Search Results (117)

The space–air–ground integrated network (SAGIN) is a key 6G architecture that provides seamless three-dimensional connectivity, exhibiting hierarchical structural symmetry between LEO satellite and HAP layers. Integrating information-centric networking (ICN) with caching on Low Earth Orbit (LEO) satellites and high-altitude platforms (HAPs) significantly enhances content distribution efficiency. Existing studies on caching mechanisms have made progress but lack optimized cache resource allocation and accurate popular content identification. Thus, an interest-aware caching scheme (ICRL) based on reinforcement learning is proposed to optimize the SAGIN’s popular content caching decisions, aiming to achieve rational symmetric allocation of cache resources across LEO and HAP layers. Different from existing RL-based caching methods, the proposed ICRL scheme considers the LEO-HAP hierarchical architecture and designs an improved reinforcement learning mechanism to adapt to the dynamic characteristics of the SAGIN. First, an air–space two-tier caching architecture is constructed to enable collaborative caching between LEO satellites and HAPs. Second, to select high-value nodes intelligently, the proposed scheme leverages a comprehensive importance model that quantitatively analyzes HAP and LEO indicators such as topology, transmission capacity, and location. Finally, a reinforcement learning-based dynamic cache mechanism is developed. It captures real-time network requests and cache states to select optimal actions and adapt to network dynamics for better content popularity matching. Extensive evaluations based on NDNSIM demonstrate that ICRL outperforms baseline schemes in terms of cache hit ratio, server load, and request latency and achieves a symmetric balance of network load and service performance in the whole SAGIN. Full article

Although Convolutional Neural Networks (CNNs) have achieved remarkable accuracy in intelligent tasks, their increasing complexity hinders low-latency execution. While edge computing mitigates the wide-area network delays typical of cloud-based inference, it remains constrained by limited computational resources when processing complex models under high concurrency. Collaborative inference has emerged as a promising paradigm to address these limitations; however, existing approaches often struggle with rigid routing, limited scalability, and inefficient resource utilization. In this paper, we propose a novel collaborative inference acceleration mechanism that integrates In-Network Computing (INC) within an Information-Centric Networking (ICN) framework. By leveraging the name-based resolution capability of ICN, our approach dynamically harnesses underutilized computational resources across distributed INC nodes, enabling flexible layer-wise offloading that transcends the limitations of static IP paths. Furthermore, a distributed decision-making and node-selection algorithm is designed to orchestrate CNN layer assignment based on real-time network conditions and node workloads. Extensive simulations on representative models demonstrate the effectiveness of the proposed method. Specifically, for the computationally intensive VGG16 model under high concurrency, the average task completion time is reduced by 43.3% and 60.2% relative to IP-based and Edge-Cloud baselines, respectively, with a load balancing fairness index maintained above 0.86. Full article

The joint optimization of computing resources and network routing constitutes a central challenge in Computing First Networks (CFNs). However, existing research has predominantly focused on computation offloading decisions, whereas the cooperative optimization of computing power and network routing remains underexplored. Therefore, this study investigates the joint routing optimization problem within the CFN framework. We first propose a computing resource scheduling architecture for CFN, termed SICRSA, which integrates Software-Defined Networking (SDN) and Information-Centric Networking (ICN). Building upon this architecture, we further introduce an ICN-based hierarchical naming scheme for computing services, design a computing service request packet format that extends the IP header, and detail the corresponding service request identification process and workflow. Furthermore, we propose Computing-Aware Routing via Graph and Long-term Dependency Learning (CRGLD), a Graph Neural Network (GNN), and Long Short-Term Memory (LSTM)-based routing optimization algorithm, within the SICRSA framework to address the computing-aware routing (CAR) problem. The algorithm incorporates a decision-making framework grounded in spatiotemporal feature learning, thereby enabling the joint and coordinated selection of computing nodes and transmission paths. Simulation experiments conducted on real-world network topologies demonstrate that CRGLD enhances both the quality of service and the intelligence of routing decisions in dynamic network environments. Moreover, CRGLD exhibits strong generalization capability when confronted with unfamiliar topologies and topological changes, effectively mitigating the poor generalization performance typical of traditional Deep Reinforcement Learning (DRL)-based routing models in dynamic settings. Full article

In recent years, there has been increasing investment in the deployment of massive commercial Low Earth Orbit (LEO) constellations to provide global Internet connectivity. These constellations, now equipped with inter-satellite links, can serve as low-latency Internet backbones, requiring LEO satellites to act not only as access nodes for ground stations, but also as in-orbit core routers. Due to their high velocity and the resulting frequent handovers of ground gateways, LEO networks highly stress mobility procedures at both the sender and receiver endpoints. On the other hand, a growing trend in networking is the use of technologies based on the Information Centric Networking (ICN) paradigm for servicing IoT networks and sensor networks in general, as its addressing, storage, and security mechanisms are usually a good match for IoT needs. Furthermore, ICN networks possess additional characteristics that are beneficial for the massive LEO scenario. For instance, the mobility of the receiver is helped by the inherent data-forwarding procedures in their architectures. However, the mobility of the senders remains an open problem. This paper proposes a comprehensive solution to the mobility problem for massive LEO constellations using the Named-Data Networking (NDN) architecture, as it is probably the most mature ICN proposal. Our solution includes a scalable method to relate content to ground gateways and a way to address traffic to the gateway that does not require cooperation from the network routing algorithm. Moreover, our solution works without requiring modifications to the actual NDN protocol itself, so it is easy to test and deploy. Our results indicate that, for long enough handover lengths, traffic losses are negligible even for ground stations with just one satellite in sight. Full article

Named Data Networking (NDN) is identified as a significant shift within the information-centric networking (ICN) perspective that avoids our current IP-based infrastructures by retrieving data based on its name rather than where the host is placed. This shift in paradigm is especially beneficial in Internet of Things (IoT) settings because information sharing is a critical challenge, as millions of IoT items create enormous traffic. Content caching in the network is another key characteristic of NDN used in IoT, which enables data storing within the network and provides IoT devices with the opportunity to address nearby caching nodes to gain the intended content, which, in its turn, will minimize latency as well as bandwidth consumption. However, effective caching solutions must be developed since cache management is made difficult by the constant shifting of IoT networks and the constrained capabilities of IoT devices. This paper gives an overview of cache strategies in NDN-based IoT systems. It emphasizes six strategy types: popularity-based, freshness-aware, collaborative, hybrid, probabilistic, and machine learning-based, evaluating their performances in terms of demands like content preference, cache update, and power consumption. By analyzing various caching policies and their performance characteristics, this paper provides valuable insights for researchers and practitioners developing caching strategies in NDN-based IoT networks. Full article

Named Data Networking (NDN) represents a promising Information-Centric Networking architecture that addresses limitations of traditional host-centric Internet protocols by emphasizing content names rather than host addresses for communication. While NDN offers advantages in content distribution, mobility support, and built-in security features, its stateful forwarding plane introduces significant vulnerabilities, particularly Interest Flooding Attacks (IFAs). These IFA attacks exploit the Pending Interest Table (PIT) by injecting malicious interest packets for non-existent or unsatisfiable content, leading to resource exhaustion and denial-of-service attacks against legitimate users. This survey examines research advances in IFA detection and mitigation from 2013 to 2024, analyzing seven relevant published detection and mitigation strategies to provide current insights into this evolving security challenge. We establish a taxonomy of attack variants, including Fake Interest, Unsatisfiable Interest, Interest Loop, and Collusive models, while examining their operational characteristics and network performance impacts. Our analysis categorizes defense mechanisms into five primary approaches: rate-limiting strategies, PIT management techniques, machine learning and artificial intelligence methods, reputation-based systems, and blockchain-enabled solutions. These approaches are evaluated for their effectiveness, computational requirements, and deployment feasibility. The survey extends to domain-specific implementations in resource-constrained environments, examining adaptations for Internet of Things deployments, wireless sensor networks, and high-mobility vehicular scenarios. Five critical research directions are proposed: adaptive defense mechanisms against sophisticated attackers, privacy-preserving detection techniques, real-time optimization for edge computing environments, standardized evaluation frameworks, and hybrid approaches combining multiple mitigation strategies. Full article

Information-centric networking (ICN) is a promising approach to address the limitations of current host-centric IP-based networking. ICN models feature ubiquitous in-network caching to provide faster and more reliable content delivery, name-based routing to provide better scalability, and self-certifying contents to ensure better security. Due to the differences in the core architecture of ICN compared to existing IP-based networks, it requires special considerations to provide quality-of-service (QoS) or quality-of-experience (QoE) support for applications based on ICNs. This paper discusses the latest advances in QoS and QoE research for ICNs. First, an overview of ICN architectures is given, followed by a summary of different factors that influence QoS and QoE. Approaches for improving QoS and QoE in ICNs are then discussed in five main categories: in-network caching, name resolution and routing, transmission and flow control, software-defined networking, and media-streaming-based strategies. Finally, open research questions for providing QoS and QoE support in ICNs are outlined for future research. Full article

The integration of blockchain with Information-Centric Networking (ICN) enhances content distribution efficiency in areas such as the Internet of Things (IoT) and 5G/6G communications. This integration implies that the network state information of ICN can significantly impact consensus efficiency. However, the Ethereum Casper FFG consensus algorithm overlooks the network heterogeneity among consensus nodes, leading to a potential bottleneck in consensus efficiency, especially when nodes with inferior network quality participate. To address this issue, this paper proposes a multidimensional reputation model based on an ICN-enabled blockchain architecture. The model combines on-chain stake and network contributions to evaluate the reputation of ICN consensus nodes. Furthermore, a reputation-based hybrid consensus mechanism, RepuICN, is introduced, which enhances the network layer of the Casper FFG algorithm. This mechanism selects higher-reputation ICN consensus nodes as proposers for checkpoint blocks, mitigating the impact of network latency fluctuations on block propagation. Additionally, RepuICN improves block propagation efficiency through ICN multicast and caching techniques. Simulation results show that, under identical conditions with a network of 5000 nodes and 2% ICN nodes, RepuICN reduces broadcast latency by 17% for regular blocks and 61.4% for checkpoint blocks and achieves 3.4 times higher transaction throughput than Casper FFG. Full article

The growing demand for large-scale data distribution and sharing presents significant challenges to content transmission within the current TCP/IP network architecture. To address these challenges, Information-Centric Networking (ICN) has emerged as a promising alternative, offering inherent support for multipath forwarding and in-network caching to improve data transmission performance. However, most existing ICN caching strategies primarily focus on utilizing resources along the default transmission path and its neighboring nodes, without fully exploiting the additional resources provided by multipath forwarding. To address this gap, we propose an efficient multipath-based caching strategy that optimizes cache placement by decomposing the problem into two steps, multipath selection and cache node selection along the paths. First, multipath selection considers both transmission and caching resources across multiple paths, prioritizing the caching of popular content while efficiently transmitting less popular content. Next, along the selected paths, cache node selection evaluates cache load based on cache utilization and available capacity, prioritizing nodes with the lowest cache load. Extensive simulations across diverse topologies demonstrate that the proposed strategy reduces data transmission latency by at least 12.22%, improves cache hit rate by at least 16.44%, and enhances cache node load balancing by at least 18.77%, compared to the neighborhood collaborative caching strategies. Full article

The advent of 6G networks and beyond calls for innovative paradigms to address the stringent demands of emerging applications, such as extended reality and autonomous vehicles, as well as technological frameworks like digital twin networks. Traditional cloud computing and edge computing architectures fall short in providing their required flexibility, scalability, and ultra-low latency. Cloud computing centralizes resources in distant data centers, leading to high latency and increased network congestion, while edge computing, though closer to data sources, lacks the agility to dynamically adapt to fluctuating workloads, user mobility, and real-time requirements. In-network computing (INC) offers a transformative solution by integrating computational capabilities directly into the network fabric, enabling dynamic and distributed task execution. This paper explores INC through the lens of information-centric networking (ICN), a revolutionary communication paradigm implementing routing-by-name and in-network caching, and thus emerging as a natural enabler for INC. We review state-of-the-art advancements involving INC and ICN, addressing critical topics such as service naming, executor selection strategies, compute reuse, and security. Furthermore, we discuss key challenges and propose research directions for deploying INC via ICN, thereby outlining a cohesive roadmap for future investigation. Full article

Information-Centric Networking (ICN) typically utilizes DRAM (Dynamic Random Access Memory) to build in-network cache components due to its high data transfer rate and low latency. However, DRAM faces significant limitations in terms of cost and capacity, making it challenging to meet the growing demands for cache scalability required by increasing Internet traffic. Combining high-speed but expensive memory (e.g., DRAM) with large-capacity, low-cost storage (e.g., SSD) to construct a hierarchical cache architecture has emerged as an effective solution to this problem. However, how to perform efficient cache management in such architectures to realize the expected cache performance remains challenging. This paper proposes a cache management scheme for hierarchical cache architectures in ICN, which introduces a differentiated replica replacement policy to accommodate the varying request access patterns at different cache layers, thereby enhancing overall cache performance. Additionally, a probabilistic insertion-based SSD cache admission filtering mechanism is designed to control the SSD write load, addressing the issue of balancing SSD lifespan and space utilization. Extensive simulation results demonstrate that the proposed scheme exhibits superior cache performance and lower SSD write load under various workloads and replica placement strategies, highlighting its broad applicability to different application scenarios. Additionally, it maintains stable performance improvements across different cache capacity settings, further reflecting its good scalability. Full article

Information-centric networking (ICN) changes the way data are accessed by focusing on the content rather than the location of devices. In this model, each piece of data has a unique name, making it accessible directly by name. This approach suits the Internet of Things (IoT), where data generation and real-time processing are fundamental. Traditional host-based communication methods are less efficient for the IoT, making ICN a better fit. A key advantage of ICN is in-network caching, which temporarily stores data across various points in the network. This caching improves data access speed, minimizes retrieval time, and reduces overall network traffic by making frequently accessed data readily available. However, IoT systems involve constantly updating data, which requires managing data freshness while also ensuring their validity and processing accuracy. The interactions with cached data, such as updates, validations, and replacements, are crucial in optimizing system performance. This research introduces an ICN-IoT method to manage and process data freshness in ICN for the IoT. It optimizes network traffic by sharing only the most current and valid data, reducing unnecessary transfers. Routers in this model calculate data freshness, assess its validity, and perform cache updates based on these metrics. Simulation results across four models show that this method enhances cache hit ratios, reduces traffic load, and improves retrieval delays, outperforming similar methods. The proposed method uses an artificial neural network to make predictions. These predictions closely match the actual values, with a low error margin of 0.0121. This precision highlights its effectiveness in maintaining data currentness and validity while reducing network overhead. Full article

Network measurement is an efficient way to understand network behavior. Traditional measurement techniques focus on internet protocol (IP) networks, where the processing capacity of network nodes is limited and primarily dedicated to packet forwarding. As a result, these techniques typically rely on end hosts or external systems to analyze traffic and evaluate network performance. This reliance introduces several challenges, such as increased measurement latency and scalability limitations, particularly in large-scale networks. With the emergence of next-generation internet architectures, especially information-centric networking (ICN), network nodes have gained enhanced capabilities, enabling measurement tasks to be performed directly at these nodes. This paper proposes a distributed measurement scheme where network nodes collaborate to monitor the packet loss rate on the intermediate link. By setting an unused bit in the packet header, the upstream node “colors” the packets into different color blocks. The minimum duration of each block is determined by the degree of reordering on the link, and the number of packets in each block must be a power of two. The downstream node recognizes blocks, assigns packets to the right block, and deduces the original number of packets for each block to calculate packet loss. Moreover, the upstream node adjusts the number of packets in each block based on the packet transmission rate on the link, aiming to balance measurement accuracy and frequency. A P4-based implementation on a BMv2 software switch is presented to demonstrate the feasibility of the proposed scheme. Simulations show that this scheme improves measurement accuracy and is more robust against packet reordering. Additionally, the proposed scheme maintains relatively low network overhead and, at higher measurement frequencies, exhibits the lowest overhead compared to existing methods. Full article

The Computing Network is an emerging paradigm that integrates network and computing resources. One of its goals is to satisfy the requirements of delay-sensitive services through network scheduling capabilities. However, traditional TCP/IP networks are deficient in accurately being aware of requirements and performing flexible routing based on service levels. Information-Centric Networking (ICN) addresses these issues through its flexible protocol design and content-based routing mechanism. Additionally, the integration of Software-Defined Networking (SDN) technology further enhances its routing flexibility. Therefore, this paper proposes an ICN-based delay-sensitive service scheduling architecture with an SDN stateful programmable data plane. The network nodes are first layered based on the type of computing clusters they are linked with, and then within each layer, they are divided into several domains according to delay constraints. Then, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm, combined with the Best-Worst Method (BWM) weighting method, is adopted to evaluate the candidate clusters, and the corresponding scheduling strategy is executed in the stateful programmable data plane. The simulation results show that compared with other scheduling architectures and traditional TOPSIS with the Entropy Weight Method (EWM), the proposed architecture and algorithm show significant advantages in reducing the overall delay of service requests and improving the scheduling success ratio, as well as the load balance of the computing clusters. Full article

How to maximize the advantages of in-network caching under limited cache space has always been a key issue in information-centric networking (ICN). Replica placement strategies aim to fully utilize cache resources by optimizing the location and quantity distribution of replicas in the network, thereby improving the performance of the cache system. However, existing research primarily focuses on optimizing the placement of replicas along the content delivery path, which cannot avoid the inherent drawback of not being able to leverage off-path cache resources. The proposals for off-path caching cannot effectively solve this problem as they introduce excessive complexity and cooperation costs. In this paper, we address the trade-off between cache resource utilization and cooperation costs by introducing a mechanism complementary to replica placement. Instead of redesigning a new caching strategy from scratch, we propose a proactive cooperative caching mechanism (called RMBCC) that involves an independent replica migration process, through which we proactively relocate replicas evicted from the local cache to neighboring nodes with sufficient cache resources. The cooperation costs are effectively controlled through migration replica filtering, migration distance limitation, as well as hop-by-hop migration request propagation. Extensive simulation experiments show that RMBCC can be efficiently integrated with different on-path caching strategies. Compared with representative caching schemes, RMBCC achieves significant improvements in evaluation metrics such as cache hit ratio and content retrieval time, while only introducing negligible cooperation overhead. Full article