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.

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.

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.

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.

The emergence of the new network paradigm of software-defined networking (SDN) provides a unified control interface for centralized machine learning and greater flexibility for solving traffic engineering (TE) problems. However, most existing machine learning solutions to SDN TE problems rely on traffic requirements and fixed link capacity, ignoring the structural information of network topology and the correlation between flows and topology. This oversight can cause rapid network performance degradation during link failures and result in poor performance when traffic demand fluctuates significantly. To overcome these challenges, this study designs a flow-based message passing neural network (Flow-MPNN) and deep reinforcement learning (DRL) to facilitate information exchange between flows. Among them, Flow-MPNN is a method we designed based on graph neural network (GNN).By gaining a better understanding of flow-to-flow interplay and structural information about the network topology. Experimental results show that compared with existing algorithms, our proposed algorithm can accommodate 5% to 8% more traffic when the network link status remains unchanged. It also reduces maximum link utilization by 25% to 35%. Importantly, our algorithm exhibits better robustness when link status changes.

A successful blend of network function virtualization (NFV) and software-defined networking (SDN) can offer flexible and varied network services, given the quick ascent of developing applications propelled by the next-generation 6G network vision. In NFV, the ordered execution of service functions that constitute emerging applications is modeled as service function chaining (SFC) and is no longer limited to traditional network functions. Nevertheless, concurrent demands for joint scheduling of network and computing resources with latency sensitivity in resource allocation frequently accompany these application demands. In this paper, we conduct SFC scheduling as an integer linear programming (ILP) problem to tackle this issue. Secondly, we provide a resource-aware SFC scheduling algorithm that combines VNF and timeslots. In order to increase the acceptance rate of latency-sensitive network services, it aims to optimize resource utilization while strictly adhering to application latency requirements by dynamically sensing the availability of resources under various time slots and, consequently, efficiently and collaboratively scheduling computing and network resources under spatiotemporal states. Experimental results demonstrate that the proposed algorithm not only maximizes resource utilization but also accommodates a larger number of latency-sensitive service requests.

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%.

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.

Low Earth Orbit (LEO) satellite networks are gaining more interest as a crucial component of future space-air-ground integrated networks. However, the traditional IP-based communication mode is not well-suited for supporting low-cost and highly reliable routing in inter-satellite packet transmission. On one hand, the centralized IP address allocation model increases server resource consumption and also leads to excessive communication between satellites. On the other hand, the single-path routing feature of IP cannot guarantee timely recovery of the path in the event of a satellite node failure. Therefore, this paper proposes a mechanism called Relative Position-based Efficient Routing (RP-ER) for LEO satellite networks. RP-ER can achieve distributed address allocation at a low cost and enable redundant routing in the event of a path failure. In particular, RP-ER first establishes the relative position model based on the laws of satellite motion. Then, the central satellite broadcasts the address allocation instructions, and each satellite reacts and disperses packets. Finally, these satellites allocate independent addresses and generate primary and backup routes simultaneously. Compared to other routing mechanisms, RP-ER utilizes fewer satellite resources during the network addressing phase. Additionally, it can establish redundant high-quality paths during the communication phase with a concise routing table.

Multipath transmission can effectively utilize multiple paths and provide high Quality of Service (QoS) performance for video streaming services. However, when multiple video streaming services are transmitted simultaneously, the network is prone to the preemption of path resources by these services, which can reduce QoS. This is because the traditional multipath scheduling algorithm aims to achieve high QoS performance for all services. Therefore, this paper proposes a Friendliness Balancing based Multipath Scheduling algorithm (FBMS) to maximize the utilization of path resources and achieve a friendly and balanced consumption of network resources. First, FBMS obtain the path resources and service requirements to build adaptation matrices. Then, FBMS considers the friendliness balancing value as the optimization objective and utilizes a two-stage evaluation-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) algorithm to assess each path. Finally, FBMS preferentially selects a single transmission path that meets the service requirements in order to avoid resource competition. If there is no qualified path, FBMS will balance the demands of each service, integrate them friendly, and schedule multiple paths for transmission. Experiments show that, compared to traditional scheduling algorithms, FBMS improves path resource utilization, reduces competition among services, and ensures high QoS performance for each service.

In-network caching is frequently used to reduce unnecessary network traffic and lower server workload by allowing content to be accessed from the caching nodes in both wired and wireless networks. The drastic increasing mobile data traffic demands have conceived cooperative content sharing between nodes for addressing the storage limitation of a single node. In this paper, we explore the problem of minimizing transmission cost among cooperative nodes by jointly optimizing caching and routing in a hybrid network with vital support of service differentiation. We show that the optimal routing policy is a route-to-least cost-cache (RLC) policy for fixed caching policy. We formulate the cooperative caching problem as a Markov decision process (MDP) with the goal of maximizing the long-term average caching reward, which is NP-hard even when assuming users demands are perfectly known. Our study then proposes C-MAAC, a collaborative multi-agent deep reinforcement learning (MADRL) algorithm employing actor-critic learning model. C-MAAC has a key characteristic of centralized training and decentralized execution, with which challenge from the unstable training process caused by simultaneous decision of all agents can be addressed. After completing the offline training process, each node independently makes caching decision online during the execution process. Simulation results demonstrate the effectiveness of our proposed algorithms under dynamic environment where user request traffic change rapidly.

The move towards sixth-generation (6G) is expecting to support not only more device connections but also the soaring multimedia big data traffic demands, which spawns the promising approach of in-network caching of information centric networks (ICN) in Internet of Things (IoT). By prefetching popular contents during off-peak traffic periods, caching nodes can make them available to users during peak periods, effectively improving the quality of experience. In this paper, considering the limited cache capacity, unknown popularity distribution as well as non-stationary user demands, we explore this problem by optimizing content caching with the objective of minimizing long-term transmission cost. The content caching process is modeled as a cooperative Markov decision process (MDP), aiming to maximize caching reward. To handle this optimization problem, we propose a deep Q network-based content caching (DQN-CC) algorithm to obtain the approximate optimal solution in an online fashion, thus the agent at the controller is able to adaptively learn and track the underlying dynamics. To update the cache of each node, a replacement rule based on the marginal gain is used, offering faster update and reduced complexity. Extensive simulations verify the superiority of the introduced design.

Telemedicine services put forward high transmission demands for network transmission, such as low latency and high throughput. However, telemedicine services suffer undesirable latency due to the high re-transmission probability caused by congestion and queuing. To reduce the probability of re-transmission, this paper firstly proposes in-band network telemetry (INT)-based delay-guaranteed transmission framework (IDTF) to make concurrent transmission control. In IDTF, we propose a proximal policy optimization (PPO)-based adaptive multipath concurrent flow scheduling algorithm (PAMA) for control policy adjustment. In detail, PAMA makes a joint minimum optimization with flow scheduling and network resource management to reduce the probability of congestion and long-time queuing. Finally, we implement extensive simulations on a programming protocol-independent packet processors (P4)-based programmable network platform to perform performance analysis. Simulation results show that PAMA outperforms existing classical algorithms in re-transmission rate, round-trip time, and throughput.

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.

In Internet of Vehicles (IoV), authentication technology provides a basic security means to achieve trusted communication between legitimate vehicles. Revocation checking for vehicle certificates is an indispensable procedure in the process of authentication to protect vehicular networks from attacks by non-legitimate vehicles. However, revocation checking introduces procedures that require additional time to process which challenges latency-sensitive applications in vehicular networks. This challenge grows more evidently if taking into consideration the factor of privacy preservation. In this paper, we propose to offload partial revocation tasks to network edges to lighten the revocation process in vehicles. Particularly, we design a dual-certificates model for the revocation offloading process and employ blockchain to achieve decentralized Certificate Revocation List (CRL) management. Finally, we build a prototype of our proposed solution based on Hyperledger Fabric using permission blockchain, and compare it with Proof-of-Work scheme in terms of blockchain synchronization latency performance.

Vehicular communication systems rely on temporary anonymous identities, i.e., pseudonyms, to avoid the possibility of tracking vehicles. If a vehicle uses only one pseudonym, an adversary can easily follow the vehicle by observing and linking messages signed under that pseudonym. Therefore, the vehicles acquire a set of pseudonyms from the Pseudonym Certificate Authority. If a vehicle would be unable to obtain these pseudonyms because of the bad communication and delay in processing from the roadside unit, it would not utilize the communication system without compromising the privacy of location. Hence, security and privacy techniques proposed for conventional vehicular ad-hoc network’s pseudonym-changing strategy may not scale well in such early deployed scenarios. This paper addresses the above-mentioned problem by offering a secure pseudonym scheme. Under this scheme, a vehicle can generate its pseudonyms using Gao Algorithm. Pseudonym Certificate Authority (PCA) issues one pseudonym for one vehicle that is sent to vehicles after encryption. The Gao Algorithm automatically generates sub-pseudonyms that are useable for 10 days. Consequently, the PCA requires less memory while providing maximum location confidentiality, and there is almost no complexity in managing pseudonyms. The Gao Algorithm uses encrypted identities so that only the central server knows the real identities of vehicles. We conduct simulations to verify the performance of the Gao Algorithm. Results show that the Gao Algorithm is expressively better than current strategies. Besides, even in the absence of roadside unit connections and low traffic on the road, the Gao Algorithm can achieve promising performance.

Network function virtualization (NFV) is a ground-breaking technology that decouples network functions (NFs) from customized hardware to support more flexible network services and network resource allocation. However, these improvements also lead to an increase in the possibility of service function chain (SFC) failure due to hardware failures, software bugs, or resource contention. This could lead to minor problems or even serious consequences. Unfortunately, the existing failure detection methods have multiple issues, such as small detection range, single detection function, heavy overhead, and low accuracy. Consequently, we propose FullSight, a feasible framework based on deep learning (DL) models that can efficiently integrate both the control plane and programmable data plane for fault detection and classification. This framework obtains the status and indicators of components and network that cause service quality performance degradation through two planes. These indicators are ultimately sent to the knowledge plane for preprocessing, dimensionality reduction, and fault analysis. In addition, we propose two algorithms based on text convolutional neural network (textCNN) and bidirectional encoder representations from transformers (BERT) to classify SFC faults. We implement and evaluate the proposed FullSight prototype extensively on a prototype with thirteen programmable switches and twenty end-hosts. Our experimental results show that FullSight can rapidly and accurately detect and identify eight categories of fine-grained SFC failures, compared with other state-of-the-art methods. Besides, compared with SFC Path Tracer and Pingmesh, our framework can reduce the average bandwidth overhead of the data plane by 57% and 84%, respectively, and achieve detection accuracy of more than 98%.

Traditional routing technologies based on a single IP address domain faces the challenge to meet the increasing demand for network services and security. Packet classification is a technique to differentiate multi-domain network traffic in a fine-grained manner using packet header fields. Packet classification requires to operate efficiently to avoid it becoming a bottleneck in the packet routing process. Tuple space search (TSS) used in SDN supports fast rule updates but low-speed packet classification. In this paper, we propose a learned Bloom filter (LBF)-based packet classification algorithm that combines LBF and TSS to promote classification speed by avoiding invalid hash table accesses. Specifically, LBF consists of multiple RNN models and one support Bloom filter (SBF), in which the learned models are trained with the positive and negative sets, and used as a pre-filter to identify the two sets. For the filter outcomes with a negative result from learned models, SBF is constructed to perform the second filtration. To ensure efficiency of RNN and SBF, we carefully select key features to be used in RNN and SBF, which can also maintain efficient search in the final stage of hash checking. Our experimental results show that the proposed algorithm saves more memory space than Tuple space pruning (TSP) given the same false positive rate. The proposed algorithm is competitive in terms of the number of memory accesses, while achieving almost one order of magnitude improvement on pre-processing time over NeuroCuts which is an advanced decision tree classifier.

Network Function Virtualization (NFV) is one of the most promising technologies which decouples Network Functions (NFs) from hardware resources to support more flexible network services and network resource allocation. However, these benefits increase the possibility of Service Function Chain (SFC) failures due to hardware failures, software defects and burst traffic, resulting in serious consequences. Unfortunately, existing failure detection methods have several issues, such as simplification of detection functionality, heavy overhead, and low accuracy. This paper introduces a framework FullSight, in which control plane and the programmable data plane can collaboratively detect failure and Deep Learning (DL) based algorithms are adopted for failure detection. FullSight can achieve an all-round perception of the state of the SFC, in which network information is acquired through the data plane, SFC components' message is obtained through the control plane. In addition, a failure detection model based on DL is established. Compared with the state-of-the-art methods, FullSight can support 8 kinds of the fine-grained failure detection. Our comprehensive evaluation of prototypes and simulations shows that FullSight can realize rapid and accurate detection and classification of diversified failures in SFCs. The bandwidth overhead reduces by 57% compared with the existing methods. Additionally, FullSight has a detection accuracy up to 93.5%.

The development of emerging network technologies represented by Software-Defined Networking (SDN) has made traditional routing technologies that are based on a single IP address domain unable to meet the increasing demand for network services and security. Packet classification is a technique to differentiate multi-domain network traffic in a fine-grained manner using packet header fields. Packet classification requires to operate efficiently to avoid it becoming a bottleneck in the packet routing process. In this paper, we propose a learned Bloom filter (LBF)-based packet classification algorithm that combines the RNN learned model with a support Bloom filter (SBF) to improve the classification accuracy. Specifically, the learned model is trained with the positive and negative sets, which is used as a pre-filter to identify the two sets. For the filter outcomes with a negative result from the learned model, SBF is constructed to perform the second filtration. To ensure efficiency of RNN and SBF, we carefully select key features to be used in RNN and SBF, which can also maintain efficient search in the final stage of rule matching. We perform simulation with various datasets showing the performance advantages of our proposed algorithm over existing solutions in terms of memory usage and classification accuracy.

Efficient transmission control is a challenging issue in vehicular networks due to the highly dynamic network environment. In this paper, we propose a Deep reinforcement learning based adaptive Transmission Scheduling Mechanism (DTSM), which is able to adaptively select different transmission control policies based on the current network status and the history data learning. In particular, we first introduce the adaptive transmission scheduling units (ATSU) in both Software-Defined Vehicular Networking (SDVN) controllers and the corresponding base stations. Based on this architecture, we formulate a mathematical model for optimal decision-making in SDVN controllers. Besides, in ATSUs, we proposed a deep Q-learning based transmission control method to dynamically adapt to the time-varying vehicular network scenarios. Simulation results verify that the proposed DTSM solution outperforms the single transmission control method of four existing benchmarks (e.g., TcpVegas, TcpBic, TcpWestwood, TcpVeno) in terms of average throughput and round-trip time.

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.

The problem of security and privacy in vehicular networks is a vital issue and it attracts increasing attention to address the security vulnerability of vehicular networks. Authentication solutions are introduced in vehicular networks to ensure that network access only comes from authorized users. Particularly, group signature not only offers authentication services in vehicular networks, but also provides conditional privacy preservation. However, the current group signature solution for authentication in vehicular networks exhibits time-consuming signature verification, which is attributed to the centralized certificate revocation list (CRL) management. To overcome this shortcoming, we propose utilizing edge computing approach and design a flexible and efficient decentralized authentication scheme (FEDAS). In the proposed architecture, a decentralized CRL management method is developed to reduce verification delay in the authentication process. In addition, transition zone is proposed to solve reliable authentication problem in border area of the group caused by decentralized architecture. We also conduct extensive simulations to show the effectiveness of our proposed scheme.

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.

With the rapid development of network, network monitoring is a significance means to ensure network security and reliability. In-band network telemetry (INT) can collect items in line-rate, and support large traffic volumes and rates network telemetry. However, existing INT monitoring schemes are quite limited in flexibly expanding the execution monitoring tasks. In this paper, we propose Fast-INT, an efficient network monitoring framework combined with learning. The goal of Fast-INT is to design a light-weight INT network collection framework by quickly implementing dynamic and scalable collection of network status information. In our approach, an INT scheduling algorithm based on reinforcement learning is designed to dynamically deploy and adjust INT monitoring tasks when dealing with network inner change event. Particularly, Fast-INT can implement specific INT monitoring tasks on target point to shorten the time of monitoring and make the network monitoring more efficient. The evaluate results show that Fast-INT has a good performance on network monitoring and achieves the goal of intelligently deploying network monitoring tasks.

Satellite networks form part of the modern mobile network. In multi-layer satellite-terrestrial networks, Contact Graph Routing (CGR) enables to calculate an efficient delivery path which depends on the contact information configured in the contact plan. Due to the rapid relative motion between satellites which belong to different layers, the inter-layer contacts can suffer frequent disruption. In order to keep the integrity of the network, the inter-layer contact must be carefully selected to maintain connectivity yet avoid congestion. In this paper, we propose a traffic aware inter-layer contact selection method (TACS) by considering the flow situation of the associated nodes which contains the queue size, flow size, the number of connected nodes and the contact duration. We verify the performance of the proposed design in our Identifier/Locator (ID/Loc) split based satellite- terrestrial network testbed with 95 simulation nodes. Experiments show that the proposed design is able to achieve balanced flow distribution among MEO layer, improve the delivery ratio and reduce the delivery delay.

Software-defined networking (SDN) simplifies the forwarding devices by introducing a centralized controller. The controller calculates routing rules for the whole network and the forwarding devices cache the routing rules. This working process leads to the new-flow attack. When malicious packets with different headers arrive at the network, they are treated as new flows. These useless flows consume lots of the resources in the controller and the forwarding devices. According to the current solution, suspicious flows are redirected to the security middleware. However, the security middleware can be a bottleneck when lots of flows are redirected to it in a short time. In this paper, we propose SmartSec to prevent the new-flow attack and optimize the security middleware at the same time. SmartSec uses the standard control link message to monitor the new-flow attack, and it achieves a low cost on the control link. Based on the monitoring results, SmartSec redirects the suspicious flows to the security middleware and monitors the workload of the security middleware. An optimization method is designed in SmartSec to reduce the workload of the security middleware. We evaluate our mechanism in both simulator and test bed. The simulation and experiment results verify the performance of SmartSec.

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.

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.

To enhance seamless provider mobility support in NDN, we propose a Novel Locator Based mobility support Approach (NLBA). In this approach, we assign an unique locator to each Access Router (AR) in NDN networks, and further modify the outgoing faces field in AR's original Forwarding Information Base (FIB), to record the mobility status and the current locator of the provider. Besides, we append an optional field to the original NDN packet, and further extend the AR with additional functionalities, such as caching or forwarding Interest packets on behalf of the provider. Our analytical investigations indicate that NLBA has lower handover cost and shorter handover latency, compared with other existing rendezvous/indirection point based mobility support approach.

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.

As a promising mobility protocol, Proxy Mobile IPv6 (PMIPv6) can provide mobility support without the involvement of the mobile nodes (MNs). It depends on the Local Mobility Anchor (LMA) and Mobility Access Gateway (MAG) to emulate the home network for the attached MNs. Based on this design principle, PMIPv6 works well only for small scale networks. However, with the rapid increase of the MNs as well as the huge traffic loads, PMIPv6 as a centralized mobility management protocol in which both the location management and dada forwarding are performed by the LMA, will induce lots of limitations, such as non-optimal routing, single point of failure, scalability and so on. Therefore, a trend to distributed mobility management (DMM) is popular, and several distributed solutions for PMIPv6 have been proposed until now. In this paper, we survey the different schemes of DMM and focus on the related solutions for PMIPv6 by qualitative analysis, and conclude the remaining challenges and open issues for future development.

Trust evaluation plays an important role in securing wireless sensor networks (WSNs), which is one of the most popular network technologies for the Internet of Things (IoT). The efficiency of the trust evaluation process is largely governed by the trust derivation, as it dominates the overhead in the process, and performance of WSNs is particularly sensitive to overhead due to the limited bandwidth and power. This paper proposes an energy-aware trust derivation scheme using game theoretic approach, which manages overhead while maintaining adequate security of WSNs. A risk strategy model is first presented to stimulate WSN nodes' cooperation. Then, a game theoretic approach is applied to the trust derivation process to reduce the overhead of the process. We show with the help of simulations that our trust derivation scheme can achieve both intended security and high efficiency suitable for WSN-based IoT networks.

ABSTRACT Protocols for sensor networks have traditionally been designed using the best effort delivery model. However, there are many specific applications that need reliable transmissions. In event‐driven wireless sensor networks, the occurrence of an event may generate a large amount of data in a very short time. Among them, some critical urgent information needs to be transmitted reliably in a timely manner. In this scenario, congestion is inevitable because of the constraints in available resources. How to control the congestion is very important for the reliable transmission of urgent information. To address this problem, we propose a queue‐based congestion detection and a multistage rate control mechanism. In our proposed mechanism, not only the current queue length but also the queue fluctuation are adopted as indications of congestion. Each sensor node evaluates its congestion level locally and determines its congestion state with a state machine. We design a multistage rate adjustment mechanism for nodes to adjust their rates depending on their congestion states. We also distinguish high‐priority critical traffic from low‐priority non‐critical traffic. Extensive simulation results confirm the superior performance of our proposed protocol with respect to throughput, loss probability, and delay.Copyright © 2012 John Wiley & Sons, Ltd.

With the increase of vehicles in city, unexpected abnormal events may occur on roads more frequently. It is necessary to disseminate abnormal traffic information to other vehicles in vehicular ad hoc networks (VANETs).With traditional geocasting protocols, the considered target region is normally single and its range is fixed. However, when an abnormal event occurs, its impacted area may include multiple streets or roads and it may last for a long time. In this paper, for multiple target regions we propose a SmartGeocast information dissemination mechanism, which contains two procedures including geocasting initialization and geocasting maintenance. In the first procedure, the abnormal traffic information can be delivered to multiple regions quickly and efficiently through path sharing and path splitting schemes. Considering that new vehicles may continuously drive into the target regions, the abnormal traffic information have to be “floating or visible” to them until the event impact gradually disappears. In the second procedure, it divides a small area in each target region for information dissemination to new arrived vehicles repeatedly, so as to furthermore reduce the message redundancy and maintenance cost. Experimental results show that the proposed SmartGeocast protocol can decrease the probability of receiving the repeated messages while try to avoid missing the important information.

When deploying wireless sensor networks (WSNs) in practical applications, access control systems can limit access to sensitive information only to trusted entities and provide a capability to resist against various attacks from malicious nodes. However, due to the characteristics of highly distributed and resource-constrained, applying conventional access control models to WSNs is significantly challenging. In this paper, a distributed and fine-grained access control model based on the trust and risk degree is proposed (TC-BAC). We first introduce a trust evaluation mechanism to meet the security requirements of access control systems. Then, a risk function is proposed to assess the behavior of nodes and evaluate the risk factor of the access. The simulation results show that TC-BAC can achieve both intended security and high efficiency of the network.

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.

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.

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.

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 an improved gradient-based micro sensor routing protocol (GMSRP) using a node sleep scheduling mechanism, to save energy for sensor nodes, called the GMSRP-SLE. This algorithm takes into account the characteristic of most applications in WSNs, i.e.,the sensor nodes are deployed randomly with some areas covered by many sensor nodes at same time. During network initialization, each sensor node in GMSRP-SLE can learn its gradient information, which is the minimal number of hops away from the sink node. Sensor nodes with the same gradients in the same layer can form a cluster according to the requirement of minimal sensing coverage ratio. One sensor node in the cluster is selected to be a cluster head, and the rest will go to sleep. Additionally, in order to balance the energy consumption and the lifetime of sensor nodes in a cluster, each sensor node will take turn to become the cluster head. Our NS-2 simulation results show that the GMSRP-SLE algorithm can work efficiently to keep some nodes sleeping in order to save more energy.

With the increasing popularity of using WLANs for Internet access, the controlled channel access mechanism in IEEE 802.11e WLANs, HCCA, has received much more attentions since its inherent centralized mechanism is more efficient in handling time-bounded multimedia traffics. So far, only a few research works address the admission control problem of variable bit rate (VBR) traffic over HCCA. These existing works consider each traffic flow individually and thus cannot exploit the statistical multiplexing gain among multiple VBR traffic flows. In this paper, we apply the existing statistical multiplexing works to the studied admission control problem with all the features of 802.11e HCCA being taken into consideration. Experimental results show that our proposed admission control achieves significant improvement in network utilization while still satisfying all the QoS requirements.

Directly implementing voice over Internet protocol (VoIP) over infrastructure wireless local area networks (WLANs) will have the bottleneck problem in the access point (AP). In this paper, we propose to use the service differentiation provided by the new IEEE 802.11e standard to solve the bottleneck problem and improve the voice capacity. In particular, we propose to allocate higher priority access category (AC) to the AP while allocating lower priority AC to mobile stations. We develop a simple Markov chain model, which considers not only the important enhanced distributed channel access (EDCA) parameters but also the channel errors. Based on the developed analytical model, we analyze the performance of VoIP over EDCA. Through appropriately selecting the EDCA parameters, we are able to differentiate the services for the downlink and the uplink. The experimental results are very promising: with the adjustment of only one EDCA parameter, we improve the VoIP capacity by 20~30%.

The IEEE 802.11 working group is currently working on the support of quality of service (QoS) in a new standard called IEEE 802.lie which introduces the so-called hybrid coordination function (HCF). A simple admission control unit has already been developed as a reference for HCF controlled channel access (HCCA) in a recent TGe draft. However, this reference scheme is inefficient because it is implemented based on the minimum physical rate from which the mobile stations' actual physical rates deviate greatly at most of the time. In this paper, we propose a physical rate based admission control scheme (PRBAC) which enhances the reference scheme by taking account of both the wireless channel characteristics and the stations' mobility. Numerical analysis and simulation results show the significant improvement of the proposed admission control scheme.

The IEEE 802.11 working group is currently working on a new standard called IEEE 802.11e to support quality of service (QoS) in WLANs. 802.11e introduces a so-called hybrid coordination function (HCF) containing two medium access mechanisms: enhanced distributed channel access (EDCA) and HCF controlled channel access (HCCA). In the EDCA mechanism, many QoS parameters are introduced including minimum contention window (CWmin), maximum contention window (CWmax), arbitration inter frame space (AIFS) and transmission opportunity limit (TXOPlimit). In this paper, we experimentally assess the MPEG-4 video streaming performance over 802.11e. In particular, we discuss in detail how the human satisfaction of streaming video is affected by the main QoS parameters in IEEE 802.11e WLANs. We measure the level of end user satisfaction together with the network performance and give recommendations regarding the network design and the parameter settings.

IEEE 802.11 wireless LAN is considered one of the most popular wireless technology all over the world because of its low cost and easy deployment. The support of quality of service (QoS) in medium access control (MAC) protocol is important in meeting the QoS requirements of real-time traffic such as guaranteed packet delay and packet loss probability. In this paper, the performance of the proposed admission control algorithm and different VBR video traffic are analyzed. In the analysis, three mean data rates of video flows ( 300 kbps, 600 kbps and 1 Mbps) were also considered.

The new standard IEEE 802.11e is specified to support quality-of-service in wireless local area networks. A comprehensive study of the performance of enhanced distributed channel access (EDCA), the fundamental medium access control mechanism in IEEE 802.11e, is reported in this paper. We present our development of an analytical model, in which most new features of the EDCA such as virtual collision, different arbitration interframe space (AIFS), and different contention window are taken into account. Based on the model, we analyze the throughput performance of differentiated service traffic and propose a recursive method capable of calculating the mean access delay. Service differentiation functionality and effectiveness of the EDCA are investigated through extensive numerical and simulation results. The model and the analysis provide an in-depth understanding and insights into the protocol and the effects of different parameters on the performance.

In this paper, we propose a new adaptive load balancing mechanism for MPLS networks in that core LSRs are not required to perform traffic engineering. Based on measurements, we obtain the average one-way delay between a pair of label switched routers (LSR), and dynamically distribute traffic load among multiple label switched paths (LSP) according to this average. Simulation results indicate that our approach improves end-to-end throughput and decreases packet loss probability. Our work demonstrates that our algorithm is robust and simple to use.