Search Results (78)

To efficiently organize large quantities of acoustic multibeam bathymetric point clouds, this paper proposes an improved oriented quadtree-based method for establishing a data indexing structure stored on a hard disk. First, the spatial characteristics of the multibeam swath data are integrated into the traditional quadtree structure, resulting in an oriented quadtree for data organization. Then, the primary orientation of the root node’s bounding box, which reflects the main orientation of the swath, is consistently applied to all child nodes, eliminating the need to calculate the orientation for each individual child node by the conventional oriented quadtree. Finally, index files containing the point cloud offset, oriented bounding box, and child node information for root, child, and leaf nodes are designed and stored in external storage. Experimental results indicate that, in terms of tree construction time, although the traditional quadtree reduces time consumption by approximately 50% compared to the improved oriented quadtree, the improved oriented quadtree still achieves a 70% reduction in time consumption compared to the conventional oriented quadtree. Regarding point cloud retrieval, within the same retrieval range, the improved oriented quadtree achieves similar average retrieval times as the conventional oriented quadtree but reduces the maximum time consumption by approximately 20.83% compared to the traditional quadtree. Furthermore, by storing the constructed index in binary format on external storage, the space occupancy was reduced by 50%. The approach effectively organizes acoustic multibeam bathymetric point clouds, providing valuable insights for enhancing point cloud retrieval efficiency and reducing data memory usage. Full article

Containerized applications are pivotal to contemporary cloud-native architectures, yet they present novel security challenges. Kubernetes, a prevalent open-source platform for container orchestration, provides robust automation but lacks inherent security measures. The intricate architecture and scattered security documentation may result in misconfigurations and vulnerabilities, jeopardizing system confidentiality, integrity, and availability. This paper analyzes the key aspects of Kubernetes security by combining theoretical examination with practical application, concentrating on architectural hardening, access control, image security, and compliance assessment. The text commences with a synopsis of Kubernetes architecture, networking, and storage, analyzing prevalent security issues in containerized environments. The emphasis transitions to practical methodologies for safeguarding clusters, encompassing image scanning, authentication and authorization, monitoring, and logging. The paper also examines recognized Kubernetes CVEs and illustrates vulnerability scanning on a local cluster. The objective is to deliver explicit, implementable recommendations for enhancing Kubernetes security, assisting organizations in constructing more robust containerized systems. Full article

Cloud computing is widely used by organizations and individuals to outsource computation and data storage. With the growing adoption of machine learning as a service (MLaaS), machine learning models are being increasingly deployed on cloud platforms. However, operating MLaaS on the cloud raises significant privacy concerns, particularly regarding the leakage of sensitive personal data and proprietary machine learning models. This paper proposes a privacy-preserving decision tree (PPDT) framework that enables secure predictions on sensitive inputs through homomorphic matrix multiplication within a three-party setting involving a data holder, a model holder, and an outsourced server. Additionally, we introduce a leaf node pruning (LNP) algorithm designed to identify and retain the most informative leaf nodes during prediction with a decision tree. Experimental results show that our approach reduces prediction computation time by approximately 85% compared to conventional protocols, without compromising prediction accuracy. Furthermore, the LNP algorithm alone achieves up to a 50% reduction in computation time compared to approaches that do not employ pruning. Full article

Scarcity of data is one of the major challenges in developing automatic computer-aided diagnosis systems, training radiologists and supporting medical research. One solution toward this is community cloud storage, which can be utilized by organizations with a common interest as a shared data repository for joint projects and collaboration. In this large database, relevant images are often searched by an image retrieval system, for which the computation and storage capabilities of a cloud server can bring the benefits of high scalability and availability. However, the main limitation in availing third party-provided services comes from the associated privacy concerns during data transmission, storage and computation. To ensure privacy, this study implements a content-based image retrieval application for finding different types of brain tumors in the encrypted domain. In this framework, we propose a perceptual encryption technique to protect images in such a way that the features necessary for high-dimensional representation can still be extracted from the cipher images. Also, it allows data protection on the client side; therefore, the server stores and receives images in an encrypted form and has no access to the secret key information. Experimental results show that compared with conventional secure techniques, our proposed system reduced the difference in non-secure and secure retrieval performance by up to 3%. Full article

Earthquakes disrupt local organic carbon distribution by stripping vegetation, destabilizing soil, and triggering landslides, leading to immediate carbon loss and potential long-term climate impacts. While remote sensing techniques effectively assess post-earthquake vegetation loss, they fail to capture subsurface carbon dynamics along vertical profiles. This study quantifies ecosystem carbon loss from the Luding Earthquake by integrating field sampling, UAV-based LiDAR, and machine learning models to assess vegetation and soil carbon stocks. Field investigations were conducted at landslide deposits, debris flow deposits, and undisturbed sites to analyze soil organic carbon and biomass carbon content. UAV-derived point cloud data improved vegetation biomass estimation, reducing sample plot overestimation by 30.4% due to uneven vegetation distribution. The results indicate that landslides and debris flows caused an 83.9–95.9% reduction in carbon storage, with the total ecosystem carbon loss estimated at 7.36 × 10⁵ Mg. This study provides a comprehensive assessment of earthquake-triggered carbon loss, offering critical insights for carbon budget research on natural disasters and the development of post-earthquake ecological restoration policies. Full article

Data sharing has dramatically promoted the efficient use of data resources. The target sharing of confidential data is increasingly becoming urgent for enterprises or organizations to solve business problems, such as data sharing between group users with the same attribute characteristics. The confidentiality and relative privacy of shared data, whether in plaintext or ciphertext, largely depend on the encryption keys used during the sharing process and the storage security of the sharing platform. In order to solve the problem of secure sharing, this paper proposes a data-sharing scheme based on group attribute characteristics. The sharer segments and encrypts the data and stores most of the data and encryption keys on the cloud platform, while a small part of the residual is stored on the edge server. The sharer specifies group users by defining user attribute values and implements access control of encryption keys and shared data through CP-ABE. In particular, the private servers of the organizations involved in data sharing act as the edge servers, which are responsible for the storage of residuals with the final authorization of data access, and try their best to ensure that the data are shared with the target users. The security analysis and data collection time overhead experiments show that the scheme further guarantees data sharing with specified target users, which is one more layer of guarantee than sharing in multi-cloud environment and cloud-encrypted sharing, and the time overhead has about a 15% improvement over sharing in a multi-cloud environment. Full article

Due to uncertain business climate, fierce competition, environmental challenges, regulatory requirements, and the need for responsible business operations, organizations are forced to implement sustainable supply chains. This necessitates the use of proper data analytics methods and tools to monitor economic, environmental, and social performance, as well as to manage and optimize supply chain operations. This paper discusses issues, challenges, and the state of the art approaches in supply chain analytics and gives a systematic literature review of big data developments associated with supply chain management (SCM). Even though big data technologies promise many benefits and advantages, the prospective applications of big data technologies in sustainable SCM are still not achieved to a full extent. This necessitates work on several segments like research, the design of new models, architectures, services, and tools for big data analytics. The goal of the paper is to introduce a methodology covering the whole Business Intelligence (BI) lifecycle and a unified model for advanced supply chain big data analytics (BDA). The model is multi-layered, cloud-based, and adaptive in terms of specific big data scenarios. It comprises business process modeling, data ingestion, storage, processing, machine learning, and end-user intelligence and visualization. It enables the creation of next-generation BDA systems that improve supply chain performance and enable sustainable SCM. The proposed supply chain BDA methodology and the model have been successfully applied in practice for the purpose of supplier quality management. The solution based on the real-world dataset and the illustrative supply chain case are presented and discussed. The results demonstrate the effectiveness and applicability of the big data model for intelligent and insight-driven decision making and sustainable supply chain management. Full article

Occasionally, four cars arrive at the four legs of an unsignalized intersection at the same time or almost at the same time. If each lane has a stop sign, all four cars are required to stop. In such instances, gestures are used to communicate approval for one vehicle to leave. Nevertheless, the autonomous vehicle lacks the ability to participate in gestural exchanges. A sophisticated in-vehicle traffic light system has therefore been developed to monitor and facilitate communication among autonomous vehicles and classic car drivers. The fuzzy logic-based system was implemented and evaluated on a self-organizing network comprising eight ESP32 microcontrollers, all operating under the same program. A single GPS sensor connects to each microcontroller that also manages three light-emitting diodes. The ESPNow broadcast feature is used. The system requires no internet service and no large-scale or long-term storage, such as the driving cloud platform, making it backward-compatible with classical vehicles. Simulations were conducted based on the order and arrival direction of vehicles at three junctions. Results have shown that autonomous vehicles at four-legged intersections can now communicate with human drivers at a much lower cost with precise position classification and lane dispersion under 30 s. Full article

In recent years, due to the significant advancements in hardware sensors and software technologies, 3D environmental point cloud modeling has gradually been applied in the automation industry, autonomous vehicles, and construction engineering. With the high-precision measurements of 3D LiDAR, its point clouds can clearly reflect the geometric structure and features of the environment, thus enabling the creation of high-density 3D environmental point cloud models. However, due to the enormous quantity of high-density 3D point clouds, storing and processing these 3D data requires a considerable amount of memory and computing time. In light of this, this paper proposes a real-time 3D point cloud environmental contour modeling technique. The study uses the point cloud distribution from the 3D LiDAR body frame point cloud to establish structured edge features, thereby creating a 3D environmental contour point cloud map. Additionally, unstable objects such as vehicles will appear during the mapping process; these specific objects will be regarded as not part of the stable environmental model in this study. To address this issue, the study will further remove these objects from the 3D point cloud through image recognition and LiDAR heterogeneous matching, resulting in a higher quality 3D environmental contour point cloud map. This 3D environmental contour point cloud not only retains the recognizability of the environmental structure but also solves the problems of massive data storage and processing. Moreover, the method proposed in this study can achieve real-time realization without requiring the 3D point cloud to be organized in a structured order, making it applicable to unorganized 3D point cloud LiDAR sensors. Finally, the feasibility of the proposed method in practical applications is also verified through actual experimental data. Full article

Although digital hoarding behavior does not directly affect physical space, with the popularization of cloud storage services, its impact on energy consumption has become increasingly significant, posing a challenge to environmental sustainability. This study focuses on the factors influencing consumer digital hoarding behavior on e-commerce platforms, aiming to provide management decision-making references for e-commerce enterprises to deal with consumer digital hoarding phenomena and improve transaction effectiveness. Based on the Motivation–Opportunity–Ability (MOA) Theory and through the Adversarial Interpretive Structure Modeling Method (AISM), this study systematically identifies and analyzes the influencing factors. The findings reveal that emotional attachment, burnout, and fear of missing out are the main motivational factors directly affecting consumer digital hoarding behavior, with strong interconnections between these factors. Perceived usefulness and platform interaction design are significant opportunity factors, indirectly affecting digital hoarding behavior by improving user experience and satisfaction. E-commerce platform convenience, anticipated ownership, perceived economic value, emotional regulation ability, auxiliary shopping decision-making, perceived behavioral control, and information organization ability are the foundational and intermediate factors. The research results emphasize the importance of understanding consumer digital hoarding behavior in the context of sustainable development. This is not only conducive to optimizing the shopping cart function and data management strategy of e-commerce platforms and improving transaction conversion rates but also provides a reference for policymakers to formulate data management and privacy protection policies. Full article

Security grids consisting of High-Definition (HD) Internet of Things (IoT) cameras are gaining popularity for organizational perimeter surveillance and security monitoring. Transmitting HD video data to cloud infrastructure requires high bandwidth and more storage space than text, audio, and image data. It becomes more challenging for large-scale organizations with massive security grids to minimize cloud network bandwidth and storage costs. This paper presents an application of Machine Vision at the IoT Edge (Mez) technology in association with a novel Grid Sensing (GRS) algorithm to optimize cloud Infrastructure as a Service (IaaS) resource allocation, leading to cost minimization. Experimental results demonstrated a 31.29% reduction in bandwidth and a 22.43% reduction in storage requirements. The Mez technology offers a network latency feedback module with knobs for transforming video frames to adjust to the latency sensitivity. The association of the GRS algorithm introduces its compatibility in the IoT camera-driven security grid by automatically ranking the existing bandwidth requirements by different IoT nodes. As a result, the proposed system minimizes the entire grid’s throughput, contributing to significant cloud resource optimization. Full article

Electronic monitoring systems (EMSs) have been widely used in global fishing vessels as an effective tool to obtain reliable information about catches and fishing operations in order to verify compliance with national and international regulations. Though EMS implementation in tuna vessels has not yet been made a mandatory requirement by tuna regional fisheries management organizations, many Chinese longliners have the system on board for traceability and safety purposes. Based on interviews with managers and skippers on Chinese tuna longliners, this paper firstly identifies the challenges in electronic monitoring management that have hindered fishermen’s confidence to implement EMS at a larger scale. Then this paper proposes a work plan to facilitate EMS implementation from the perspective of Chinese tuna longliners, in which the adoption of EM standards, specifications, and procedures, and identification of EM data fields are the top priorities. To fully address concerns raised by tuna longliners, a cloud computing platform for EMS data storage, transmission, and review could help to protect data safety and confidentiality. Artificial intelligence technology is recommended to increase cost-efficiency in data review procedures, in addition to compliance incentives and financial incentives from policy makers. Full article

Digitalization has been increasingly recognized for its role in addressing numerous societal and environmental challenges. However, the rapid surge in data production and the widespread adoption of cloud computing has resulted in an explosion of redundant, obsolete, and trivial (ROT) data within organizations’ data estates. This issue adversely affects energy consumption and carbon footprint, leading to inefficiencies and a higher environmental impact. Thus, this opinion paper aims to discuss the challenges and potential solutions related to the environmental impact of file storage on the cloud, aiming to address the research gap in “digital sustainability” and the Green IT literature. The key findings reveal that technological issues dominate cloud computing and sustainability research. Key challenges in achieving sustainable practices include the widespread lack of awareness about the environmental impacts of digital activities, the complexity of implementing accurate carbon accounting systems compliant with existing regulatory frameworks, and the role of public–private partnerships in developing novel solutions in emerging areas such as 6G technology. Full article

Spatial planning requires ensuring the legality, uniformity, authority, and relevance of data. Blockchain technology, characterized by tamper-proofing, complete record-keeping, and process traceability, may effectively organize and manage spatial planning data. This study introduces blockchain technology to address common spatial planning problems, such as planning overlaps and conflicts. We developed a block structure, chain structure, and consensus algorithms tailored for spatial planning. To meet the data management requirements of these structures, we devised a primary unit division method based on the space and population standards of the 15 min life circle, using the Point Cloud Density Tiler. The validation experiments were conducted using the Hyperledger Fabric 2.0 technology framework in Changsha City, Hunan Province, China, with the division method validated against the number and distribution of public service facilities. The validation results show that during the data storage process, the block size remains below 1.00 MB, the data redundancy is up to 21.30%, the consensus verification rate is 150.33 times per second, the block generation rate is 20.83 blocks per minute, and the equivalent data throughput is 12.21 transactions per second. This demonstrates that the proposed method effectively addresses the challenges of block size, data redundancy, consensus algorithm efficiency, and data throughput in blockchain technology. The findings demonstrate that the structures ensure legal, uniform, and authoritative spatial planning, and advance the application of blockchain technology in relevant fields. Additionally, we explored the application of a blockchain data structure in spatial planning monitoring and early warning. This technology can be further studied and applied in related fields. Full article

A Data Warehouse (DW) is a centralized database that stores large volumes of historical data for analysis and reporting. In a world where enterprise data grows exponentially, new architectures are being investigated to overcome the deficiencies of traditional Database Management Systems (DBMSs), driving a shift towards more modern, cloud-based solutions that provide resources such as distributed processing, columnar storage, and horizontal scalability without the overhead of physical hardware management, i.e., a Database as a Service (DBaaS). Choosing the appropriate class of DBMS is a critical decision for organizations, and there are important differences that impact data volume and query performance (e.g., architecture, data models, and storage) to support analytics in a distributed cloud environment efficiently. In this sense, we carry out an experimental evaluation to analyze the performance of several DBaaS and the impact of data modeling, specifically the usage of a partially normalized Star Schema and a fully denormalized Flat Table Schema, to further comprehend their behavior in different configurations and designs in terms of data schema, storage form, memory availability, and cluster size. The analysis is done in two volumes of data generated by a well-established benchmark, comparing the performance of the DW in terms of average execution time, memory usage, data volume, and loading time. Our results provide guidelines for efficient DW design, showing, for example, that the denormalization of the schema does not guarantee improved performance, as solutions performed differently depending on its architecture. We also show that a Hybrid Processing (HTAP) NewSQL solution can outperform solutions that support only Online Analytical Processing (OLAP) in terms of overall execution time, but that the performance of each query is deeply influenced by its selectivity and by the number of join functions. Full article