Search Results (22)

This paper presents the design of G-IDSS (Graph-based InnoCyPES Data Storage Service): a novel, distributed data storage service that is built around a P2P network overlay to support handling distributed data. G-IDSS is accessible through a standard command-line interface and is based on a graph database to support the schema-less management of the distributed data stored among peers. The mechanisms to facilitate the execution of complex queries requiring distributed data integration and fusion are also presented and discussed. Besides the design, this work also provides relevant details related to the implementation of G-IDSS, reflecting several use cases that demand data that are distributed across different, even geographically spread, locations. G-IDSS scales to thousands of peers in an overlay, it is able to run distributed queries and can integrate data that are stored in different sources. Full article

As a multi-ethnic border region of China, Xinjiang hosts revolutionary cultural tourism resources (RCTRs) that embody historical memory and the spirit of frontier reclamation, while also playing a strategic role in strengthening national identity and maintaining regional stability. Yet, their spatial distribution is highly uneven due to geographical, historical, and socio-economic constraints. This study analyzes 135 representative sites using a dual framework of spatial pattern analysis and driving mechanism quantification. Nearest neighbor index, imbalance index, Lorenz curve, geographic concentration index, kernel density estimation, and hotspot analysis results reveal a clustered “multi-core–peripheral attenuation” pattern with pronounced regional disparities. GIS-based overlay analysis identifies natural thresholds of moderate elevation (834–2865 m) and gentle slopes (0–8.65°), while socio-economic factors such as transportation corridors and population density amplify clustering effects. Geographic Detector results confirm road network density (q = 0.85, p < 0.01) and historical site density (q = 0.79, p < 0.01) as dominant drivers, with interactions between natural and social factors enhancing explanatory power above 0.90. These findings highlight the coupled influence of topographic suitability and socio-economic accessibility. Policy recommendations include optimizing road network layouts, adopting tiered heritage protection, and fostering cross-regional cooperation. The study provides scientific evidence for balanced development and sustainable conservation of RCTRs, contributing to the achievement of sustainable development goals (SDGs) related to cultural heritage, regional equity, and inclusive growth. Full article

This paper proposes a multi-hop peer-to-peer (P2P) video streaming architecture designed to support dynamic, conversation-aware communication. The primary contribution is a decentralized system built on WebRTC that eliminates reliance on a central media server by employing super node aggregation. In this architecture, video streams from multiple peer nodes are dynamically routed through a group of super nodes, enabling real-time reconfiguration of the network topology in response to conversational changes. To support this dynamic behavior, the system leverages WebRTC data channels for control signaling and overlay restructuring, allowing efficient dissemination of topology updates and coordination messages among peers. A key focus of this study is the rapid and efficient reallocation of network resources immediately following conversational events, ensuring that the streaming overlay remains aligned with ongoing interaction patterns. While the automatic detection of such events is beyond the scope of this work, we assume that external triggers are available to initiate topology updates. To validate the effectiveness of the proposed system, we construct a simulation environment using Docker containers and evaluate its streaming performance under dynamic network conditions. The results demonstrate the system’s applicability to adaptive, naturalistic communication scenarios. Finally, we discuss future directions, including the seamless integration of external trigger sources and enhanced support for flexible, context-sensitive interaction frameworks. Full article

Recent studies highlight artificial intelligence’s ability to identify ventricular dysfunction via electrocardiograms (ECGs); however, specific indicative waveforms remain unclear. This study analysed ECG and echocardiography data from 17,422 cases in Japan and Germany. We developed 10-layer convolutional neural networks to detect left ventricular ejection fractions below 50%, using four-fold cross-validation. Model performance, evaluated among different ECG configurations (3 s strips, single-beat, and two-beat overlay) and segments (PQRST, QRST, P, QRS, and PQRS), showed two-beat ECGs performed best, followed by single-beat models, surpassing 3 s models in both internal and external validations. Single-beat models revealed limb leads, particularly I and aVR, as most indicative of dysfunction. An analysis indicated segments from QRS to T-wave were most revealing, with P segments enhancing model performance. This study confirmed that dual-beat ECGs enabled the most precise ventricular function classification, and segments from the P- to T-wave in ECGs were more effective for assessing ventricular dysfunction, with leads I and aVR offering higher diagnostic utility. Full article

The increasing complexity of modern networks and their evolving needs demand flexible, high-performance packet processing solutions. The P4 language excels in specifying packet processing in software-defined networks (SDNs). Field-programmable gate arrays (FPGAs) are ideal for P4-based packet parsers due to their reconfigurability and ability to handle data transmitted at high speed. This paper introduces three FPGA-based P4-programmable packet parsing architectural designs that translate P4 specifications into adaptable hardware implementations called base, overlay, and pipeline, each optimized for different packet parsing performance. As modern network infrastructures evolve, the need for multi-tenant environments becomes increasingly critical. Multi-tenancy allows multiple independent users or organizations to share the same physical network resources while maintaining isolation and customized configurations. The rise of 5G and cloud computing has accelerated the demand for network slicing and virtualization technologies, enabling efficient resource allocation and management for multiple tenants. By leveraging P4-programmable packet parsers on FPGAs, our framework addresses these challenges by providing flexible and scalable solutions for multi-tenant network environments. The base parser offers a simple design for essential packet parsing, using minimal resources for high-speed processing. The overlay parser extends the base design for parallel processing, supporting various bus sizes and throughputs. The pipeline parser boosts throughput by segmenting parsing into multiple stages. The efficiency of the proposed approaches is evaluated through detailed resource consumption metrics measured on an Alveo U280 board, demonstrating throughputs of 15.2 Gb/s for the base design, 15.2 Gb/s to 64.42 Gb/s for the overlay design, and up to 282 Gb/s for the pipelined design. These results demonstrate a range of high performances across varying throughput requirements. The proposed approach utilizes a system that ensures low latency and high throughput that yields streaming packet parsers directly from P4 programs, supporting parsing graphs with up to seven transitioning nodes and four connections between nodes. The functionality of the parsers was tested on enterprise networks, a firewall, and a 5G Access Gateway Function graph. Full article

Multi-access edge computing (MEC) enhances service quality for users and reduces computational overhead by migrating workloads and application data to the network edge. However, current solutions for task offloading and cache replacement in edge scenarios are constrained by factors such as communication bandwidth, wireless network coverage, and limited storage capacity of edge devices, making it challenging to achieve high cache reuse and lower system energy consumption. To address these issues, a framework leveraging cooperative edge servers deployed in wireless access networks across different geographical regions is designed. Specifically, we propose the Distributed Edge Service Caching and Offloading (DESCO) network architecture and design a decentralized resource-sharing algorithm based on consistent hashing, named Cache Chord. Subsequently, based on DESCO and aiming to minimize overall user energy consumption while maintaining user latency constraints, we introduce the real-time computation offloading (RCO) problem and transform RCO into a multi-player static game, prove the existence of Nash equilibrium solutions, and solve it using a multi-dimensional particle swarm optimization algorithm. Finally, simulation results demonstrate that the proposed solution reduces the average energy consumption by over 27% in the DESCO network compared to existing algorithms. Full article

In the frame of a regional study that is aimed at defining the landslide susceptibility in the upper Susa Valley (Western Italian Alps), this paper investigated the variation in the fracture network of rock masses in correspondence to faults zones. Fracture networks were characterized at sites located along transects that are oriented normally to fault traces and scattered in their adjacent sectors with the scope to be able to define and estimate the variation in fracture abundance in the bedrock. Fracture collection using a traditional geomechanical survey was combined with topological analysis to estimate the degree of fracturing of rock masses in terms of fracture intensity (P₂₁) and connectivity. Several sites were selected, and the variation in fracturing intensity values and degree of connectivity allowed for the defining of “high fracturing zones” (HFZ) near fault traces moving from the background values of adjacent rock masses. Considering the variation in fracture intensity, a range of 400 m to 150 m HFZ can be defined, with considerable differences between the areas analysed. The values recorded show a high irregular variability in fracture intensity in correspondence to the mapped faults due to the interference and overlay of structures related to the complexity setting of the fault damage zone. Full article

Cloud-computing capabilities have revolutionized the remote processing of exploding volumes of healthcare data. However, cloud-based analytics capabilities are saddled with a lack of context-awareness and unnecessary access latency issues as data are processed and stored in remote servers. The emerging network infrastructure tier of fog computing can reduce expensive latency by bringing storage, processing, and networking closer to sensor nodes. Due to the growing variety of medical data and service types, there is a crucial need for efficient and secure architecture for sensor-based health-monitoring devices connected to fog nodes. In this paper, we present publish/subscribe and interest/resource-based non-DHT-based peer-to-peer (P2P) RC-based architecture for resource discovery. The publish/subscribe communication model provides a scalable way to handle large volumes of data and messages in real time, while allowing fine-grained access control to messages, thus enabling heightened security. Our two − level overlay network consists of (1) a transit ring containing group-heads representing a particular resource type, and (2) a completely connected group of peers. Our theoretical analysis shows that our search latency is independent of the number of peers. Additionally, the complexity of the intra-group data-lookup protocol is constant, and the complexity of the inter-group data lookup is O(n), where n is the total number of resource types present in the network. Overall, it therefore allows the system to handle large data throughput in a flexible, cost-effective, and secure way for medical IoT systems. Full article

The aim of the present study was to evaluate the fatigue to cyclic and static resistance of indirect restorations with different preparation designs made either of lithium disilicate (LS) or polymer-infiltrated ceramic network (PICN). Eighty-four (n = 84) molars were chosen, endodontically treated, and prepared with standardized MOD cavities. The molars were randomly divided into 6 study groups (n = 14) taking into account the “preparation design’’ (occlusal veneer with 1.2 mm occlusal thickness; overlay with 1.6 mm occlusal thickness; adhesive crown with 2 mm occlusal thickness) and the “CAD/CAM material’’ (E-max CAD, Ivoclar vivadent; Vita Enamic, Vita). A fatigue test was conducted with a chewing simulator set at 50 N for 1,500,000 cycles. Fracture resistance was assessed using a universal testing machine with a 6 mm diameter steel sphere applied to the specimens at a constant speed of 1 mm/min. A SEM analysis before the fracture test was performed to visually analyze the tooth-restoration margins. A statistical analysis was performed with a two-way ANOVA and a post-hoc pairwise comparison was performed using the Tukey test. The two-way ANOVA test showed that both the preparation design factor (p = 0.0429) and the CAD/CAM material factor (p = 0.0002) had a significant influence on the fracture resistance of the adhesive indirect restorations. The interaction between the two variables did not show any significance (p = 0.8218). The occlusal veneer had a lower fracture resistance than the adhesive crown (p = 0.042) but not lower than the overlay preparation (p = 0.095). LS was more resistant than PICN (p = 0.002). In conclusion, in the case of endodontically treated teeth, overlay preparation seems to be a valid alternative to the traditional full crown preparation, while occlusal veneers should be avoided in restoring non-vital molars with a high loss of residual tooth structure. LS material is more resistant compared to PICN. Full article

Accurate estimation of forest above-ground biomass (AGB) is critical for assessing forest quality and carbon stocks, which can improve understanding of the vegetation growth processes and the global carbon cycle. Landsat 9, the latest launched Landsat satellite, is the successor and continuation of Landsat 8, providing a highly promising data resource for land cover change, forest surveys, and terrestrial ecosystem monitoring. Regression kriging was developed in the study to improve the AGB estimation and mapping using the Landsat 9 image in Wangyedian forest farm, northern China. Multiple linear regression (MLR), support vector machine (SVM), back propagation neural network (BPNN), and random forest (RF) were used as the original models to predict the AGB trends, and the optimal model was used to overlay the results of kriging interpolation based on the residuals to obtain the new AGB predictions. In addition, Landsat 8 images in Wangyedian were used for comparison and verification with Landsat 9. The results showed that all bands of Landsat 8 and Landsat 9 maintained a high degree of uniformity, with positive correlation coefficients ranging from 0.77 to 0.89 (p < 0.01). RF achieved the highest estimation accuracy among all the original models based on the two data sources. However, kriging regression can significantly reduce the estimation error, with the root mean square error (RMSE) decreasing by 55.4% and 51.1%, for Landsat 8 and Landsat 9, respectively, compared to the original RF. Further, the R² and the lowest RMSE for Landsat 8 were 0.88 and 16.83 t/ha, while, for Landsat 9, they were 0.87 and 17.91 t/ha. The use of regression kriging combined with Landsat 9 imagery has great potential for achieving efficient and highly accurate forest AGB estimates, providing a new reference for long-term monitoring of forest resource dynamics. Full article

Decentralized networks bring us many benefits, but as networks evolve, many nodes either actively or passively become unreachable behind an NAT or a firewall. This has become a hindrance to the development of decentralized networks, where peer-to-peer communication data transfer between unreachable nodes cannot be accomplished, whether in decentralized file systems, decentralized social, or decentralized IoT. The existing scheme requires a series of centralized servers or requires network-wide flooding for consensus data, which can lead to the loss of decentralized nature of the network and cause flooding bottlenecks, contrary to the design concept of decentralization. In this paper, our proposed scheme uses a structured P2P overlay network to store the indexes of unreachable nodes in the whole network, so that the characteristics of a decentralized network are still maintained while ensuring the efficiency of lookup. When nodes communicate, the transmission channel is established so that both nodes continuously transmit data streams peer-to-peer without relying on the central server. Moreover, the scheme guarantees the security and privacy of nodes’ data transmission and the P2P overlay network without relying on centralized trusted institutions. Finally, we deploy a real cluster environment to verify the effectiveness of each module at different network sizes and prove the overall feasibility of the scheme. The scheme has certain advantages over existing solutions in terms of security, privacy, communication efficiency, device democracy, etc. Full article

Since the inception of the Internet of Things (IoT), we have adopted centralized architecture for decades. With the vastly growing number of IoT devices and gateways, this architecture struggles to cope with the high demands of state-of-the-art IoT services, which require scalable and responsive infrastructure. In response, decentralization becomes a considerable interest among IoT adopters. Following a similar trajectory, this paper introduces an IoT architecture re-work that enables three spheres of IoT workflows (i.e., computing, storage, and networking) to be run in a distributed manner. In particular, we employ the blockchain and smart contract to provide a secure computing platform. The distributed storage network maintains the saving of IoT raw data and application data. The software-defined networking (SDN) controllers and SDN switches exist in the architecture to provide connectivity across multiple IoT domains. We envision all of those services in the form of separate yet integrated peer-to-peer (P2P) overlay networks, which IoT actors such as IoT domain owners, IoT users, Internet Service Provider (ISP), and government can cultivate. We also present several IoT workflow examples showing how IoT developers can adapt to this new proposed architecture. Based on the presented workflows, the IoT computing can be performed in a trusted and privacy-preserving manner, the IoT storage can be made robust and verifiable, and finally, we can react to the network events automatically and quickly. Our discussions in this paper can be beneficial for many people ranging from academia, industries, and investors that are interested in the future of IoT in general. Full article

In this paper, we present a privacy-preserving scheme for Overgrid, a fully distributed peer-to-peer (P2P) architecture designed to automatically control and implement distributed Demand Response (DR) schemes in a community of smart buildings with energy generation and storage capabilities. To monitor the power consumption of the buildings, while respecting the privacy of the users, we extend our previous Overgrid algorithms to provide privacy preserving data aggregation (PP-Overgrid). This new technique combines a distributed data aggregation scheme with the Secure Multi-Party Computation paradigm. First, we use the energy profiles of hundreds of buildings, classifying the amount of “flexible” energy consumption, i.e., the quota which could be potentially exploited for DR programs. Second, we consider renewable energy sources and apply the DR scheme to match the flexible consumption with the available energy. Finally, to show the feasibility of our approach, we validate the PP-Overgrid algorithm in simulation for a large network of smart buildings. Full article

Vehicular ad-hoc networks (VANET) enable vehicles to exchange information on traffic conditions, dynamic status and localization, to enhance road safety and transportation efficiency. A typical VANET application is platooning, which can take advantage of exchanging information on speed, heading and position to allow shorter inter-vehicle distances without compromising safety. However, the platooning performance depends drastically on the quality of the communication channel, which in turn is highly influenced by the medium access control protocol (MAC). Currently, VANETs use the IEEE 802.11p MAC, which follows a carrier sense multiple access with collision avoidance (CSMA/CA) policy that is prone to collisions and degrades significantly with network load. This has led to recent proposals for a time-division multiple access (TDMA)-based MAC that synchronize vehicles’ beacons to prevent or reduce collisions. In this paper, we take CSMA/CA and two TDMA-based overlay protocols, i.e., deployed over CSMA/CA, namely PLEXE-slotted and RA-TDMAp, and carry out extensive simulations with varying platoon sizes, number of occupied lanes and transmit power to deduce empirical models that provide estimates of average number of collisions per second and average busy time ratio. In particular, we show that these estimates can be obtained from observing the number of radio-frequency (RF) neighbours, i.e., number of distinct sources of the packets received by each vehicle per time unit. These estimates can enhance the online adaptation of distributed applications, particularly platooning control, to varying conditions of the communication channel. Full article

According to the IHO (International Hydrographic Organization) S-44 standard, hydrographic surveys can be carried out in four categories, the so-called orders—special, 1a, 1b, and 2—for which minimum accuracy requirements for the applied positioning system have been set out. These amount to, respectively: 2 m, 5 m, 5 m, and 20 m at a confidence level of 0.95. It is widely assumed that GNSS (Global Navigation Satellite System) network solutions with an accuracy of 2–5 cm (p = 0.95) and maritime DGPS (Differential Global Positioning System) systems with an error of 1–2 m (p = 0.95) are currently the two main positioning methods in hydrography. Other positioning systems whose positioning accuracy increases from year to year (and which may serve as alternative solutions) have been omitted. The article proposes a method that enables an assessment of any given navigation positioning system in terms of its compliance (or non-compliance) with the minimum accuracy requirements specified for hydrographic surveys. The method concerned clearly assesses whether a particular positioning system meets the accuracy requirements set out for a particular IHO order. The model was verified, taking into account both past and present research results (stationary and dynamic) derived from tests on the following systems: DGPS, EGNOS (European Geostationary Navigation Overlay Service), and multi-GNSS receivers (GPS/GLONASS/BDS/Galileo). The study confirmed that the DGPS system meets the requirements for all IHO orders and proved that the EGNOS system can currently be applied in measurements in the orders 1a, 1b, and 2. On the other hand, multi-GNSS receivers meet the requirements for order 2, while some of them meet the requirements for orders 1a and 1b as well. Full article