Search Results (230)

The growing adoption of the Internet of Things (IoT) in healthcare has led to a surge in real-time data from wearable devices, medical sensors, and patient monitoring systems. This latency-sensitive environment poses significant challenges to traditional cloud-centric infrastructures, which often struggle with unpredictable service demands, network congestion, and end-to-end delay constraints. Consistently meeting the stringent QoS requirements of smart healthcare, particularly for life-critical applications, requires new adaptive architectures. We propose ML-RASPF, a machine learning-based framework for efficient service delivery in smart healthcare systems. Unlike existing methods, ML-RASPF jointly optimizes latency and service delivery rate through predictive analytics and adaptive control across a modular mist–edge–cloud architecture. The framework formulates task provisioning as a joint optimization problem that aims to minimize service latency and maximize delivery throughput. We evaluate ML-RASPF using a realistic smart hospital scenario involving IoT-enabled kiosks and wearable devices that generate both latency-sensitive and latency-tolerant service requests. Experimental results demonstrate that ML-RASPF achieves up to 20% lower latency, 18% higher service delivery rate, and 19% reduced energy consumption compared to leading baselines. Full article

Traffic congestions due to construction and maintenance works of road infrastructure cause travel delays, unpredictability and less tolerant road users. Bicyclists are more flexible with road closures, shifting to alternative routes, public transport and other active transport depending on the infrastructure, quality and transport services. However, the mixed traffic environment near road closures increases the safety risks for bicyclists. Traditional traffic monitoring systems rely on costly and demanding intrusive sensors. The application of wireless sensors and machine learning algorithms can enhance the analysis and prediction ability of traffic distribution and characteristics in the proximity of road closures. This paper applies artificial neural networks (ANNs) coupled with a Generalised Delta Rule (GDR) algorithm to analyse the sensor traffic data before, during and after the closure of the Mill Road Bridge in Cambridge City in the United Kingdom. The ANN models show that the traffic volume of motorbikes (44%) and buses (34%) and the proximity of Mill Road Bridge (39%) are significant factors affecting bicycle traffic before the closure. During the bridge closure, the proximity of the bridge (99%) and traffic volume of large rigid vehicles (51%) are the most important factors of bicycle distribution in nearby streets leading cyclists to unsafe detours. After the reopening of the Mill Road Bridge, unclear signage caused continued traffic impact, with motorbikes (17%) and large vehicles (24%) playing the most significant role in the spatial distribution of bicycle traffic. This paper emphasises safety concerns from mixed traffic and highlights the importance of cost-effective sensor-based traffic monitoring and analysis of the sensor data using neural networks. Full article

In this study, we develop a simulation of Contact Graph Routing (CGR) for data communication between Mars, Earth, and relay satellites. Due to the changing of the satellites’ distances to Mars and Earth, respectively, there are specific contact windows between NASA’s Mars rovers and orbiting relay satellites, and specific contact windows between these relay satellites and NASA’s global system of antennas on Earth. The barrier in communication develops delays caused by link propagation, so it needs a Delay Tolerant Network (DTN) for routing networks among the nodes (satellites and antennas), which is the concept of storing and forwarding data whenever the windows are open. We construct an efficient algorithm for CGR, which puts all objects into a general framework of numbered nodes, so that we can easily develop another application of a network with a larger number of nodes. Simulated data are generated randomly to mimic the unpredicted data volumes that are sent from Mars to Earth. We construct some cases involving delivering data for one Martian day, and the simulation performs well in carrying, storing, and forwarding data from Mars to Earth, even though the relay satellites are not able to contact Earth for a period of time. Full article

Unmanned Surface Vehicles (USVs) have emerged as vital tools in marine monitoring and management due to their high efficiency, low cost, and flexible deployment capabilities. This paper presents a systematic review focusing on four core areas of USV applications: communication networking, navigation, control, and data-driven operations. First, the characteristics and challenges of acoustic, electromagnetic, and optical communication methods for USV networking are analyzed, with an emphasis on the future trend toward multimodal communication integration. Second, a comprehensive review of global navigation, local navigation, cooperative navigation, and autonomous navigation technologies is provided, highlighting their applications and limitations in complex environments. Third, the evolution of USV control systems is examined, covering group control, distributed control, and adaptive control, with particular attention given to fault tolerance, delay compensation, and energy optimization. Finally, the application of USVs in data-driven marine tasks is summarized, including multi-sensor fusion, real-time perception, and autonomous decision-making mechanisms. This study aims to reveal the interaction and coordination mechanisms among communication, navigation, control, and data-driven operations from a system integration perspective, providing insights and guidance for the intelligent operations and comprehensive applications of USVs in marine environments. Full article

With the deep integration of distributed network technology and intelligent medical care, how to achieve efficient collaboration under the premise of safeguarding data security and system efficiency has become an important challenge for intelligent medical diagnosis systems. The traditional practical Byzantine fault tolerance (PBFT) algorithm has difficulty meeting the demands of large-scale distributed medical scenarios due to high communication overhead and poor scalability. In addition, the existing improvement schemes are still deficient in dynamic node management and complex attack defence. To this end, this paper proposes the VS-PBFT consensus algorithm, which fuses a verifiable random function (VRF) and reputation mechanism, and designs a distributed intelligent medical diagnosis collaboration system based on this algorithm. Firstly, we introduce the VRF technique to achieve random and unpredictable selection of master nodes, which reduces the risk of fixed verification nodes being attacked. Secondly, we construct a dynamic reputation evaluation model to quantitatively score the nodes’ historical behaviors and then adjust their participation priority in the consensus process, thus reducing malicious node interference and redundant communication overhead. In the application of an intelligent medical diagnosis collaboration system, the VS-PBFT algorithm effectively improves the security and efficiency of diagnostic data sharing while safeguarding patient privacy. The experimental results show that in a 40-node network environment, the transaction throughput of VS-PBFT is 21.05% higher than that of PBFT, the delay is reduced by 33.62%, the communication overhead is reduced by 8.63%, and the average number of message copies is reduced by about 7.90%, which demonstrates stronger consensus efficiency and anti-attack capability, providing the smart medical diagnosis collaboration system with the first VS-PBFT algorithm-based technical support. Full article

Communications in delay-tolerant networking (DTN) environments like deep space face significant challenges due to immense distances and the intermittent nature of links. Overcoming these issues requires moving beyond the assumptions of immediacy and reliability that underpin traditional terrestrial Internet Protocol (IP) networks. Historically, deep-space networks have relied on custom architectures or protocols like the Bundle Protocol (BP) to address these challenges; however, such solutions impose the constraint that nodes must implement the chosen protocol for proper operation, thereby not providing interoperability with standard IP-based nodes. This paper proposes an alternative approach, leveraging innovations in IP version 6 (IPv6) and Internet Control Message Protocol version 6 (ICMPv6) to integrate delay-tolerant features directly at Layer 3. By embedding these functionalities within the existing IPv6 framework, the proposed IP-compliant solution enhances interoperability, with terrestrial networks enabling DTN nodes to seamlessly communicate with compliant IPv6 nodes. This study provides a detailed comparison of the capabilities of IPv6 and BP version 7, highlighting gaps and opportunities. Based on this analysis, a node architecture is designed to implement the necessary functionalities for DTN, paving the way for more seamless integration of deep-space and terrestrial networks while reducing complexity and improving scalability. Full article

Distributed Kubernetes clusters provide robust solutions for geo-redundancy and fault tolerance in modern cloud architectures. However, default scheduling mechanisms primarily optimize for resource availability, often neglecting network topology, inter-node latency, and global resource efficiency, leading to suboptimal task placement in multi-region deployments. This paper proposes network-aware scheduling plugins that integrate heuristic, metaheuristic, and linear programming methods to optimize resource utilization and inter-zone communication latency for containerized workloads, particularly Apache Spark batch-processing tasks. Unlike the default scheduler, the presented approach incorporates inter-node latency constraints and prioritizes locality-aware scheduling, ensuring efficient pod distribution while minimizing network overhead. The proposed plugins are evaluated using the kube-scheduler-simulator, a tool that replicates Kubernetes scheduling behavior without deploying real workloads. Experiments cover multiple cluster configurations, varying in node count, region count, and inter-region latencies, with performance metrics recorded for scheduler efficiency, inter-zone communication impact, and execution time across different optimization algorithms. The obtained results indicate that network-aware scheduling approaches significantly improve latency-aware placement decisions, achieving lower inter-region communication delays while maintaining resource efficiency. Full article

Low Earth Orbit (LEO) satellite networks are promising for satellite-based cloud platforms. Due to frequent link switching and long transmission distances in LEO satellite networks, applying the TCP/IP architecture introduces challenges such as packet loss and significant transmission delays. These issues can trigger excessive retransmissions, leading to link congestion and increased data acquisition delay. Deploying Named Data Networking (NDN) with connectionless communication and link-switching tolerance can help address these problems. However, the existing congestion control methods in NDN lack support for congestion avoidance, lossless forwarding, and tiered traffic scheduling, which are crucial for achieving low-delay operations in satellite-based cloud platforms. In this paper, we propose a Congestion Control method with Lossless Forwarding (CCLF). Addressing the time-varying nature of satellite networks, CCLF implements zero packet loss forwarding by monitoring output queues, aggregating packets, and prioritizing packet scheduling. This approach overcomes traditional end-to-end bottleneck bandwidth limitations, enhances network throughput, and achieves low-delay forwarding for critical Data packets. Compared with the Practical Congestion Control Scheme (PCON), the CCLF method achieves lossless forwarding at the network layer, reduces the average flow completion time by up to 41%, and increases bandwidth utilization by up to 57%. Full article

The proliferation of smart devices such as sensors and communication devices has necessitated the development of networks that can adopt device-to-device communication for delay-tolerant data transfer and energy efficiency. Therefore, there is a need to develop opportunistic networks to enhance energy efficiency through improved data routing. A sensor device equipped with computing, communication, and mobility capabilities can opportunistically transfer data to another device, either as a direct recipient or as an intermediary forwarding data to a third device. Routing algorithms designed for such opportunistic networks aim to increase the probability of successful message transmission by leveraging area information derived from historical data to forecast potential encounters. However, accurately determining the precise locations of mobile devices remains highly challenging and necessitates a robust prediction mechanism to provide reliable insights into mobility encounters. In this study, we propose incorporating a random forest regressor (RFR) to predict the future location of mobile users, thereby enhancing message routing efficiency. The RFR utilizes mobility traces from diverse users and is equipped with sensors for computing and communication purposes. These predictions improve message routing performance and reduce energy and bandwidth resource utilization during routine data transmissions. To evaluate the proposed approach, we compared the predictive performance of the RFR against existing benchmark schemes, including the Gaussian process, using real-world mobility data traces. The mobility traces from the University of Southern California (USC) were employed to underpin the simulations. Our findings demonstrate that the RFR significantly outperformed both the Gaussian process and existing methods in predicting mobility encounters. Furthermore, the integration of mobility predictions into device-to-device (D2D) communication and traditional internet networks showed potential energy consumption reductions of up to one-third, highlighting the practical benefits of the proposed approach. The contribution of this research is that it highlights the limitations of existing mobility prediction models and develops new resource optimization and energy-efficient opportunistic networks that overcome these limitations. Full article

Modulation format identification (MFI) is one of the most critical functions embedded in digital coherent receivers in elastic optical networks (EONs). In view of inherent amplitude and phase characteristics of received signals, different modulation formats exhibit a set of notable features in the polar coordinate system, based on which an MFI scheme incorporating the Gaussian weighted k-nearest neighbors (KNN) algorithm was proposed to identify polarization division multiplexed (PDM)-QPSK/-16QAM/-32QAM/-64QAM/-128QAM signals. The performance of the proposed scheme was numerically verified in 28GBaud coherent optical communication systems. The numerical simulation results show that, to achieve 100% correct identification rates for all of the five modulation formats, the required minimum optical signal-to-noise ratios (OSNRs) were less than their relevant thresholds corresponding to the 20% forward error correction (FEC). The tolerable ranges of the residual chromatic dispersion (CD) for QPSK, 16QAM, 32QAM, 64QAM, and 128QAM were −1920 ps/nm~1920 ps/nm, −720 ps/nm~360 ps/nm, −1200 ps/nm~1680 ps/nm, −600 ps/nm~360 ps/nm, and −600 ps/nm~480 ps/nm, respectively. Meanwhile, the results demonstrate the maximum tolerable differential-group delay (DGD) for the QPSK, 16QAM, 32QAM, 64QAM, and 128QAM signals were 34 ps, 16 ps, 20 ps, 6 ps, and 1.2 ps, respectively. In addition, the simulated results also show that the proposed MFI scheme is robust against the fiber nonlinearities, even if the launch power is increased to 4 dBm. Full article

This paper considers a decentralized and autonomous wireless network of low SWaP (size, weight, and power) fixed-wing UAVs (unmanned aerial vehicles) used for remote exploration and monitoring of targets in an inaccessible area lacking communication infrastructure. Here, the UAVs collaborate to find target(s) and use routing protocols to forward the sensed data of target(s) to an aerial base station (BS) in real-time through multihop communication, which can then transmit the data to a control center. However, the unpredictability of target locations and the highly dynamic nature of autonomous, decentralized UAV networks result in frequent route breaks or traffic disruptions. Traditional routing schemes cannot quickly adapt to dynamic UAV networks and can incur large control overhead and delays. In addition, their performance suffers from poor network connectivity in sparse networks with multiple objectives (exploration and monitoring of targets), which results in frequent route unavailability. To address these challenges, we propose two routing schemes: Pipe routing and TC-Pipe routing. Pipe routing is a mobility-, congestion-, and energy-aware scheme that discovers routes to the BS on-demand and proactively switches to alternate high-quality routes within a limited region around the routes (referred to as the “pipe”) when needed. TC-Pipe routing extends this approach by incorporating a decentralized topology control mechanism to help maintain robust connectivity in the pipe region around the routes, resulting in improved route stability and availability. The proposed schemes adopt a novel approach by integrating the topology control with routing protocol and mobility model, and rely only on local information in a distributed manner. Comprehensive evaluations under diverse network and traffic conditions—including UAV density and speed, number of targets, and fault tolerance—show that the proposed schemes improve throughput by reducing flow interruptions and packet drops caused by mobility, congestion, and node failures. At the same time, the impact on coverage performance (measured in terms of coverage and coverage fairness) is minimal, even with multiple targets. Additionally, the performance of both schemes degrades gracefully as the percentage of UAV failures in the network increases. Compared to schemes that use dedicated UAVs as relay nodes to establish a route to the BS when the UAV density is low, Pipe and TC-Pipe routing offer better coverage and connectivity trade-offs, with the TC-Pipe providing the best trade-off. Full article

The integrated heterogeneous 7G/8G networks may face multiple challenges for reliable data delivery such as link disruption, intermittent link availability, long latency and a highly lossy channel. Delay tolerant networking (DTN) was proposed as a highly reliable networking technology for space networks that will be part of future 7G/8G networks. In this paper, an experimental evaluation of transmission control protocol (TCP)-based DTN (i.e., running TCP at the transport layer of DTN) for space-vehicle communications in the cislunar domain is presented. The impact of link disruption is also considered. The evaluation was conducted using the DTN protocol suites over a realistic experimental testbed. The study results show that TCP-based DTN works effectively for space-vehicle communications in cislunar domain in the presence of a link disruption event. However, a roughly exponential goodput decrease is observed with a linear increase in link delay from 1250 ms to 5 s. Full article

This paper introduces RWA-BFT, a reputation-weighted asynchronous Byzantine Fault Tolerance (BFT) consensus algorithm designed to address the scalability and performance challenges of blockchain systems in large-scale IoT scenarios. Traditional centralized IoT architectures often face issues such as single points of failure and insufficient reliability, while blockchain, with its decentralized and tamper-resistant properties, offers a promising solution. However, existing blockchain consensus mechanisms struggle to meet the high throughput, low latency, and scalability demands of IoT applications. To address these limitations, RWA-BFT adopts a two-layer blockchain architecture; the first layer leverages reputation-based filtering to reduce computational complexity by excluding low-reputation nodes, while the second layer employs an asynchronous consensus mechanism to ensure efficient and secure communication among high-reputation nodes, even under network delays. This dual-layer design significantly improves performance, achieving higher throughput, lower latency, and enhanced scalability, while maintaining strong fault tolerance even in the presence of a substantial proportion of malicious nodes. Experimental results demonstrate that RWA-BFT outperforms HB-BFT and PBFT algorithms, making it a scalable and secure blockchain solution for decentralized IoT applications. Full article

Modern electrical grids are evolving towards distributed architectures, necessitating efficient and reliable state synchronization mechanisms to maintain structural and functional consistency. This paper investigates the application of conflict-free replicated data types (CRDTs) for representing and synchronizing the states of distributed electrical grid systems (DEGSs). We present a general structure for DEGSs based on CRDTs, focusing on the Convergent Replicated Data Type (CvRDT) model with delta state propagation to optimize the communication overhead. The Observed Remove Set (ORSet) and Last-Writer-Wins Register (LWW-Register) are utilized to handle concurrent updates and ensure that only the most recent state changes are retained. An actor-based framework, “Vigilant Hawk”, leveraging the Akka toolkit, was developed to simulate the asynchronous and concurrent nature of DEGSs. Each electrical grid node is modelled as an independent actor with isolated state management, facilitating scalability and fault tolerance. Through a series of experiments involving 100 nodes under varying latency degradation coefficients (LDK), we examined the impact of network conditions on the state synchronization efficiency. The simulation results demonstrate that CRDTs effectively maintain consistency and deterministic behavior in DEGSs, even with increased network latency and node disturbances. An effective LDK range was identified (LDK effective = 2 or 4), where the network remains stable without significant delays in state propagation. The linear relationship between the full state distribution time (FSDT) and LDK indicates that the system can scale horizontally without introducing complex communication overhead. The findings affirm that using CRDTs for state synchronization enhances the resilience and operational efficiency of distributed electrical grids. The deterministic and conflict-free properties of CRDTs eliminate the need for complex concurrency control mechanisms, making them suitable for real-time monitoring and control applications. Future work will focus on addressing identified limitations, such as optimizing message routing based on the network topology and incorporating security measures to protect state information in critical infrastructure systems. Full article

Future 7G/8G networks are expected to integrate both terrestrial Internet and space-based networks. Space networks, including inter-planetary Internet such as cislunar and deep-space networks, will become an integral part of future 7G/8G networks. Vehicle-to-everything (V2X) communication networks will also be a significant component of 7G/8G networks. Therefore, space networks will eventually integrate with V2X communication networks, with both space vehicles (or spacecrafts) and terrestrial vehicles involved. DTN is the only candidate networking technology for future heterogeneous space communication networks. In this work, we study possible concatenations of different DTN convergence layer protocol adapters (CLAs) over a cislunar relay communication architecture. We present a performance characterization of the concatenations of different CLAs and the associated data transport protocols in an experimental manner. The performance of different concatenations is compared over a typical primary and secondary cislunar relay architecture. The intent is to find out which network relay path and DTN protocol configuration has the best performance over the end-to-end cislunar path. Full article