethical considerations and guiding organizations in navigating opportunities and challenges.

Reconstruction of dense time series high spatial resolution NDVI data using a spatiotemporal optimal weighted combination estimation model based on Sentinel-2 and MODIS

A regular, dense time series of the normalized difference vegetation index (NDVI) is a crucial remote sensing parameter that provides insights into the growth status of plants across different temporal and spatial scales. However, the production of high-resolution, equal-interval, and dense-time-series NDVI products currently faces technical challenges, limiting their application in agriculture and forestry-related fields. This study proposes a new method for reconstructing a dense Sentinel-2 NDVI time series based on the spatiotemporal optimal weighted combination estimation model (SOWCEM). The SOWCEM is developed using state-of-the-art spatial and temporal reconstruction algorithms. This model considers the distribution characteristics of errors in spatiotemporal dimensions, allowing for adaptive determination of the optimal combination weight at the pixel level. Then, the reconstructed regular dense-time-series NDVI data covering the region for five days/time are evaluated through a comparative analysis. The experimental results demonstrate that the regional NDVI reconstruction image based on the SOWCEM algorithm has a determination coefficient (R2) of 0.9082, a root mean square error (RMSE) of 0.0403, and a comprehensive overall error (ERGAS) of 5.7726. Compared with single spatiotemporal dimension models, the SOWCEM algorithm shows an average improvement of 5.78% in R2, while the RMSE and ERGAS decrease by 6.5% and 6.86%, respectively. Moreover, the stability and robustness of the models are also enhanced. These results indicate that the SOWCEM algorithm can effectively achieve a high-quality fusion reconstruction of regional images, significantly enhancing the accuracy of high-spatial-resolution NDVI dense temporal spectral reconstruction. Furthermore, it exhibits clear superiority and good applicability. This algorithmic framework provides a feasible approach for the dense reconstruction of high-spatial-resolution regular time-series NDVI data, enabling more accurate and efficient high-resolution NDVI remote sensing monitoring services in related field applications. The core code of SOWCEM is available at https://github.com/GISerZkun/SOWCEM.

Analysis of the Effectiveness of Based Public Services Information and Communication Technology (ICT)

This research is entitled: Analysis of the Effectiveness of Public Services Based on Information and Communication Technology (ICT): Study of the National Public Service Complaint Management System for People's Online Aspiration and Complaint Services (SP4N LAPOR) at the Communication and Information Service of the Riau Islands Province. The aim of the research is to determine the effectiveness of public services that utilize information and communication technology (ICT) with the SP4N LAPOR Application service at the Riau Islands Province Communication and Informatics Service (Diskominfo). The SP4N LAPOR Application is an application that can be used for category purposes. reporting in the form of aspirations and appreciation, as well as requests for public information and complaints and reports in the reporting completion category. and one of the government's efforts to provide high-quality public services for the community. In measuring the level of program effectiveness, researchers used Champbel theory (in Mutiarin and Arif, 2014). Also this research is descriptive in nature by applying qualitative methods. Informants were determined by applying purposive sampling techniques. In data collection, research instruments were used, including field observations and interviews with several informants. Then documentation studies are used for the documents in the research. Meanwhile, data analysis techniques are carried out through data reduction, displaying data and drawing conclusions on research that has been carried out. In the research results, it was found that the effectiveness of ICT- based public services with the SP4N LAPOR application in the Riau Islands Province Communication and Information Service (Diskominfo) was running quite effectively, although there were a number of obstacles, including limited reliability human resources in the field of information technology and obstacles. others in the form of the Riau Islands Provincial Government, especially Diskominfo, do not yet have an Information and Communication Technology (ICT) master plan and an Electronic Based Service System (SPBE) architecture.

Assessment of the Requirements of Natural Resources Using the Community Around Khow Noi-Khow Pradoo Non-Hunting Area of Phisanulok Province, Thailand

This study aimed to assess natural resource needs in the Khow Noi-Khow Pradoo non-hunting area (KNKPNHA) to understand the importance of using these natural resources. The assessment was based on local resource funds combined with the quality standards set by environmental agencies. The methodology involved collecting data through group interviews with academics and local stakeholders, along with individual interviews with villagers living in communities around KNKPNHA. Semi-structured interviews, check-lists, questionnaires, and the analysis of conceptual framing were used. KNKPNHA covers an area of 13516.8 hectares. The main land cover types in the area are mixed deciduous forest (major) and deciduous forest (minor). The primary occupation of villagers in KNKPNHA is the collection of wild edible plants from the forest, and is the next most important occupation is tourist services. The villagers can be classified into three categories: (1) those who hold land and utilize the forest; (2) distant has influence on understanding of space application regulations and services; and (3) those whose household income is related to their condition. It was found that villagers in KNKPNHA rely on wild edible plants from the forest. However, the results of the study show that perceptions of the importance of collecting fuel and herbal medicine from the forest is decreasing. Future research should investigate resource use behavior, including conservation management, with the participation of locals in the KNKPNHA and help communities to monitor and safeguard resources alongside government personnel.

Internal Medicine and Internal Medicine-Pediatrics Residency Program Website Traffic Patterns Across Two Institutions and Five Electronic Residency Application Service (ERAS) Cycles.

Background Since the start of the COVID-19 pandemic in Spring 2020, medical residency program recruitment has become increasingly web-based due to the transition to virtual interviews. Although social media use by residency programs has soared during this time, applicant surveys show that official program websites remain the most used online resource. According to survey-based studies, the content that applicants favor on program websites tends to mirror their priorities in choosing programs. However, it is unclear whether applicant-reported priorities in program choice and website content truly drive program website traffic. In this analysis, we will elucidate website traffic patterns from two Internal Medicine residency programs and two Internal Medicine-Pediatrics programs, both in terms of the thematic content of high-traffic pages and changes in traffic patterns throughout the Electronic Residency Application Service (ERAS) application cycle. We will provide novel, data-driven guidance to residency program leadership regarding website content. Methodology For each of the four programs included in the analysis, total pageviews on a monthly basis were obtained for the annual ERAS application cycles taking place from 2017 through 2022. For one Internal Medicine program, data was only available for its current website from 2020 to 2022. The mean monthly pageviews were calculated for each page within each website. The total site traffic trends across each year and within high-traffic months were totaled as well. Results As expected, the highest traffic period for all programs in all years was the days before the application deadline, with a secondary, smaller spike in traffic around Match Day. In general, the most popular pages for all four programs were thematically in line with the top five applicant priorities in the 2022 National Resident Matching Program applicant survey, namely, geographic location, goodness of fit, program reputation, work/life balance, and current program residents. Three of the websites featured unique content that unexpectedly proved to be as popular as the top survey-reported topics, such as pages related to a new major clinical site, a new integrated subspecialty pathway, and, most profoundly, a pipeline program for certain applicants from groups that are underrepresented in medicine. Alumni career content was also heavily trafficked across all four programs. Conclusions Program directors should plan twice-yearly updates to residency program websites, timed to be finished by the start of the ERAS cycle in the fall, and again just before Match Day in March. Program directors should include specific, up-to-date information about unique program features. Future research should incorporate a more diverse variety of programs, software-based page content analysis, and traffic source data.

MONITORING ADMINSTRASI JARINGAN DENGAN MIKROTIK DAN TELEGRAM BOT PADA INTERNET SERVICE PROVIDER

Network management using MikroTik devices at Lajur Net still has shortcomings. Although internet access is available around the clock, the limited number of employees makes it difficult for managers to access computers to manage MikroTik. Administrators hope to manage the MikroTik network remotely through end devices. Currently, accessing MikroTik online incurs high operational costs for renting public IPs and web-based management application services. This study aims to utilize Telegram Bot for network management and monitoring that can be accessed remotely via the internet. The use of Telegram Bot for network management at Lajur Net can be successfully implemented. The implementation of Telegram Bot includes managing interfaces, hotspots, ICMP, and monitoring the resources of MikroTik RouterBoard devices in the Lajur Net network. Telegram Bot can be accessed remotely through the internet and works well on the Lajur Net network. The implementation of Telegram Bot includes interface administration, hotspots, ping, and monitoring the resources of MikroTik RouterBoard devices. The additional firewall feature allows network administrators to add and delete filter rules within the Lajur Net network security system. Moreover, Telegram Bot utilizes button features on Telegram messages to facilitate usage without having to type all commands. Thus, Telegram Bot offers an efficient and effective solution in MikroTik network management at Lajur Net, reducing the need for expensive public IPs and web-based management applications. This demonstrates that the use of chatbot technology can provide significant benefits in network administration, especially for internet service providers with limited resources. Through this research, it is hoped that the implementation of Telegram Bot can serve as a reference for developing more innovative and efficient network management in the future.

Revisiting Differences in Fourth-Year Orthopaedic Away Rotation Opportunities and Fees Among Allopathic and Osteopathic Medical Students.

Fourth-year away rotations are an important modifiable variable proven to increase students' opportunities to match into orthopaedic surgery. The purpose of this study was to determine differences in away rotation eligibility requirements and cost of rotation between allopathic and osteopathic students during the 2023 application cycle. Eligibility requirements and fees were then compared with the 2021 application cycle. A cross-sectional study was performed during the 2023 application cycle of all nonmilitary, Accreditation Council for Graduate Medical Education (ACGME)-accredited orthopaedic surgery residency programs (n = 194). Each program's website, affiliated school of medicine's website, visiting student application service portal, and Residency Explorer tool were searched for eligibility criteria, associated rotation fees, and other rotation requirements. Two-sample Z tests for proportions were utilized to compare differences in programs with differing requirements for students based on academic degree type. Data were compared statistically with previously reported data from the 2021 application cycle. In 2023, there were more programs that restricted osteopathic medical students from away rotations than programs that restricted allopathic medical students (12/194, 6.2% vs. 0/194, 0.0%; p < 0.001). All 12 programs were formerly ACGME-accredited before the integration into a single accreditation system. There was a decrease in the number of programs restricting osteopathic medical students from away rotations compared with the 2021 application cycle (18/194, 9.3% vs. 12/194, 6.2%; p = 0.254). Fees associated with away rotations ranged from $25 to $4,000 for both allopathic and osteopathic students. The number of programs that charged osteopathic medical students higher rotation fees than programs that charged allopathic students when compared with the 2021 application cycles decreased (1/194, 0.5% vs. 5/194, 2.6%; p = 0.049). While some programs continue to have away rotation eligibility requirements that prohibit osteopathic medical students from rotating, only one residency program currently charges osteopathic medical students a higher fee to rotate than allopathic medical students.

Do Program Directors of Anesthesiology Residency Programs Interpret Narrative Letters of Recommendation as Intended?

Background Letters of recommendation (LORs) are an important part of the application process for medical residency programs with most specialties preferring a narrative format. Given the inherent subjectivity of narrative LORs, the current study sought to determine whether the intended messages of narrative LORs written for applicants to anesthesiology residency programs are accurately interpreted by readers. Methodology Anonymous online surveys were sent via the Qualtrics platform to program directors (PDs) of the Accreditation Council for Graduate Medical Education-accredited anesthesiology residency programs in the Mid-Atlantic region as designated by the Electronic Residency Application Service, which consists of the states of New York, Pennsylvania, and New Jersey. Each PD participant received five surveys, each of which was attached to a de-identified LOR that was written by another PD located at an institution in the same region. Both the letter writer and study participants were asked to score LORs on a Likert-like scale. Participants were additionally asked whether the LORs, if received, would influence their decision to either offer an interview to the applicant or to rank the applicant. Finally, participants were asked to note any specific words or phrases within the LORs that they found to be particularly impactful. Results Overall, 10 of 34, 29.41%, PDs responded to the survey. There was a high correlation between the LOR intent and the respondents' assigned rating (Spearman's rho = 0.7973, p < 0.001). Responses were more accurate for "outstanding and excellent" LORs compared to the lower three categories. Results were unaffected after adjusting for respondents' years of experience as PDs. Additionally, 71.6% indicated that the LORs would influence the decision about offering an interview, and 56.5% stated that the LORs would influence a ranking decision. Conclusions Our results indicate that respondents' perception of LORs correlated strongly with the intent of the writer. Additionally, respondents seemed to value LORs for interview and ranking decisions.

Effectiveness of Digital Sharia Pawnshop Services in Increasing The Number of Customers Pegadaian Syariah

Technology continues to develop, this is proven by technological advances in the form of applications and online transactions. However, not all technology provides convenience and has a positive impact on its users if not handled properly. Therefore, this thesis aims to reveal the effectiveness of the online services provided by Pegadaian Syariah Siringo-ringo through the Pegadaian Syariah Digital Service (PSDS) Application Service in increasing the number of customers. The background to the problem in this research is that errors often occur in applications and services that do not comply with procedures. The data collection method uses qualitative methods with observation, interviews and documentation.The research results show that this application is largely operating according to its intended use, although it is not yet fully effective in facilitating transactions. This application is not really effective in increasing the number of customers because customers still have minimal knowledge regarding the function of this application. Then the increase in the number of customers did not increase too much, but there continued to be an increase every year, be it gold savings customers, pawnshops, etc.

Association Between ERAS Application Changes and Unfilled Positions in the 2024 Family Medicine Match.

The number of family medicine positions unfilled in the Main Match has increased from 2019 to 2023. In the 2023-2024 family medicine application season, the specialty adopted the changes in the Electronic Residency Application Service (ERAS or MyERAS), which included geographic preferences and offered applicants five program signals. We compared the number of unfilled positions from prior years to results of the 2024 family medicine Match after the adoption of these changes. We analyzed publicly available data from the National Resident Matching Program to compare the number of positions filled in the Main Match from 2019 to 2024. We estimated the number of expected positions filled using multiple linear regression and application data from ERAS and the number of available positions in the Match. A total of 636 positions were unfilled in the 2024 family medicine Main Match. Our statistical analysis, based only on the number of applicants and the number of available positions, predicted 630.65 unfilled slots with a 95% confidence interval from 605.53 to 655.77. The 2024 family medicine position fill rate decreased slightly at 87.8% compared to 88.7% in the 2023 Match. The number of family medicine programs that did not fill in the Main Match increased in 2024 to 241 (30.3% of programs) compared to 217 (28.1%) in 2023. MyERAS changes in the 2023-2024 family medicine application season, including geographic preferences and program signaling, were not associated with a change in the number of programs or positions filled in the family medicine Main Match.

Optimasi Layanan Aplikasi Digital Pasca Pandemi Melalui Kolaborasi Organisasi Gojek-Tokopedia

The COVID-19 pandemic has profoundly changed the business landscape, especially in the use of digital applications. During the pandemic period, digital applications have become the main means for individuals and organizations to stay connected, transact and carry out various activities. However, the pandemic has also exposed several weaknesses in digital application infrastructure and services, such as reduced performance and weaknesses in data security. In facing these challenges, inter-organizational collaboration becomes increasingly important. Such collaboration can help in establishing new industry standards and increase user confidence. On the other hand, innovation in digital application services is also key in meeting user needs after the pandemic. The impact of post-pandemic organizational changes also affects application usage patterns and demand for digital services. This research uses a quantitative survey method by collecting data through questionnaires to understand user attitudes and perceptions towards post-pandemic digital application services.

Sentiment Analysis of Alfagift Application User Reviews Using Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) Methods

The rapid advancement of mobile apps has emerged as an important aspect of the routine of internet-connected users. In Indonesia, many companies are introducing their apps to improve the quality of service for users, and Alfamart is one of them. However, users have identified many shortcomings in these apps. This feedback is provided by users on the review feature of the Alfagift app on the Google Play Store. This research aims to apply a sentiment analysis approach to identify the application's shortcomings so that developers can understand the aspects that need to be improved to improve the quality of application services. The research stages include data collection, preprocessing, labeling, weighting, classification of LSTM and SVM methods, and performance evaluation using a confusion matrix. The dataset consists of 1000 reviews obtained through web scraping techniques. This research uses the Lexicon-based method to classify the dataset into positive, negative, and neutral categories. The analysis results show that 801 data are classified as positive sentiment, 77 as negative sentiment, and 122 as neutral sentiment. Based on testing, both SVM and LSTM methods show good performance. The best accuracy results were obtained using the SVM method, which amounted to 83.5%. Meanwhile, the LSTM method achieved an accuracy of 82%.

Analisis Sentimen Ulasan Aplikasi PosPay untuk Meningkatkan Kepuasan Pengguna dengan Metode K-Nearest Neighbor (KNN)

PT Pos Indonesia has launched a digital pospay service. Users who have a positive experience are more likely to return to application. User perceptions analysis can be known from the Review sentiments. Review sentiments that are classified as positive and negative are really needed by developers to improve services (user satisfaction). The research aims to increase user satisfaction of the PosPay application based on the application's review data. The source of data is a review of the pospay application at Google play store. The method used quantitative study method that is K-Nearest Neighbor (K-NN) that classify objects based on learning data that are closest to the object. Research variable is the word from user commentary that associated with the pospay application services. Application review data in scrapping, preprocessing, splits data (train data and test data). Supervised learning (TF-IDF and K-NN) prepared with python programming provides data visualizing. The research results show that the sentiment of Pospay application users tends to be positive. K-NN classification model produces 91% accuracy, 90% precision and recall by 99%. The key word of positive sentiment is: easy, helpful, transaction. Keyword negative sentiment: balance, pay, login.

Congestion-aware Spatio-Temporal Graph Convolutional Network-based A* Search Algorithm for Fastest Route Search

The fastest route search, which is to find a path with the shortest travel time when the user initiates a query, has become one of the most important services in many map applications. To enhance the user experience of travel, it is necessary to achieve accurate and real-time route search. However, traffic conditions are changing dynamically, and the frequent occurrence of traffic congestion may greatly increase travel time. Thus, it is challenging to achieve the above goal. To deal with it, we present a congestion-aware spatio-temporal graph convolutional network-based A* search algorithm for the task of fastest route search. We first identify a sequence of consecutive congested traffic conditions as a traffic congestion event. Then, we propose a spatio-temporal graph convolutional network that jointly models the congestion events and changing travel time to capture their complex spatio-temporal correlations, which can predict the future travel-time information of each road segment as the basis of route planning. Further, we design a path-aided neural network to achieve effective origin-destination (OD) shortest travel-time estimation by encoding the complex relationships between OD pairs and their corresponding fastest paths. Finally, the cost function in the A* algorithm is set by fusing the output results of the two components, which is used to guide the route search. Our experimental results on the two real-world datasets show the superior performance of the proposed method.

PATH-10. ACCELERATING COMPREHENSIVE CNS TUMOR MOLECULAR DIAGNOSTICS WITH RAPID-CNS2 AND MNP-FLEX: A PROSPECTIVE MULTI-CENTER VALIDATION

Abstract BACKGROUND The 2021 WHO classification update highlights the necessity of integrating molecular alterations for precise central nervous system (CNS) tumor diagnoses. However, current molecular reporting methods are hindered by significant initial investment, labor-intensive protocols, and lengthy turnaround times. Methylation-based classification has emerged as a pivotal diagnostic tool but is currently limited to array-based techniques. This necessitates exploration of novel technologies to streamline molecular analysis. METHODS We implemented Rapid-CNS2 - our adaptive sampling-based nanopore sequencing workflow- on 190 adult and pediatric samples at University Hospital Heidelberg and University of Nottingham. Intraoperative potential was assessed through real-time analysis followed by 24-hour sequencing for comprehensive genomic insights. Additionally, we developed MNP-Flex, a platform-agnostic version of the Heidelberg methylation classifier covering 184 CNS tumor classes. We evaluated MNP-flex on a global cohort of over 78,000 samples from methylation arrays, whole genome bisulfite sequencing, nanopore whole genome sequencing, methylation panels and Rapid-CNS2. RESULTS Rapid-CNS2 validation yielded accurate integrated diagnoses in all 190 samples. Within a crucial 30-minute timeframe, we reported accurate methylation families and arm-level copy number profiles followed by next-day reporting of fine-grained methylation classification, SNVs, focal CNVs, MGMT status, fusions and novel structural variants. Moreover, MNP-Flex achieved 92% accuracy over the validation dataset spanning over 78,000 samples from five different technologies. CONCLUSIONS The adoption of Rapid-CNS2 and MNP-Flex enables rapid intraoperative broad methylation classification and copy number alteration reporting within 30 minutes, with additional clinically relevant, fine-grained molecular insights available the following day. It offers clinicians rapid access to comprehensive molecular information critical for treatment decisions. Furthermore, MNP-Flex extends the utility of the Heidelberg methylation classifier to diverse sequencing-based data. By overcoming the limitations of currently available methods, our workflow represents a paradigm shift in the field, promising improved management of CNS tumor patients. Rapid-CNS2 can be executed with a single command, while MNP-Flex is publicly available as a web service, enhancing accessibility and usability for clinical applications.

Lyapunov-guided Deep Reinforcement Learning for service caching and task offloading in Mobile Edge Computing

With the development of Internet of Things (IoT) networks and Mobile Edge Computing (MEC), many computing-intensive applications have been developed in large quantities. Due to the heterogeneity of tasks, different application services are required to perform each task. Caching application services and related data in edge servers is challenging. Hence, we study the service cache placement and task offloading problem in IoT networks. Since IoT devices and edge servers with limited storage resources can only cache a few services at the same time, we formulate the service cache placement and task offloading of IoT devices problem to minimize task service delay with long-term energy constraint of IoT devices, which is a mixed integer nonlinear programming problem. To solve this problem, an online Deep Reinforcement Learning guided by the Lyapunov optimization framework algorithm (LYADRL) is proposed. We first build a virtual queue model to decouple the problem by Lyapunov optimization technique to transform the problem into a single time slot optimization problem. Then, we use Deep Reinforcement Learning techniques to find the optimal edge service caching and task offloading policies for each time slot. Simulation results show that our algorithm can reduce the service delay compared with other benchmark algorithms.

HCSMBO: A hybrid cat swarm and monarch butterfly optimization algorithm for energy consumption optimization in industrial internet of things

Energy consumption optimization is crucial for improving the quality of application services in the industrial Internet of Things (IIoT) environment. Traditional optimization methods often adopt blind search strategies, which leads to a significant waste of computing resources and low efficiency. This paper proposes a Hybrid Cat Swarm Optimization and Monarch Butterfly Optimization (HCSMBO), which combines the advantages of Cat Swarm Optimization (CSO) and Monarch Butterfly Optimization (MBO) to solve this problem. By utilizing the characteristics of swarm intelligence, including strong spatial search ability, high stability, and fast convergence, HCSMBO focuses on detailed optimization of the coverage, efficiency, and energy balance coefficient of Support Vector Machine (SVM) networks. By adjusting the weight factors of the fitness function, this algorithm can effectively find the global optimal solution. The experimental results confirm that HCSMBO significantly improves the intelligence, accuracy, and stability of task scheduling in resource management configuration, while achieving lower energy consumption. The research results of this paper are of great significance for improving the service quality of IIoT applications, meeting personalized business needs, and improving resource utilization and execution efficiency. In addition, they also provide new ideas and methods for energy consumption optimization research in the future IIoT.