The emergence of Intent-based Networking(IBN)-enabled Healthcare Internet of Things (H-IoT) environments brings new challenges and opportunities for deploying intelligent medical image compression systems in real-time and privacy-sensitive scenarios. However, most existing medical image compression approaches are manually designed and optimized without accounting for the high dimensionality of medical data and the strict deployment constraints in heterogeneous IBN environments, resulting in suboptimal performance and limited adaptability. To address these challenges, we propose a novel implicit neural representation (INR)-based framework for automated medical image compression, leveraging the powerful continuous signal modeling capabilities of INRs to achieve high fidelity on high-dimensional medical images while maintaining compactness and adaptability. Our framework integrates an evolutionary architecture search strategy with privacy-constrained optimization and parameter quantization, enabling the architectures that balance reconstruction quality, latency, communication cost, and privacy. To validate the effectiveness of the framework, we design and conduct comprehensive simulation experiments in realistic multi-hospital IBN environments. The results demonstrate that our INR-based designs outperform traditional and data-driven baselines in both objective metrics and deployment feasibility under constrained resources.

The core function of quantum networks is to establish high-fidelity quantum entanglement for long-distance communication. However, the main challenge is to efficiently allocate resources under limited conditions, maximize throughput, satisfy end-to-end (E2E) fidelity requirements, and prevent quantum decoherence caused by inefficient routing algorithms. Current research focuses on optimizing either throughput or fidelity, with a lack of approaches that optimize both simultaneously; furthermore, existing algorithms suffer from high computational complexity. To tackle these challenges, this study proposes a Satisfying Fidelity Threshold Routing and Adaptive Purification Strategy (SFTRAP). SFTRAP maximizes throughput for each request by selecting multiple paths and dynamically choosing links for entanglement purification based on the current state of link resources, thus minimizing throughput loss while satisfying fidelity threshold. The strategy also adaptively adjusts the number of purification rounds according to the fidelity threshold, thereby optimizing the time required for deep purification and enhancing algorithmic efficiency. For multi-request scenarios, SFTRAP employs a priority sorting mechanism that takes into account both path cost and path freedom, which refines request scheduling and path selection to create more efficient request combinations, thus further boosting the overall network throughput. Simulation results indicate that SFTRAP surpasses state-of-the-art methods in terms of both throughput and algorithmic efficiency, highlighting its potential for optimizing resources in quantum networks.

The Internet of Medical Things (IoMT) has transformed traditional healthcare systems by enabling real-time monitoring, remote diagnostics, and data-driven treatment. However, security and privacy remain significant concerns for IoMT adoption due to the sensitive nature of medical data. Therefore, we propose an integrated framework leveraging blockchain and explainable artificial intelligence (XAI) to enable secure, intelligent, and transparent management of IoMT data. First, the traceability and tamper-proof of blockchain are used to realize the secure transaction of IoMT data, transforming the secure transaction of IoMT data into a two-stage Stackelberg game. The dual-chain architecture is used to ensure the security and privacy protection of the transaction. The main-chain manages regular IoMT data transactions, while the side-chain deals with data trading activities aimed at resale. Simultaneously, the perceptual hash technology is used to realize data rights confirmation, which maximally protects the rights and interests of each participant in the transaction. Subsequently, medical time-series data is modeled using bidirectional simple recurrent units to detect anomalies and cyberthreats accurately while overcoming vanishing gradients. Lastly, an adversarial sample generation method based on local interpretable model-agnostic explanations is provided to evaluate, secure, and improve the anomaly detection model, as well as to make it more explainable and resilient to possible adversarial attacks. Simulation results are provided to illustrate the high performance of the integrated secure data management framework leveraging blockchain and XAI, compared with the benchmarks.

Live video streaming has grown significantly, especially on networks with limited bandwidth. Traditional video streaming methods, which drop less important frames, often struggle with high latency. We propose ReparoV2, a novel approach designed specifically for live streaming environments. On the client side, ReparoV2 selectively omits video frames at the source, which substantially reduces bandwidth usage without significantly degrading the QoE. ReparoV2 implements a real time Video Frame Discarding (VFD) algorithm, which categorizes even-indexed frames into three types: preserving, recovering using neural networks and substituting with the previous frame, which is corresponding to the high, medium and low degree of difference between two adjacent frames. Additionally, to gain a better QoE, ReparoV2 integrates an adaptive bitrate strategy and introduces multiple discrete low-frame-rate encoding modes, balancing video quality and bandwidth reduction when the network bandwidth is limited. On the server side, ReparoV2 uses a Video Frame Interpolation Deep Neural Network (VFI-DNN) and frame-copying to reconstruct dropped frames, ensuring a smooth viewing experience. It updates the VFD model based on received video chunks and sends the updated model back to the client. ReparoV2 outperforms conventional Dynamic Adaptive Streaming over HTTP (DASH), achieving higher Structural Similarity Index Measure (SSIM) (+0.024), lower bandwidth usage (-23.19%), and improved QoE (+26.66%) on 25fps videos under 0.974Mbps average bandwidth.

The integration of software-defined networking (SDN) and the Internet of Things (IoT) presents significant challenges in large-scale healthcare systems, particularly in terms of optimizing resource allocation, managing energy consumption (EC), and ensuring real-time data processing. This research introduces an AI-driven resource management framework designed to address these challenges. Using autonomous aerial vehicles (AAVs) for aerial computing, the framework optimizes energy usage, reduces network latency, and enhances anomaly detection through machine learning models. Key contributions include dynamic allocation of bandwidth and processing resources, adaptive power management, and real-time traffic prediction, ensuring high Quality of Service (QoS) even in resource-constrained environments. The simulation results demonstrate a 10%–15% reduction in EC, 15% decrease in latency, and improved real-time data processing, making the system ideal for critical healthcare applications such as telemedicine and remote monitoring. The framework offers a scalable solution to efficiently manage the growing number of IoT devices and AAVs, while also maintaining a low-latency secure service delivery.

Medical supply chains face critical challenges in balancing transparent data access with privacy protection while maintaining environmental sustainability. Organizations must share crucial information like production specifications and environmental monitoring data, yet protect sensitive information such as proprietary formulations and patient records. Traditional access control mechanisms use centralized approaches that lack transparency, create single points of failure, and cannot adapt to the complex data sharing requirements of Artificial Intelligence (AI)-based Internet of Things (IoT) (AIoT) -enhanced green medical supply chains. To address these challenges, we propose green blockchain hierarchical AIoT-enhanced medical supply chain (G-BHAIMS), a novel framework that enables secure, energy-efficient data access control. G-BHAIMS employs a multi-chain architecture that segregates sensitive medical data storage from access control operations, while integrating environmental monitoring through AIoT devices. Our framework introduces a hierarchical access control model with data classification based on sensitivity levels and ecological impact factors, implemented through energy-efficient smart contracts. Experimental results demonstrate that G-BHAIMS achieves a 98.5% correct access decision rate, outperforming the best baseline method by 2.5 percentage points. The framework maintains an average execution time of 22ms (21.4% faster than comparable systems) and throughput above 95 TPS under heavy loads. Most significantly, G-BHAIMS reduces energy consumption by 17.8% and decreases carbon emissions by 16.1% compared to the best alternative approach, aligning security requirements with green supply chain principles.

Rising trends in Industrial Internet-of-things (IIoT), flexible manufacturing, and edge computing present unprecedented opportunities for advancing healthcare manufacturing systems. However, orchestrating resources efficiently while maintaining stringent healthcare quality standards poses significant challenges, particularly in environments where manufacturing processes must adapt quickly to varying production requirements. These challenges are further complicated by the limited computational resources of IIoT devices and the need for real-time processing in medical device production and pharmaceutical manufacturing. To address these challenges, we propose a distributed healthcare-aware deep learning resource orchestration (DH-DLRO) algorithm for edge computing-enabled healthcare IIoT flexible manufacturing systems. Our approach constructs a joint optimization problem for task offloading decisions and resource allocation, specifically tailored to healthcare manufacturing requirements. The algorithm employs multiple parallel deep neural networks to generate efficient offloading decisions while considering healthcare-specific quality constraints and manufacturing precision requirements. The algorithm reduces energy consumption by 25-30% while maintaining medical device manufacturing precision standards, shows remarkable stability across varying task sizes (7000-25000 bytes), and exhibits robust performance under different system parameter configurations. DH-DLRO maintains consistent quality of service levels above 0.95 for medical device assembly tasks while achieving optimal CPU utilization patterns between 60-80%, demonstrating its effectiveness in balancing computational efficiency with healthcare manufacturing quality requirements.

The unparalleled possibilities of Internet of Vehicles (IoV) development prompt the enhancement of in-vehicle medical emergency response. Nevertheless, the IoV environment is still affected by data privacy, latency, and network instability, which hamper effective and reliable emergency medical systems. In this regard, this article suggests the emergency-aware distributed edge intelligence (DEI) for medical response (EDEM) framework, a novel approach leveraging DEI to address these challenges. Specifically, EDEM introduces a hierarchical edge collaborative computing architecture that dynamically constructs learning domains based on a comprehensive medical data capability model. The framework incorporates an in-vehicle medical data reliability model and tailored latency and energy consumption models to optimize resource allocation and response times. Then, a deep-reinforcement-learning-based node selection algorithm ensures efficient task distribution across the network. Finally, EDEM’s dual-layer federated learning model features an emergency-aware adaptive aggregation mechanism and an adaptive medical model updating scheme for cross-domain scenarios, complemented by an emergency-weighted asynchronous model fusion approach. The superiority of EDEM over state-of-the-art methods is demonstrated through simulation results showing up to a 15% increase in model accuracy, a 30% reduction in response times, and a 20% better resource utilization efficiency. This implies that it can greatly enhance speed, accuracy, and reliability for in-vehicle emergency responses within IoV environments.

With the rapid advancement of technologies such as B5G/6G and edge computing, network scenarios are becoming increasingly complex and diverse, leading to the emergence of slicing networks. Virtualizing applications into distinct categories and establishing corresponding network slices ensures performance to a certain extent. However, the challenges posed by the complex slicing environment demand more fine-grained routing control and higher costs to locate requested content or services, areas where current state-of-the-art methods fall short. To address these challenges, this work introduces a system framework that integrates the principles of Segment Routing over IPv6 (SRv6). An SRv6 optimization layer is created between the control and infrastructure layers to manage slices effectively and enhance routing control. Additionally, we propose a novel policy routing method based on zero-trust and Graph Convolutional Network (GCN) technology. This method transforms actions into policies that can be flexibly deployed on SRv6 nodes, segment by segment. These actions encompass both routing and security measures, allowing for dynamic and flexible deployment of policies on each segment to achieve the desired goals. This integration of segment routing and zero-trust principles simplifies implementation and enhances security. Comprehensive experiments were conducted to evaluate the proposed method. The results demonstrate significant improvements over state-of-the-art methods, including a higher service acceptance rate, better resource utilization, and reduced average latency and packet loss rate.

Electrocardiogram (ECG) analysis and diagnosis are important auxiliary means for preventing and detecting cardiovascular diseases. Traditional approaches often face challenges due to the sheer volume of data, difficulty in extracting meaningful features, limitations in model complexity, and the requirement for real-time analysis in clinical settings. This paper presents a pioneering approach for automatic ECG diagnosis through the application of a novel Multiscale Deep Neuro-fuzzy Network (MDNFN) structure. The MDNFN is designed to address the complexity of arrhythmia classification by incorporating deep learning and fuzzy logic processing across multiscale feature extraction. To optimize the performance of the MDNFN, an innovative model optimization technique based on the Particle Swarm Optimization (PSO) algorithm is introduced, offering an efficient exploration of the parameter space. Extensive experiments across diverse datasets validate the superior performance of the proposed model compared to existing methods. The MDNFN demonstrates heightened accuracy and robustness, supported by its adaptability to different frequency and time scales inherent in ECG signals. The study establishes the model's efficacy through comprehensive experimentation, providing compelling evidence for its potential application in real-world clinical scenarios.

The design of effective multimodal feature fusion strategies is the key task for multimodal learning, which often requires huge computational costs with extensive expertise. In this paper, we seek to enhance multimodal learning via hierarchical fusion architecture search with inconsistency mitigation. Different from previous works, our Hierarchical Fusion Multimodal Neural Architecture Search (HF-MNAS) considers the inconsistency in modalities and labels, and fine-grained exploitation in multi-level fusion architectures. Specifically, it disentangles the hierarchical fusion problem into two-level (macro- and micro-level) search spaces. In the macro-level search space, the high-level and low-level features are extracted and then connected in a fine-grained way, where the inconsistency mitigation module is designed to minimize discrepancies between modalities and labels in cell outputs. In the micro-level search space, we find that different intermediate nodes in the cells exhibit different importance degrees. Then, we propose an importance-based node selection mechanism to form the optimal cells for feature fusion. We evaluate HF-MNAS on a series of multimodal classification tasks. Empirical evidence shows that HF-MNAS achieves competitive trade-off performance across accuracy, search time, and inference speed. In particular, HF-MNAS consumes minimal computational cost compared with state-of-the-art MNASs. Furthermore, we theoretically and experimentally verify that the modality-label inconsistency deteriorates the overall fusion performance of models such as accuracy and F1 score, and demonstrate that the proposed inconsistency mitigation module could effectively mitigate this phenomenon.

Medical image analysis plays a crucial role in healthcare systems of Internet of Medical Things (IoMT), aiding in the diagnosis, treatment planning, and monitoring of various diseases. With the increasing adoption of artificial intelligence (AI) techniques in medical image analysis, there is a growing need for transparency and trustworthiness in decision-making. This study explores the application of explainable AI (XAI) in the context of medical image analysis within medical cyber-physical systems (MCPS) to enhance transparency and trustworthiness. To this end, this study proposes an explainable framework that integrates machine learning and knowledge reasoning. The explainability of the model is realized when the framework evolution target feature results and reasoning results are the same and are relatively reliable. However, using these technologies also presents new challenges, including the need to ensure the security and privacy of patient data from IoMT. Therefore, attack detection is an essential aspect of MCPS security. For the MCPS model with only sensor attacks, the necessary and sufficient conditions for detecting attacks are given based on the definition of sparse observability. The corresponding attack detector and state estimator are designed by assuming that some IoMT sensors are under protection. It is expounded that the IoMT sensors under protection play an important role in improving the efficiency of attack detection and state estimation. The experimental results show that the XAI in the context of medical image analysis within MCPS improves the accuracy of lesion classification, effectively removes low-quality medical images, and realizes the explainability of recognition results. This helps doctors understand the logic of the system's decision-making and can choose whether to trust the results based on the explanation given by the framework.

The rapid integration of cyber-physical systems (CPS) in urban transportation networks has revolutionized emergency medical services (EMS), enhancing response time and resource allocation. However, this interconnectedness exposes critical infrastructure to sophisticated cyber-attacks, potentially compromising patient safety and operational efficiency. The aim of this work is to develop a secure and efficient control method for EMS in transportation CPS (T-CPS) that can maintain optimal performance while defending against sophisticated, stealthy cyber-attacks. We propose a novel secure output-feedback control method for EMS (SOFC-EMS) in T-CPS that leverages the Kullback-Leibler divergence to characterize attack stealthiness and employs dynamic output-feedback control to maintain system stability and performance. Our approach utilizes ellipsoidal invariant reachable sets to analyze system behavior under various attack scenarios and optimizes controller parameters through convex optimization techniques. Simulation results show that the proposed SOFC-EMS method significantly reduces the reachable set volume, indicating improved system security. The method also performs better in practical EMS scenarios, reducing average ambulance response time and maintaining higher system safety scores under increasing attack frequencies. We demonstrate the method’s adaptability to different urban traffic patterns and attack intensities through consistent performance across various system parameters. While our simulations demonstrate promising results in a simplified urban grid, further research is needed to validate the method’s effectiveness in more complex, real-world urban environments.

Researchers have proposed to exploit label correlation to alleviate the exponential-size output space of label distribution learning (LDL). In particular, some have designed LDL methods to consider local label correlation. These methods roughly partition the training set into clusters and then exploit local label correlation on each one. Each sample belongs to one cluster and therefore has only one local label correlation. However, in real-world scenarios, the training samples may have fuzziness and belong to multiple clusters with blended local label correlations, which challenge these works. To solve this problem, we propose in LDL fuzzy label correlation (FLC)-each sample blends, with fuzzy membership, multiple local label correlations. First, we propose two types of FLCs, i.e., fuzzy membership-induced label correlation (FC) and joint fuzzy clustering and label correlation (FCC). Then, we put forward LDL-FC and LDL-FCC to exploit these two FLCs, respectively. Finally, we conduct extensive experiments to justify that LDL-FC and LDL-FCC statistically outperform state-of-the-art LDL methods.

Researchers have suggested leveraging label correlation to deal with the exponentially sized output space of label distribution learning (LDL). Among them, some have proposed to exploit local label correlation. They first partition the training set into different groups and then exploit local label correlation on each one. However, these works usually apply clustering algorithms, such as -means, to split the training set and obtain the clustering results independent of label correlation. The structures (e.g., low rank and manifold) learned on such clusters may not efficiently capture label correlation. To solve this problem, we put forward a novel LDL method called LDL by partitioning label distribution manifold (LDL-PLDM). First, it jointly bipartitions the training set and learns the label distribution manifold to model label correlation. Second, it recurses until the reconstruction error of learning the label distribution manifold cannot be reduced. LDL-PLDM achieves label-correlation-related partition results, on which the learned label distribution manifold can better capture label correlation. We conduct extensive experiments to justify that LDL-PLDM statistically outperforms state-of-the-art LDL methods.

Abstract With the promotion and development of oversized data technology, many data analysis platforms based on super large data storage and computing frameworks have emerged in the industry. While the platforms with oversized economic data analysis combined with machine learning models are still relatively lacking. And oversized data also brings a new problem, that is the security of economic development. It is an important and difficult task to analyse and detect risks from oversized economic data. Based on machine learning, data analysis, economic market and other multidisciplinary fields, this paper proposes a machine learning method, which is a genetic algorithm (GA) based feature selection method: FSGA. This method abstracts every possible feature selection result into an individual in GA, generates a population through genetic operation, and measures the merits of the individual through fitness. In addition, this paper has conducted multitudinous simulation experiments on the GA‐based FSGA method and the traditional LSTM data analysis method respectively. The accuracy rate and other indicators are obtained by comparing the training. The experimental results show that the GA‐based FSGA machine learning method has higher prediction accuracy when analysing oversized economic data. And it is practical to accelerate the development of digital economy.

In the rapidly evolving field of smart healthcare, integrating modern information technologies, such as Internet of Things, big data, and AI, has significantly enhanced the quality, efficiency, and accessibility of medical services. However, this technological advancement also brings substantial security challenges, particularly regarding protecting electronic medical records (EMRs) from phishing attacks. This article presents a provably secure anti-phishing scheme to safeguard EMRs in smart healthcare systems. By utilizing authentication and key agreement technology, our proposed scheme ensures mutual authentication between users and servers, leveraging elliptic curve encryption, symmetric encryption, hash functions, and XOR operations to secure session keys and verify the legitimacy of medical records. Our scheme addresses critical security challenges, including phishing attacks, stolen mobile device attacks, offline password guessing attacks, replay attacks, and temporary information leakage. The real-oracle-random (ROR) model validates the correctness and security of our scheme, confirming its robustness against common cybersecurity threats. Performance evaluations demonstrate that our scheme provides enhanced security and offers lower time and communication costs compared to existing methods. This makes it a highly efficient and practical solution for safeguarding patient data in smart healthcare environments, ultimately contributing to the reliability and trustworthiness of smart healthcare systems.

The emergence of metaverse applications and services heralds a new era of immersive driving experiences in future vehicular ad hoc networks (VANETs). This unprecedented metaverse-infused driving experience, however, requires high-throughput transmissions for real-time high-quality 360° video streaming, which challenges today’s limited bandwidth resources provisioned by wireless communication infrastructure. To this end, this article introduces virtual road, real connection (VRRC), a novel framework for enhancing the metaverse-infused driving experience in VANET through the Internet of Robotic Things (IoRT). In the face of limited bandwidth resources, VRRC effectively addresses the challenge of high-throughput demand from two key perspectives. First, VRRC reduces redundant transmission by implementing a graph neural network (GNN)-based vehicle clustering method for dynamic multicast group formation, taking into account both the geographical status of vehicles and their communication patterns. Second, VRRC aggregates bandwidth resources across various channels by employing a multiagent reinforcement learning (MARL)–based multipath packet scheduling policy to adapt to heterogeneous channel conditions and dynamic vehicular mobility. Extensive experiments with real-world vehicular traces validate the effectiveness of VRRC and demonstrate its outperformance in reducing redundant traffic by 54% and improving overall throughput by 28%. VRRC represents a substantial leap forward in the integration of the metaverse experience into VANET.

Vehicular edge computing (VEC) systems face challenges in providing real-time intelligent transportation services due to limited computing resources at VEC servers, which lead to excessive delays or denial of services, especially for latency-critical tasks. This article proposes an augmented intelligence of things (AIoT) framework to enable priority-aware task offloading in VEC for vehicle road cooperation systems, maximizing overall system rewards under latency constraints. The framework incorporates an advanced dynamic resource management mechanism that adapts to real-time data and optimizes resource allocation using augmented intelligence models. The joint priority-aware application offloading and resource optimization problem is formulated as a constrained Markov decision process, and a deep Q-network (DQN)-based learning algorithm is employed to optimize the allocation of communication and computational resources based on application priorities and real-time channel/queue state information. Simulation results demonstrate that the proposed algorithm achieves significant improvements in weighted carrying capacity, high/low-priority task drop rates, and high/low-priority task queuing delays under varying overall task arrival rates, proportions of high/low-priority tasks, vehicle density, and task size compared to benchmark schemes. The proposed AIoT-enhanced DQN-based learning algorithm advances the field of VEC systems for vehicle road cooperation, offering practical advantages, such as increased efficiency, reduced latency, and improved resource utilization, ultimately enhancing user experience and enabling real-world applications in intelligent transportation systems.

The proliferation of multimedia-enabled IoT devices and edge computing enables a new class of data-intensive applications. However, analyzing the massive volumes of multimedia data presents significant privacy challenges. We propose a novel framework called generative adversarial privacy (GAP) that leverages generative adversarial networks (GANs) to synthesize privacy-preserving surrogate data for multimedia analytics across the IoT-Edge continuum. GAP carefully perturbs the GAN's training process to provide rigorous differential privacy guarantees without compromising utility. Moreover, we present optimization strategies, including dynamic privacy budget allocation, adaptive gradient clipping, and weight clustering to improve convergence and data quality under a constrained privacy budget. Theoretical analysis proves that GAP provides rigorous privacy protections while enabling high-fidelity analytics. Extensive experiments on real-world multimedia datasets demonstrate that GAP outperforms existing methods, producing high-quality synthetic data for privacy-preserving multimedia processing in diverse IoT-Edge applications.

The exponential growth of mobile data traffic in next generation networks has led to a significant increase in energy consumption, posing critical challenges for network operators. We propose DLLF-2EN, a novel energy-efficient framework that integrates deep learning-based load forecasting, an advanced power consumption model, and a comprehensive energy-saving strategy to address this issue. The load forecasting technique utilizes deep convolutional neural network and long short-term memory model, which is based on deep learning. This model is capable of capturing the spatiotemporal dependencies present in network traffic data. The power consumption model accurately characterizes the base stations’ static and dynamic power consumption components, facilitating the assessment of energy efficiency under various network scenarios. The energy-saving strategy combines base station sleep mode with discontinuous transmission and reception, as well as lightweight transmission of common signals, dynamically adapting the network operation based on the predicted traffic load. Furthermore, DLLF-2EN incorporates an intelligent power management system that leverages machine learning algorithms to continuously monitor the network, analyze collected data, and make optimal energy-saving decisions in real-time. Simulation demonstrate that the superior performance of DLLF-2EN in terms of load forecasting accuracy and energy efficiency compared to state-of-the-art baseline methods. The proposed framework represents a comprehensive solution for energy-efficient and sustainable next generation mobile networks, addressing the critical challenges of minimizing energy consumption while meeting the growing demands for high-quality mobile services.

SummaryThe mobile video quality can be improved by video super-resolution (SR) especially when bandwidth is limited. To achieve real-time SR, the latest work, ClassSR (CVPR 19), divides frames into equal-size image blocks (IBs), and different-complexity SR models are used respectively to reduce the computational burden.

The user experience of mobile web video streaming is often impacted by insufficient and dynamic network bandwidth. In this paper, we design Bidirectionally Optimized Super-Resolution (BiSR) to improve the quality of experience (QoE) for mobile web users under limited bandwidth. BiSR exploits a deep neural network (DNN)-based model to super-resolve key frames efficiently without changing the inter-frame spatial-temporal information. We then propose a downscaling DNN and a mobile-specific optimized lightweight super-resolution DNN to enhance the performance. Finally, a novel reinforcement learning-based adaptive bitrate (ABR) algorithm is proposed to verify the performance of BiSR on real network traces. Our evaluation, using a full system implementation, shows that BiSR saves 26% of bitrate compared to the traditional H.264 codec and improves the SSIM of video by 3.7% compared to the prior state-of-the-art. Overall, BiSR enhances the user-perceived quality of experience by up to 30.6%.

Applying the Multiple Drones System (MDS) to perform the repetitive and dangerous tasks for human in many complex environments has become a trend all around the world, due to the increasing capacity of mobile communication and the increasing intelligence of drone robots. However, to fulfill the target with less cost as much as possible, drones need to collaborate deeply with each other to make the optimal decision, which is now important and challenging. In this work, we focus on addressing the efficient task allocation among large-scale drones with the object of minimizing the resource waste and cost, which is proved to be NP-hard. Specifically, we first introduce the Digital Twin (DT) technology to dynamically construct the spatial structure for drones, in which a density clustering based algorithm is proposed to decompose the large-scale task allocation problem among all drones into smaller sub-problems among partial drones. Then, for each sub-problem, we propose an improved auction algorithm to allocate the sub-tasks to local drones according to the task difficulty and drone ability. The experimental results indicate that the proposed method outperforms the state-of-the-art methods in terms of the moving distance, resource utilization and task completion time, etc.

Information Centric Networking (ICN) has the characteristics of supporting mobility naturally. To make the most of 5G and ICN, this work intends to propose a 5G-enabled and mobility supported routing based on ICN to satisfy the requirements of various new network applications. In particular, the corresponding routing method is designed leveraging the ant swarm behavior. Specifically, we firstly formulate a corresponding networking model, during which four node tables are designed for routing. Then the routing method is designed based on the ant swarm behavior. Lastly, the experiments are carried out over DFN topology and the results indicate that the proposed method can significantly improve the performance.

Information-Centric Networking (ICN) provides scalable and efficient content distribution at the Internet scale due to in-network caching and native multicast. To support these features, a content router needs high performance at its data plane, which consists of three forwarding steps: checking the Content Store (CS), then the Pending Interest Table (PIT), and finally the Forwarding Information Base (FIB). In this work, we build an analytical model of the router and identify that CS is the actual bottleneck. Then, we propose a novel mechanism called “NB-Cache” to address CS’s performance issue from a network-wide point of view. In NB-Cache, when packets arrive at a router whose CS is fully loaded, instead of being blocked and waiting for the CS, these packets are forwarded to the next-hop router, whose CS may not be fully loaded. This approach essentially utilizes Content Stores of all the routers along the forwarding path in parallel rather than checking each CS sequentially. NB-Cache follows a design pattern of on-demand load balancing and can be formulated into a non-trivial N-queue bypass model. We use the Markov chain to establish its theoretical base and find an algorithm for automated transition rate matrix generation. Experiments show significant improvement of data plane performance: 70% reduction in round-trip time (RTT) and 130% increase in throughput. NB-Cache decouples the fast packet forwarding from the slower content retrieval thus substantially reducing CS’s heavy dependency on fast but expensive memory.

Traffic optimization in Information Centric Networks (ICN) inevitably involves making full use of in-network storages. Although ICN provides efficient content delivery and traffic offloading by exploiting nearby cached content and reducing redundant transmissions for same content requests, it inherently follows an opportunistic fashion to utilize in-network caches. In this paper, we improve network traffic distribution for ICN by leveraging multicasting. Specifically, introducing Software-Defined Networking (SDN), a virtual point-based algorithm is proposed to achieve many-to-many multicasting with global optimization. Furthermore, we propose a tunnel-based bandwidth allocation mechanism to improve the scalability of the system. Extensive simulation results show that multicast tree can be constructed optimally and meanwhile our proposal significantly improves network performance in terms of load balancing and network utilizazation.

Information-Centric Networking (ICN) provides scalable and efficient content distribution at the Internet scale due to its in-network caching and native multicast capabilities. To support these features, a content router needs high performance at its data plane, which consists of three forwarding steps: checking the Content Store (CS), then the Pending Interest Table (PIT), and finally the Forwarding Information Base (FIB). While prior works focus on performance optimization of a single step, we build an analytical model of content router's entire data plane and identify that CS is the actual bottleneck in the pipeline. Compared with PIT and FIB, CS is more challenging because it has more data to read/write, may have more entries in its table to store and lookup, and needs to organize content objects to sustain frequent cache replacement. Then, we propose a novel mechanism called "NB-Cache" to address CS's performance issue from a network-wide point of view rather than a single router's. In NB-Cache, when packets arrive at a router whose CS is fully loaded, instead of being blocked and waiting for the CS, these packets are forwarded to the next-hop router, whose CS may not be fully loaded. This approach essentially utilizes Content Stores of all the routers along the forwarding path in parallel rather than checking each CS sequentially. Our experiments show significant improvement of data plane performance: 70% reduction in round-trip time (RTT) and 130% increase in throughput.

In this paper, we design an LAP based routing algorithm in General Networks (GN) to solve the problem of information consistency of the full network under OSPF with the following operations: (i) decomposing GN into one or more Single-link Networks (SNs) with the approach of depth-first walk, (ii) re-composting the SNs to a network with regular topology structure by adding links, (iii) searching the undirected complete graph of three nodes round by round until it converges to a simple network topology based on region binding, and (iv) processing different converged network topologies with different LAP based routing algorithms. The proposed algorithm is compared with Dijkstra algorithm over some random network topologies. Simulation results show that the proposed algorithm can solve the problem of information consistency of the full network under OSPF and has better performance than Dijkstra algorithm.