Traditional key agreement-based encryption mechanisms in public security communications struggle to address increasingly sophisticated network attacks and real-time threats due to infrequent key updates and exposure risks. To overcome these limitations, this study proposes a Chaotic Encryption-based Tunneling Method (CETM) at the network layer. CETM integrates a six-dimensional (6D) hyperchaotic system with the AES-256 algorithm, leveraging the system’s extreme sensitivity to initial conditions to generate high-strength key sequences, eliminating the need for traditional negotiation. A self-synchronizing key update mechanism is also introduced, allowing both parties to update keys automatically without network transmission, thereby eliminating the risk of key leakage. The proposed system demonstrates strong complexity and randomness, as validated by multiple chaotic metrics including the Lyapunov exponent, Shannon entropy, correlation coefficient, permutation entropy, and key space. The generated keys pass both the SP800-22 and FIPS 140-2 tests, meeting established security standards. Performance evaluations across local, cloud, and satellite environments show that CETM significantly outperforms conventional methods in terms of bandwidth efficiency, latency, jitter, and packet loss.

In heterogeneous wireless emergency networks, network fault diagnosis plays a critical role in ensuring reliable and secure communication. To improve network transmission quality, the complexity of network equipment—both in hardware and software design—has increased, which inevitably gives rise to equipment failures with complex root causes, significantly elevating the difficulty of fault diagnosis. Current fault diagnosis algorithms are inadequate for addressing the challenges in fault diagnosis of complex emergency access networks, primarily due to their high diagnostic costs and low accuracy. In this study, we first propose a diagnosis framework and a Deterministic Fault Propagation (DFP) model, and a Hierarchical Fault Diagnosis Framework. Second, we develop three algorithms to construct a Fault Cause Relationship Graph, which supports identifying the logical relationships among various fault causes associated with a specific fault. Third, we propose a Fault Diagnosis algorithm based on Relational Graph Inference (FDRGI). Finally, we conduct extensive experiments in real-world wireless access networks. The experimental results demonstrate that our algorithm satisfies the requirements for root cause diagnosis of access failures in emergency networks, and outperforms other comparative algorithms in terms of diagnostic cost and accuracy: it reduces the average diagnostic cost by 13.71%-69.88% and improves the average diagnostic accuracy by 39.06%-1.98-fold.

In this paper, we explore the fine-grained channel state information (CSI) obtained through the sensing function in an reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system to support the efficient covert communications in the system. We first construct a new fine-grained CSI model for the RIS-assisted ISAC system and propose a novel covert communication scheme based on the new CSI model. We then develop theoretical models for the detection error probability, Cramér-Rao Bound and covert rate to depict the covertness, sensing and covert communication performances under the proposed scheme. Based on these theoretical models, we further formulate an optimization problem for covert rate maximization through optimizing the reflection coefficient in RIS and the transmit powers for covert/probing signals. With the help of the homogenization for quadratic constrained quadratic programming, semi-definite relaxation and Dinkelbach transform, an efficient alternating optimization (AO) algorithm is devised to tackle this complex optimization problem. Finally, extensive numerical results are presented to demonstrate the performance enhancement for covert communication in the RIS-assisted ISAC system from exploring the fine-grained CSI and AO-based parameter optimization therein.

As an important support of next-generation network technologies, edge computing provides crucial support for holographic communication services (HCSs) by offering low latency and high computing power. However, in resource-constrained environments, the deployment of HCSs is significantly affected by the coordinated scheduling of computational and transmission resources, as well as the selection of multi-task processing ratios, which in turn curtails service latency and user experience. For this purpose, this paper proposes an efficient edge network offloading and orchestration framework that jointly optimizes adaptive video processing, multi-task offloading, and resource allocation strategies. Specifically, we model computation offloading and video processing as a stochastic optimization problem to maximize system utility, which is defined as a weighted difference between network satisfaction and user utility. Based on Lyapunov optimization theory, the original long-term optimization problem is decomposed into single time slot subproblem, and further reformulated as a Markov Decision Process (MDP). To effectively solve this problem, we design a Graph attention-driven Computing and Networking Coordination based policy optimization Orchestration algorithm (GCNCO). The multi-dimensional policy joint optimization is achieved by fully sensing the contextual relationship of services and network state distribution. Finally, we compare the proposed algorithm with several benchmark algorithms and validate it using a prototype system. Experimental results demonstrate that GCNCO outperforms existing approaches in terms of convergence speed and performance stability, significantly enhancing system utility and improving the success performance of HCS orchestration.

The rise of latency-sensitive and bandwidth-intensive services has driven hardware accelerator adoption in cloud networks. Programmable data plane (PDP) switches achieve significant performance gains in high-performance cloud computing but face fixed pipeline constraints that limit flexibility in multi-tenant service function chaining (SFC) with dynamic function compositions. Existing approaches either exhaust resources through redundant embeddings or degrade performance via packet recirculation. This paper presents PBox, a framework enabling flexible SFC orchestration across multiple PDP switches by optimizing network function (NF) execution orders to minimize pipeline traversals-the dominant end-to-end processing delay source. This requires co-designing NF embedding with routing strategies. PBox contributes: (i) an optimized Network Service Header design supporting one-pass matching of multiple NFs through per-bit activation, reducing packet matching overhead by 45-60%; (ii) a nested optimization formulation capturing interdependence between long-term pipeline orchestration and short-term flow routing decisions; and (iii) a sampling-based genetic algorithm with statistical robustness guarantees, achieving fast switch configuration while adapting to dynamic service patterns. Evaluations on BMv2 and Intel Tofino demonstrate 33-79% completion time reduction, 46% capacity increase, and 78% line-rate efficiency, validating cloud-scale deployment potential.

Accurate mobile network traffic prediction is crucial for transit infrastructure service optimization in industrial informatization. Traditional linear models fail to capture complex non-linear dynamics, while existing deep learning methods struggle with rapid temporal changes, signal fluctuations, and diverse network conditions, limiting real-time applicability. To address these challenges, this paper proposes a hybrid model integrating Convolutional Neural Networks (CNNs) and Transformers, tailored for High-Speed Railway (HSR) environments. The proposed hybrid model is evaluated using both public datasets and a real-world HSR dataset collected through empirical field measurements, it not only achieves state-of-the-art (SOTA) predictive accuracy, reducing root mean square error by 4.7% over strong baselines in the challenging HSR environment, but also delivers this performance with superior computational efficiency, achieving over 3.6 times lower inference latency than leading SOTA models. This establishes an optimal performance-to-cost ratio, demonstrating its practical value for real-time HSR systems.

Mobile edge caching plays a crucial role in traffic offloading for access networks. By storing frequently requested content items close to subscribers, it significantly reduces data retrieval latency, mitigates backhaul congestion, and alleviates the load on remote servers. Among various caching strategies, the two-tier Least Recently Used (2-LRU) policy has been widely adopted due to its efficient popularity-aware filtering capability while maintaining$\mathcal {O}(1)$computational complexity. However, conventional 2-LRU often allocates an equal number of entries to both LRU tiers, overlooking the potential cache hit ratio gains achievable through asymmetric tier-size configurations. Therefore, in this paper, we present a comprehensive analysis of 2-LRU under asymmetric tier sizing, and leverage the obtained insights to further guide performance optimization. In particular, we first construct a discrete-time Markov chain model to characterize the state transitions of 2-LRU and derive a closed-form expression for its cache hit probability, i.e., the hit ratio of its second-tier LRU ($C_{2}$). We then perform extensive simulations to validate accuracy of the proposed model and investigate the optimal size settings for the first-tier LRU ($C_{1}$). Building on the key implications of the associated results, we further propose 2LRU-$\Delta C_{1}$, an enhanced 2-LRU scheme that dynamically adjusts the size of$C_{1}$to accelerate the population of$C_{2}$with popular content data, thereby improving the cache hit ratio of$C_{2}$. Finally, we implement 2LRU-$\Delta C_{1}$in NS-3 and evaluate its performance against several baseline strategies, including LRU, 2-LRU ($C_{1}=C_{2}$), LFU, and a DRL-based policy. Corresponding results have confirmed the efficiency of our proposed scheme.

Uncrewed aerial vehicle (UAV) has emerged as a promising solution for automating railway inspections due to its high mobility, flexible deployment, and reduced labor cost. In this paper, we investigate UAV-assisted railway inspections, which include object recognition, humidity monitoring, and critical infrastructure modeling, each with distinct data volumes and computational requirements. Particularly, we introduce a UAV-assisted railway inspection framework. Different types of sensors are divided into several clusters. The UAV departs from the hive, flies over each cluster to collect their computational requirements, and performs task offloading before returning to the hive. This process is formulated as a joint optimization problem of trajectory planning and task offloading to minimize the weighted sum of latency and energy consumption. Considering the constrained computing and storage capabilities of UAVs, it is crucial but challenging to develop a lightweight yet high-performing solution for the multi-objective optimization problems. As such, a novelArtificial General Intelligence (AGI)-orientedTransformer (AoT) algorithm is proposed to solve the optimization problem. It uses an encoder-only architecture to process either sensor location or task features, and then directs the encoded outputs to different output heads to make decisions on UAV trajectory and task offloading. Simulation results demonstrate that the proposed AoT algorithm outperforms benchmark algorithms in terms of trajectory length and average offloading cost.

In-vehicle networks (IVNs) face growing threats from advanced cyber-attacks, particularly stealthy masquerade attacks that mimic legitimate message patterns. This paper proposes STATGRAPH, a fine-grained intrusion detection frame work based on multi-view statistical graph learning over the Controller Area Network (CAN) messages within IVNs. STAT GRAPH constructs two graphs per detection window: a Timing Correlation Graph (TCG) capturing temporal ID dependencies, and a Coupling Relationship Graph (CRG) modeling short term contextual relations. TCG and CRG are further used to generate graph structure encoding payload variations and embedded signal co-occurrence. A lightweight multi-layered Graph Convolutional Network (GCN) is then applied to classify each message, leveraging the expressive representations from TCG and CRG. To ensure effectiveness against diverse attacks, we evaluate STATGRAPH on two real-world CAN datasets featuring five underexplored masquerade attacks. Experimental results show that STATGRAPH significantly improves detection granularity and outperforms state-of-the-art methods, with F1-score gains of 7% and 22%, while maintaining the highest accuracy. Code is available at https://github.com/wangkai-tech23/StatGraph

For the computation offloading via device-to-device (D2D) terminals and edge servers in a resource-constrained wireless network (RCWN), mobile users can choose to offload their tasks to nearby D2D terminals or edge servers according to quality of service (QoS) requirements (e.g., load balancing at the network edge) by mobile edge computing. To this end, we first formulate computation offloading as a multi-user collaborative resource dynamic management optimization problem that aims to maximize user satisfaction utility function, carefully considering critical issues like the non-uniform distribution of computational resources, user's risk awareness, and the dynamic changes between computing-intensive regions and computing-sparse regions. This is a nonlinear and nonconvex optimization problem, which is generally difficult to be solved. We then construct a resource management scheme for resource allocation of the edge server based on convex optimization. Furthermore, we propose a dynamic offloading update strategy achieving the maximum of user satisfaction utility function based on game theory. The simulation results are presented to show that our proposed method can increase the total system satisfaction utility by nearly 20% and reduce the system energy consumption by nearly 10% compared to the benchmark methods.

Cloud Manufacturing (CMfg) has revolutionized traditional manufacturing by enabling resource sharing across factory boundaries. As industry increasingly adopts cloud-edge-device collaborative ecosystems, effective authentication and key agreement (AKA) mechanisms play a critical role in safeguarding security across these complex multi-factory environments. Existing schemes, however, focus primarily on isolated binary security relationships (e.g., device-edge, device-cloud, or edge-cloud pairs individually), neglecting the integrated cloud-edge-device collaborative security demands inherent in multi-factory environments, while often relying on trusted authorities and high-overhead cryptographic mechanisms. This leads to redundant authentication processes, increased latency, and security vulnerabilities as devices must separately establish connections with each entity. To bridge these gaps, this paper introduces OMEGA: a comprehensive Cloud-Edge-Device Authentication and Key Agreement scheme for collaborative multi-factory manufacturing that pioneers an integrated security architecture. OMEGA’s distinctive advantage lies in its ability to establish all necessary secure connections (device-edge, device-cloud, and edge-cloud) through a single authentication request from the smart manufacturing device (SMD), significantly reducing authentication overhead. By leveraging lightweight hash operation, OMEGA creates a cohesive security fabric that enables SMDs to concurrently access specialized capabilities from multiple clouds while leveraging edge computing for time-sensitive operations. Security analysis using both Real-Or-Random (ROR) model and ProVerif formal verification tool demonstrates OMEGA achieves robust security while performance evaluation confirms its superior efficiency in industrial environments.

Multipath cooperation technology alleviates transmission pressure by leveraging path diversity. However, in next-generation service-oriented environments with computation-intensive services, the limited computing capability of transmission paths can degrade end-to-end service quality, even when bandwidth is sufficient. This issue becomes more pronounced in dynamic mobile scenarios, where fluctuating link status and computational resources introduce new challenges in path selection. To address these challenges, we propose a novel path selection approach that jointly considers both network and computation constraints for computation-intensive services. First, we construct a computation-integrated multipath transmission framework to support real-time monitoring of both link-level computing capabilities and network conditions. Second, we introduce a packet structure embedding device identifiers and computing capability, enabling adaptive scheduling. Finally, we develop a computing capability-constrained delay-minimizing packet scheduler (C2-DMPS) to balance bandwidth and computational load, ensuring low-latency transmission for emerging service demands. The results demonstrate the critical role of computational capacity in maintaining service performance, especially under volatile network conditions, highlighting potential risks to service continuity in next-generation environments.

6G-driven compute-intensive applications require the collaboration of communication and computing to achieve optimal performance. Such collaboration drives integrated sensing, communication, and computing to support service requirement sensing and on-demand computing task steering within the network. To this end, the Computing-Aware Network proposes to incorporate computing information into the network layer address called service identifier (SID), to implement serviceoriented SID anycast. This integration aims to naturally support dynamic task steering using the anycast mechanism. However, SID represents an abstract service rather than a specific host, making SID anycast incompatible with the TCP communication patterns used by existing socket-based applications. To address this challenge, this paper introduces Srvcast, a host-transparent and stateful anycast solution. Srvcast consists of two stages: WAN routing and edge network forwarding. In WAN routing, it employs a novel service-oriented routing mechanism to ensure connection affinity for anycast. In the edge network forwarding, Srvcast presents ServiceNAT, a P4-based address translation mechanism that enhances SID compatibility with socket-based applications. To implement Srecast, a prototype system is built in a practical WAN environment. The results demonstrate that Srvcast outperforms existing solutions in terms of system complexity and socket connection establishment time. Srvcast can maintain the flexibility of SID anycast while ensuring compatibility with TCP communication patterns at a lower cost.

Industrial Internet of Things (IIoT) applications, such as industrial process control, demand ultra-high reliability and bounded delay. The Reliable and Available Wireless (RAW) initiative within the IETF DetNet working group addresses these needs by applying IEEE 802.15.4 time-slotted channel hopping (TSCH) technology and leveraging techniques like Packet Replication, Elimination, and Ordering Functions (PREOF) to ensure deterministic performance for IIoT. However, while PREOF improves reliability, its redundant transmission mechanism inevitably increases energy consumption, conflicting with the energy constraints of TSCH nodes. The existing multipath routing approaches struggle to address this challenge, failing to jointly consider both energy efficiency and deterministic performance. Additionally, these approaches often overlook the delay variation caused by multipath transmissions of different lengths—a key factor that can undermine deterministic performance by increasing buffering requirements and affecting the predictability of data flows. In this paper, we investigate a multipath optimization problem aiming at improving energy efficiency and minimizing delay variation while meeting the requirements of bounded reliability and delay for deterministic flows. Considering the above multipath routing optimization problem, which aims to satisfy multiple objectives under multiple constraints, is typically NP-hard, solving these challenges with traditional methods is highly complex. Thus, we further propose a Energy-Efficient Multi-path Routing (EEMR) algorithm that utilizes deep reinforcement learning (DRL) to optimize the multipath selection, effectively enhancing energy efficiency for deterministism. EEMR can be extended to solve optimization problems in holistic-deterministic multi-domain scenarios, such as smart factories integrating 5G and DetNet. We compare the performance of our proposed method with several baseline methods. Empirical evaluations show that EEMR significantly reduces energy comsumption and delay variation compared to baseline methods under various environment settings.

Practical applications in mobile ad-hoc networks (MANETs) require the support of diverse network services, e.g., host-centric, content-centric, and location-centric routing and forwarding services. However, existing solutions are typically designed over a single network service rather than integrated ones. To embed diverse network services into MANETs, the major challenge is enabling interoperability among various network-layer (L3) protocols without suffering complexity and scalability issues. In this article, we propose OpenL3, a programmable L3 approach to support the coexistence of diverse network services in MANETs. Specifically, OpenL3 first abstracts key attributes from network entities, such as content, locations, or groups of devices. These attributes are embedded into a network address, named multidimensional identifier (MID), to control the routing and forwarding processes. Then, a distributed MID mapping system is established to facilitate efficient MID registration and query. Based on the MID, a programmable routing and forwarding scheme is proposed, which incorporates a lightweight packet processing design using a P4 programmable data plane to enable interoperability among various L3 protocols. A cluster of SDN-based control plane devices collaboratively distribute flow rules to manage data plane behavior. Furthermore, a prototype system is built to implement and evaluate the proposed solutions. Experimental results show that OpenL3 outperforms the existing solutions in terms of end-to-end latency and network throughput while being deployable in MANETs without modifications to network protocols or sockets.

In the current era of rapid advancements in Artificial Intelligence of Things (AIoT), with the increase in cloud data center operations and the limited security computing capabilities of AIoT terminal devices, link flooding attack (LFA) has emerged as a complex and stealthy new threat. However, the existing defense methods based on programmable networks usually have issues of slow offline inference and delayed defense activation. To address these issues, we propose a collaborative programmable defense framework (CPDTG) to predict, detect, and mitigate LFA. First, an early attack intention prediction model based on temporal graph learning (TGL) is proposed to accurately locate attacks and promptly activate defenses to save resource consumption during idle time. Second, a switch-native clustering algorithm independent of the global perspective is introduced for line-speed detection of LFA. The unsupervised algorithm does not rely on labeled datasets for training, which enhances its robustness against differentiated attack scenarios. Third, we propose a distributed defense mechanism that achieves the pushback deployment of adaptive rate-limiting strategies. Compressing the potential attack vector space effectively increases the difficulty of launching rolling attacks. Extensive experimental validation demonstrates the effectiveness of the proposed CPDTG in predicting and defending against LFA.

Low Earth orbit (LEO) satellite networks enhance terrestrial connectivity by providing global coverage and low-latency communication. However, their highly dynamic topology, time-varying propagation delays, and constrained bandwidth severely limit the efficiency of conventional centralized routing, underscoring the necessity for adaptive and distributed strategies that can operate effectively under partial observability. Multi-agent reinforcement learning (MARL) offers a promising foundation for such strategies by enabling decentralized, context-aware decision-making based on local information. Nevertheless, existing MARL-based routing approaches often struggle to maintain accurate congestion awareness, reconcile conflicting objectives, and ensure stable convergence in large-scale LEO constellations. To address these challenges, we present POMAP, a packet routing framework that integrates Pareto optimization with multi-agent proximal policy optimization (MAPPO) to achieve efficient and stable trade-offs across multiple key performance metrics. Specifically, we propose a dynamic multi-attribute graph model for LEO satellite networks that simultaneously captures communication delay and energy consumption. Within this framework, each satellite node is represented as a G/G/1/K queue equipped with active queue management and scheduled using weighted priority queueing, thereby enabling precise characterization and control of packet queueing behavior. We formulate the packet routing problem as a partially observable Markov decision process that jointly minimizes delay, energy consumption, and packet loss rate, and apply MAPPO to optimize the resulting policy. Extensive simulations on realistic satellite network topologies demonstrate that the proposed method achieves better convergence stability, improved Pareto front coverage, and enhanced overall network performance compared with state-of-the-art baselines.

Artificial intelligence of things (AIoT) is a new networking paradigm incorporating AI and IoT, empowering multiple industries. Due to the high value of AI infrastructure in AIoT, its security issues are becoming increasingly prominent. A new type of covert DDoS attack, link flooding attack (LFA), is emerging as a vital threat. It congests critical links to AI infrastructure by manipulating multiple heterogeneous terminals to send legitimate low-speed traffic to cut off the connection of AI infrastructure while hiding itself. To quickly mitigate the LFA-induced congestion, this paper presents a learning-based proactive and adaptive LFA mitigation mechanism in AIoT. Specifically, a link suspicious level evaluation scheme based on graph autoencoder is first proposed. The potential risk links are identified by mining the link traffic features in the attack preparation and synthesizing two types of reconstruction errors, which is helpful for early to support rapid response to subsequent attacks. Second, a local traffic engineering model is presented based on maximizing the benefit of defenders. To solve the model to obtain the mitigation strategy, a solution based on deep reinforcement learning is designed to make real-time optimal local traffic path assignment decisions. Simulation results demonstrate that the proposed scheme can quickly perceive LFA and effectively resist the link congestion caused by LFA.

Given the delay-critical nature of AI-driven industrial automation applications, industrial cyber-physical systems are evolving from centralized cloud automation to decentralized cloud-fog automation to reduce model inference delay. However, traditional centralized deep federated learning is not wellsuited for this evolution, primarily due to scalability and delay issues caused by centralized parameter synchronization. Thus, we introduce a decentralized deep federated learning (DDFL) architecture. While DDFL resolves scalability and delay concerns, decentralized parameter synchronization amplifies the delay imbalance impact caused by uneven computing and network loads. Additionally, traditional single-task load balancing approaches with fixed load balancing weights face challenges posed by diverse delay requirements across different model training tasks. To overcome these challenges, we formulate a hybrid multi-task Markov decision process with the objective of minimizing flexibly weighted computing and network load. We further propose a hybrid multi-task deep reinforcement learning (MTDRL) scheme based on the importance-weighted actor-learner architecture, which trains a hybrid-MTDRL decision model to select fog servers and paths with balanced loads suited to diverse delay requirements. Realistic trace-based simulation and testbed evaluation results demonstrate that hybrid-MTDRL outperforms benchmarks in load balancing and reducing training delay.

With the rapid growth of low earth orbit (LEO) satellites, enabling LEO AI inference becomes a fast-increasing trend. However, due to resource heterogeneity, scheduling complexity, and fast movement, how to decide the place of executing each AI inference task is nontrivial in LEO systems. In this paper, we propose LEOEdge, an edge-assisted AI inference system for LEO satellites. We first introduce the adaptive modeling technologies that automatically generate the model for each satellite according to its computation resources. We then propose a layered scheduling optimization scheme to schedule the AI inference task in a distributed manner. LEOEdge also designs a seamless data transmission scheme to avoid transmission failure due to the LEO satellite movement. We conduct a series of simulation tests to validate the performance of the proposed LEOEdge, in terms of the neural network searching efficiency, average time execution latency, and delivery latency.

With the rapid development of emerging services such as cellular vehicle-to-everything and immersive video service, network connections have further evolved from tangible physical connections to intangible virtual connections such as content, services, and computing resources, and the application scenarios have become more abundant. The mobile ultra-service, which is characterized by ultra-low latency, ultra-high reliability, and ubiquitous mobility, is becoming one of the most representative traffic types. However, the existing mobile network architecture has not evolved sufficiently to meet the specific requirements of these mobile ultra-services, the mobility anchors introduce unnecessary node and link latency, leaving space for further optimization. A compact network architecture (ComArch) is proposed in this paper for ultralow-latency services with ubiquitous mobility. ComArch is designed with a mapping control plane and a generalized forwarding plane to collaboratively implement packet forwarding in mobile scenarios. The generalized forwarding plane handles packet forwarding, while the mapping control plane manages terminals’ identifier and locator mapping entries. The node latency introduced by mobility anchors is eliminated, and an efficient routing scheme is proposed to find the optimal mandatory nodes in the forwarding path, thereby reducing unnecessary link latency. Experimental results show that ComArch can effectively reduce end-to-end delay while saving resources.

Deterministic network services play a vital role for supporting emerging real-time applications with bounded low latency, jitter, and high reliability. The deterministic guarantee is penetrated into various types of networks, such as 5G, WiFi, satellite, and edge computing networks. From the user’s perspective, the real-time applications require end-to-end deterministic guarantee across the converged network. In this paper, we investigate the end-to-end deterministic guarantee problem across the whole converged network, aiming to provide a scalable method for different kinds of converged networks to meet the bounded end-to-end latency, jitter, and high reliability demands of each flow, while improving the network scheduling QoS. Particularly, we set up the global end-to-end control plane to abstract the deterministic-related resources from converged network, and model the deterministic flow transmission by using the abstracted resources. With the resource abstraction, our model can work well for different underlying technologies. Given large amounts of abstracted resources in our model, it is difficult for traditional algorithms to fully utilize the resources. Thus, we propose a deep reinforcement learning based end-to-end deterministic-related resource scheduling (E2eDRS) algorithm to schedule the network resources from end to end. By setting the action groups, the E2eDRS can support varying network dimensions both in horizontal and vertical end-to-end deterministic-related network architectures. Experimental results show that E2eDRS can averagely increase 1.33x and 6.01x schedulable flow number for horizontal scheduling compared with MultiDRS and MultiNaive algorithms, respectively. The E2eDRS can also optimize 2.65x and 3.87x server load balance than MultiDRS and MultiNaive algorithms, respectively. For vertical scheduling, the E2eDRS can still perform better on schedulable flow number and server load balance.

Low Earth orbit satellite networks (LSNs) are envisioned as key enablers of 6 G by offering ubiquitous, low-latency connectivity. Their mesh topology enables multipath differential routing, which improves bandwidth utilization and reduces transmission delay. However, the growing demand for data and the dynamic, self-organizing nature of LSNs pose significant challenges for joint multipath routing and traffic scheduling under strict latency and energy constraints. To address these challenges, this paper proposes a multipath routing optimization (MRO) and traffic scheduling method tailored for multipath differential routing. Specifically, a dynamic multi-attribute graph model is developed to precisely capture the dynamic properties of LSNs. Building on this model, a MRO algorithm, integrated with a Stackelberg game framework, is introduced. The MRO algorithm employs a decomposition-based approach to identify multiple optimal paths that minimize delay and energy consumption, while the Stackelberg game framework ensures efficient traffic distribution across these paths. Numerical results demonstrate that the proposed approach significantly outperforms existing baseline methods, achieving cumulative reward improvements of 26.77% to 43.8% across four real-world network topologies and exhibiting better Pareto front coverage. Furthermore, by leveraging the rapid convergence properties of the Stackelberg game model, the proposed method enhances network throughput by 12% to 43% and reduces transmission time by 14% to 49%.

The surge in mobile application services has led to diversified traffic and increased demands on network resources. Traditional multipath algorithms, designed for resource integration through subflow scheduling across paths, struggle with disharmonious transmission caused by terminal mobility and differentiated path resources. Especially when differentiated services are transmitted concurrently, disharmonious transmission can give rise to resource contention, causing a large number of subflows to congest a single path and leading to performance degradation. To mitigate these challenges, this paper introduces HarmonyPath, a fine-grained flexible multipath transmission mechanism that can ensure harmonious resource occupation. Specifically, HarmonyPath firstly employs an in-band telemetry protocol to gather path resource information, generating a network resource distribution map. Based on this map, it flexibly allocates path resources according to the network resource distribution and service requirements. Then, HarmonyPath establishes a collaborative matching model for service demands and path resources. Through matrix transformation and calculation, it rapidly generates and deploys the scheduling strategy. To further alleviate service contention, HarmonyPath employs heuristic algorithms to optimize the scheduling strategy and achieve precise multipath transmission. Experiments demonstrate that HarmonyPath surpasses traditional algorithms in the multipath transmission of differentiated services, offering flexible service resource guarantees and enhancing network resource utilization efficiency.

To reduce redundant traffic transmission in both wired and wireless networks, optimal content placement problem naturally occurring in many applications is studied. In this paper, considering the limited cache capacity, unknown popularity distribution and non-stationary user demands, we address this problem by jointly optimizing content caching and routing with the objective of minimizing transmission cost. By optimizing the routing with theroute-to-least cost-cachepolicy, the content caching process is modeled as a Markov decision process (MDP), aiming to maximize caching reward. However, the optimization problem consists of multiple nodes selecting caching contents, which leads to the combinatorial increase of the number of action dimensions with the number of possible actions. To handle this curse of dimensionality, we propose an intelligent caching algorithm by embedding action branching architecture into a dueling double deep Q-network (D3QN) to optimize caching decisions, and thus the agent at the controller can adaptively learn and track the underlying dynamics. Considering the independence of each branch, a marginal gain-based replacement rule is proposed to satisfy cache capacity constraint. Our simulation results show that compared with the prior art, the caching reward and hit rate of the proposed algorithm are increased by 35.3% and 33.6% respectively on average.

The Crowd-edge (CE) computing paradigm facilitates the utilization of the computational resources through simultaneously relying the edge computing and the collaboration among various mobile devices (MDs). Most existing works, focusing on offloading tasks from device to edge servers by centralized solutions, are unable to distribute tasks to massive MDs in CE. Meanwhile, designing a decentralized task offloading solution enabling task subscribers to individually make offloading decisions can be challenging given the randomness of crowd resource provisioning and limited knowledge of global status variations. In this paper, we propose a decentralized crowd-edge task offloading solution that enables users to optimally offload tasks to the CE in a distributed manner. Specifically, we formulate the corresponding problem as a stochastic optimization with partially observable status. By observing network and process delays at the crowd side, we further reform the optimization forms and provide a novel approximation policy, enabling users to optimize their offloading strategy based on local observations without interaction with each other. We then solve this task offloading problem by developing a Mixed Multi-Agent Proxy Policy Optimization algorithm (mixed MAPPO). Extensive testing, including numerical and system-level simulations, was conducted to validate the performance of the proposed algorithm in terms of task delay (including the processing delay and transmission delay), load rate, and resource utilization.

Satellite-terrestrial integrated network (STIN) is a full-scale communication paradigm, which can support joint information processing and seamless service provision by leveraging satellites' wide coverage and terrestrial networks' high capacity. The existing STIN operates with insufficient synergy in transmission scheduling, impacting resource allocation efficiency and transmission delay optimization, particularly in complex transmission scenarios. In this paper, we designDeterministic STIN (DetSTIN), a novel architecture for STIN, along with two algorithms tailored for transmission scheduling to collaboratively optimize resource adaptation and service flow scheduling. Specifically, the DetSTIN enables the smooth interconnection and integration of heterogeneous networks by providing layered deterministic services. Besides, a genetic-based resource adaptation algorithm is designed for fixed-mobile-satellite heterogeneous networks to reduce resource allocation overhead while maintaining the network performance. Furthermore, we propose a deep reinforcement learning-based differentiated scheduling algorithm to solve the routing-queue two-dimensional decision problem to differentially optimize transmission delay of service flows, thus obtaining higher transmission scheduling benefit. By addressing resource adaptation and differentiated scheduling synergistically, the proposed solution achieves reduced resource allocation overhead and increased transmission scheduling benefit, ultimately leading to increased network operation revenue of the DetSTIN. Simulation results demonstrate that the proposed solution delivers effective performance across various flow proportions, and as the number of flows increases, the network operation revenue exhibits a noticeable improvement, compared with benchmark algorithms.

Geo-Distributed Machine Learning (GDML) aims to train large-scale machine learning models across geographically dispersed datacenters. However, the performance of GDML systems is constrained by the limited Wide Area Network (WAN) bandwidth and the presence of the straggler problem. Existing GDML designs often show contradictory effects in addressing these challenges, while in-network computing attempts are typically restricted to single datacenter environments rather than the more complex GDML scenarios. To overcome these limitations, this paper proposes L3DML to facilitate GDML using the P4-based Software-defined Network (SDN). Our approach incorporates three key innovations. Firstly, we introduce a novel network addressing scheme that enables location-specific in-network gradient aggregation for GDML, eliminating the need for parameter servers. Secondly, we utilize the P4 data plane to integrate lossless gradient transmission within switches. Thirdly, we address the straggler problem by employing a unique Deep Reinforcement Learning (DRL) model set and a corresponding rate synchronization routing approach. L3DML is implemented on a prototype system consisting of several Intel Tofino switches and the Spirent network emulator. Experimental results indicate that L3DML outperforms existing solutions in terms of goodput, model accuracy, and training speed gain for large-scale GDML.

Service Function Chaining (SFC) is widely deployed by telecom operators and cloud service providers, offering traffic QoS guarantees and other additional functions for various applications. The network state at the time of SFC deployment can differ significantly from the runtime conditions, leading to excessive resource allocation and consequent energy waste. The existing SFC reconfiguration methods face the challenge of meeting the latency requirements of delay-sensitive applications while achieving significant energy savings. This paper proposes FR-SFCO, a flow rate-aware SFC offloading framework on programmable data planes for delay-sensitive flows. Specifically, we designed a TCAM-friendly table matching method for FR-SFCO to reduce the flow entries needed for SFC offloading in programmable switches and support larger numbers of offloaded SFC. Then, we proposed a dual-threshold-based offloading trigger mechanism that, according to the real-time traffic arrival rate, can fast offload SFC flows before they default to servers. Building on this, we propose DQN-AOTA, an adaptive offloading thresholds adjustment algorithm based on Deep Q-Learning, which can wisely change the offloading thresholds by interacting with a dynamic network traffic environment to minimize the packet loss and long-term energy consumption. Finally, we build a testbed using BMv2 software switches and Docker containers for extensive evaluation. The experimental results demonstrate the effectiveness of our solution which not only meets the latency constraints for delay-sensitive SFC flows but also reduces energy expenditure by at least 14.6%.

Natural disasters pose a significant threat to human life. In these extreme conditions, terrestrial networks frequently become incapacitated, hindering the provision of essential communication and computing services required for emergency response efforts. In recent years, the rapid advancement of drone technology, coupled with the maturation of lightweight communication and computing equipment, has led to the emergence of unmanned aerial vehicle (UAV) networks as a crucial asset in disaster rescue missions. These networks provide significant advantages, including rapid response times, flexible deployment capabilities, and heightened resilience to complex terrains, showcasing considerable potential for further development. UAV networks exemplify resource-constrained systems where efficient scheduling of computing resources is vital. Especially in emergency rescue scenarios, this complexity is exacerbated by the diverse range of tasks, varying demands, and stringent real-time requirements. Effectively managing and allocating the computational resources of drones is essential for maximizing their operational efficiency in response to the intricate dynamics of disaster situations. To improve the computational service efficiency of the network, this paper proposes an emergency rescue UAVs network architecture. Additionally, we investigate a joint optimization approach for task planning and SFC scheduling. Current research on SFC scheduling primarily focuses on ground data center networks, with comparatively limited investigation into UAV networks. Fully considering the mobility of computing nodes, as well as the wireless transmission modes within the aerial environment, we establish a joint optimization model for task planning and SFC scheduling aiming at minimizing the total weighted end-to-end delay. Then we design the A3C based algorithm to learn the optimization strategy. Simulation results are presented to demonstrate the superiority of the proposed approach in the aspect of total weighted end to end delay and training time against other benchmark algorithms.

Delay-sensitive applications like telemedicine and VR/AR intensify competition for network resources and elevate congestion risks, particularly in mobile networks with highly dynamic link conditions. Traditional end-to-end congestion control methods suffer from prolonged response times, rendering them ineffective for Delay-sensitive applications. To this end, this paper proposes a novel In-Network Congestion Control (INCC) mechanism that accelerates congestion control by enabling network nodes to proactively identify bottlenecks and promptly notify end-hosts. Unlike traditional end-host-centric approaches, INCC facilitates collaborative congestion decision-making between end-hosts and in-network unit. INCC classifies congestion into two phases: “yellow” and “red” based on the local queue length bottleneck awareness and global congestion flow bottleneck statistics. For the “yellow” local congestion phrase, we design an in-network local control algorithm that performs proactive packet dropping and rate adjustment to mitigate emerging congestion. For the “red” global congestion phrase, we design an end-host and network cooperative global congestion control algorithm to make precise sending rate adaptation by proactive bottleneck awareness. We implement INCC via Linux kernel modifications and design three experiments to compare with Cubic, NewReno, and BBR. Experimental results demonstrate INCC has good performance on round-trip time and throughput, achieving 99.03% scheduling fairness in flow contention scenarios. Additionally, INCC has low execution overhead on CPU utilization and realize microsecond computational latency.

The widespread adoption of real-time services on the Internet has aroused interest in the study of low-latency and deterministic communications. Deterministic guarantee over wide-area networks (WANs), the primary infrastructure for communications, is essential to achieving end-to-end deterministic transmission. However, applying off-the-shelf deterministic schemes to WANs is challenging due to the statistical multiplexing nature of WANs and the non-periodic nature of WAN traffic. In this paper, we propose a novel deterministic framework for WANs, named DetWAN, which guarantees the timely delivery of WAN traffic via deadline-aware routing and scheduling. We design a coordinated earliest deadline first (CEDF) scheduling scheme in the data plane of the DetWAN, which provides determinism for non-periodic deadline-constrained traffic while following statistical multiplexing. To precisely estimate the capacity of deadline-constrained traffic that the DetWAN can satisfy, we derive an end-to-end deadline satisfiability criterion in the DetWAN by introducing the deadline curve into traffic modeling. Based on the criterion, we formulate the deadline-aware routing and scheduling problem as a stochastic optimization problem to maximize the timely delivery ratio. Furthermore, we propose a distributed admission control algorithm based on multi-agent deep reinforcement learning in the control plane to solve the problem in a highly autonomous manner. The algorithm can jointly determine optimal routes and per-hop deadline budgets for traffic flows in a decentralized mode. Extensive evaluation results validate the deterministic guarantee as well as the high throughput of the DetWAN and show that the proposed admission control algorithm can significantly improve the timely delivery ratio compared with benchmarks in WAN scenarios.

The data consistency protocol is a core component of distributed data services that provide fault-tolerance and data consistency across distributed data centers and even edge networks. Raft is a popular approach due to its ease of implementation and superior performance. However, Raft adopts a sequential log entry processing strategy, where log entries without dependencies are not allowed to be processed in parallel, limiting system performance in high-concurrency scenarios. To address this challenge, researchers propose Raft-based protocols that supportout-of-orderapply(OOApply), which is called OORaft. Existing OORaft protocols adopt the Paxos-style election and replication process to merge missing entries on leader candidates. It leads to problems such as extra overhead on dependency analysis, availability when the network is partitioned, and incomplete correctness verification. This paper proposes aconciseparalleledRaftprotocol called CP-Raft, which is the first OORaft protocol to focus on dependency analysis overhead and to use full TLA$^+$validation for the leader election process. Specifically, 1) CP-Raft proposes a Raft-aligned three-step election method that significantly simplifies the difficulty of understanding and solves the availability problem when the network is partitioned. 2) CP-Raft applies a leader-side bitmap-based dependency analysis and representation method to break through the performance bottleneck caused by the high overhead of dependency analysis. 3) CP-Raft discusses why existing methods cannot achieve OORaft correctness verification using TLA$^+$in a limited time and uses phased verification methods to ensure its correctness. Finally, we implement CP-Raft based on an open-source Raft protocol and discuss its potential performance bottlenecks in various scenarios. The experimental results under high dependency strength workloads demonstrate that CP-Raft achieves 1.5× transaction per second (TPS) performance of DP-Raft and 2× of ParallelRaft-CE. It also provides better availability than state-of-the-art OORaft protocols.

Unmanned Aerial Vehicles (UAVs) provide a flexible working paradigm for device-cloud communication. Besides working as a relay between devices and clouds, UAVs can also provide mobile edge computing (MEC) services. In this paper, we investigate a computation offloading problem for UAV-assisted MEC networks in which local devices, UAVs, and clouds collaboratively process computing tasks to achieve energy-saving and latency reduction. In such green UAV-assisted MEC networks, we treat the same energy consumption of task processing differently due to processing location and assign different weights to the energy consumption of devices, UAVs, and clouds. Specifically, we propose a two-stage computation offloading framework including 1) the device clustering stage to determine the device cluster connected to certain UAVs and 2) the network operation stage to conduct computation offloading. We formulate the offloading process as a stochastic optimization problem to minimize the offloading cost. Furthermore, we decouple the optimization problem into a UAV selection subproblem and an offloading decision subproblem. Particularly, for the former subproblem, we employ a simulated annealing-based algorithm to minimize the total transmit energy of devices and UAVs. For the latter, we utilize a proximal policy optimization-based offloading algorithm to ascertain the processing locations of computing tasks. Simulation results show that the proposed algorithm outperforms in terms of energy reservation and latency reduction.

At present, multipath transmission realized by multi-interface devices and bandwidth aggregation technology meets user demand for high-bandwidth communication in 6G wireless networks. However, multipath transmission systems face the threat of single-point failure by multi-interface servers themselves. Existing solutions for such failure are not suitable for multipath transmission scenarios, and this seriously limits the ability to bandwidth aggregation and reduces the reliability and invulnerability of the multipath transmission system in 6G wireless networks. In this paper, we propose a novel Intelligent Device Disaster Recovery (Multi-ID2R) mechanism to solve the single-point failure in multipath and aggregated environments for the first time. In particular, we establish the Multi-Dimensional Parameter Joint Analysis model (MDPJA) and propose an algorithm for judging the running state of multi-interface devices. The algorithm takes into account the different network parameters of the paths, including delay, packet loss rate, and throughput. Moreover, an intelligent switching mechanism based on service quality is designed. Multi-ID2R comprehensively considers the characteristics of the business and the current parameters of multipath networks to determine the moment of switching to flexibly adjust switching strategies. Finally, we deploy the mechanism on multi-interface servers in actual networks. Experiments demonstrate that, compared with Virtual Router Redundancy Protocol, Gateway Load Balancing Protocol, and Hot Standby Router Protocol, Multi-ID2R effectively improves the reliability and invulnerability of multi-interface server in 6G wireless networks.

Integration of the fifth-generation mobile communication technology (5G) into time-sensitive networking (TSN) was first proposed in the 3GPP Release 16. However, this conceptual proposal lacks of detailed designs to guarantee bounded latency and high reliability of this integration. In this paper, we study a deterministic scheduling problem for TSN-5G integrated networks in industrial Internet of things (IIoT) scenarios, in which a unified control plane jointly allocates the time-frequency resources for TSN and 5G to support deterministic end-to-end transmission. Specifically, we design a novel control architecture, i.e., centralized network and distributed user, for the integrated networks to reduce the signaling overhead. Moreover, we formulate a stochastic optimization problem for IIoT scenarios to maximize the number of successfully scheduled flows as well as realize throughput fairness for wired and wireless equipment. Since the resource allocation of TSN and 5G are coupled, this problem is NP-hard. We propose a dueling double deep Q network (D3QN) based Joint Resource Allocation (DJRA) algorithm. By leveraging two convolution-enhanced neural networks, with their parameters periodically synchronized, the accuracy of the estimated Q-value can be increased and the convergence speed of DJRA can be accelerated. Simulation results show that the proposed algorithm can facilitate efficient cooperation between TSN and 5G as compared to the other heuristic and learning-based algorithms.

The development of immersive video service and large-scale cluster computing technology further expand the potential application scope of time-sensitive networks (TSN). In the delivery network for these emerging services, the Ultra-Service Flow (USF), which is characterized by ultra-high bandwidth and deterministic latency has become the most representative traffic type. Therefore, the route scheduling for hybrid deployment of Regular Service Flow (RSF) and USF has become an unavoidable issue within a deterministic domain. To resolve this issue, a multi-objective optimization model for joint routing of hybrid service flows is studied in this paper. Subsequently, an effective algorithm is designed to discover feasible routing solutions using a deep reinforcement learning approach with a transformer framework, followed by optimization utilizing NSGA-II. The simulation results indicate that, our proposed algorithm exhibits superior overall performance and enhanced generalization capabilities. It effectively reduces the overall latency of RSF by 10.526% and the path blocking degree of USF by 14.10256%, while significantly increasing the available bandwidth rate by 14.286%.

The rapid growth of mobile internet is driving the expansion of big data. The massive amount of data generated by mobile interconnections is stored on remote servers using Fiber Channel (FC) Storage Area Networks (SANs). Our literature study reveals that a significant portion of this data is either stored in plaintext or experiences reduced throughput when encryption devices are integrated into the network, posing potential security risks. To address the vulnerabilities of plaintext data storage and improve throughput performance, we design a high-performance data encryption device using advanced FPGA technology. Positioned between the server and the disk array, this encryption device secures the data written to the disk array. It achieves a data throughput rate of up to 11.21 Gbps by adopting high-performance hardware platform and designing pipeline-based data processing. It also improves the ability of data encryption device to resist side channel attack by designing diverse SM4 circuit consist of different S-boxes.

The increasing number of IoT devices in the network brings new challenges to the network carrying capacity of intelligent edge computing, and the complicated network services make the demand for network resources in industrial production scenarios or ordinary network users often exceed the carrying capacity of the edge computing network. To alleviate this problem, this paper proposes an intelligent edge computing architecture that introduces network service identification, extracts and analyses the data characteristics of network traffic, and designs appropriate algorithms to classify network traffic into six different service types. This enables real-time and computing-requiring tasks to be prioritised in the network. Using two machine learning algorithms, KNN and MLP, a model validation is carried out on the constructed dataset, and the results show the effectiveness of the method, with the correct rate of data validation reaching 85%, which is more than 5% higher than the correct rate of direct classification of the specified applications, and the accuracy can be as high as 97% in certain scenarios.

The widespread adoption of intelligent Internet of Things (IoT) has sparked increased efforts to foster extensive data interaction and collaboration across diverse fields, leading to a trust crisis in cross-domain scenarios. Moreover, cross-domain collaboration increases the complexity of key management, especially in resource-constrained IoT environments where high computational costs are impractical. This situation poses risks of key leakage and inefficient key updates. This paper introduces DKGAuth, a blockchain-based method for distributed key generation and authentication tailored for resource-constrained cross-domain intelligent IoT systems. Initially, we propose a lightweight cross-domain authentication architecture based on blockchain to address the trust crisis effectively among different domains in the intelligent IoT. Secondly, building upon this architecture, we introduce a distributed key generation method that revolutionizes the key infrastructure to address key management concerns. Additionally, we design an algorithm to combine key factors, minimizing costs associated with both key generation and updates. Finally, we establish a simulation environment to assess the computational, storage, and read/write overheads of our approach. In the same configuration, compared to other solutions, the efficiency of key updates improves by 83% when updated 100 times.

As a key enabling technology in intelligent heterogeneous Internet of Things (IoT), edge caching provides important support for reducing core network load and improving network service efficiency, especially for high bandwidth demand services represented by multimedia applications. However, external time-varying information is hard to be obtained comprehensively in a complicated heterogeneous IoT environment. Meanwhile, there exists the substitutability of content (e.g., videos with different bitrates), which is difficult to make caching decisions online in real-time to achieve fast feedback with low latency and avoid useless deployment. To this end, this article designs a transcoding-enabled online cache scheme for IoT video service with cloud–edge–terminal collaboration. First, we design a variable bitrate video routing strategy to dynamically retrieve content from cloud/edge according to user demands. Furthermore, the video caching problem is considered as an online convex optimization problem to learn utility gradient and determine the optimal caching strategy in real-time without any prior information. On this basis, we extend the problem to elastic networks with dynamic available resources and prove the sublinear regret and sublinear constraint violation. Finally, we summarized five video request data sets and carried out differentiated multiple verifications based on different request habits and content requirements. Compared with the most advanced algorithms in terms of delay, we evaluated the performance advantages of the proposed scheme.

In software defined network/network function virtualization (SDN/NFV)-enabled cloud environment, cloud services can be implemented as service function chains (SFCs), which consist of a series of ordered virtual network functions. However, due to fluctuations of cloud traffic and without knowledge of cloud computing network configuration, designing SFC optimization approach to obtain flexible cloud services in dynamic cloud environment is a pivotal challenge. In this paper, we propose a fault tolerance oriented SFC optimization approach based on deep reinforcement learning. We model fault tolerance oriented SFC elastic optimization problem as a Markov decision process, in which the reward is modeled as a weighted function, including minimizing energy consumption and migration cost, maximizing revenue benefit and load balancing. Then, taking binary integer programming model as constraints of quality of cloud services, we design optimization approaches for single-agent double deep Q-network (SADDQN) and multi-agent DDQN (MADDQN). Among them, MADDQN decentralizes training tasks from control plane to data plane to reduce the probability of single point of failure for the centralized controller. Experimental results show that the designed approaches have better performance. MADDQN can almost reach the upper bound of theoretical solution obtained by assuming a prior knowledge of the dynamics of cloud traffic.

Geocast is a one-to-many communication paradigm that enables the transmission of data packets to a designated area rather than an IP address. The most common geocast solutions rely on the application-layer Geolocation-to-IP database. But these IP-based approaches cannot cope with the challenges of flexibility and mobility in a granularity-customizable geocast scenario. While some non-IP network-layer (L3) attempts have resulted in low addressing accuracy and poor routing scalability. Besides, the clean-slate design is incompatible with the existing network. To address these issues, this paper proposes an innovative network-layer geographic addressing scheme that leverages P4-based Software Defined Networks (SDN) to enable flexible geocast with high accuracy. Based on the aggregation relationship of the geographic area, a network-layer routing strategy is designed to enhance routing scalability. Compatibility is improved by deploying the network-layer designs only at the network edge where granularity-customizable geocast is implemented, without requiring changes to the current IP infrastructure. Then, the network-layer functions are integrated with an application-layer mapping service to support intercommunication between different network edges. Furthermore, a prototype system is built to implement and evaluate the proposed L3Geocast, which outperforms the existing approaches in terms of communication latency and mapping overhead.

Multipath transmission brings strong bandwidth aggregation capability for services in wireless networks. Nonetheless, the heterogeneous nature of paths and the motion of terminals results in varying transmission delays, leading to out-of-order (OFO) delivery and transmission quality decrease. Traditional algorithms, limited in their scope, fail to strike a balance between high bandwidth and low OFO extent. Recent studies have focused on utilizing learning algorithms to find a multi-performance joint optimal transmission strategy. In light of this, this paper proposes a framework called DRL-based OFO-Friendly Multipath Scheduling (DOFMS) to ensure high bandwidth and low OFO extent transmission in mobile heterogeneous networks. In particular, the framework introduces a novel OFO evaluation index to assess the degree of OFO more accurately. To achieve elastic scheduling, the framework employs the Double Deep Q Network (DDQN) to dynamically regulate the scheduling ratio. Recognizing the dynamic and unpredictable nature of path delays, an asynchronous module is introduced to enhance learning accuracy. Experimental results demonstrate that the framework reduces the OFO rate by 25% compared to traditional bandwidth aggregation algorithms, while maintaining low bandwidth and packet loss rates. Furthermore, compared to conventional OFO avoidance algorithms, the framework improves bandwidth by 4% and reduces fluctuation by 90%.

In this paper, we present a three-layer (i.e., device, field, and factory layers) deterministic federated learning (FL) framework, named DetFed, which accelerates collaborative learning process for ultra-reliable and low-latency industrial Internet of Things (IoT) via integrating 6G-oriented Time-sensitive Networks (TSN). Utilizing dispersive local data, industrial IoT devices distributively train a deep neural network (DNN) model, and the updated model parameters are aggregated at their associated field servers every round or at a centralized factory server every a few rounds. Aiming at optimizing the learning accuracy of FL without affecting the co-transmission of burst traffic (e.g., safety-critical traffic), an integrated TSN is considered to establish connections among the three layers, where a cyclic queuing and forwarding mechanism is deployed in each switch to support deterministic model parameter transmission with microsecond-level delay and near-zero packet loss requirements. To improve the FL performance, we formulate a multi-objective stochastic optimization problem to simultaneously maximize the scheduling success ratio and learning accuracy while satisfying the deterministic requirements of delay, jitter, and packet loss. Since the objective function is implicit and the available time slots of the considered TSN in each FL round are temporally correlated, the problem is difficult to solve in real time. Therefore, we transform the problem into a Markov decision process formulation and propose a dynamic resource scheduling algorithm, based on deep reinforcement learning, to make optimal resource scheduling decisions while adapting to device heterogeneity and network dynamics. Experimental results based on real-world dataset demonstrate that the proposed DetFed significantly accelerates FL convergence and improves learning accuracy as compared to state-of-the-art benchmarks.

Multipath transmission is a critical enabling technology to enhance QoE for edge users. The packet scheduler plays an irreplaceable role in overcoming heterogeneity and dynamicity in multipath transmission. However, current schedulers depend on an inaccurate delay estimation and lack systematic traffic intensity awareness, performing poorly in wireless heterogeneous networks (HetNets). In this paper, we propose a novel traffic-aware two-level packet scheduler (TA2LS) to address the problem and improve aggregated bandwidth while trading off delay. In particular, we design a multipath transmission state machine (MTSM) to perceive link traffic intensity. MTSM replaces network prediction algorithms by identifying the contribution of each link in multipath transmission in a cost-effective way. Further, we propose a scheduling mechanism based on a two-level optimal-path evaluation method (2LOSM) to adjust the packet scheduling policy adaptively. 2LOSM increases the priority of links with low traffic intensity during scheduling, improving aggregated bandwidth performance and reducing end-to-end delay. We have built a real-world 4G/5G/WiFi testbed and deployed 47 dynamic scenarios to evaluate TA2LS and other five schedulers. In 4G/5G/WiFi scenarios, TA2LS improves aggregated bandwidth by 10.32%–48.27% compared to the second-best scheduler and reduces end-to-end delay by 5.04%–39.98% under the premise of fewer or equivalent overheads.

Task cooperation emerges as a efficacious strategy for the execution of intricate tasks within the context of challenging wireless edge networks characterized by limited resources and intermittent infrastructure connections. Presently, TCP/IP-based solutions encounter issues related to suboptimal utilization of network resources and a substantial dependency on infrastructure connections. In light of this, Information-Centric Networking (ICN) has surfaced as a promising architectural paradigm aimed at mitigating these challenges. In ICN-based task cooperation, the data reuse characteristic of ICN enhances the efficiency of network resource utilization. However, this also introduces plausible security vulnerabilities to the reused data, encompassing eavesdropping attacks and unauthorized access attacks. In this paper, we propose an ICN-based secure task cooperation scheme to mitigate the above threats without compromising the efficiency of task execution. We present the task cooperation model that quantifies the cost of securing data reuse in task execution. We also introduce a specific naming convention to support the acquisition of collaborative task requests and keys related to the task cooperation. Besides, we introduce a novel design for an enhanced name-based access control scheme that ensure both data confidentiality and access control in collaborative tasks accessed by the same sub-policy, streamlining the encryption process for content keys. Security analysis and experimental results demonstrate that our scheme effectively safeguards the security of data reuse. Furthermore, compared to existing schemes, our scheme incurs lower cost associated with computation and security.

Recent advances in Cloud Computing have shown great power in enhancing intelligent devices to support various applications. Nevertheless, conventional Cloud Computing fails to keep up with the ever-advancing requirements of efficient task execution, mainly resulting from its drawbacks in communication delay. To this end, multi-layer cloud computing with local, edge, and remote data centers has gained high interest yet remains challenging because of the inherent complexity of cross-layer orchestration. In particular, with more participants involved, it is nontrivial to achieve customized service provision while guaranteeing system stability. Hence, we address the workload scheduling issue in the multi-layer cloud paradigm in this paper, with task offloading and service reconfiguration considered jointly. We first formulate it as a stochastic optimization problem, where statistical service requirements are imposed on queue lengths. Then, we divide the original optimization into three individual low-complex sub-problems with optimal solutions provided. To improve system performance, we introduce a request-rejecting mechanism that augments our approach with delay-optimality. Theoretical analysis confirms that our approaches can guarantee system stability and are asymptotically optimal within a small gap from the optimum. Finally, we validate the efficiency of our approaches through extensive simulation results in performance guarantees and customized workload scheduling.

Remote surgery is an emerging medical business derived from information networking technology and plays an increasingly essential role in the medical system. In remote surgery, it is imperative to facilitate cross-regional information transmission and processing by leveraging medical information networks to establish a collaborative service model served by multiple data centers in different regions, enabling collaboration and support for surgery operations. Additionally, the implementation of service function chain scheduling technology is crucial for the efficient allocation of computing resources of data centers. In this paper, we design a novel multi-cloud collaborative medical information network framework. Based on this framework, the service function chain (SFC) scheduling problem is investigated to minimize the total weighted end-to-end delay. To solve the scheduling problem, the original problem is reformulated as a Multiple Markov Decision Process (MMDP). Then, a multiple-state-action deep reinforcement learning (MSA-DRL) algorithm is developed to learn the best scheduling policy. Simulation results are presented to demonstrate the superiority of the proposed approach in the aspect of total weighted end to end delay against other benchmark algorithms.

The multipath transmission structure decouples network services from a single transmission carrier, which has great potential for shaping a more secure and efficient 6G network. Existing multipath transmission schemes face challenges such as network heterogeneity, perception lag, and additional scheduling delay, which limits their ability to improve bandwidth aggregation capacity and information security. To address these issues, we propose the Sequential Reinforcement Evolution (SRE) scheme, which utilizes deep reinforcement learning to predict the value of future scheduling actions based on past network states. The SRE scheme regards improving bandwidth aggregation capacity and anti-eavesdropping ability as optimization goals, and designs a semi-symmetric attention recurrent neural network (SARNN) to better mine the sequential nature of the scheduling process. The SRE scheme utilizes approximately 500 million real network data points to pre-train the SARNN model, and performs cycle optimization during the actual deployment process. Experimental results show that SRE significantly outperforms state-of-the-art scheduling schemes with a 32% increase in bandwidth aggregation and a 117% increase in traffic security dispersion with minimal impact on latency.

With the development of economic globalization and emerging applications such as transnational communication and webcasting, inter-cloud link transmission has become a key factor for green networking. Optimized transmission policies can effectively reduce network energy consumption and improve resource utilization by dynamically adjusting the optimal network paths. In this paper, we collect first-hand and over-million-level network datasets from practical nodes on three continents (Asia, Europe, and North America), and analyze the characteristics of international links among cloud centers in different continents. According to the actual test results, we discover that the network transmission quality strongly correlates with factors such as time zone and cloud service provider. Besides, we conclude that the quality of inter-cloud networks can be effectively improved according to regional activity and triangular routing. Based on the in-depth observation, we further discuss the optimization directions of network transmission and provide relevant suggestions. This work is of significant reference value for research on green transmission of inter-cloud links.

Multipath transmission is an important solution in 6G wireless networks to improve communication resource convergence among heterogeneous networks. The appearance of out-of-order (OFO) packets is a problem in multipath transmission, especially in heterogeneous wireless networks, where network states fluctuate frequently. Existing schedulers attempt to solve the OFO problem by designing an “in-order” strategy. However, these “in-order” strategies lose their roles in jittered networks, resulting in poor throughput performance. In this study, we propose a novel packet scheduling strategy, called a multipath-state machine (MSM) scheduler. MSM prioritizes improving throughput utilization over ensuring packets arrive in order. Based on whether links are fully utilized, MSM divides link utilization states into 5 phases, including establishment, idle, busy, congestion, and risk. MSM prefers underutilized links. Furthermore, MSM chooses the one with the shortest transmission delay when multiple paths are in the same utilization state. Experiments show that MSM provides a more stable and higher bandwidth utilization than typical algorithms in various networks with heterogeneous characteristics such as delay, bandwidth, and jitter.

Edge caching has been regarded as a promising technique for low-latency, high-rate data delivery in future networks, and there is an increasing interest to leverage Machine Learning (ML) for better content placement instead of traditional optimization-based methods due to its self-adaptive ability under complex environments. Despite many efforts on ML-based cooperative caching, there are still several key issues that need to be addressed, especially to reduce computation complexity and communication costs under the optimization of cache efficiency. To this end, in this paper, we propose an efficient cooperative caching (FDDL) framework to address the issues in mobile edge networks. Particularly, we propose a DRL-CA algorithm for cache admission, which extracts a boarder set of attributes from massive requests to improve the cache efficiency. Then, we present an lightweight eviction algorithm for fine-grained replacements of unpopular contents. Moreover, we present a Federated Learning-based parameter sharing mechanism to reduce the signaling overheads in collaborations. We implement an emulation system and evaluate the caching performance of the proposed FDDL. Emulation results show that the proposed FDDL can achieve a higher cache hit ratio and traffic offloading rate than several conventional caching policies and DRL-based caching algorithms, and effectively reduce communication costs and training time.

Network Functions Virtualization (NFV) replaces the specialized hardware with the software-based forwarding to promise the flexibility, scalability and automation benefits. With an increasing range of applications, NFV must ultimately forward packets at rates that are comparable to the native and specialized hardware-based approaches. However, the transition packet forwarding from specialized hardware to software-based has turned out to be more challenging than expected. Thus, NFV acceleration is desperately needed to play a crucial role in the development of NFV. It is an interesting issue how to address the persistent performance tuning in a way that provides far greater flexibility to meet the demands of power. The existing developments are very inefficient, since that the uncontrollable and unanticipated performance regressions frequently occur. Besides, the environments for full system simulations are traditionally expensive and time consuming to evaluate the system performance. In this paper, we propose the methodology named as “NFV Acceleration via Lean Measurements (NALM)” to tune the performance for the NFV acceleration. NALM provides a holistic measurement approach through combining individual measures to quickly identify the bottlenecks, which can help developers with a better understanding of the design tradeoffs. Moreover, the environments for large scale performance simulation are replaced by a debugger. Thus, the waste is eliminated in terms of time consumption and infrastructure costs of the full system simulation. The systematic analysis of the multi-cores speedup ratio highlights the potential optimization space and rules. We further propose the improvement recommendations on efficient practices. The experiments evaluate the specific effects, and the relationship between the metrics and forwarding performance.

Deterministic transmission guarantee in time-sensitive networks (TSN) relies on queue models (such as CQF, TAS, ATS) and resource scheduling algorithms. Thanks to its ease of use, the CQF queue model has been widely adopted. However, the existing resource scheduling algorithms of CQF model only focus on periodic time-triggered (TT) flows without consideration of bursting flows. Considering that the bursting flows often carry high-priority data in real systems, in this paper we investigate the mixed-flow (i.e., TT and bursting flows) scheduling problem in CQF-based TSN aiming to maximize the number of schedulable flows and system load balance while satisfying the deterministic demands of delay, jitter, and reliability for both TT and bursting flows. Unfortunately, it is challenging to schedule the mixed flows with the original CQF model because of the huge difference between TT and bursting flows. To resolve this problem, we firstly design an enhanced Multi-CQF model to satisfy the basic demands of bursting flows sent at any time without affecting the deterministic transmission of TT flows. Given the complexity of mixed-flow scheduling and the proposed queue model, it is difficult for traditional algorithms to fully utilize network resources. Thus, we further propose a uline time-correlated uline DRL uline resource uline scheduling (TimeDRS) algorithm to optimize the resource allocation. TimeDRS can be extended to other time-related resource scheduling scenarios, such as TDMA-based scheduling. Experimental results demonstrate that our proposed approaches can greatly reduce frame loss and end-to-end latency for bursting flows, and well balance runtime and schedulability compared with state-of-the-art benchmarks.

Space/aerial-assisted internet of things (IoT) is promising to provide extensive coverage and heterogeneous network services. However, it also faces the risk of eavesdropping attacks due to the peculiarity of highly open transport. In this paper, we propose a combating eavesdropping solution with resilient multipath (CERM) for space/aerial-assisted IoT. Firstly, we analyze the dynamics of space/aerial-assisted IoT and build a betweenness centrality based eavesdropping probability model. Furthermore, we formulate multipath selection problem as an integer optimization by minimizing eavesdropping probability. Based on this, we propose a programmable CERM solution to flexibly schedule multipath traffic to reduce eavesdropping risk. Extensive experimental results verify the proposed CERM solution decreases eavesdropping probability as well as increases transmission throughput compared with the traditional single-path and Round-Robin multipath solutions.

Cognitive Radio (CR) enabled vehicles in Vehicle Networks can use multiple Radio Access Networks (RANs) for data transmission. The simultaneous use of multiple RANs for transmission requires the design of a specific multi-path transmission protocol. Many scholars have studied the scheduling algorithm to improve the quality of multi-path transmission. However, most of the existing scheduling algorithms are difficult to deal with the challenges brought by the diversity and heterogeneity of the vehicle network. To deal with these challenges, this paper proposes an IP layer Deep Reinforcement Learning (DRL) multi-path transmission scheduling algorithm named Adaptive Dynamic Scheduling Algorithm (ADSA), which can dynamically generate the optimal scheduling policy through the interaction between agent and network environment. This paper first models the data packet scheduling strategy of multi-path transmission into an optimization problem of multi-path transmission efficiency. Then this paper transforms the optimization problem into a DRL problem and finds the optimal scheduling strategy through DRL model training. This paper evaluates the network performance of ADSA in different network scenarios compared with traditional scheduling algorithms. Simulation results show that ADSA increases the throughput by 8.9 Mbps compared with the three traditional scheduling algorithms and reduces the transmission delay by 4.3 ms.

Since Internet of Things (IoT) communications can enjoy many advantages brought by content-centric networking (CCN) in nature, there is an increasing interest on their integration for better information retrieval and distribution. Nevertheless, different from the conventional multimedia traffic of which contents are hardly changed, IoT data are always transient and updated by their producers according to the actual situation. As a result, if without any effective countermeasures, outdated copies are inevitably stored by CCN routers and then distributed to the associated consumers, degrading both caching efficiency and user experience. In fact, most of related policies take little account of information freshness for cached contents, and how to tackle transient IoT data in CCN is still an ignored but crucial issue required for further explorations. Therefore, in this article, we propose an efficient popularity-based cache consistency management scheme, which aims to guarantee freshness of IoT data returned by on-path routers and avoid heavy signalling costs introduced at the same time. Extensive simulations were performed under both real-world scare-free and binary-tree topologies, and corresponding results have proved the efficiency of the proposed scheme in timely evictions of outdated IoT data stored by CCN in-network caching.

Network softwarization is one of the key enablers of the future Internet evolution, also supporting the road from the fifth generation (5G) to the next-generation communication systems, namely 6G, with their main objective of bringing hyper-connected experience to every corner of society.

Considering the fluctuations of network traffic and dynamics of unknown underlying network state, designing a elastic control model with long-term high Quality of Service (QoS) and low network cost has become a pivotal problem in Software Defined Network/Network Functions Virtualization (SDN/ NFV) network. Based on the problem, we design the multiagent Deep Reinforcement Learning based Elasticity Control approach (DRLEC). Considering multi-objective of maximizing revenue benefit and minimizing migration cost, the optimization problem for elasticity control is modeled as a Markov Decision Process (MDP). Then, taking the binary integer programming model as constraints, DRLEC is designed to solve the optimization problem of maximizing long-term profit. Experimental results demonstrate that DRLEC shows better performance than heuristics and single-agent DQN algorithm. Moreover, DRLEC can nearly achieve the upper bound of the theoretical solution, which is obtained by assuming knowing the dynamics of network traffic in advance.

Multipath transmission is considered one of the promising solutions to improve wireless resource utilization where there are many kinds of heterogeneous networks around. Most scheduling algorithms rely on real-time network metrics, including delay, packet loss, and arrival rates, and achieve satisfying results in simulation or wired environments. However, the implicit premise of a scheduling algorithm may conflict with the characteristics of real heterogeneous wireless networks, which has been ignored before. This paper analyzes the real network metrics of three Chinese heterogeneous wireless networks under different transmission rates. To make the results more convincing, we conduct experiments in various scenarios, including different locations, different times of the day, different numbers of users, and different motion speeds. Further, we verify the suitability of a typical delay-aware multipath scheduling algorithm, Lowest Round Trip Time, in heterogeneous networks based on the actual data measured above. Finally, we conclude the characteristics of heterogeneous wireless networks, which need to be considered in a well-designed multipath scheduling algorithm.

As the demand for real-time and high-quality social network services in mobile communications continues to grow, the performance defects of single-path transmission networks have become more and more prominent. At the same time, multipath transmission provides people with the possibility of stable and smooth communication in mobile wireless social networks. However, since it is usually difficult to obtain frequently varying delays along each path, packets always appear out of order during the communication of heterogeneous social networks, which will cause additional waiting delays in the receiving process. Therefore, it is still a very challenging task to construct a high-bandwidth and low-latency multipath transmission mechanism for social networks in mobile communications. According to the behavioral features of packets of social networks in mobile scenarios, a social network model BAH that reveals wireless social networks bottlenecks is established. Subsequently, this paper proposes a bottleneck-aware algorithm BFDE, which utilizes the one-way delay of periodically probing to derive the features of the wireless social networks bottleneck, so as to achieve an accurate estimation of the delay of each path. In the analysis and simulation, we compared it with the baseline EDPF and proved that the BFDE algorithm can achieve effective scheduling in complex and changeable mobile wireless social networks, thereby effectively increasing the multipath aggregation bandwidth, and has a strong robustness.

State-of-the-art mechanisms against eavesdropping first encrypt all packet payloads in the application layer and then split the packets into multiple network paths. However, versatile eavesdroppers could simultaneously intercept several paths to intercept all the packets, classify the packets into streams using transport fields, and analyze the streams by brute-force. In this article, we propose a programming protocol-independent packet processors (P4)-based network immune scheme (P4NIS) against the intractable eavesdropping. Specifically, P4NIS is equipped with three lines of defenses to provide a softwarized network immunity. Packets are successively processed by the third, second, and first line of defenses. The third line basically encrypts all packet payloads in the application layer using cryptographic mechanisms. Additionally, the second line re-encrypts all packet headers in the transport layer to distribute the packets from one stream into different streams, and disturbs eavesdroppers to classify the packets correctly. Besides, the second line adopts a programmable design for dynamically changing encryption algorithms. Complementally, the first line uses programmable forwarding policies which could split all the double-encrypted packets into different network paths disorderly. Using a paradigm of programmable data planes-P4, we implement P4NIS and evaluate its performances. Experimental results show that P4NIS can increase difficulties of eavesdropping and transmission throughput effectively compared with state-of-the-art mechanisms. Moreover, if P4NIS and state-of-the-art mechanisms have the same level of defending eavesdropping, P4NIS can decrease the encryption cost by 69.85%-81.24%.

It is well known that concurrent multipath transfer (CMT) can improve the transmission rate. However, due to multiple heterogeneous paths from users to the access network, a large number of out-of-order packets significantly degrade the overall transmission reliability. Cybertwin provides a potential solution to alleviate the packet out-of-order problem by accurately detecting and perceiving the path state. In this article, we investigate the data scheduling problem and propose a learning-based cybertwin-driven CMT algorithm to obtain the optimal data scheduling policy. In particular, we first formulate the data scheduling problem as an integer linear programming by taking the QoS metrics into account. To cope with the packet out-of-order problem in CMT, we propose a reliable cybertwin-CMT with deep reinforcement learning (CMT-DRL) algorithm to determine the data scheduling decisions. The proposed algorithm takes multipath throughput, end-to-end delay, and packet loss rate into account. Besides, CMT-DRL adopts an asynchronous learning framework to efficiently execute data collection, packet scheduling, and neural network training in sequence by decoupling model training and execution. We conduct extensive experiments in a P4-based programmable network platform. Experimental results indicate that the CMT-DRL outperforms the existing benchmarks in terms of the number of out-of-order packets, round-trip time, and throughput.

In this paper, we aim to make the best joint decision of device selection and computing and spectrum resource allocation for optimizing federated learning (FL) performance in distributed industrial Internet of Things (IIoT) networks. To implement efficient FL over geographically dispersed data, we introduce a three-layer collaborative FL architecture to support deep neural network (DNN) training. Specifically, using the data dispersed in IIoT devices, the industrial gateways locally train the DNN model and the local models can be aggregated by their associated edge servers every FL epoch or by a cloud server every a few FL epochs for obtaining the global model. To optimally select participating devices and allocate computing and spectrum resources for training and transmitting the model parameters, we formulate a stochastic optimization problem with the objective of minimizing FL evaluating loss while satisfying delay and long-term energy consumption requirements. Since the objective function of the FL evaluating loss is implicit and the energy consumption is temporally correlated, it is difficult to solve the problem via traditional optimization methods. Thus, we propose a “Reinforcement on Federated” (RoF) scheme, based on deep multi-agent reinforcement learning, to solve the problem. Specifically, the RoF scheme is executed decentralizedly at edge servers, which can cooperatively make the optimal device selection and resource allocation decisions. Moreover, a device refinement subroutine is embedded into the RoF scheme to accelerate convergence while effectively saving the on-device energy. Simulation results demonstrate that the RoF scheme can facilitate efficient FL and achieve better performance compared with state-of-the-art benchmarks.

Faced with fluctuating network traffic and unknown underlying network traffic dynamics, developing an effective orchestration model with low network cost is still a critical issue in Network Functions Virtualization (NFV)-enabled networks. Thus we propose a Deep Reinforcement Learning based Quality of Service (QoS)/Quality of Experience (QoE)-Aware Adaptive Online Orchestration (DRL-QOR) approach to adapt to the real- time network variations. We formulate the stochastic resource optimization as a Parameterized Action Markov Decision Process (PAMDP), with QoE and specific QoS requirements as key factors in formulating the reward function, aiming to maximize QoE while satisfying QoS constraints. Then we propose DRL-QOR to solve the Non-deterministic Polynomial hard (NP-hard) problem with consideration of improving the long-term profits, where deep neural network combinatorial optimization theory is extended under the constraints of the binary integer programming model. Extensive experimental results in real USANET topology demonstrate that our proposed DRL-QOR converges fast during the training process. Compared with other benchmarks that only consider the current system performance, it shows good performance in QoE provisioning and QoS requirements maintenance for orchestrating SFCs.

The Softwarization of networks is enabled by the SDN (Software-Defined Networking), NV (Network Virtualization), and NFV (Network Function Virtualization) paradigms, and offers many advantages for network operators, service providers and data-center providers. Given the strong interest in both industry and academia in the softwarization of telecommunication networks and cloud computing infrastructures, a series of special issues was established in IEEE Transactions on Network and Service Management, which aims at the timely publication of recent innovative research results on the management of softwarized networks.

The rapid development of the transportation industry has brought about the demand for massive data transmission. In order to make use of a large number of heterogeneous network resources in vehicular network, the research of applying network coding to multipath transmission has become a hot topic. Network coding can better solve the problems of packet reordering and low aggregation efficiency. The determination of coding scale is the key to network coding scheme. However, the existing research cannot adapt to the different characteristics of network resources in vehicular network, leading to larger decoding time cost and lower bandwidth aggregation efficiency. In this paper, we propose a network coding scheme called Delay Determined Group Size (DDGS), which could adaptively adjust the coding group according to the heterogeneous wireless networks state. The mathematical analysis and process design of the DDGS scheme are discussed in detail. Through a large number of simulations, we proved that the DDGS scheme is significantly superior to other coding group determination schemes in terms of decoding time cost and bandwidth aggregation efficiency.

Recent advances in the Internet of Things (IoT) technologies have enabled ubiquitous smart devices to sense and process various kinds of data. However, these innovations also raise the concern of efficient data transmission. Tackling the above issue is nontrivial since the resource constraints and environmental randomness in IoT require a lightweight transmission scheme while guaranteeing system stability. In this paper, we formulate the transmission scheduling problem of multi‐interface IoT devices as a concave optimization, aimed at accommodating the randomness of the IoT environment within the network capacity. By applying the Lyapunov optimization technique, we divide the stochastic problem into a series of low‐complex subproblems, which can be individually solved per time slot, and develop a dynamical control algorithm that does not require a priori knowledge such as link states. Theoretical analysis shows that our algorithms nicely bound the average queue length and are asymptotically optimal. Finally, extensive simulation results verify the theoretical conclusions and validate the effectiveness of the proposed algorithm.

As the demand of mobile services for high bandwidth grows, aggregation of various wireless network resources in hybrid communication system becomes a trend. Network coding has been widely studied to solve the packet reordering, low aggregation efficiency problems which are brought by network heterogeneity. Even if the determination of coding scale is the core issue of network coding, current research can not adapt to heterogeneous wireless channels, resulting in low bandwidth aggregation and decoding efficiency. Therefore, we propose a cross-layer network coding scheme called Delay Determined Group Size (DDGS) scheme, which adaptively adjusts the coding scale to solve the problem of overall performance degradation caused by heterogeneous characteristics of wireless channels. It can get the utmost out of the performance improvement brought by network coding, to better avoid packet reordering as well as reduce the receiving delay at the receiving end. The simulation results show that DDGS is significantly superior to network coding schemes in existing state-of-the-art solutions.

In this paper, we investigate the joint task assignment and resource allocation for deep neural network (DNN) inference in the device-edge-cloud based industrial Internet of things (IIoT) networks. To efficiently orchestrate the limited spectrum and computing resources in IIoT networks for massive DNN inference tasks, a resource management problem is formulated with the objective of maximizing the average inference accuracy while satisfying the quality-of-service of DNN inference tasks. Considering the strict delay requirements of inference tasks, we transform the formulated problem into a Markov decision process, and propose a deep deterministic policy gradient based learning algorithm to obtain the solution rapidly. Simulation results show that the proposed algorithm can achieve high average inference accuracy.

To satisfy the various quality-of-service (QoS) re-quirements with minimum network costs, network functions virtualization (NFV) is proposed as an emerging wireless architecture that migrates network functions from dedicated hardware appliances to software instances running in virtual computing platforms. One crucial issue in NFV is to solve the orchestration of virtualized network functions (VNFs) to reduce costs and to improve the management flexibility of telecommunications service providers (TSPs). Particular, multiple objectives are required to be considered for orchestrating VNFs in order to achieve overall system performance. This can be optimally solved in small scale using integer linear programming (ILP) algorithms with high accuracy but low time efficiency. On the other hand, heuristic algorithms can be applied for solving part of the objectives in NFV resource allocation with high time efficiency but low accuracy. To tackle the above challenges, QMORA, a ${Q}$-learning based multi-objective resource allocation approach, is proposed to solve multi-objective optimization in NFV orchestration (NFVO) efficiently and accurately. Particularly, the approach includes reinforcement learning module and VNFs placement module. Reinforcement learning module is responsible for generating the “best” candidate paths. VNFs placement module is responsible for selecting optimal nodes on the generated candidate paths to host VNFs required for flows. The simulation results in the real ISP topology show that the proposed QMORA can balance the multi-objective including maximizing number of flows admitted to the network, minimizing path stretch, balancing the load among VNF instances and minimizing link occupation rate compared with other heuristic approaches.

The Internet has made a significant success, which is on the basis of TCP/IP stacks. However, due to the dramatic development of the Internet of Things and 5G, giving rise to the continuous expansion of the network scale and the emergence of new applications, it becomes more and more complicated to manage the Internet. Specifically, the best-effort model and the device-centric working manner have become the inhibitors to meet the demands of the intelligent and coordinated transmission under heterogeneous networks in the future. In this paper, we proposed a novel Internet architecture named Smart Integration Identifier Networking (SINET-I) after comprehensively summarizing the related researches of the future Internet. SINET-I enhanced the ability of network automation and realized heterogeneous networks collaboration via introducing intent scheme and making full use of machine learning technologies. The experiment results show that SINET-I performs well in coordinated transmission across different heterogeneous protocols scenario and the available bandwidth of multi-paths is more than 2 times of that of single-path.

Ubiquitous power Internet of Things (IoT) is suffering unprecedented constraints and reliable tracing is a typical example. Motivated by software-defined and function virtualization capabilities of edge-cloud interplay, we propose a smart collaborative tracking scheme by investigating advanced parameter prediction skills and improved particle filter approaches. First, the range-based positioning issues are transformed into the vector nonlinear suboptimal estimation problem based on information fusion. Second, the importance of density function is provisioned to calculate locations and trajectories of the mobile node by obtaining cubature points, updating state estimation, and revising vector estimation. The Gauss-Newton iterative method has been utilized to achieve higher accuracy. Third, we implement our scheme into the simulation platform and prototype system. The practical deployment has been validated from multiple perspectives. Comparing with existing candidates, experimental results illustrate that the proposed algorithm is able to enhance the performance and demonstrate acceptable reliability. Potential usages are being expected in dynamic surveillance, equipment maintenance, and other emerging IoT scenarios.

Artificial intelligence is being utilized in multipath industrial networks to enhance service supporting ability. However, existing obstacles in controlling receive buffer restrict throughput even when higher bandwidth is available. Therefore, in this article, we propose a smart collaborative automation (SCA) scheme to improve resource usage and overcome buffer limitations. First, a mathematical model is established to describe primary system operations with considerations of chunk loss. The inf-supremum methodology and probability theory are adopted to track congestion window variations. Second, differences in disordered chunk expectations are analyzed to locate the critical condition of round numbers. Specific algorithm details are provided via simplifying comparison to achieve comprehensive policy selections. Third, evaluation topologies and environments are created with reasonable parameter settings. Validation results demonstrate that model-driven SCA can reduce unexpected occupations at the receiver-side. Comparing to intuition-driven schemes, overall performances, in terms of the sender's transmission capacity and receiver's buffer utilization, are improved under different experimental configurations.

The Internet of Things (IoT) has gained great attention in recent years, due to its significant role in industry innovations and promotions. However, it is still facing many technical challenges before fully gaining ground, mainly resulting from limited computational and energy resources of IoT devices and best-effort underlying network paradigms. Thanks to the emerging edge computing that optimizes the cloud computing by processing data at edge networks, IoT devices can offload computation-intensive tasks to their assigned edge computing servers with response time guaranteed and energy consumption saved. As a result, how to perform task offloading by IoT devices has become a key challenge widely discussed. Nevertheless, most of the existing works focus on the tradeoff between executing a task locally and remotely through techniques, such as optimization and game theory, rather than related theoretical model to analyze communication procedures of offloading policies. Thus, in this paper, we propose a multiqueue model to explore the impact of offloading policies on performance of the IoT devices with their assigned edge computing server. Particularly, we consider two simple policies, namely Locality-First policy and Probability-based policy, and obtain their analytic solution of the task mean response time and energy consumption of the IoT devices and edge computing server. Extensive simulations are performed and related results have proved accuracy of the proposed model.

The fifth-generation (5G) network cloudification enables third parties to deploy their applications (e.g., edge caching and edge computing) at the network edge. Many previous works have focused on specific service strategies (e.g., cache placement strategy and vCPU provision strategy) for edge applications from the perspective of a certain third party by maximizing its benefit. However, there is no literature that focuses on how to efficiently allocate resources from the perspective of a mobile network operator, taking the different deployment requirements of all third parties into consideration. In this paper, we address the problem by formulating an optimization problem, which minimizes the total deployment cost of all third parties. To capture the deployment requirements of the third parties, the applications that they want to deploy are classified into two types, namely, computation-intensive ones and storage-intensive ones, whose requirements are considered as input parameters or constraints in the optimization. Due to the NP-hardness and non-convexity of the formulated problem, we have designed an elitist genetic algorithm that converges to the global optimum to solve it. Extensive simulations have been conducted to illustrate the feasibility and effectiveness of the proposed algorithm.

The great improvements in both satellite and terrestrial networks have motivated the academic and industrial communities to rethink their integration. As a result, there is an increasing interest on how to combine broadband satellite networks with the clean-slate terrestrial ones, especially with clouds leveraging SDN (Software-Defined Networking) and NFV (Network Functions Virtualization) techniques, for better network openness, flexibility, elasticity and controllability. In this way, customized SFCs (Service Function Chaining) can be deployed at terrestrial and satellite ground segment clouds on demand, significantly reducing OPEX and CAPEX (Operational and Capital Expense). Nevertheless, how to efficiently leverage cloud substrate resources and deploy required SFCs is still challenging, as many issues such as system cost and revenue are involved. Therefore, in this paper, we focus on SFC mappings at SDN/NFV-based terrestrial and satellite ground clouds, and propose a related approach that considers both SF (Service Function) multiplexing and SFC merging, aiming to improve resource utilization efficiency of underlying substrate networks. Extensive simulations are performed and numerical results have verified benefits of the proposed SFC mapping approach.

Information-Centric Networking (ICN) provides great potential to promote the development of the Internet of Things (IoT) due to its multicast nature and mobility support. However, the stateful forwarding peculiarity introduces new varietal attacks named Interest Flooding Attacks (IFA), which is stealthy but destructive for the resource-limited IoT devices. In this paper, we propose a lightweight BLoom-filter based Attack Mitigating (BLAM) mechanism to reduce the detecting memory cost, while guaranteeing both the detecting accuracy and delay. Specifically, each IoT node employs a small Bloom filter to check attack behaviors instead of the traditional memory-consuming operations, i.e., recording malicious requests. Bloom filter values by hashing the published data names with a set of hash functions, are encapsulated and distributed via a new message named Ba-NACK. Based on this design, two specific schemes are further proposed for the attack detecting and Bloom filter updating. We formulate the memory cost minimum problem and theoretically analyze that BLAM can reduce the memory cost. We also implement BLAM in a realistic network testbed to evaluate its performance. The results show that BLAM reduces the memory cost by 78.6%, and reduces the delay from millisecond to microsecond with slight sacrifice of the accuracy by 0.4% compared with other state-of-the-art mechanisms.

The prosperity of Internet of Things (IoT) and the success of rich Cloud services have expedited the emergence of a new computing paradigm called Fog computing, which promotes the processing of data at the proximity of their sources. Complementary to the Cloud, Fog promises to offer many appealing features, such as low latency, low cost, high multitenancy, high scalability, and to consolidate the IoT ecosystem. Although the Fog concept has been widely adopted in many areas, a comprehensive realization has yet been adequately researched. To address all these issues, in this paper, object virtualization is investigated to overcome obstacles resulting from resource constraints on sensory-level nodes while service virtualization is explored to easily create tailored applications for end users. Moreover, network function virtualization is studied to perform the flexibility of network service provisioning. Grounded on object virtualization, network function virtualization and service virtualization, a layered framework that encompasses smart objects, Fog and Cloud is presented to illustrate the realization of virtual Fog along IoT continuum. This proposed virtual Fog framework is applied to a smart living case for verification, then quantitative analysis is conducted to demonstrate the low latency, low operating expense, high multitenancy and scalability, followed by an experimental evaluation to further confirm that delay and jitter can be decreased through virtualization.

Information-centric networking (ICN), as a novel network paradigm, is expected to natively support mobility, multicast, and multihoming in vehicular ad hoc networks (VANETs). In this paper, the adoption of ICN principles for multimedia streaming in multihomed VANETs is investigated, with a major emphasis on the tradeoff between the quality of experience and energy efficiency (EnE). To formalize this problem, a cost optimization model is first proposed, based on queueing theory arguments. Then, a novel green information-centric multimedia streaming (GrIMS) framework is designed to drive the system toward optimal working points in practical settings. GrIMS consists of three enhanced mechanisms for on-demand cloud-based processing, adaptive multipath transmission, and cooperative in-network caching. Finally, a massive simulation campaign has been carried out, demonstrating that, thanks to its core components, the GrIMS enables flexible multimedia service provisioning and achieves an improved performance in terms of start-up delay, playbacks continuity, and EnE with respect to state-of-the-art solutions.

Information centric network (ICN) is becoming a prevailing design for future Internet to provide data objects to end customers. One crucial problem in ICN is to design routing and forwarding strategy so that traffic can be transmitted efficiently even with the dynamics of network link. In this paper, we consider content centric network (CCN), which is one of the promising candidate architecture design for ICN. Particularly, we consider the scalable CCN with name mapping system, which improves scalability by using router Identifier as name prefix to forward interests. We address the issue of improving link failure resiliency for CCN by designing a dynamic interest transmission approach. We propose the link failure resilient interest transmission algorithm (LFRIT) to enhance link failure resiliency by considering both network traffic and link status information. Additionally, we propose the advanced design of LFRIT (A-LFRIT), which calculates and updates the forwarding table centrally for link updates to further improve network robustness. Numerous simulations are conducted to evaluate the proposed approach under realistic network topologies with respect to packet loss, latency, and network link utilization. Simulation results demonstrate that the packet loss probability and data retrieval delay of A-LFRIT can reduce to 50% of the basic CCN and multi-repository single path approaches.

Vehicle‐assisted data offloading is envisioned to significantly alleviate the problem of explosive growth of mobile data traffic. However, due to the high mobility of vehicles and the frequent disruption of communication links, it is very challenging to efficiently optimize collaborative offloading from a group of vehicles. In this paper, we leverage the concept of Software‐Defined Networking (SDN) and propose a software‐defined collaborative offloading (SDCO) solution for heterogeneous vehicular networks. In particular, SDCO can efficiently manage the offloading nodes and paths based on a centralized offloading controller. The offloading controller is equipped with two specific functions: the hybrid awareness path collaboration (HPC) and the graph‐based source collaboration (GSC). HPC is in charge of selecting the suitable paths based on the round‐trip time, packet loss rate, and path bandwidth, while GSC optimizes the offloading nodes according to the minimum vertex cover for effective offloading. Simulation results are provided to demonstrate that SDCO can achieve better offloading efficiency compared to the state‐of‐the‐art solutions.

Information-Centric Networking (ICN) introduces ubiquitous in-network caching to reduce network load and improve Quality of Service (QoS). This peculiarity requires high-speed caching technologies to support wire-speed and large-amount data forwarding, which brings new challenges to existing routers. To promote practical ICN deployment, many emerging researches focus on how to accelerate caching. In this paper, we propose a novel two-layer High-speed Caching scheme (HCaching), which leverages the characteristics of both SRAM and DRAM to accelerate caching for ICN routers. In particular, using DRAM as a primary cache and SRAM as a secondary one, HCaching is able to: (i) reduce excessive utilization of high-cost SRAM, (ii) speed up access of DRAM, (iii) and improve total network throughput. We implement and analyze HCaching performance by comparing with another two state-of-the-art solutions. The results show that HCaching achieves an improved throughput by 3-10 times faster than the compared solutions.

Due to the complexity and fluctuation of the wireless network state in high-speed mobility scenarios, the existing works related to network selection face a great challenge for selecting the accurate network in terms of the imprecise information and mobility. Therefore, we design a novel dynamic imprecise-aware network selection approach, named FSNS by taking advantage of fuzzy logic and utility function of multiple attributes. Our proposed approach not only copes with imprecise network information but also dynamically adapts to the high-speed mobility scenarios, which are not presented for the existing proposals. In this paper, we compare FSNS approach with an enhanced TOPSIS method through simulation experiments of two types of services. The results demonstrate that FSNS outperforms TOPSIS for a preferable decision to keep relatively stable and reduce abnormal selection. The conclusions of experimental results have some extent pragmatic value because the simulation imitates network state in the high-speed mobile environment by real world data from high-speed railways.

Widespread deployment of High-Speed Railway (HSR) in recent years brings strong demand for high-quality onboard Internet services. However, the wireless link between the train and Base Station (BS) suffers from numerous problems, including frequent handover, severe Doppler shift, and great penetration loss, etc. It is still a challenge to provide HSR passengers with high-quality Internet services. In this paper, the Packet Loss Rate (PLR) of HSR networks is measured along Beijing-Shanghai railway line. Measurement results indicate that PLR remains at a high level for a long period and changes frequently in a large interval. To address this problem, we focus on frame loss problem of train-to-BS wireless link and propose a novel Loss-Aware Adaptive Scalable Transmission mechanism (LAAST) to fit for HSR networks. In LAAST, variable number of frame copies are transmitted according to Frame Loss Probability (FLP) to improve the scalability and efficiency of transmission over train-to-BS link. The optimal relationship between frame duplication number and FLP is derived through nonlinear programming model. Simulations demonstrate the effectiveness and fitness of LAAST for HSR networks.

Due to the severe environment along the High-Speed Railway (HSR), it is essential to research an efficient HSR communication system. In our previous work, we collected and analyzed an amount of the first hand dataset of signal intensity in HSR networks. We first observed that the link status variation presented an obvious Two-Time-Scale characteristics. However, that work did not analyze the cause of the Two-Time-Scale characteristics clearly. In this work, we focus on the fundamental cause of the periodic Two-Time-Scale characteristics, and make a lot of in-depth studies on this interesting phenomenon. Furthermore, we rebuild Two-Time-Scale characteristics by leveraging the relationship between the link state variation and the geographical position along HSR lines. In particular, considering the distribution of urban areas and rural ones along the HSR, a periodic distance based small time-scale model and a path-loss based large time-scale model are proposed respectively. Simulation results show the proposed models can perfectly explain the Two-Time-Scale characteristics and predict HSR link quality.

Due to the widespread research on Smart Identifier NETwork (SINET), its security has received much attention recently. But most of those attempts consider SINET security from the specific attack perspective. To the best of our knowledge, none so far has paid attention to the security analysis and modeling of separation mechanisms in SINET. Therefore, this paper provides a different approach to security analysis based on Petri net. Our objective is to analyze the separation mechanisms security via the combination of model and state. This method represents the network structure and state transferring by way of Petri net. In addition, it introduces the security analysis method of tokens to explore the potential threatens. Finally, we analyze SINET via the combination of the number, logic, and time series of tokens in Petri net, and present the results. Our results are very promising in using such models to achieve such security objectives.

As an important component of the intelligent transportation system, internet‐of‐vehicles technology has attracted considerable attention. The dissemination of event‐driven emergency messages with high reliability and low delay is vital to ensure traffic safety and improve traffic efficiency in urban environment. In this study, the authors propose a reliable emergency message dissemination protocol taking into account the urban road characteristics and scalability requirements, which consists of a layout‐aware ready‐to‐broadcast‐emergency‐message and clear‐to‐broadcast‐emergency‐message handshake mechanism and a redundant relay node adaptation mechanism. Finally, the simulation results confirm the feasibility and effectiveness of the proposed scheme.

In this paper, we study the source mobility problem that exists in the current named data networking (NDN) architecture and propose a proxy-based mobility support approach named PMNDN to overcome the problem. PMNDN proposes using a proxy to efficiently manage source mobility. Besides, functionalities of the NDN access routers are extended to track the mobility status of a source and signal Proxy about a handoff event. With this design, a mobile source does not need to participate in handoff signaling which reduces the consumption of limited wireless bandwidth. PMNDN also features an ID that is structurally similar to the content name so that routing scalability of NDN architecture is maintained and addressing efficiency of Interest packets is improved. We illustrate the performance advantages of our proposed solution by comparing the handoff performance of the mobility support approaches with that in NDN architecture and current Internet architecture via analytical and simulation investigation. We show that PMNDN offers lower handoff cost, shorter handoff latency, and less packet losses during the handoff process.

Software-defined networking (SDN) is a promising network paradigm for future Internet. The centralized controller and simplified switches replace the traditional complex forwarding devices, and make network management convenient. However, the switches in SDN currently have limited ternary content addressable memory to store specific routing rules from the controller. This bottleneck provokes cyber attacks to overload the switches. Despite existing some countermeasures for such attacks, they are proposed based on simplified attack patterns. In this paper, we review the table-overflow attack using a sophisticated attack pattern. In the attack pattern, attack flows are targeted at their middle hops instead of endpoints. We first define potential targets in the network topology, then we propose three specific traffic features and a monitoring mechanism to detect and locate the attackers. Further, we propose a mitigation mechanism to limit the attack rate using the token bucket model. With the control of token add rate and bucket capacity, it avoids the table overflow on the victim switch. Extensive simulations in different types of topologies and experiments in our testbed are provided to show the performance of our proposal.

Due to the complexity and fluctuation of the wireless network statuses in the high-speed railway scenario, the existing works of the network selection problem in heterogeneous wireless networks face two major challenges, that is, the imprecise statuses and mobility. In this paper, we propose FSNS, a novel dynamic imprecise-aware network selection approach to solve the problems. The imprecise statuses are inferred by fuzzy rules and the status-awareness feature of the status monitor module enables a dynamic network selection. Through the status monitor and fuzzy processing modules, FSNS is formulated as utility functions for meeting quality-of-service (QoS) requirements. What is more, we carry out plenty of simulation experiments and compare FSNS approach with an enhanced TOPSIS method and fuzzy MADM (FMADM) scheme through simulation experiments of two types of services. The results indicate that FSNS outperforms both TOPSIS and FMADM for a good performance improvement and a preferable decision to keep relative stability and reduce abnormal selections. The conclusion of experimental results has some extent pragmatic value since the network statuses of the simulation are complex and fluctuate by real-world data from high-speed railways.

As a promising future Internet architecture, Named Data Networking (NDN) naturally enables consumer mobility but leaves source mobility challenging due to the binding between content identifier and locator. Most of source mobility solutions in literature adopt similar idea with the Mobile IP and suffer from several problems, like non-optimal routing, severe scalability, single point of failure etc. To address this issue, we build a distributed scalable mobility management framework based on threefold separation mechanisms to improve the content source mobility management without changing the original NDN's name-based communication paradigm. The proposed scheme supports fast handover and shortest path communication by splitting content locator and identifier as well as isolating content source's dynamics out of the routing plane. Numerical comparisons show that the proposed scheme outperforms the other baseline schemes in terms of handover latency, communication latency.

Information-Centric Networking (ICN) has emerged as a promising way for the efficient content delivery over the Internet, and it can be seen as a super large-scale caching distributed system. However, as one of the most important problems, the cache consistency issue, which refers to whether cached contents in routers are outdated, is still not investigated thoroughly in ICN. Thus, in this paper, we propose a cost-effective Popularity-based Cache Consistency (PCC) mechanism to guarantee the freshness of cached contents in ICN routers. PCC is able to balance the trade between the consistency strength and related costs since it only maintains the strong consistency for popular contents while the weak for unpopular ones. Besides, we improve another two cache consistency mechanisms used in the web caching, namely Polling-Every-Time (PET) and Time-To-Live (TTL), to be suitable for ICN, and use them as the benchmarks for comparisons with PCC. To evaluate their performance, we firstly analyse the costs of these mechanisms including the user latency in terms of hop counts and corresponding signaling overheads, and then conduct extensive simulations using a real topology. The simulation results show the high efficiency of PCC compared with the improved PET and TTL.

A Distributed Denial of Service (DDoS) attack against controllers is one of the key security threats of Software-Defined Networking (SDN). The breakdown of a controller may disrupt a whole SDN network. Nowadays, a novel DDoS means is that the attackers may generate vast new low-traffic flows to trigger malicious flooding requests to overload the controllers. It is difficult to prevent this attack, as the attackers may connect to any interface of any switch in an SDN network. In this paper, we propose an effective detection method, which is designed to detect the DDoS attack and to further locate the compromised interfaces the malicious attackers have connected. We first classify the flow events associated with an interface, then make a decision using Sequential Probability Ratio Test (SPRT), which has bounded false negative and false positive error rates. In addition, we evaluate the performance of the proposed method using DARPA Intrusion Detection Data Sets. We also discuss and compare our method to three other detection methods, which are based on the percentage, count, and entropy of the flows, respectively, and demonstrate the superiority of our method in terms of promptness, versatility and accuracy.

Software Defined Network (SDN) is able to provide better network management and higher utilization for data center. However the centralized control of entire network may trigger large overhead and limit the scalability of control plane. In this paper, we propose an enhanced mechanism of elephant flow scheduling in SDN-based data center. The mechanism can efficiently reduce the overhead and improve the scalability of control plane by using Parametric Minimum Cross Entropy (PMCE) algorithm. We describe the proposed approach in detail, and evaluate it in OMnet++ to verity its feasibility and effectiveness. Numerical results show that the benefits of our scheme are better than previous methods and the extra delay caused by PMCE algorithm is controllable.

Vehicular ad hoc network (VANET) is an important component for advancing the intelligent transportation system (ITS) to improve the traffic safety and enrich driving experience. In VANET safety applications, reliable and rapid dissemination of event-driven emergency messages is of great significance to obtain the traffic safety and efficiency. In this paper, we propose a RBEM/CBEM handshake mechanism to enhance the broadcast protocol, which is dedicated to the emergency message broadcast in urban road environment. The reliability of emergency message dissemination can be improved by reducing the packet delivery failures caused by the collisions. We design a process of broadcasting emergency messages through the urban roads, taking into account the road characteristics and scalability requirements. We conduct simulation to confirm the feasibility and effectiveness of the proposed mechanism.

Multipath TCP (MP-TCP) algorithms are supposed to be fair to single-path TCP and utilize multiple paths to support high quality end-to-end services. Existing MP-TCP algorithms in literature are confronted with the problems: 1) MP-TCP can be excessively aggressive towards single-path TCP in their coexisting environments; 2) they sometimes fail to balance congestion on multiple paths; 3)their performance is prone to degradation under the circumstances of path heterogeneity, wireless network environments and bandwidth-intensive applications. In this paper, we propose a fluid-based MP-TCP algorithm to address the above problems. Our algorithm has a unique stable equilibrium and serves the design goals of fairness and congestion-balancing. Simulation results show that our algorithm achieves improvement of TCP-friendliness and window fluctuation performance in different scenarios.

Multipath congestion control algorithms are supposed to be friendly to traditional single-path TCP (SP-TCP). Existing multipath Additive Increase Multiplicative Decrease algorithms (MP-AIMD) for multipath TCP (MP-TCP) are confronted with the problems: 1) they are unfair to SP-TCP when the multiple available paths have heterogeneity, e.g. different RTT; 2) they all use packet losses as congestion signals and induce spurious backoffs on a wireless link with high error rate; 3) congestion window fluctuation reduces their potential to support smooth high-quality service such as multimedia applications. This paper proposes a path quality based multipath AIMD (AIMD-PQ) to tackle the above problems. We utilize the round trip time (RTT) on each sub-path to formulate the path quality estimation. AIMD-PQ balances the loads among its sub-paths, moves traffic off the most congested sub-path, and triggers Multiplicative Decrease by both packet loss and path quality signals. Simulation results show that AIMD-PQ improves both the TCP-friendliness and window fluctuation performance of MP-TCP in a heterogeneous wireless network environment.

The growing availability of various wireless access technologies promotes increasing demand for mobile video applications. Stream control transmission protocol (SCTP)-based concurrent multipath transfer (CMT) improves the wireless video delivery performance with its parallel transmission and bandwidth (BW) aggregation features. However, the existing CMT solutions deployed at the transport layer only are not accurate enough due to lower layer uncertainties, such as variations of the wireless channel. In addition, CMT-based video transmission may use excessive BW in comparison with the popular Transmission Control Protocol (TCP)-based flows, which results in unfair sharing of network resources. This paper proposes a novel cross-layer fairness-driven (CL/FD) SCTP-based CMT solution (CMT-CL/FD) to improve video delivery performance, while remaining fair to the competing TCP flows. CMT-CL/FD utilizes a cross-layer approach to monitor and analyze path quality, which includes wireless channel measurements at the data-link layer and rate/BW estimations at the transport layer. Furthermore, an innovative window-based mechanism is applied for flow control to balance delivery fairness and efficiency. Finally, CMT-CL/FD intelligently distributes video data over different paths depending on their estimated quality to mitigate packet reordering and loss, under the constraint of TCP-friendly flow control. Simulation results show how CMT-CL/FD outperforms existing solutions in terms of both video delivery performance and TCP-friendliness.

Stream Control Transport Protocol (SCTP)-based multimedia streaming has gained variety of attentions and resulted in many peer-reviewed publications. However, there is no MAC-SCTP cross-layer path switching strategy appropriate for wireless networking, where wireless error tends to occur frequently due to the intrinsic wireless link characteristics. As a remedy, we in this paper propose a novel Cross-layer Cognitive SCTP (SCTP-C2) for efficient multimedia data delivery by jointly considering the characteristics of MAC layer and transport layer. A Cross-layer Path Switching Trigger (CPST) is designed in SCTP-C2to improve the efficiency of the path switching mechanism and further provide an optimal congestion window (cwnd) fast recovery scheme after path switching. A Congestion-aware Multimedia Data Distributor (CMDD) is introduced in SCTP-C2to overcome a "hot-potato" congestion problem and enable an optimal transmission behavior by identifying network congestion. The results gained by a close realistic simulation topology show that how SCTP-C2outperforms existing SCTP protocol in terms of consumers' experience of quality for multimedia streaming service.

Content-Centric Networking (CCN) has emerged as a promising paradigm in current Internet Due to the interest-driven, we concern more about the content itself rather than the place it located, and therefore, CCN supports subscriber mobility natively, however, it remains a major challenge in publisher mobility. In this paper, we propose a hierarchical mobility management scheme for CCN (HMMCCN) which contains an overlay mapping structure to store the old-to-new name binding relationship and perform the mobility management for mobile nodes. The main goals of this paper are: (a) to propose a mobility scheme suitable for all mobile nodes in CCN, both publishers and subscribers; (b) to describe the hierarchical mobility management procedure during intra-domain and inter-domain handoff; (c) to establish an analytical model and formulate the location update signaling cost.

Content-Centric Networking (CCN) attracts much attention in the ongoing research area of the future Internet. CCN treats content as the first class entity of the network and attempts at addressing challenges in content distribution scalability, mobility and security. Mobile consumers of content may be well served in CCN due to the receiver-driven paradigm. Mobile sources of content, however, face a number of intractable problems, since content names are used both for routing of Interests and content identifying. In this paper, we borrow the idea of data and control plane separation and locator/identifier separation to design a source mobility management scheme in CCN. We design the basic architecture, and describe the handoff processes for the intra-domain movement and inter-domain movement. Finally, numerical results are presented.

Recent work has shown that Proxy Mobile IPv6 (PMIPv6) is a promising mobility management protocol for its salient features, such as supporting unmodified Mobile Node (MN). However, PMIPv6 does not consider the multicast routing support. Moreover, current research mainly concerns on the multicast receiver mobility, but it is a critical and challenging issue to ensure service connectivity for mobile multicast senders, which has not been addressed well. In this paper, we propose an efficient multicast sender mobility scheme for PMIPv6 (PMIP-BT), in which the multicast data can be transmitted through the PMIPv6 tunnel and the multicast sender mobility is transparently enabled in the PMIPv6 networks. Numerical results show that the proposed scheme outperforms the current scheme in terms of signaling cost.

Information-centric networking (ICN) and software defined networking (SDN) are two novel network paradigms that the networking community is actively investigating. Because of their salient features, there are increasing attempts to integrate ICN and SDN. In this paper, we show how a recently proposed future Internet architecture (called CoLoR) makes it efficient to integrate SDN and ICN. In particular, we show how CoLoR reduces the flow setup delay, the number of flow setup requests, and the number of flow entries.

Content Centric Networking (CCN) is one of the representative Information Centric Networking (ICN) architectures. In CCN, data objects are accessed instead of end hosts, and each router maintains a large size of routing table consisting of all name prefixes announced by content servers. To improve the scalability of CCN, an alternative name space relating to Router IDentifier (RID) has been proposed for forwarding the interest instead of using the name prefix of data object, and a name mapping system is applied for mapping data objects related names to RID related names. In this paper we propose dynamic interest transmission scheme in CCN with name mapping (DIT-NM), in which interest can be forwarded towards dynamically chosen RID through dynamically chosen interface associated with the RID according to real-time monitoring network status and traffic load information. The performance of the proposed scheme is evaluated using realistic network topologies with respect to latency and network link utilization. We also compare with the basic CCN model and multi-repository single path (MRSP) to highlight the advantages of our scheme. Achieved results demonstrate the effectiveness of the proposed scheme in reducing latency, improving robustness and balancing overall traffic distribution in name-mapping CCN environment.

Recently there has been a new emerging trend in integrating Information Centric Networking (ICN) and Software Defined Networking (SDN) together in the future internet research area. Software defined information centric networking (SD-ICN) may face more serious scalability problem in control plane compared with traditional SDN environment due to new features about in-network cache and content-based communication. In this paper, we address the control plane scalability problem from viewpoint of ICN/SDN integration and propose a scalable area-based hierarchical architecture (SAHA) for controller deployment in SD-ICN. The SAHA supports scalable awareness of network resources and content resources, as well as guarantees efficient interest matching and resource adaptation. Simulation experiments under OMNET++ show that the proposed SAHA can achieve good scalability in resource awareness and content-based communication.

Software-defined networking (SDN) has attracted many research interests among the networking community and has been deployed in many small to moderate networks. Although many approaches have been proposed to address the scalability issue of SDN, however, it is still an open problem whether SDN can be applied to large scale networks such as Tier-1 Internet service providers (ISP) when it works in passive mode. To address this problem, we in this paper analyze the number of flow entries that a switch needs to maintain and the number of flow setup requests per second that a controller needs to deal with, based on real data traces collected from a link between two routers in a Tier-1 ISP. The results show that, if we store a unique flow entry for every flow, SDN cannot be applied to large ISPs. In addition, if switches are required to deal with 12-tuple flow entries, SDN still cannot be applied to large Tier-1 ISPs even if we only setup unique flow entries for flows whose sizes are larger than 100 MB. However, if switches are required to deal with only 2-tuple flow entries, SDN can be applied to large Tier-1 ISPs if we only setup unique flow entries for flows whose sizes are larger than 10 MB.

The packet loss and handover tend to occur often in burst in heterogeneous wireless network. The Sender-based transport control mechanisms make current SCTP cannot provide an expected adaptive transmission rate adjustment and recovery strategy to ensure the users' quality of experience for multimedia streaming service due to the abrupt and frequent transmission rate fluctuation. Moreover, current SCTP solutions seldom consider balancing the overhead and sharing the load between the sender and receiver. In this paper, we propose a novel receiver-driven SCTP-based multimedia delivery solution which runs some important functions at receiver including: 1) appropriate sending rate estimation and advertisement, supported by a designed receiver-based sending rate estimator; and 2) primary path selection and fast recovery, enabled by a developed receiver-assisted path switch trigger. The simulation results show that how the proposed solution outperforms existing SCTP protocol in terms of multimedia delivery performance.

Online content propagation gives rise to tremendous data explosion and requires efficient management for a large amount of network resources, after next generation network service predomination. Especially in the age of social network, the way of content propagation and consumption has significantly changed from requesting to sharing. Since massive users are tending to influenced by the trends in social community and mainstream media, content caching becomes an effective method for providing better quality of service for such social relationships. A key challenge is traditional caching strategies cannot meet the dynamic variation in geo-social environments. In this paper, we propose a dynamic social content caching scheme with social user mobility. We employ a combination of cooperative filtering recommendation and cache update algorithm to effectively predict and manage cache updates. Simulation results show that our caching method over performs than classical caching methods that rely on the historical popularity prediction.

Routing in mobile ad hoc networks (MANETs) is an extremely challenging issue due to the features of MANETs. In this paper, we present a novel bio-inspired hybrid routing protocol (B-iHRP) supporting multimedia delivery based on zone routing framework, ant colony optimization (ACO) and physarum autonomic optimization (PAO). B-iHRP divides network topology into a series of zones subjectively. Within a zone, the route table of central node is proactively maintained by perceptive ants which can sense link status metrics through cross-layer perception to assess the discovered routes. Among zones, perceptive ants are sent to reactively find routes to destinations as well as assess the discovered routes with the metrics by source nodes. Afterwards, B-iHRP uses PAO to select the optimal one from the found routes and optimize autonomically the local routes during the course of multi-zone communication sessions. Simulation results show how B-iHRP can achieve the effective performance compared to existing state-of-the-art algorithms.

Content-Centric Networking (CCN) is an entirely novel networking paradigm, in which packet forwarding relies upon lookup operations on content names directly instead of fixed-length host addresses. The unique features of CCN names, i.e., variable length, huge cardinality, and hierarchical structure, introduce new challenges that could hinder the deployment of such a new architecture at the Internet scale. In this paper, we make an in-depth study of characteristics of large-scale CCN names, and propose a simple yet efficient CCN-customized name lookup engine (named by TB2F), which capitalizes the strengths of Tree-Bitmap (TB) and Bloom-Filter (BF) mechanisms, while counteracts their main limitations. To this end, TB2F splits CCN prefix into a constant size T-segment and a variable length B-segment with a relative short length, which are treated using TB and BF, respectively. Furthermore, an optimal length of the T-segment is found to improve the lookup efficiency. Experimental comparisons with respect to the reference Name Prefix-Trie and Bloom-Hash have been also carried out. The results show that TB2F properly configured has good scalability and efficiency by (i) speeding up lookup operations and reducing the false positive rate with respect to Bloom-Hash; (ii) requiring less memory than Name Prefix-Trie; (iii) achieving a low overhead in updating operations in the large scale case.

As a promising solution to the scalability issue of the current routing infrastructure, locator/identifier separation has gained variety of attentions and resulted in thousands of peer-reviewed publications. However, there is still significant ongoing work addressing many challenges of the secure Locator/Identifier Separation Context (LISC). In this paper, we propose a novel Cognitive Adaptive Access-Control solution (CAAC) for a secure LISC with three modules, which are Tag-aware Access-Control module (TAC) that devotes to generate user tag (UTag) and service tag (STag) by cognizing their natural and dynamic attributes, Adaptive Policy Generation paradigm (APG) that serves to select proper policy instance for adaptive and intelligent access control, and Cooperative Decision Making module (CDM) that contributes to provide efficient decision-making by multi-peer parallel cooperation. We implement the designed CAAC in our identifier-based network platform to verify its advantages.

In this paper, we propose TCP-friendly CMT, a novel TCP-friendly Stream Control Transmission Protocol (SCTP)-based Concurrent Multipath Transfer (CMT) solution necessitating the following aims: (i) fairness to TCP flows, (ii) load sharing, and (iii) improve multimedia delivery performance. To satisfy the first requirement, a Weighted Moving congestion window (WM-cwnd) based Additive Increase and Multiplicative Decrease (AIMD)-enhanced congestion control mechanism is designed to make TCP-friendly CMT preserve fairness to TCP flows. A newly WM-cwnd-based data distribution algorithm is further introduced in TCP-friendly CMT to make proper load sharing and improve multimedia delivery performance. Finally, a proposal for saving energy is introduced. The simulation results show how the proposed TCP-friendly CMT solution improves the data delivery performance, as well as users' quality of experience for multimedia streaming service while still remaining fair to the competing TCP flows.

Concurrent Multipath Transfer (CMT), enabled by Stream Control Transmission Protocol (SCTP), is considered as one preferred data-transport mode due to increased available bandwidth. However, CMT performance degrades seriously in terms of data reordering due to path dissimilarity and frequent packet loss from wireless unreliability. Most relevant solutions follow the packet sequence numbers and thereby focus on strict in-order reception and packet-specific retransmission. Passively adapting to the network conditions, those approaches are not general and well enough responding to the dynamicity of wireless environment. By applying Network Coding (NC) to CMT, this paper proposes a progressive end-to-end solution (CMT-NC) to those problems in heterogeneous wireless networks. CMT-NC is capable of avoiding reordering and compensating lost packets. Further, an innovative group-based transmission management mechanism enhances the robustness and reliability of data transfer. Simulation results show how by using CMT-NC significant improvements in comparison to another state-of-the-art solution are obtained.

In this paper, we present a novel bio-inspired trusted routing framework (B-iTRF) which composed of trust mechanism and routing strategy. For trust mechanism, B-iTRF monitors neighbors' behavior in real time and then assesses neighbors' trust value based on the priori knowledge. For routing strategy, each node finds routes to the Sink based on ant colony optimization. In the process of path finding, B-iTRF senses and calculates the metrics of the found routes to support the route selection. Moreover, B-iTRF also assesses the availability of route based on Physarum autonomic optimization to maintain the route table. Simulation results show that B-iTRF can achieve the effective performance compared to existing state-of-the-art algorithms.

High-quality multimedia streaming services in Vehicular Ad-hoc Networks (VANETs) are severely hindered by intermittent host connectivity issues. The Information Centric Networking (ICN) paradigm could help solving this issue thanks to its new networking primitives driven by content names rather than host addresses. This unique feature, in fact, enables native support to mobility, in-network caching, nomadic networking, multicast, and efficient content dissemination. In this paper, we focus on exploring the potential social cooperation among vehicles in highways. An ICN-based COoperative Caching solution, namely ICoC, is proposed to improve the quality of experience (QoE) of multimedia streaming services. In particular, ICoC leverages two novel social cooperation schemes, namely partner-assisted and courier-assisted, to enhance information-centric caching. To validate its effectiveness, extensive ns-3 simulations have been executed, showing that ICoC achieves a considerable improvement in terms of start-up delay and playback freezing with respect to a state-of-the-art solution based on probabilistic caching.

ABSTRACT There is an increasing consensus that the locator/identifier separation of IP address is necessary to resolve the scalability issues of current Internet routing architecture. After identifiers are separated from locators, an identifier‐to‐locator mapping service must be employed to map identifiers onto locators. From this point, this paper proposes an anomaly detection and response approach based on mapping requests. By using the cumulative sum algorithm for change point detection, this approach introduces the anomalous traffic detection of mapping requests to diagnose the aberrant network behaviors. Once alarming, two effective response methods can be chosen to control the anomalous attack traffic in real time. Furthermore, in order to decouple the mapping request traffic from the mapping cache, this approach not only takes into account the mapping cache timeout but also puts forward a practical mapping request threshold algorithm. In particular, our simulation results show that, compared with the anomaly detection approach based on network traffic, the proposed approach is more advantageous and efficient. In addition, we also discuss the possible false positive and false negative problems, which may be caused by some accidental phenomena. Copyright © 2014 John Wiley & Sons, Ltd.

The original architecture of content-centric network CCN may suffer from interest flooding attacks. In this paper, we focus on one type of interest flooding attacks called denial of service against content source DACS attack. To damage CCN, it floods a large number of malicious interests requesting content that does not exist, which guarantees that no cache hit can occur at routers until these malicious interests reach the target content source. Thus, it can directly exhaust the resource of the victim. To counter it, we propose a threshold-based detecting and mitigating TDM scheme. The basic idea is to detect DACS attack on the basis of the frequency that pending interest table items in CCN routers expire recording this frequency by introducing two counters with their corresponding thresholds and one indicator for counter mode and to mitigate it by implementing the rate limiter in each router. From the viewpoint of a CCN router, we analyze the performance of TDM in terms of detection ability and effect on mitigating malicious traffic. In addition, we briefly analyze the overhead of TDM. The results show that TDM achieves high detection ability and good effect on mitigating malicious traffic while bringing in small overhead on countering DACS attack. To the best of our knowledge, this is the first attempt to design a detailed scheme embedded with corresponding algorithms on countering this attack. Copyright © 2013 John Wiley & Sons, Ltd.

Virtual machine (VM) live migration, the key problem of modern virtualized data centers, is a challenging task since 1) Frequent traffic across data center between coupling VMs limits the efficiency of current methods. 2) Most existing approaches suffered from poor scalability issues as multi-objective optimization is still an open question in these designs. To address these problems, in this paper, a novel multi-objective VM migration algorithm is proposed. Given the definition of dominant resource fairness, a max-min fair model subject to server-side constraints is introduced. Then, we further formulate the VM migration as an optimization problem which considers application dependencies to reduce network traffic caused by migration. By incorporating the two basic VM migration algorithms, we conduct a joint formulization for maximizing the utilization of physical machines while minimizing the traffic burden across dependent VMs. The simulation result demonstrates the accuracy of the theoretic model and it is shown that our proposed method decreases network traffic by up to 82.6%, significantly improving the efficiency of data centers.

Extension for the multi-homing feature of Stream Control Transport Protocol (SCTP), Concurrent Multipath Transfer (CMT) can achieve bandwidth aggregation by making use of parallel transmisson over selected paths. However, if CMT-based path selection depended solely upon the information provided by transport layer, it cannot really make the desired bandwidth aggregation. Motivated by the urgent needs of cross-layer bandwidth aggregation and the advances of Ant Colony Optimization (ACO) in network selection, this paper proposes a novel ACO based cross-layer bandwidth aggregation scheme for efficient Concurrent Multipath data Transfer (CMT-ACO) in wireless transmission. CMT-ACO provides an efficient data delivery with two modules, which are ACO-based Efficiency Aware model (ACO-EA) that devotes to sense paths' transmission efficiency(supported by a cross-layer factor) and reduce ``ping-pongquot; path switching (enabled by a stabilization factor), and ACO-based Bandwidth Aggregation scheme (ACO-BA) that contributes to provide a cross-layer optimal bandwidth aggregation scheme. The results gained by a close realistic simulation topology show that how CMT-ACO outperforms existing CMT protocol in terms of performance and quality of service in multi-homed SCTP-based wireless networks.

Providing IP multicast to Mobile Nodes has drawn significant attention and so far many approaches have been proposed. However, current mobile multicast schemes mainly focus on the multicast receiver mobility support based on the host-based mobility management protocol. Recent work has shown that ensuring service connectivity for mobile multicast sources is a challenging issue and is still largely open. With the rapid development of the network-based mobility solution, multicast source mobility support schemes in Proxy Mobile IPv6 (PMIPv6) networks are needed urgently. In this paper, a base solution (BS) and a direct multicast routing scheme (DMRS) are proposed for multicast source mobility in PMIPv6 networks. In order to transmit multicast data through the PMIPv6 tunnel, we adopt the Multicast Listener Discover (MLD) Proxy function in the BS. In the DMRS, locally optimized routing for traffic flows can be provided. The performance of the proposed schemes is evaluated by theoretical analysis and implementation. These results show that our proposed schemes outperform the previous solutions in terms of the signaling cost and the BS has lower multicast handover delay than the DMRS.

There is a trade-off between routing efficiency and energy equilibrium for sensor nodes in wireless sensor networks (WSNs). Inspired by the large and single-celled amoeboid organism-slime mold physarum polycephalum, this paper presents a novel physarum-inspired routing protocol (P-iRP) for WSNs to address the above issue. In P-iRP, a sensor node selects its proper next hop by using a proposed physarum-inspired selecting next hop model (PSN), which considers comprehensively the distance, energy residue and location of the next hop. We introduce the PSN's routing selecting strategy and detail PiRP's algorithms. Simulation results show how P-iRP can achieve the effective trade-off between routing efficiency and energy equilibrium compared to existing classical algorithms.

With feature of flows across multiple interfaces based on the multi-homing feature of Stream Control Transport Protocol (SCTP), Concurrent Multipath Transfer (CMT) has been regarded as a promising protocol for content-rich multimedia data delivery under stringent bandwidth, delay, and loss wireless environment. However, current CMT researches mostly pay attention to improve CMT protocol itself depended solely upon the information provided by transport layer. In this paper, we propose a novel MAC-SCTP based Cross-layer Cognitive CMT (CMT-CC) for efficient multimedia distribution in varying wireless transmission. A Cross-layer Quality Sense Model (CQSM) is designed in the CMT-CC to cognize the paths' quality and select candidate paths for multimedia content delivery. By condition-aware cognitive ability to distinguish the causes of transmission condition change, a further proposed Intelligent Multimedia Content Distributor (IMCD) makes adaptive multimedia delivery behaviors in compliance with real-time wireless condition. Results obtained by a close realistic simulation topology show how the CMT-CC outperforms existing CMT approach in terms of users' of quality of experience for multimedia streaming services.

In this paper, we consider employing a multi-element antenna (MEA) with close spacing to tackle the challenging channel estimation (CE) in highly mobile OFDM systems. Instead of large spacing for diversity, we propose to place the adjacent elements with one symbol distance in the moving direction to observe the quasi-duplicated channels in temporal difference of one symbol period. In exploiting the quasi-duplicated channels, we developed a novel CE-symbol detection (SD) iteration that cooperates with the standardized comb-type pilots to track fast varying channels. From simulation results, we show the proposed system outperforms the conventional receiver of two antennas with spatial diversity in highly mobile channels as long as the mutual coupling effects with the close-spaced elements are restricted.

Information-Centric Networking (ICN) is a clean-slate networking architecture that puts information is focus instead of addressed hosts. Construction of content retrieval model to estimate the delivery performance is challenging in this ICN-based Mobile Ad hoc Networks (MANETs). In this paper, we propose a novel content retrieval model (PRCRM) for Information-Centric MANETs (ICMs) in 2-dimensional case. By investigating the distribution of content popularity, receiver-driven mechanism, content caching and replacement mechanism and generalized mobility model in 2-dimensional space, PRCRM constructs a novel content retrieval model based on the content hit/miss probability to estimate the content retrieval-related performance. We evaluate PRCRM by comparing its performance with another state of the art solution in terms of RTT and throughput. Simulation results demonstrate PRCRM's rationality and validity and it is shown that PRCRM is available to analyze content retrieval in ICMs.

Mobile devices equipped with multiple network interfaces can increase their throughput by making use of parallel transmissions over multiple paths and bandwidth aggregation, enabled by the stream control transport protocol (SCTP). However, the different bandwidth and delay of the multiple paths will determine data to be received out of order and in the absence of related mechanisms to correct this, serious application-level performance degradations will occur. This paper proposes a novel quality-aware adaptive concurrent multipath transfer solution (CMT-QA) that utilizes SCTP for FTP-like data transmission and real-time video delivery in wireless heterogeneous networks. CMT-QA monitors and analyses regularly each path's data handling capability and makes data delivery adaptation decisions to select the qualified paths for concurrent data transfer. CMT-QA includes a series of mechanisms to distribute data chunks over multiple paths intelligently and control the data traffic rate of each path independently. CMT-QA's goal is to mitigate the out-of-order data reception by reducing the reordering delay and unnecessary fast retransmissions. CMT-QA can effectively differentiate between different types of packet loss to avoid unreasonable congestion window adjustments for retransmissions. Simulations show how CMT-QA outperforms existing solutions in terms of performance and quality of service.

The locator/ID separation protocol (LISP) proposed for addressing the scalability issue of the current Internet has gained much interest. LISP separates the identifier and locator roles of IP addresses by end point identifiers (EIDs) and locators, respectively. In particular, while EIDs are used in the application and transport layers for identifying nodes, locators are used in the network layer for locating nodes in the network topology. In LISP, packets are tunneled from ingress tunnel routers (ITRs) to egress tunnel routers in a map-and-encapsulation manner. For this purpose, an ITR caches on demand some mappings between EIDs and locators. Since hosts roam from place to place, however, their EID-to-locator mappings change accordingly. Thus, an ITR cannot store a mapping permanently but maintains for every mapping a timer whose default value is set to a given cache timeout. If the cache timeout for a mapping is too short, an ITR frequently queries the mapping system (control plane), resulting in a high traffic load on the control plane. On the other hand, if the cache timeout for a mapping is too long, the mapping could be outdated, resulting in packet loss and associated overheads. Therefore, it is desirable to set appropriate cache timeout for mapping items. In this paper, we analytically determine the optimal cache timeout for EID-to-locator mappings cached at ITRs to minimize the control plane load while remaining efficient for mobility. The results presented here provide valuable insights and guidelines for deploying LISP.

The energy efficiency of modern data centers has become a practical concern and has attracted significant attention in recent years. In contract to existing solutions that primarily focuses on only one specific aspect of management to reduce energy consumption, this paper explores the balance between server energy consumption and network energy consumption to present an energy-aware joint virtual machine (VM) placement. Given the definition of VM placement fairness, the basic algorithm of VM placement which fulfills server energy consumption constraints is conducted. Then, we further formulate the VM placement as an optimization problem which considers application dependencies to reduce network energy consumption. We design a joint algorithm that efficiently solves the VM placement problem for very large problem sizes. Using simulations, we conduct a comparative analysis on the impact of the data center architectures, server constraints and application dependencies on the potential performance gain of energy-aware VM placement. Compared to existing generic methods, we show a significant performance improvement such as efficiently reducing the number of physical machines used to save server energy consumption, decreasing the communication distance between VMs to obtain data center network energy consumption efficiency, improving scalability of data centers.

With the growth of different design goals and application requirements, wireless sensor networks (WSNs) are receiving sustained attentions in the recent low-cost industrial automation systems. Moreover, Cognitive Radio (CR) technology gives us a possibility to maximize the utilization efficiency of the limited spectrum resources. However, because the wireless devices coexist in the same radio environment, there are harmful channel conflicts among users, and the increasing radio systems causes great contribution to the increasing energy consumption. In order to realize the industrial circumstance, we complete three major works in this paper. First of all, we describe a practical model of cumulative interferences from the entire cognitive radio-based industrial wireless sensor networks (CR-IWSNs). Then, based on the interference model and the interference avoidance purpose, we propose a self-configurable power control scheme to address the communication requirements on both interference temperature and secondary network Quality-of-Service. Finally, Nonlinear Programming is used to model the scheme and a distributed algorithm is given to solve the problem. Several simulations are given to verify the effectiveness of the proposed power control algorithm on optimizing the total system throughput and energy consumption. Results show that the throughput could be improved and the energy consumption could be reduced with the guarantee that the users are without interference.

In this paper, we investigate resource block assignment in femtocell networks. A resource block assignment algorithm is designed to avoid co-channel intercell interference and ensure service quality for femtocell networks with dense and random femto deployments. We first formulate the optimization problem as the integer linear programming (ILP) on resource block assignment. The goal of the optimization formulation is to maximize the overall utilization of resource blocks with quality of service (QoS) constraints. We propose an efficient and simple algorithm termed interference-aware resource block assignment (IARBA). By considering conditions of resource blocks, the proposed approach achieves better resource block efficiency and assignment within QoS requirements. Our analytical and simulation results show that IARBA not only provides interference-free resource block assignment but also outperforms existing schemes in terms of average throughput with comparable complexities.

In highly mobile OFDM systems, the carrier frequency offsets (CFO) with Doppler spreads for downlink detection can be considerably large, which degrades the frequency alignment for uplink transmission, particularly in employing directional antennas for inter-carrier interference (ICI) reduction. In prior works, the directional antenna was investigated with appropriate frequency alignment in receiver's local oscillator to efficiently reduce ICI in fast time varying OFDM systems. To resolve the optimal frequency offsets problem with Doppler spreads, this paper develops a simple scheme to capture instant Doppler power spectrum density (PSD) through moving directional antennas with arbitrary gain patterns. Thus, the optimal aligning frequency is derived as the center of gravity of the Doppler PSD. Simulations show our approach acquires the highest carrier to interference (C/I) ratio and the lowest bit error rates (BER) compared with other approaches.

The ad hoc network is composed of multiple sensor nodes to serve various applications, such as data collection or environmental monitoring. In many applications, the sensor nodes near the boundary of the deployment region provide biased or low-quality information because they have limited number of neighboring nodes and only partial information is available. Hence, the boundary recognition is an important issue in the ad hoc networks. By the statistical approach in high node density networks, Fekete's pioneer work identified the boundary node by number of neighboring nodes and using a specific threshold. By exploiting the number of nodes in the two-hop region, our proposed algorithm has significant improvement of boundary recognition contrasted with Fekete's algorithm in the low-density network. Given the information topology and the cost function, the analyses provide a framework to obtain the optimal threshold for boundary recognition. Besides, the simulation results reveal the proposed algorithm has greater than 90% detection rate and lower than 10% false alarm rate.

This paper presents a category of detection schemes for Multiple-Input Multiple-Output (MIMO) system called Parallel Sphere Decoder (PSD). Compared to the conventional depth-first sphere decoder with Schnorr-Euchner enumeration (SE-SD), the proposed PSD algorithms use parallel computations and achieve approximately 50% searching time reductions under the same amount of computations. Namely, in hardware implementation, the proposed work provides trade-off between computational time and computing units. Simulations of the proposed algorithms in 4×4 16-QAM and 3×3 64-QAM MIMO systems show the searching time reductions of the proposed algorithms while maintaining ML performances.

This paper proposes cooperative opportunistic routing (COR), a throughput improvement scheme for the cooperative opportunistic routing in multi-hop wireless mesh networks (WMNs). We investigate the two major issues in opportunistic routing, the selection and the prioritization metric for the candidate set. The COR is presented to select and prioritize the candidate node with minimum expected cost. This candidate selection with low expected cost on each transmission constructs a throughput efficient routing path. The COR's robust packet handling strategy is also proposed to avoid duplicated transmission without forwarding list. With more efficient candidate set and packet handling, the average throughput improves by 76% and the end-to-end delay is reduced by 15% in our simulation results.

The use of nuclear energy to generate electric power is crucial in meeting the high energy demand of modern economy. The dependability analysis of nuclear power plants has been a critical issue and the reactor containment is the most important safety structure acting as a barrier against the release of radioactive material to the environment. In this paper, we analyze the dependability of the reactor containment. We also propose a tool for design, implementation, and V&V to enhance the dependability of reactor containment through an integrated leakage rate test. Our practical experiences in the on-site tests are also discussed.

This work proposes an adaptive intercell interference avoidance scheme for the self-organization in Orthogonal Frequency Division Multiple Access (OFDMA) femtocell networks. Due to the expected large number of user-deployed cells, the femtocell networks suffers from the intercell interference problem. In this paper, we define the self-organizing resource allocation problem to maximize resource efficiency with OFDMA architecture. The proposed problem formulation supports the desired Quality of Service (QoS) criteria while satisfying reliability constraints. We develop an autonomous resource allocation algorithm to pursue the most efficient frequency allocation. From the simulation results, the proposed approaches can increase the system throughput by over 13%, while the femtocell interference can be avoided completely.

The most important goal in event-driven wireless sensor networks(EWSNs) is to transmit the event information to users timely. One of the most important factors that would make it possible to reach this goal is the design of efficient detecting protocols. In this paper we present an efficient event detecting protocol(EEDP) designed for event monitoring applications based on geographical routing. In the event area, each node broadcasts its primary detection result to make a decision corporately. And then nodes will choose the nearest node to destination as forwarder to reduce the delivery time. To improve the event detection probability, EEDP makes final decisions combing with other single decisions. EEDP is a highly efficient and accurate protocol for the event detection applications where the constraints of end-to-end delay and reliability are stringent.

The use of nuclear energy to generate electric power is crucial in meeting the high energy demand of modern economy. The dependability of nuclear power plants has been a critical issue and the reactor containment is the most important safety structure acting as a barrier against the release of radioactive material to the environment. In this paper, we propose a practical framework for design, implementation, and V&V to enhance the dependability of reactor containment through an integrated leakage rate test.

The ad hoc sensor network has been widely applied to various applications, such as environmental data collection and surveillance. To enable these applications, the accurate localization of sensor nodes is crucial. The DV-Hop provides a basic scheme to retrieve the localization information without GPS. The DV-Hop scheme requires the anchor nodes to be localized in advance, and the locations of the anchor nodes are used to localize other unknown nodes. The hop count between two anchor nodes can be exchanged through multi-hop routing. Using the hop counts and locations of other anchor nodes, the anchor nodes obtain the average distances per hop among one another. The distance estimation errors are caused by the uncertainties in per-hop distance estimation and the communication ranges. We propose a scheme where a node ranks the orders of its neighbor nodes through exchanging neighbor information locally. The neighbor information with orders provides useful information for node localization. Besides, the analysis reveals the probability among the order distance and the node density. Therefore, the distance estimation is improved by using the order information of neighbor nodes. The simulation results show more than 22% error reduction compared with the DV-Hop.

Lane tracing is the problem of estimating the geometric shape of the lane boundaries based on the image grabbed by a camera on board a vehicle. In this paper, a real-time video-based lane detection method is presented. By analysing the result of lane tracing, the behaviour of the vehicle is traced and the focus windows are placeable, which provides useful information to the next round detection recursively. The system is porting on both PC and iPhone 3G to verify the recognition rate and performance through field testing. With the knowledge of road model, it is applicable to the marked and the unmarked roads, as well as the dash and the solid paint line roads. Experimental results show that the proposed method is robust and the performance is capable for practical applications.

Recent work has shown that sink mobility along a constrained path can improve the energy efficiency in wireless sensor networks. However, due to the path constraint, a mobile sink with constant speed has limited communication time to collect data from the sensor nodes deployed randomly. This poses significant challenges in jointly improving the amount of data collected and reducing the energy consumption. To address this issue, we propose a novel data collection scheme, called the Maximum Amount Shortest Path (MASP), that increases network throughput as well as conserves energy by optimizing the assignment of sensor nodes. MASP is formulated as an integer linear programming problem and then solved with the help of a genetic algorithm. A two-phase communication protocol based on zone partition is designed to implement the MASP scheme. We also develop a practical distributed approximate algorithm to solve the MASP problem. In addition, the impact of different overlapping time partition methods is studied. The proposed algorithms and protocols are validated through simulation experiments using OMNET++.

As a promising network-based mobility management method that does not require active participation of mobile nodes (MNs), Proxy Mobile IPv6 (PMIPv6) is attracting considerable attention among the telecommunication and Internet communities. It remains an open issue how to build a scalable PMIPv6 domain that is able to support a large number of MNs while keeping handover delays low. In this paper, we propose an approach for building Scalable And Robust PMIPv6 (SARP) domains. We propose that every mobility access gateway (MAG) in a SARP domain also functions as a local mobility anchor (LMA), and is organized into a virtual ring with all other MAGs. Consistent hashing is used to efficiently distribute the mapping between each MN and its LMA to all MAGs. A MAG finds an MN's LMA by sending a query message to the virtual ring. Our analysis verifies the robustness and scalability of SARP. We also propose two handover procedures for SARP and show that they achieve low handover delays.

Abstract Mobile IPv6 (MIPv6) is a host‐based mobility support specification which has been approved by the IETF as the standardized solution of global mobility management in IPv6 network for Mobile Node (MN). However, MIPv6 handover procedure results in a large handover delay. To improve its performance, some MIPv6 variants such as Fast Handover for MIPv6 (FMIPv6) and Hierarchical MIPv6 (HMIPv6) were proposed. However, they all require the MN to support the mobility management protocols which increase the difficulty of management and deployment. Recently, IETF NETLMM workgroup published the Proxy Mobile IPv6 (PMIPv6) which is a network‐based localized mobility management to provide the mobility support for mobile host without the involvement of the mobility signaling. In this paper, we analyze the singling cost of PMIPv6 and implement it in our test‐bed to evaluate its performance. The results show that the PMIPv6 has lower signaling cost and packet delivery cost, and it can improve the handover performance of UDP and TCP than the other mobility management protocols under low‐delay networks, as for the wide area networks, it needs to introduce some mechanism like fast handover to further improve its performance. Copyright © 2009 John Wiley & Sons, Ltd.

Mobile multicast becomes a research hotspot with the development of wireless and mobile technologies, and it is based on the traditional multicast protocols and mobility management protocols to provide the multicast services for mobile subscribers. Several mobile multicast methods were proposed in the past few years, but most of them are based on the mobile IPv6 and its alternatives which require the mobile hosts to support the mobility function. Recently, the proxy mobile IPv6 (PMIPv6) was proposed to provide the mobility support for mobile node with or without mobility involvement, and the previous studies have shown that the PMIPv6 can improve the handover performance. However, the PMIPv6 mainly concerns on the unicast routing support and little considers the multicast routing. In this paper, we study the multicast support in PMIPv6 and propose two multicast methods called the MAG (mobile access gateway)-based method and LMA (local mobility anchor)-based method, and analyze their performance under different scenarios. The analytical results show that the LMA-based method is suitable for the bigger PMIPv6 domain, and larger network topology scenarios (more than 105), whereas the MAG-based method is used for the smaller PMIPv6 domain.

Because of its salient features such as ease of deployment, Proxy Mobile IPv6 (PMIPv6) is a promising solution for mobility management and is attracting considerable attention among the telecommunication and Internet communities. To the best of our knowledge, however, it is still an open issue how to build a scalable PMIPv6 domain so that a PMIPv6 domain is able to support as many mobile nodes (MNs) as possible while keeping low handover delay. In this paper, we propose an approach for building Scalable And Robust PMIPv6 (SARP) domains. We propose that every mobile access gateway (MAG) behaves as both an MAG and a local mobility anchor (LMA). All MAGs in a proxy mobility domain then organize into a ring and (key = MN-identifier, value = MN's LMA) pairs are distributed to all MAGs using consistent hashing. In this way, every MAG can send proxy binding update messages to the LMA identified by hashing the MN-identifier of the MN. We show that SARP is robust, scalable, and has very low handover delay. In particular, our results show that with SARP, a single PMIPv6 domain is able to support 108MNs.

In monitoring wireless sensor networks (WSNs), the occurrence of an emergency will generate a large amount of data in a very short time. Among them, some urgent information is more critical and needs to be transmitted out reliably as soon as possible. In this scenario, congestion is inevitable due to the limited resources, especially near the sink. To address this problem, we propose a novel reliable transmission protocol for urgent information (RETP-UI) in WSNs. In RETP-UI, not only the current queue length but also the ratio of queue length's fluctuation is adopted as indication of congestion. Each sensor node evaluates its congestion level locally and determines its congestion state with a state machine. Furthermore, we use a multistage rate adjustment mechanism to adjust each node's sending rate cooperatively according to different congestion states. Finally, with extensive simulation results we illustrate that the performance of RETP-UI is significantly improved over traditional protocols.

By identifying the shortcomings of using RTT to evaluate the quality of different paths in a multipath scenario, we propose a Relative Delay Estimator (RDE) to compare the relative one way delay of different paths without clock synchronisation. This estimator enables the comparison and selection of the best forward and backward paths, in terms of delay. As an initial application of RDE, we design a novel retransmission policy (NcRDE). The main novelty of this policy is that, from the multiple paths available, the path chosen for retransmission is according to the value of one way delay. We also present an extension to this scheme that takes path failures into account (PF-NcRDE). Simulation results show that, when compared with recently proposed retransmission policies, NcRDE can improve throughput when the different paths have different forward and backward delays. Also, in case of path failure PF-NcRDE enhances the performance significantly over NcRDE.

In this paper, we propose a mobile multicast architecture with management support. In this architecture, a powerful entity called Multicast Controller (MC) is deployed which handles most of the multicast management-related tasks. Based on this architecture, the mobile multicast routing protocol called Fast Chain Mobile Multicast (FCMM) is proposed, which processes the handover in advance and spans the multicast tree using the forwarding chain. Our analysis demonstrates that the FCMM protocol outperforms other related protocols and is a promising alternative for providing efficient and flexible mobile multicast services.

Wireless sensor network (WSN) is the connection between the physical world and mankind. Particularly, environmental monitoring and devices automatic control of intelligent building based on wireless sensor network is considered as one of the most crucial applications. It can perceive many kinds of environmental parameters and feedback control information to some devices to provide comfortable environment to people. However, it is difficult to deploy a WSN in the buildings because there usually are many wireless LAN devices used in the buildings, which bring serious frequency interferences. In this paper, we conduct a real intelligent building wireless sensor network (IBWSN) for environmental monitoring and air-conditioning automatic control. In order to ensure the effectiveness of this system, actual spectrum analysis is developed. Performance evaluation proves that the presented IBWSN can satisfy the needs of the proposed applications.

In this paper, we propose a network mobility supporting scheme (N-NEMO) in Proxy Mobile IPv6 (PMIPv6) network, which is an issue still up in the air for the PMIPv6. In the N-NEMO, a tunnel splitting scheme is used to differentiate the inter-Mobility Access Gateway (MAG) and intra-MAG mobility. The performance analysis and comparison between other related schemes show that N-NEMO reduces the signaling cost significantly. Besides, it enhances the efficiency and scalability to provide the comprehensive network mobility in the PMIPv6 context.

Source address authentication is very important to change current serious network security situation. In this article, we propose a new source address validation scheme based on IBS algorithm and study its security using SVO logic. We find that our scheme can successfully guarantees authenticity of IP packet’s information source.

Recent work shows that sink mobility along a constrained path can improve the energy efficiency in wireless sensor networks. However, due to the path constraint, a mobile sink with constant speed has limited communication time to collect data from the sensor nodes deployed randomly. This poses significant challenges in simultaneously improving the amount of data collected and reduction in energy consumption. To address this issue, we propose a novel data collection scheme, called the maximum amount shortest path (MASP), that increases network throughput as well as conserves energy to optimize the assignment of sensor nodes. MASP is formulated as an integer linear programming problem and then solved with the help of a genetic algorithm. A two-phase communication protocol is designed to implement the MASP scheme. Simulations experiments using OMNET++ show that MASP outperforms the shortest path tree (SPT) and static sink methods in terms of system throughput and energy efficiency.

Abstract Proxy mobile IPv6 (PMIPv6) is a network‐based mobility protocol where the mobility management signaling is performed by a network entity on behalf of the node requiring mobility itself. To the best of our knowledge, no studies have been conducted in the area of proxy MIPv6 authentication method. This paper proposes an authentication method in proxy MIPv6. In addition to providing access authentication, the proposed authentication method prevents threats such as replay attack and key exposure. Also, we develop analytic models for the authentication latency and cost analysis. Then, the impacts of mobility and traffic parameters on the authentication cost and latency are analyzed, respectively. Copyright © 2008 John Wiley & Sons, Ltd.

It is commonly recognized that today's Internet routing and addressing system is facing serious scaling problems, which are mainly caused by the overloading of IP address semantics. That is, an IP address represents not only the location but also the identity of a host. To address this problem, several recent schemes propose to replace the IP namespace in today's Internet with a locator namespace and an identity namespace. The locator namespace consists of locators that are used to represent the locations of hosts. On the other hand, the identity namespace consists of identifiers that are used to represent the identities of hosts. For these schemes to work, there must be a mapping system that can supply an appropriate locator for any given end point identifier (EID). While prior related works mainly focus on aggregable EIDs, several recent works proposed the use of self-certifying EIDs for purpose of security and privacy. However, self-certifying EIDs are flat, unstructured and prior proposals cannot be used to deal with flat EIDs. In this paper, we propose a Distributed hash table (DHT)-based identifier-to-locator mapping scheme to resolve a locator for a flat identifier. We evaluate the performance of the proposed scheme. We show that, besides the capability to support flat EIDs, the scheme has good scalability and low resolution delay. We also show that the scheme is robust and can efficiently support mobility.

The Border Gateway Protocol (BGP) is the only inter-domain routing protocol currently. BGP allows ASes to select and propagate routes based on flexible and locally defined policies. But the flexibility and freedom of policies can lead to routing instability, even policy disputes among ASes cause inter- domain routing oscillations. Recent studies either enforce global constraints on policies without freedom and privacy, or require expensive memory consumption and huge message overhead. In this paper, we propose a solution that operates with small overhead, guarantees safe convergence, and preserves policy freedom and privacy as much as possible. It uses a distributed mechanism for detecting and solving policy disputes at run time. Only when the policy-induced oscillations exist, ASes suppress the dispute routes for safety.

Network mobility (NEMO) aims managing seamless connectivity for a group of mobile terminals as a whole mobile network. Multiple care of addresses (MCoA) registration scheme was also proposed to address seamless Internet connectivity issue. In this paper we first analyze the upload and download packet loss of mobile router (MR) in NEMO basic support protocol. Then the seamless Internet connectivity of MR which is equipped with WLAN, CDMA and GPRS interfaces is studied and experimented on our platform successfully. Finally, we analyze and compare the performance of packet loss ratio between uni-interfaced and multiple-interfaced MR, and results indicate that multiple interfaced MR can increase the performance of MR obviously when it moves across heterogenous overlay access networks.

The next generation of mobile/wireless communication system is expected to include heterogeneous broadband wireless networks that will coexist and use a common IP core to offer a diverse range of high data rate multimedia services to end users with contemporary mobile devices. The mobile devices will be equipped with multiple network interfaces since the networks have characteristics that complement each other. This requires the provision of seamless vertical handover. In this paper, a probability driven handover scheme which uses the mobile's speed (SPHO) is developed to support seamless and adaptive handover management between WLAN and UMTS. Extensive simulation experiments show that this scheme effectively achieves seamless handover between heterogeneous networks and guarantees quality of communication during handover.

Proxy Mobile IPv6 is a network-based mobility protocol where the mobility management signaling is performed by a network entity on behalf of the node requiring mobility itself. To the best of our knowledge, no studies have been conducted in the area of Proxy Mobile IPv6 authentication method. This paper proposes an authentication method in Proxy Mobile IPv6. In addition to providing access authentication, the proposed authentication method prevents threats such as replay attack and key exposure. Also, we develop analytic models for the authentication latency and cost analysis. Then, the impacts of mobility and traffic parameters on the authentication cost and latency are analyzed, respectively.

Intrusion detection system (IDS) has recently become a heated research topic due to its capability of preventing attacks from malicious network users. A pattern matching intrusion detection system has been proposed in this paper. The pattern matching based NIDS consists of four modules: collection module, analyze module, response module and attack rule library. We base this system on CIDF architecture. Realizing that string matching is the bottleneck, our system has improved the performance of detection engines due to an improved algorithm based on the current BM algorithm. Testing results demonstrate superior performance in terms of the detection speed of IDS

Decreasing communication and management cost is a key issue of the research of Internet mobility management. Hierarchical mobile IPv6 (HMIPv6) has been proposed to reduce the number of location registration messages in backbone network by using hierarchy agent, but resources consumption inside the hierarchical domain is increased as expense. In this paper, a concept of integrated optimization is proposed, in which network cost and bandwidth consumption of delivering management messages and data payload is optimized both inside and outside hierarchical domain in a certain degree. Mathematic models of HMIPv6 are built on the viewpoint of entire network resource, and the judgment rule is obtained in result, which determines when HMIPv6 is adopted preferably. Based on the rule, intelligent mobility management scheme (IMMS) is designed, which allows a mobile node to select a suitable mobility management mechanism from mobile IPv6 and HMIPv6 according to its working parameters. Only mobile node's protocol stack needs to be changed in IMMS and co-operation ability is assured.

Usability is one of the important problems in Ku wideband land mobile satellite communication design. The difference between the mobile satellite communication and the fixed satellite communication is that the earth station is in motion when it works in mobile satellite communication. In addition, there are multi-path fading, Doppler shift, shadowing effect and rain attenuation in land mobile satellite communication. In this paper, the usability of the communication system is emphasized and appropriate measures based on four major characters in the Ku band land mobile satellite communication system is taken, and the system usability is then calculated, finally the calculation result is analyzed.