Storage Usage Research Articles

Actual usage assessment among cloud storage consumers in the Philippines using a machine learning ensemble approach

Cloud storage has been widely considered among developed and developing countries due to its ability to provide a platform for large data and information storage. Developing countries like the Philippines have started using this storage and have only since considered the free services. With the aim to understand utility for development and continuous patronage, there has been lacking evidence in the intention and actual use of cloud storages. The need for study is evident to promote and develop concrete strategies for cloud storage uptake, even if payment is needed for extra storage. This study analyzed the antecedents of actual use behavior of cloud storage in a developing country like the Philippines using a machine learning ensemble (MLE). With 616 valid responses, a total of 33,264 datasets were processed to analyze the actual use of cloud storage among Filipinos, measured using the integrated extended technology acceptance model and valence framework. With an average accuracy of 93% and 90% for the MLE considered, results have presented consistent output of voluntariness, subjective norm, perceived benefit, perceived usefulness, and perceived ubiquity to be contributing factors affecting actual use behavior. It could be posited that both personal and professional usage of cloud storage has been considered by users. In addition, due to people’s readiness to use technology nowadays, the adoption of which is relatively convenient for them. Evident from the findings, further technological infrastructure is needed to be enhanced in the country for a more positive continuous intention. Therefore, the application of the integrated framework may be used and expanded for other technology utilities in different countries. Lastly, practical and managerial insights were built on the results to provide strategies and development needed for marketing, utility, and application.

Representing Long Volumetric Video with Temporal Gaussian Hierarchy

This paper aims to address the challenge of reconstructing long volumetric videos from multi-view RGB videos. Recent dynamic view synthesis methods leverage powerful 4D representations, like feature grids or point cloud sequences, to achieve high-quality rendering results. However, they are typically limited to short (1~2s) video clips and often suffer from large memory footprints when dealing with longer videos. To solve this issue, we propose a novel 4D representation, named Temporal Gaussian Hierarchy, to compactly model long volumetric videos. Our key observation is that there are generally various degrees of temporal redundancy in dynamic scenes, which consist of areas changing at different speeds. Motivated by this, our approach builds a multi-level hierarchy of 4D Gaussian primitives, where each level separately describes scene regions with different degrees of content change, and adaptively shares Gaussian primitives to represent unchanged scene content over different temporal segments, thus effectively reducing the number of Gaussian primitives. In addition, the tree-like structure of the Gaussian hierarchy allows us to efficiently represent the scene at a particular moment with a subset of Gaussian primitives, leading to nearly constant GPU memory usage during the training or rendering regardless of the video length. Moreover, we design a Compact Appearance Model that mixes diffuse and view-dependent Gaussians to further minimize the model size while maintaining the rendering quality. We also develop a rasterization pipeline of Gaussian primitives based on the hardware-accelerated technique to improve rendering speed. Extensive experimental results demonstrate the superiority of our method over alternative methods in terms of training cost, rendering speed, and storage usage. To our knowledge, this work is the first approach capable of efficiently handling hours of volumetric video data while maintaining state-of-the-art rendering quality.

Crack Detection and Cost Analyzer Using AI

Crack Vision is an innovative AI-powered Android application developed to detect and analyze wall cracks, providing users with real-time, automated repair cost estimates. By leveraging advanced object detection models such as YOLOv7 and Mask R-CNN, Crack Vision enhances both the accuracy and efficiency of crack detection, enabling precise measurement of crack dimensions, severity assessment, and quick identification of structural issues. This application simplifies the inspection and budgeting process for wall repairs, making it an essential tool for homeowners, contractors, and structural engineers who seek to minimize reliance on manual inspection methods. The core functionality of Crack Vision combines high-resolution image processing with state-of-the-art machine learning algorithms, ensuring reliable detection across various wall types and crack textures. Using computer vision and deep learning techniques, the app can differentiate between structural and non-structural cracks, thereby offering a comprehensive solution for repair prioritization. Furthermore, Crack Vision integrates with a robust backend that stores user data, allowing users to track the condition of their walls over time. This feature aids in preventive maintenance by identifying trends in crack expansion and deterioration, potentially mitigating costly repairs. Additionally, the application provides a detailed breakdown of estimated repair costs based on crack size, depth, and type, empowering users to make informed decisions about necessary repairs. Crack Vision is designed with user-friendliness in mind, featuring an intuitive interface that guides users through the inspection process. The app also supports multilingual options and customizable settings to cater to diverse user needs. In sum, Crack Vision redefines wall crack inspection, offering a rapid, accurate, and accessible solution for both casual users and industry professionals, ultimately contributing to improved building safety and maintenance efficiency

Indentity Management System Using Blockchain and Survey

Abstract—This project aims to develop a web3 platform that stores user credentials on the blockchain, providing high levels of security and privacy. Using a range of tools and technologies, including Metamask, RemixIDE, Ganache, Node.js, Solidity for smart contracts, HTML, and CSS, the platform offers a user-friendly interface that enhances the user experience. Smart contracts are used to ensure that user credentials are only visible to the individual user, providing a high level of security and privacy. This platform has the ability to revolutionize how users interact with online services and manage their digital identities, reducing costs, increasing trust, and improving expandability. The implementation of this project has demonstrated the overall benefits of blockchain and smart contracts in virtual identity management, including increased security, improved privacy, and enhanced user experience. The platform has the potential for further development and expansion, including the integration of biometric authentication, artificial intelligence and machine learning algorithms, and the expansion to include a range of online services. Overall, this project has demonstrated the significant potential of blockchain technology and smart contracts in digital identity management and has the ability to shift the way users communicate with online services, offering a one-stop-shop for their online needs. Keywords—Block chain, metamask, ganache, remix ide, solidity

Tiny Pointers

This paper introduces a new data-structural object that we call the tiny pointer. In many applications, traditional \(\log n\) -bit pointers can be replaced with \(o(\log n)\) -bit tiny pointers at the cost of only a constant-factor time overhead and a small probability of failure. We develop a comprehensive theory of tiny pointers, and give optimal constructions for both fixed-size tiny pointers (i.e., settings in which all of the tiny pointers must be the same size) and variable-size tiny pointers (i.e., settings in which the average tiny-pointer size must be small, but some tiny pointers can be larger). If a tiny pointer references an item in an array filled to load factor \(1-\delta\) , then the optimal tiny-pointer size is \(\Theta(\log\log\log n+\log\delta^{-1})\) bits in the fixed-size case, and \(\Theta(\log\delta^{-1})\) expected bits in the variable-size case. Our tiny-pointer constructions also require us to revisit several classic problems having to do with balls and bins; these results may be of independent interest. Using tiny pointers, we apply tiny pointers to five classic data-structure problems. We show that: A data structure storing \(n\) \(v\) -bit values for \(n\) keys with constant-factor time modifications/queries can be implemented to take space \(nv+O(n\log^{(r)}n)\) bits, for any constant \(r\gt0\) , as long as the user stores a tiny pointer of expected size \(O(1)\) with each key—here, \(\log^{(r)}n\) is the \(r\) -th iterated logarithm. Any binary search tree can be made succinct, meaning that it achieves \((1+o(1))\) times the optimal space, with constant-factor time overhead, and can even be made to be within \(O(n)\) bits of optimal if we allow for \(O(\log^{*}n)\) -time modifications—this holds even for rotation-based trees such as the splay tree and the red-black tree. Any fixed-capacity key-value dictionary can be made stable (i.e., items do not move once inserted) with constant-factor time overhead and \((1+o(1))\) -factor space overhead. Any key-value dictionary that requires uniform-size values can be made to support arbitrary-size values with constant-factor time overhead and with an additional space consumption of \(\log^{(r)}n+O(\log j)\) bits per \(j\) -bit value for an arbitrary constant \(r\gt0\) of our choice. Given an external-memory array \(A\) of size \((1+\varepsilon)n\) containing a dynamic set of up to \(n\) key-value pairs, it is possible to maintain an internal-memory stash of size \(O(n\log\varepsilon^{-1})\) bits so that the location of any key-value pair in \(A\) can be computed in constant time (and with no IOs). In each case tiny pointers allow for us to take a natural space-inefficient solution that uses pointers and make it space-efficient for free.

Pengaruh Store Atmosphere dan Kualitas Produk Terhadap Kepuasan Pelanggan pada Eiger Adventure Flagship Store Merr Surabaya: Studi pada Eiger Adventure Flagship Store Merr Surabaya

This research aims to determine and analyze the influence of store atmosphere on customer satisfaction at the Eiger Adventure Flagship Store Merr Surabaya. The method used is a quantitative research method which has a positivist philosophy, used in research on certain populations or samples, sampling techniques are generally carried out randomly, data collection uses research instruments, quantitative or statistical data analysis aims to test predetermined hypotheses. With the results of this research, there is high customer satisfaction with Eiger Adventure Flagship products at the Merr Surabaya Store for Eiger product users because of the Store Atmosphere and Product Quality which makes consumers feel satisfied with Eiger products. So it is hoped that in the future Eiger will be able to maintain and continue to improve product quality so that consumers do not feel disappointed with Eiger products and also the Eiger store will still be able to maintain and also renew the store atmosphere that has been implemented now because from the results of this research almost all respondents like the atmosphere of the Eiger store. Because with quality products and a pleasant store atmosphere, it will create customer satisfaction with a feeling of satisfaction felt by customers, so customers will become loyal to the Eiger brand.

Development of high-efficient asphalt pavement modeling software for digital twin of road infrastructure

To develop digital twin (DT) of road infrastructure, one critical element is computation of pavement responses (strains, stresses, and deflections) under traffic and environmental loading. This study aims to develop high-efficient asphalt pavement modeling software based on semi-analytical finite element method (SAFEM) for DT application. The algorithms address important aspects in vehicle-tire-pavement interaction modeling, such as dynamic vehicular loading, three-dimensional (3-D) non-uniform tire contact stress, viscoelastic behavior of asphalt material, and interface bonding condition. The simulation accuracy is verified by comparison with full-scale test and field measurements, and the relative differences are around 5 % to 20 %. Techniques including optimized discrete Fourier transform, parallel computing, graphics processing unit (GPU) acceleration, and sparse matrices are implemented for computation efficiency. As compared to the traditional 3-D FEM, SAFEM shows significant savings in computation time and storage usage. The high efficiency and accuracy make the software full of potential to be applied for DT of roadway infrastructure.

A novel Deep High-level Concept-mining Jointing Hashing Model for unsupervised cross-modal retrieval

Unsupervised cross-modal hashing has achieved great success in various information retrieval applications owing to its efficient storage usage and fast retrieval speed. Recent studies have primarily focused on training the hash-encoded networks by calculating a sample-based similarity matrix to improve the retrieval performance. However, there are two issues remain to solve: (1) The current sample-based similarity matrix only considers the similarity between image-text pairs, ignoring the different information densities of each modality, which may introduce additional noise and fail to mine key information for retrieval; (2) Most existing unsupervised cross-modal hashing methods only consider alignment between different modalities, while ignoring consistency between each modality, resulting in semantic conflicts. To tackle these challenges, a novel Deep High-level Concept-mining Jointing Hashing (DHCJH) model for unsupervised cross-modal retrieval is proposed in this study. DHCJH is able to capture the essential high-level semantic information from image modalities and integrate into the text modalities to improve the accuracy of guidance information. Additionally, a new hashing loss with a regularization term is introduced to avoid the cross-modal semantic collision and false positive pairs problems. To validate the proposed method, extensive comparison experiments on benchmark datasets are conducted. Experimental findings reveal that DHCJH achieves superior performance in both accuracy and efficiency. The code of DHCJH is available at Github.

PROBLEMATIC ASPECTS OF DEVELOPING APPLICATIONS FOR GENERATING AND STORING USER PASSWORDS

In today’s digital world, the security of personal data is becoming an increasingly pressing issue. Apps for storing user passwords are an important tool in ensuring the security of online accounts, but their development is not without problematic aspects. This article discusses the key challenges developers face when building these applications. Particular attention is paid to storage security, authentication mechanisms, as well as usability and integration with other services. By analyzing these issues, you can identify the main challenges and find ways to solve them, contributing to the creation of more reliable and user- friendly applications for storing user passwords. This article provides an overview of the challenges that arise when developing applications to store user passwords and provides recommendations for solving them. The article discusses such important aspects as data protection, prevention of cyberattacks, compliance with security standards, and usability for the end user. Understanding these challenges and being able to solve them plays a key role in creating trustworthy and effective applications that can provide a high level of security and convenience for users in their everyday online experience. The article describes the practical development of an application for generating complex passwords, as well as their further secure storage in an encrypted database with subsequent integration with third-party applications for more secure storage. The article is of great practical significance. Keywords: generating passwords, storing passwords, cryptography, safety, encryption.

AI-Enhanced Smart Cooking Pot: A Culinary Companion with Intelligent Sensing

This research introduces SmartCook, an avant-garde kitchen appliance that amalgamates sensor technologies, artificial intelligence (AI), and machine learning to redefine the art of cooking. The device employs an array of sensors, including temperature, moisture, infrared, proximity, electronic tongue, and electronic nose, ingeniously positioned in the lid with foodgrade silicon transparent sealing. This design ensures a non-intrusive manner of sensing, preserving the integrity of both the food and the sensors. SmartCook operates in two modes: Cooking Mode, where experienced users store their personalized cooking patterns, and Guide Mode, designed for beginners seeking expert guidance. The AI processing unit, analysing vapours produced during cooking, intricately captures the nuances of smell, texture, timing, temperature, and moisture to create and store unique recipe patterns for each prepared meal. The addition of a texture-sensing camera placed in the lid, also sealed with foodgrade silicon, enhances the system’s capability to analyze and replicate food textures. This innovative approach transforms SmartCook into a comprehensive, guided, and customizable culinary assistant. The system’s safety mechanisms, including automatic shut-off, prioritize user safety throughout the cooking process. SmartCook revolutionizes home cooking, transforming it into an interactive, guided, and enjoyable journey. With its emphasis on replicating the unique patterns of individual cooks, SmartCook introduces a novel approach to the culinary arts, empowering users to virtually learn and recreate meals from their favourite chefs. This research presents a ground-breaking contribution to the evolving landscape of smart kitchen appliances, offering users an innovative and enjoyable way to explore the world of gastronomy.

Efficient skin lesion segmentation with boundary distillation.

Medical image segmentation models are commonly known for their complex structures, which often render them impractical for use on edge computing devices and compromising efficiency in the segmentation process. In light of this, the industry has proposed the adoption of knowledge distillation techniques. Nevertheless, the vast majority of existing knowledge distillation methods are focused on the classification tasks of skin diseases. Specifically, for the segmentation tasks of dermoscopy lesion images, these knowledge distillation methods fail to fully recognize the importance of features in the boundary regions of lesions within medical images, lacking boundary awareness for skin lesions. This paper introduces pioneering medical image knowledge distillation architecture. The aim of this method is to facilitate the efficient transfer of knowledge from existing complex medical image segmentation networks to a more simplified student network. Initially, a masked boundary feature (MBF) distillation module is designed. By applying random masking to the periphery of skin lesions, the MBF distillation module obliges the student network to reproduce the comprehensive features of the teacher network. This process, in turn, augments the representational capabilities of the student network. Building on the MBF distillation module, this paper employs a cascaded combination approach to integrate the MBF distillation module into a multi-head boundary feature (M2BF) distillation module, further strengthening the student network's feature learning ability and enhancing the overall image segmentation performance of the distillation model. This method has been experimentally validated on the public datasets ISIC-2016 and PH2, with results showing significant performance improvements in the student network. Our findings highlight the practical utility of the lightweight network distilled using our approach, particularly in scenarios demanding high operational speed and minimal storage usage. This research offers promising prospects for practical applications in the realm of medical image segmentation.

Forensic analysis and data decryption of tencent meeting in windows environment

Video conferencing applications have become ubiquitous in the post-COVID-19 era. Remote meetings, briefing sessions, and lectures are gradually becoming part of our culture. Thus, the amount of user data that video conferencing applications collect and manage has increased, and such data can be used as digital evidence. In this study, we analyzed Tencent Meeting, the most widely used video conferencing application in China, to identify the data stored on the user's disk by the application. Tencent Meeting stores user information and the chat history during a video conference on local storage. We found that Tencent Meeting suffers from a vulnerability in the process of encrypting and storing the user data, which can be exploited by anyone who can access and decrypt the user's data. We expect that our findings to help digital forensics investigators conduct efficient investigations when applications are used for malicious purposes.

Optimasi Otentikasi Jaringan Wireless Menggunakan Standar Protokol 802.1x dan PEAP

The internet is currently a basic need in processing and managing information, one way to connect to the internet is to use radio frequency and transmission as a delivery media or also called WiFi. WiFi has many advantages such as being concise in use and easy to maintain, but WiFi has a problem, namely digital data transferred via a wireless network (WiFi) is easy to steal or sniff. A protocol standard is needed that can guarantee the security of the wireless connection, the 802.1x protocol standard and PEAP can be one solution to this problem. In the implementation of the 802.1x protocol standard, a topology is needed that involves a radius server as a place to store user accounts, an authentication system as the initial gate to validate user accounts and the users themselves. Daloradius is used as a tool to manage user accounts. This study succeeded in optimizing authentication on wireless networks using the 802.1x and PEAP protocol standards.

Efficient Heuristic Management of Cloud Data Lakes for Enhanced Big Data Handling in Cloud Computing Environments

Efficient control of cloud data lakes is important for improving big data dealing in cloud computing settings. Cloud data lakes serve as stores for vast amounts of organized, semi-structured, and unstructured data from varied sources, offering scaling and freedom in data storage and processing. However, the sheer amount and range of data offer difficulties in improving storage, access, and analysis processes. This study suggests a heuristic method to handle these issues by improving the order and recovery of data within cloud data lakes. The heuristic method focuses on automatically handling data splitting, storage, and caching strategies based on data access trends and task factors. By leveraging heuristic principles, such as adaptable splitting and predictive caching, the suggested method aims to reduce delay and improve total query speed. Key components of the heuristic management include adaptable splitting methods that actively change data partition sizes based on access frequency and data distribution patterns. This flexible method ensures that frequently accessed data is easily available, while less accessed data is efficiently saved to improve storage usage. Furthermore, predictive storing methods expect future data access patterns using machine learning models or previous data trends. By strategically saving relevant datasets in memory or high-speed storage, the system reduces delay for repeated questions, thereby improving real-time data processing capabilities. The success of the suggested heuristic management method is tested through models and benchmarks using real big data tasks. Comparative research against standard static splitting and caching methods shows significant changes in query response times and resource utilization efficiency.

Accelerating User Profiling in E-Commerce Using Conditional GAN Networks for Synthetic Data Generation

Abstract This paper presents the findings of a study on the profiling of online store users in terms of their likelihood of making a purchase. It also considers the possibility of implementing this solution in the short term. The paper describes the process of developing a profiling model based on data derived from monitoring user behaviour on a website. During the customer’s subsequent visits, information is collected to identify the user, record their behaviour on the page and the fact that they made a purchase. The model requires a substantial amount of training data, primarily related to the purchase of products. This represents a small percentage of total website traffic and requires a considerable amount of time to monitor user behaviour. Therefore, we investigated the possibility of using the Conditional Generative Adversarial Network (CGAN) to generate synthetic data for training the profiling model. The application of GAN would facilitate a more expedient implementation of this model on an online store website. The findings of this study may also prove beneficial to webshop owners and managers, enabling them to gain a deeper insight into their customers and align their price offers or discounts with the profile of a particular user.

The Influence Of Advertising Creativity, Brand Trust, And Brand Image On Consumer Purchase Intentions Of Cosmetic Products (A Study On Sociolla Store Users At Ska Mall Pekanbaru)

This study aims to understand the impact of advertising creativity, brand trust, and brand image on consumer purchasing interest in cosmetic products at the Sociolla Store, SKA Mall Pekanbaru. The development of digital economy has changed the way of trading, including in the cosmetic industry with the emergence of e-commerce platforms such as Sociolla. In the context of tight competition, understanding the factors that influence consumer purchasing interest becomes important. This study involved 120 respondents from Sociolla Store users at SKA Mall Pekanbaru. Data were collected using an online questionnaire assessing respondents' perceptions of advertising creativity, brand trust, brand image, and purchase interest. Data analysis was conducted using multiple regression to assess the influence of independent variables on the dependent variable. The results showed that the majority of visitors or consumers of Sociolla at SKA Mall Pekanbaru are individuals of relatively young age, especially in the age range of 26 to 35 years, with a background of higher education and diverse professions. Validity and reliability tests of the instrument showed that the questionnaire used has good validity and reliability. Data analysis showed that advertising creativity, brand trust, and brand image significantly influence consumer purchasing interest in cosmetic products. Advertising creativity plays an important role in attracting attention and influencing consumer purchasing interest. Brand trust also plays a role in forming long-term relationships between consumers and brands, while brand image creates unique perceptions and influences consumer preferences. In conclusion, advertising creativity, brand trust, and brand image have a significant influence on consumer purchasing interest in cosmetic products at the Sociolla Store, SKA Mall Pekanbaru. These results can be valuable guidance for cosmetic companies in enhancing their marketing strategies in the digital era

Efficient suppression algorithms for preserving trajectory privacy

Concerns about the security of trajectory data have surfaced amid rising public awareness of privacy protection. In this study, we focus on how to protect personal information effectively when adversaries have access to a portion of users’ trajectory data. To resolve this issue, we design a graph-based index structure to store users’ trajectory data and develop a novel subtrajectory upper bound estimation method and corresponding pruning strategies. Using a graph-based index structure and pruning strategies, we propose a global suppression algorithm and a local suppression algorithm to prevent personal information from being extracted from the original trajectory data. Experimental results show that the maintenance cost of the graph-based index structure is low when performing global and local suppression, and that the pruning strategies effectively eliminate unnecessary computation of non-upper-bound subtrajectories. Therefore, the execution times of the proposed algorithms are far shorter than those of existing algorithms.

Cloud Storage Cost Optimization: Advanced Techniques and Case Studies

The growing usage of cloud storage has revolutionized data management by offering unparalleled scalability and accessibility. However, optimizing cloud storage prices remains challenging, sometimes leading to significant unforeseen costs. This article explores a wide range of advanced techniques and practical case studies that enable enterprises to effectively navigate the intricacies of cost optimization across top cloud providers, including Google Cloud Platform (GCP), Microsoft Azure, and Amazon Web Services (AWS). Beyond selecting the most appropriate storage type for individual workloads (block, blob, or file storage), we explore advanced strategies that unlock substantial cost savings. From leveraging contractual negotiations for high-volume storage users to utilizing dedicated data transfer devices for streamlined on-premises migration, this paper equips readers with a diverse arsenal of optimization tools. We explore best practices for data lifecycle management, such as tiered backups and granular retention policies, to optimize beyond merely technical solutions. This way, we ensure that data is held at the most affordable tier depending on its frequency of access and compliance with legal requirements. By consistently analyzing cloud storage consumption and adapting responsive strategies to changing requirements, enterprises may retain optimal performance while ensuring long-term financial sustainability. This paper serves as a valuable resource for organizations seeking to master the intricacies of cloud storage cost optimization. By implementing the advanced techniques and best practices outlined herein, cloud users can confidently unlock significant cost savings and maximize the return on their cloud storage investments.

Enhanced MNB Method for SPAM E-mail/SMS Text Detection Using TF-IDF Vectorizer

Spam, whether in the form of SMS or email, poses significant threats by compromising user privacy and potentially leading to unauthorized access to personal data. In the era of smartphones, where users store sensitive information, the risk of cyber-attack through spam messages is heightened. This research addresses the pressing issue of spam SMS and email detection using a dataset comprising 5574 messages from reputable sources. The collection includes contributions from the National University of Singapore SMS Corpus, Grumble text Website, Caroline Tag’s PhD Theses, and SMS Spam Corpus v.0.1 Big. With a meticulous approach encompassing data cleaning, balancing, preprocessing, and exploratory data analysis, the research employs the TF-IDF (Term Frequency and Inverse Document Frequency) vectorizer to enhance the model’s ability to capture the importance of individual words. This foundational work sets the stage for evaluating various machine learning models, including Support Vector Machine, Multinomial Naïve Bayes, Decision Tree, Logistic Regression, Random Forest, AdaBoost, K-Nearest Neighbors, XGBoost, Gradient Boost Classifier, Bagging Classifier, and Extra Tree Classifier. Notably, the Multinomial Naïve Bayes model emerges as a standout performer with 100% accuracy and 97% precision in phishing detection. The research introduces an intuitive user interface, facilitating real-time interactivity for model assessment and offering valuable insights for cybersecurity applications. The study contributes to the advancement of robust cybersecurity systems, emphasizing precision and accuracy in spam SMS and email text detection.

Untraceable blockchain-assisted authentication and key exchange in medical consortiums

Medical consortium provides tamper-proof data sharing of patient medical records. Since the communications of patients’ devices with hospital servers are accomplished through insecure mediums, scholars have suggested secure authentication protocols to protect the communication links. Yet careful analysis of cutting-edge blockchain-assisted schemes reveals they are susceptible to flooding, ephemeral secret leakage, and tracing/tracking attacks. Besides, they cannot provide a proper balance between security and resource efficiency. This paper introduces a blockchain-assisted authentication protocol that not only defends against potential attacks, but also significantly improves efficiency in computation, communication, and storage usage on the blockchain. Besides, patients are able to receive continuous services from multiple hospitals after just one registration on a cloud server, with the blockchain ensuring the authenticity and traceability of authentication records. We present both formal and informal proof of the proposed protocol and conduct experimental evaluations using ARM and Hyperledger Fabric to provide realistic security and performance insights.