SDN data plane generally utilizes TCAM to accommodate flow tables for fast packet classification, which also leads to serious problem of high energy consumption. Existing techniques are difficult to stably achieve satisfactory energy-saving effect especially under network traffic jitters. To address this problem, this paper first designs an elastic energy-saving cache to always keep sufficient number of active exact flows for stably high cache hit rates even under network traffic jitters. Particularly, we adaptively adjust the number of cache segments, in terms of the relationship between current cache hit rate and its preset expected range, to maintain high cache hit rate. Meanwhile, we regulate the threshold of packet inter-arrival time for identifying active exact flows, in accordance with current cache occupancy rate, to match the number of active exact flows with cache capacity. Furthermore, we theoretically derive cache occupancy rate based on randomly mapping assumption of each active exact flow and cache hit rate based on the assumption of flow activity degree model. Subsequently, we build an elastically energy-saving flow table storage architecture, by applying the elastic energy-saving cache to always enable a majority of incoming packets to bypass energy-hungry TCAM flow table lookups. Finally, we set up an experimental SDN platform to evaluate its performance on real network traffic traces. Experimental results indicate that our built flow table storage architecture steadily achieves high energy saving rates around 82.43% even under network traffic jitters, with the increase of 6.32˜7.55% compared to the state-of-the-art one.

Multivariate time series data often demonstrate sparse and irregular characteristics in real-world signal processing applications, making anomaly detection challenging. This paper introduces STMG-AD, a spatio-temporal mixed graph neural controlled differential equation method with adaptive connection sampling, designed specifically for anomaly detection in irregular multivariate time series. By integrating causal graphs with graph attention networks and employing adaptive connection sampling coupled with Monte Carlo dropout, STMG-AD can enhance the robustness and accuracy of anomaly detection. Experiments on various real-world datasets demonstrate its superiority over existing methods in detecting anomalies in irregular multivariate time series.

Large Language Models (LLMs) have emerged as a disruptive technology for Natural Language Processing (NLP), achieving success in NLP-related generative applications. However, the potential capability of LLMs in other domains remains largely unexplored. To explore the potential of task scheduling with LLMs, we model a typical task scheduling scenario in cloud computing and transfer scheduling problems as natural language prompts. Afterward, the knowledge and reasoning abilities of LLMs are enabled to generate scheduling decisions. Six well-known and open-source LLMs are integrated into our framework to perform experimental studies, and the results are evaluated from multiple perspectives and compared with each other. Besides, traditional heuristic algorithms and a basic Reinforcement Learning (RL) method are all performed for comparison. Our results demonstrate: 1) compared to most heuristic methods, the decisions made by LLMs achieve better scheduling performance; 2) compared to the basic RL method, LLMs exhibit better generalization on various workload patterns; 3) the larger parameter size of the LLMs has, the better scheduling performance it achieves. To the best of our knowledge, our experimental study is the first exploration to apply LLMs in task scheduling. Our findings highlight the promising potential of LLMs as a novel approach to task scheduling, offering new avenues for research and practice.

Computing Power Networks (CPNs) present unique scheduling challenges due to the tight coupling of compute and network resources, exacerbated by highly heterogeneous task requirements and significant intra-cycle resource state fluctuations. Addressing these core CPN-specific issues, this paper proposes JARS (Joint Adaptive compute Routing and Scheduling), an intelligent scheduling method based on resource state evolution and heterogeneous task awareness. JARS first identifies the distinct characteristics of heterogeneous tasks—such as compute-intensive, data-intensive, and latency-sensitive types—through dynamic clustering. Crucially, it introduces a real-time resource state evolution mechanism that captures the immediate impact of each scheduling decision within a single cycle, thus overcoming the state perception lag inherent in traditional batch schedulers. We conducted extensive experimental evaluations on six real-world network topologies using a well-defined synthetic workload model that emulates diverse CPN task mixes. Results show that JARS significantly outperforms existing methods, achieving a$\text{1. 8 \%} - \text{2 4 9. 9 \%}$improvement in task success rate and a 9.0%$-84.7 \%$reduction in average completion time, demonstrating its effectiveness and robustness for CPN environments.

The extensive deployment of drone swarms in emergency response and logistics applications presents stringent requirements for mobile ad-hoc network protocols, particularly due to their large-scale and highly dynamic characteristics. As a widely adopted protocol in industrial applications, BATMAN-ADV (Better Approach To Mobile Ad-Hoc Networking Advanced) employs a fixed-interval transmission mechanism for control packets, which leads to inefficient utilization of bandwidth resources. Therefore, we propose ROBIN, a distributed Multi-Agent Reinforcement Learning(MARL) optimization method. Our approach enables each drone to act as an independent agent, autonomously adjusting the control packet transmission interval and optimizing transmission strategies based on localized network states. The solution maintains the protocol’s inherent millisecond end-to-end latency while establishing a dynamic balance between bandwidth consumption and latency performance. Moreover, our method features extremely low resource consumption, making it suitable for drone with limited resources. Experimental results show that ROBIN reduces control packet overhead by 75.9% across diverse network scales and mobility scenarios, while maintaining only 2.29% CPU utilization and 0.25% memory consumption per node on commercial drones.

The rapid proliferation of IoT technologies intensifies network anomaly detection challenges because of high-dimensional and redundant data. To address this issue, we propose causal gray wolf optimization (CGWO), a framework that integrates the global search capabilities of gray wolf optimization (GWO) with causal inference to distinguish causal features from spurious correlations. CGWO employs a four-stage process: (1) data preprocessing with causal weight transformation, (2) causal analysis via potential outcome models (POMs), (3) feature subset optimization using a fitness function balancing accuracy and dimensionality, and (4) anomaly detection with an enhanced convolutional autoencoder (ECAE). By prioritizing causal relationships over statistical correlations, CGWO minimizes redundant features while increasing model interpretability. When evaluated with benchmark datasets (NSL-KDD and UNSW-NB15), CGWO achieves 99.8% accuracy (14 features) for 5-class classification with NSL-KDD and 94.2% accuracy (10 features) for 10-class classification with UNSW-NB15, outperforming conventional methods by more than 6%. The framework reduces feature dimensions by 65–80% without performance loss, demonstrating robustness, computational efficiency (120–150 s per dataset), and scalability for edge computing. These results validate the effectiveness of CGWO in balancing dimensionality reduction and interpretable model design for complex network environments.

It is difficult for existing Internet of Things (IoT) intrusion detection systems to simultaneously identify and classify network anomalies, especially when the classification of unknown attacks is required, which brings great risks to the use of IoT devices. This article applies transformers to decouple false associations by causal reasoning to obtain an intelligent interpretable IoT detection system that can classify known attacks and identify unknown attacks. To achieve these goals, a causal transformer-based intelligent detection system for IoT devices is proposed. The system is divided into three main modules. First, training is conducted based on known traffic types with prior knowledge, and then the detection samples containing unknown attack types are classified into known traffic types. Second, the causal feature distribution of known traffic types is learned based on causal attention, and the causal feature distribution differences between normal and abnormal traffic samples are amplified with the minimax strategy to distinguish their types. Then, all traffic samples different from the known types are integrated into unknown types for causal transformer classification until there is only one type. Validation is performed on three broad and representative IoT datasets, and the results show that the causal transformer detection system can not only correctly classify known attacks but also achieve a 100% success rate in identifying cyberattacks on IoT datasets. In addition, more than 99% of unknown attack types can be effectively identified and classified, providing timely and effective guidance for cybersecurity defense.

With the continuous development of Internet of Things (IoT) technology, an increasing number of devices are connected to the internet, generating large amounts of highdimensional redundant information. Moreover, significant environmental and device heterogeneity leads to nonindependent and identically distributed (N-IID) samples. These challenges compromise the stability and causal interpretability of existing IoT detection methods, limiting their effectiveness in providing actionable insights for network security defense. To address these limitations, we propose a causal interpretabilitydriven IoT abnormal traffic detection approach. Central to this method is the adoption of structural causal models (SCMs), which are chosen for their ability to explicitly model direct causal linkages, suppress confounding effects, ensure robust cross-deployment detection, and enable counterfactual reasoning for precise attack attribution. The approach first eliminates spurious feature associations via Fourier transformation, then constructs and prunes SCMs using causal effect analysis, KNN, and counterfactual diagnosis to restore genuine causal relationships between anomalies and traffic features. Experiments on CI-CIDS2019, ToNIoT, and NSL-KDD datasets demonstrate effective noise reduction, redundancy elimination, and causal relationship recovery. Notably, detection accuracy improves by >19% on NSL-KDD data under polluted conditions, while maintaining stability and providing clear causal explanations for IoT network anomalies.

To meet the access demands of massive terminal users, the space-based Internet of Things (IoT) requires sufficient frequency resources for allocation. However, the frequency resources that are currently available have already been allocated to a great extent. Furthermore, the utilization rate of the allocated frequency resources is low. To support massive user access under restricted frequency resources, this work proposes a scheme based on Doppler frequency offset (DFO) pre-compensation to enhance spectrum utilization efficiency. By calculating the relative motion between the satellite and the transmitting terminal, combined with the length and transmission rate of the message, the optimal compensation value of the Doppler frequency deviation is determined. The frequency-protection interval is reduced. Simulation results show that the pre-compensation method can expand the user access volume by 90–400 times. Properly selecting the number of message splits and transmission rate to perform DFO pre-compensation calculations can increase user access by an additional 45% or more. This method improves the spectrum utilization efficiency and provides a solution to the challenge of access by a large number of users.

In the paradigm of Software-Defined Networking (SDN), its data plane generally perform packet forwarding based on flow table lookup on TCAM with high energy consumption. Popular energy-saving methods employ caching techniques for most packets to bypass energy-intensive TCAM lookups. However, existing energy-saving caches cannot adapt to network traffic fluctuation with sufficient utilization of cache space due to non-negligible hash conflicts. To overcome this issue, we design a traffic-aware energy-saving cache with optimal utilization for TCAM flow tables in SDN data plane. In particular, we first devise a nearly conflict-free hashing algorithm for the cache called FelisCatus, which provides three candidate locations for each incoming flow by adjacent hopping, and searches for an empty or replaceable entry for each conflicting flow by co-directional kicking. Then, we propose an adaptive adjustment mechanism of flow activity criterion, i.e., packet inter-arrival time threshold, for enabling the cache to consistently accommodate the most active exact flows in network traffic. Furthermore, we build an energy-efficient SDN flow table storage architecture by applying the above cache and exploiting the accessing features of different memories. Finally, we verify the performance of our designed energy-saving cache and flow table storage architecture by experiments with backbone network traffic traces. Experimental results indicate that, our designed energy-saving cache obtains stable and high hit rates around 75% even under network traffic fluctuation, and our proposed flow table storage architecture achieve high energy saving rates around 71%, with the increase of 7.89% compared to state-of-the-art ones.

Service function chain placement is crucial to support services flexibility and diversity for different users and vendors. Specifically, this problem is proved to be NP-hard. Existing deep reinforcement learning based methods either can only handle a limited number of objectives, or their training time are too long. Concomitantly, they are unable to satisfy when the number of objectives is dynamic. It is necessary to model service function chain placement as a multi-objective problem. The multi-objective problem can decomposed into multiple sub-problems by the weight vectors. In this paper, we first reveal the relationship between weight vectors and solution position, which can reduce the training time to gain a better placement model. Then, we design a novel algorithm for the service function chain placement problem, called rzMODRL. The weight vectors are divided into zones for training in parallel, and the order is defined for the final models located at the end of a training process, which can save time and improve the quality of the model. Dynamic objective placement method is based on the high-dimensional model to avoid retraining for a low-dimensional placement. Evaluation results show that the proposed algorithms improve the service acceptance ratio up to 32% and the hyper-volume values with 14% in the multi-objective service function chain placement, where hyper-volume has been widely applied to evaluate the convergence and diversity simultaneously in multi-objective optimization. And it is also effective in solving the dynamic objective service function chain placement problem that the difference of average hyper-volume values is 10.44%.

To address the cybersecurity problems caused by encrypted traffic, current attack detection systems (ADSs) focus on non-decryption-based detection. However, non-decryption systems face problems including large training sample imbalances, which seriously affect ADS performance. To address these problems, we propose a structural causal model (SCM) for cyberattacks and define detection tasks such as eliminating noise features and providing interpretability. First, the influence of causal features on the results is enhanced by weighting the causal features, and low-weight noise features are removed to improve the causal interpretability of the detection results. Furthermore, samples are generated through a Wasserstein generative adversarial network (WGAN) that learns the causal feature distribution to balance the training samples. Finally, the detection performance is evaluated through the F1 score and interpretability indices. Our method achieves F1 score improvements on all datasets, and the causal relationships between cyberattacks and feature anomalies are explained through causal effects.

Networks Anomaly Detection is very critical to ensure the security in IoT. However, the noise in training and detection samples varies due to differences in scenarios and devices, and this different noise information corresponds to distinct false correlation relationships. This leads to a lack of stability in existing detection models based on correlation reasoning. To address these issues, in this paper, we propose a novel causal diffusion approach to detect anomalies in IoT. The model first generates independent features by adding noise to remove false correlations and subsequently addresses the problems of Not Independent and Identically Distributed (N-IID) sample distributions by calculating the causal effect relationships between the labels and features to remove noise. Finally, through the validation of one broad and representative network intrusion detection dataset, the experimental results show that method can achieve a maximum detection rate of >99% in the actual different network environments in CICIDS2019.

Multi-access edge computing (MEC) presents computing services at the edge of networks to address the enormous processing requirements of intelligent applications. Due to the maneuverability of unmanned aerial vehicles (UAVs), they can be used as temporal aerial edge nodes for providing edge services to ground users in MEC. However, MEC environment is usually dynamic and complicated. It is a challenge for multiple UAVs to select appropriate service strategies. Besides, most of existing works study UAV-MEC with the assumption that the flight heights of UAVs are fixed; i.e., the flying is considered to occur with reference to a two-dimensional plane, which neglects the importance of the height. In this paper, with consideration of the co-channel interference, an optimization problem of energy efficiency is investigated to maximize the number of fulfilled tasks, where multiple UAVs in a three-dimensional space collaboratively fulfill the task computation of ground users. In the formulated problem, we try to obtain the optimal flight and sub-channel selection strategies for UAVs and schedule strategies for tasks. Based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, we propose a curiosity-driven and twin-networks-structured MADDPG (CTMADDPG) algorithm to solve the formulated problem. It uses the inner reward to facilitate the state exploration of agents, avoiding convergence at the sub-optimal strategy. Furthermore, we adopt the twin critic networks for update stabilization to reduce the probability of Q value overestimation. The simulation results show that CTMADDPG is outstanding in maximizing the energy efficiency of the whole system and outperforms the other benchmarks.

No abstract available.

The essence of the User Plane Function (UPF) is strong forwarding, and virtualisation architecture by software implemented can not meet high-performance requirements. Thus, hardware acceleration becomes an option. However, existing offloading schemes as an accelerator are not prominent in the latency. In this paper, we implement a demo called cUPFCard to offload UPF into a smart NIC based on FPGA platform, which can provide higher throughput with lower latency. Experiments show that cUPFCard is feasible in the real network. Moreover, the throughput is improved 24 times, and the latency is decreased 41 times.

In recent years, frequent network attacks have seriously threatened the interests and security of humankind. To address this threat, many detection methods have been studied, some of which have achieved good results. However, with the development of network interconnection technology, massive amounts of network data have been produced, and considerable redundant information has been generated. At the same time, the frequently changing types of cyberattacks result in great difficulty collecting samples, resulting in a serious imbalance in the sample size of each attack type in the dataset. These two problems seriously reduce the robustness of existing detection methods, and existing research methods do not provide a good solution. To address these two problems, we define an unbalanced index and an optimal feature index to directly reflect the performance of a detection method in terms of overall accuracy, feature subset optimization, and detection balance. Inspired by DNA computing, we propose intelligent attack detection based on DNA computing (ADDC). First, we design a set of regular encoding and decoding features based on DNA sequences and obtain a better subset of features through biochemical reactions. Second, nondominated ranking based on reference points is used to select individuals to form a new population to optimize the detection balance. Finally, a large number of experiments are carried out on four datasets to reflect real-world cyberattack situations. Experimental results show that compared with the most recent detection methods, our method can improve the overall accuracy of multiclass classification by up to 10%; the imbalance index decreased by 0.5, and 1.5 more attack types were detected on average; and the optimal index of the feature subset increased by 83.8%.

User Plane Function (UPF) plays a crucial role in the 5G core network, which is vital to improve UPF performance. In this prototype, we provide novel UPF architecture with a programmable switch. The implemented UPF prototype can provide up to 100Gbps matching throughput within 1 microsecond(us) latency. Moreover, the prototype is feasible in the real network.

Packet classification shows a key role in kinds of network functions, such as access control, routing, and quality of service (QoS). With the rapid growth of the network size, users have to ignore some fields in packet classification due to resource constraints. In addition, some fields may not always be available in some networks. However, traditional packet classification algorithms can hardly handle packet classification if some fields are missing. In this paper, we propose a novel model to build a robust classifier. In the classifier, we utilize the advantage of Recursive Flow Classification (RFC) in handling fields concurrently. Then, we design a new workflow to deal with field missing based on flows. In addition, two complementary bitmap models are designed to accelerate matching packets to flows, and a buffer mechanism is introduced to further improve the classification accuracy. Our experiments show that the proposed classifier can classify packets with an accuracy of 94%-99.5% when the field missing probability is lower than 0.3.

In recent years, network function virtualization has attracted massive attention in academia and industry,and the virtual network functions placement problem is one of them. Reinforcement learning has been widely applied in network control and decision, which can learn the optimal policy according to the environment feedback automatically. This paper presents a new summary of the virtual network functions placement problem based on reinforcement learning. We will give a detailed description of how to use reinforcement learning to solve virtual network function placement in different scenarios, then the prospect of further research is forecasted preliminarily.

Satellite Internet has broad emerging applications in aviation, marine, forest, disaster emergency and other fields. Since the openness of satellite Internet, it is extremely vulnerable to eavesdropping, replay, impersonation and other attacks, which may cause significant privacy and security concerns. To prevent malicious nodes from accessing and attacking satellite Internet, many authentication schemes have been proposed, but few of them consider the authentication and security of satellite nodes. In addition, most schemes assume that key management in network control center (NCC) is absolutely secure. But if it is maliciously attacked and the public-private key pairs are leaked, the entire system will crash immediately. In this paper, we propose CLASSI, a novel, secure and efficient certificateless authentication scheme for satellite Internet. CLASSI no longer assigns the complete public and private keys when users, satellites and gateway stations register, but uses their identity information to generate partial private keys. We make full use of the computing and storage capabilities of satellites to give satellites the ability to authenticate. We theoretically prove the security of our scheme through formal analysis and prove that our scheme can resist various of attacks. We also conducted extensive experiments and compared CLASSI with the existing schemes. The experimental results demonstrate that CLASSI is more suitable for user access in satellite Internet when meeting more security attributes.

Network technology is the basis for large-scale high-efficiency network computing, such as supercomputing, cloud computing, big data processing, and artificial intelligence computing. The network technologies of network computing systems in different fields not only learn from each other but also have targeted design and optimization. Considering it comprehensively, three development trends, i.e., integration, differentiation, and optimization, are summarized in this paper for network technologies in different fields. Integration reflects that there are no clear boundaries for network technologies in different fields, differentiation reflects that there are some unique solutions in different application fields or innovative solutions under new application requirements, and optimization reflects that there are some optimizations for specific scenarios. This paper can help academic researchers consider what should be done in the future and industry personnel consider how to build efficient practical network systems.

Edge computing, which provides computation services at the edge of networks, has become a promising method to meet the massive computation demands of Internet of Things (IoT). To make full use of resources, a computation offloading scheme is needed in edge computing system. In this work, we propose an event-driven computation offloading scheme for the first time. Compared with the existing time-driven schemes, the proposed scheme has a smaller implementation complexity in some scenarios with computation-intensive task computing. In the proposed scheme, the priority of different tasks is jointly considered. To decide the optimal offloading action of the scheme, we formulate the offloading problem as a semi-Markov decision process (SMDP). Then, a model-based method is proposed to derive the optimal offloading policy under fully explored system by addressing the challengs of modeling. On the other hand, considering partially explored system, we propose an online double deep Q-network algorithm, which can deal with the poor scalability of the standard Q-learning algorithm, to derive the optimal offloading policy. In addition, we also introduce some tricks to accelerate the learning procedure. The simulation results show the superior performance of our proposed scheme.

In this paper, the problem of distributed opportunistic channel access in wireless cooperative networks is investigated. To cope with the energy limitation problem of relay nodes, radio-frequency (RF) energy harvesting is considered, and thus, no external energy is needed for each relay node. Then, a novel distributed opportunistic scheduling (DOS) scheme is proposed. In the scheme, users contend for the channel access opportunity by random access, and then, the user with a successful contention makes a decision whether to give up the opportunity after probing the source-to-relay link and relay-to-destination link by following a strategy. To maximize the average throughput of the network, the optimal strategy of the proposed scheme, which is to help the user to decide whether to give up the transmission opportunity, is derived by optimal stopping theory. The obtained optimal strategy has a threshold-based structure, and thus, it is easy to implement in practice. In addition, the threshold can be calculated off-line by a proposed low-complexity algorithm. Simulation results are provided to demonstrate the superior performance of the proposed DOS scheme.

Cognitive radio technology can largely enhance spectrum utilization efficiency by dynamic spectrum access. In cognitive radio, spectrum sensing is essential to protect the transmission of primary users (PUs). To improve the sensing accuracy, cooperative sensing has been introduced in the literature. However, there are still some challenges on cooperative sensing, especially in Cognitive Radio Ad Hoc Networks (CRAHNs) in which a centralized coordinator does not exist. In this paper, we deal with the challenges of cooperative sensing in CRAHNs, with focus on sensing data fusion and security. An overview of existing research efforts is given first, and thus, the research challenges are identified and discussed in details. To solve those challenges, we propose a cooperative sensing scheme for CRAHNs. In order to reduce the communication overhead, we partition the secondary users (SUs) to several clusters, and in each cluster, a cluster head is selected to serve as the representative for the cluster. An efficient consensus-based method with security consideration is proposed to obtain the accurate final sensing result. Extensive simulation is conducted based on real scenarios to evaluate the performance of the proposed scheme.

Mobile edge computing (MEC) has recently emerged as a promising paradigm to meet the increasing computation demands in Internet of Things (IoT). However, due to the limited computation capacity of the MEC server, an efficient computation offloading scheme, which means the IoT device decides whether to offload the generated data to the MEC server, is needed. Considering the limited battery capacity of IoT devices, energy harvesting (EH) is introduced to enhance the lifetime of the IoT systems. However, due to the unpredictability nature of the generated data and the harvested energy, it is a challenging problem when designing an effective computation offloading scheme for the EH MEC system. To cope with this problem, we model the computation offloading process as a Markov decision process (MDP) so that no prior statistic information is needed. Then, reinforcement learning algorithms can be adopted to derive the optimal offloading policy. To address the large time complexity challenge of learning algorithms, we first introduce an after-state for each state-action pair so that the number of states in the formulated MDP is largely decreased. Then, to deal with the continuous state space challenge, a polynomial value function approximation method is introduced to accelerate the learning process. Thus, an after-state reinforcement learning algorithm for the formulated MDP is proposed to obtain the optimal offloading policy. To provide efficient instructions for real MEC systems, several analytical properties of the offloading policy are also presented. Our simulation results validate the great performance of our proposed algorithm, which significantly improves the achieved system reward under a reasonable complexity.

One-to-many group communication is a performance bottleneck for high-performance data center applications, due to sending massive data from one source to hundreds of receivers. The state-of-the-art solutions utilize either electrical packet switch (EPS) or optical circuit switch (OCS) multicast to accelerate massive data disseminations. However, there exist competitions between multicast and unicast flows at core EPSes in the electrical multicast. Moreover, the optical multicast suffers from a non-negligible reconfiguration delay and exclusive optical links. In this paper we present iCAST, a system for reducing multicast flow completion time (MFCT) on a generic hybrid EPS/OCS network, which has multiple EPSes and OCSes supporting multicast. iCAST constructs multicast trees by integrating the electrical and optical multicast to fully utilize network resources, and seamlessly schedules flows between the static electrical and dynamic optical networks to reduce the configuration overhead. We evaluate the performance by implementing a small-scale hybrid EPS/OCS testbed and extending the high-performance framework MPICH to support iCAST. Experiments show that iCAST outperforms one order of magnitude in reducing MFCT compared with the ring algorithm. We also develop an event-based flow level simulator to evaluate the performance of iCAST at the scale of thousands of servers. Simulation results show that iCAST reduces the average MFCT by 32% and 28% compared to OCS and EPS respectively, and significantly outperforms binomial tree and ring algorithm by up to 64% and 46% respectively.

Control framework design is critical to future SDN/NFV-enabled satellite network. However, the current multi-layer constellation based solutions have drawbacks in terms of availability, latency, openness, etc. The key contribution in this work is the study of an efficient control framework, which logically consists of two parts: entity part and overlay part. The entity part is a novel heterogeneous single-layer LEO satellite network, while the overlay part implements a virtualized overlay network. We have implemented a lightweight prototype of our framework and compared it with a GEO satellite based solution (i.e. OpenSAN). We demonstrate proof-of-concept that our framework performs better than OpenSAN with respect to control latency and avoids potential system bottleneck.

The proposed MSN architecture is intended to directly address the challenge of mobility, which refers to the motion of users as well as the dynamics of the satellite constellation. A virtual access point layer consisting of fixed virtual satellite network attachment points is superimposed over the physical topology in order to hide the mobility of satellites from the mobile endpoints. Then the MSN enhances endpoint mobility by a clean separation of identity and logical network location through an identity-to-location resolution service, and taking full advantage of the user's geographical location information. Moreover, a SDN based implementation is presented to further illustrate the proposal.

Abstract People‐centric sensing (PCS), which collects information closely related to human activity and interactions in societies, is stepping into a flourishing time. Along with its great benefits, PCS poses new security challenges such as data integrity and participant privacy. Hierarchical identity‐based signature (HIBS) scheme can efficiently provide high‐integrity messaging, secure communication, and privacy protection to PCS. However, key escrow problem and low computation efficiency primarily hinder the adoption of HIBS scheme. In this paper, we propose an escrow‐free online/offline HIBS scheme for securing PCS. By utilizing user‐selected‐secret signing algorithm and splitting the signing phase into online and offline procedures, our scheme solves the key escrow problem and achieves high scheme performance. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, we propose a rollback traffic avoidance method for the snapshot routing in cyclic mobile networks. Since the snapshot routing tables are switched simultaneously, part of the traffic may be sent back on some links if the new routing path contains the same links but with reversed forwarding direction against the old one. Since the selection of routing paths to avoid the rollback traffic is NP-hard, we propose an approximate algorithm called Inter-Snapshot Rollback Traffic Avoidance (ISRTA), to pursuit the approximate optimal solutions. Evaluation is performed based on the typical cyclic mobile network -- Iridium satellite system, and the simulation results show that our method can efficiently eliminate the rollback paths and traffic in the Iridium system.

In this paper, we propose an offline rollback traffic avoidance method for the snapshot routing in cyclic mobile networks. Since the snapshot routing tables are switched simultaneously, part of the traffic may be sent back on some links if the new routing path contains the same links but with reversed forwarding direction against the old one. To avoid the rollback traffic, we first formulate the rollback traffic avoidance problem as an integer programming problem which is NP-hard, and then we propose an approximate algorithm called Inter-Snapshot Rollback Traffic Avoidance (ISRTA), to pursuit the approximate optimal solutions. Simulation results show that our method can efficiently eliminate the rollback paths and traffic in the Iridium system. Although the global average network delay is increased slightly, the extra end to end delay caused by rollback traffic is eliminated and the packet loss rate is reduced.

In this paper, we propose FlyCast, an architecture using the physical layer of free-space optics (FSO) to accelerate multicast communication. FlyCast leverages off-the-shelf devices (e.g. switchable mirror, beam splitter) to physically split the FSO beam to multi receivers on demand, which enables to build dynamical multicast trees in physical layer and accelerates multicast communications. We demonstrate the feasibility of FlyCast through our theoretical analysis and the proof-of-concept prototype.

Cooperative wireless networking, which is promising in improving the system operation efficiency and reliability by acquiring more accurate and timely information, has attracted considerable attentions to support many services in practice. However, the problem of secure cooperative communication has not been well investigated yet. In this paper, we exploit physical layer security to provide secure cooperative communication for wireless ad hoc networks (WANETs) where involve multiple source-destination pairs and malicious eavesdroppers. By characterizing the security performance of the system by secrecy capacity, we study the secrecy capacity optimization problem in which security enhancement is achieved via cooperative relaying and cooperative jamming. Specifically, we propose a system model where a set of relay nodes can be exploited by multiple source-destination pairs to achieve physical layer security. We theoretically present a corresponding formulation for the relay assignment problem and develop an optimal algorithm to solve it in polynomial time. To further increase the system secrecy capacity, we exploit the cooperative jamming technique and propose a smart jamming algorithm to interfere the eavesdropping channels. Through extensive experiments, we validate that our proposed algorithms significantly increase the system secrecy capacity under various network settings.

In recent years, with the rapid development of satellite technology including On Board Processing (OBP) and Inter Satellite Link (ISL), satellite network devices such as space IP routers have been experimentally carried in space. However, there are many difficulties to build a future satellite network with current terrestrial Internet technologies due to the distinguished space features, such as the severely limited resources, remote hardware/software upgrade in space. In this paper, we propose OpenSAN, a novel architecture of software-defined satellite network. By decoupling the data plane and control plane, OpenSAN provides satellite network with high efficiency, fine-grained control, as well as flexibility to support future advanced network technology. Moreover, we also discuss some practical challenges in the deployment of OpenSAN.

This work presents a novel quantum security VoIP phone, called Qphone. Qphone integrates quantum key distribution (QKD) and VoIP steganography, and achieves peer-to-peer communication with information-theoretical security (ITS) guaranteeing. Qphone consists of three parts, a real-time QKD system, RT-QKD, a steganography software, VS-Phone, and an audio encryption and authentication hardware, AE-KEY. RT-QKD explores QKD technologies, and is able establish a shared key between two peers ensuring ITS. VS-Phone utilizes VoIP steganography to protect transmission channels of sensitive information. Qphone can provide efficient and real-time security protections to meet different security demands.

Routing is a challenge problem in Delay-tolerant networks (DTNs) due to the intermittent connectivity environment. To cope with it, both single-copy and multi-copy routing protocols have been proposed. The difference between them is how to handle with the messages that have been forwarded to the next hop. These messages will be removed from buffer immediately in the single-copy protocols, while be kept and forwarded to other relays in the multi-copy cases. However, there is a gap between the two protocols. In our previous work, we have indicated it and proposed a Snail Crawling(SC) method to fill the gap. In this paper, we indicate that the SC method is just an enhanced method but not a routing protocol. By adopting heuristic strategies in messages distribution of SC, we get a new routing protocol One Leader at One Time(OLOT), in which only one node can forward message to another relay at the same time. Simulation results show that both the delivery rate and the overhead of OLOT are better than the SC method.

Multi-tenancy and lack of network performance isolation among tenants together make the public cloud vulnerable to attacks. This paper studies one of the potential attacks, namely, low-rate denial-of-service (DoS) attack (or \textit{Shrew} attack for short), in cloud data center networks (DCNs). To explore the feasibility of launching Shrew attack from the perspective of a normal external tenant, we first leverage a loss-based probe to identify the locations and capabilities of the underlying bottlenecks, and then make use of the low-latency feature of DCNs to synchronize the participating attack flows. Moreover, we quantitatively analyze the necessary and sufficient traffic for an effective attack. Using a combination of analytical modeling and extensive experiments, we demonstrate that a tenant could initiate an efficient Shrew attack with extremely little traffic, e.g., milliseconds-long burst traffic, which imposes significant difficulty for the switching boxes and counter-DoS mechanisms to detect. We identify that both the conventional protocol assumption and new features of DCNs enable such Shrew attack, and new techniques are required to thwart it in the DCNs.

SRAM-based FPGA is more sensitive to multiple bit upset, and the possibility of accumulation of memory's upset is high. In order to improve the ability that SRAM-based FPGA is more stable to multiple upset, this paper presents a new type design of multiple errors correction. The design combines BCH(15,7) code which can correct two errors and the improved TMR technology and achieves detecting and correcting multiple bit upset. Compared with the classical Hamming codes and extended Hamming codes, it has the advantage of correcting multiple bit upset. And compared with the traditional TMR, it can effectively determine the validity of the data after voting. Meanwhile, the design writes back the right data when error happens to avoid the accumulation of errors.

Recently, streaming steganography has attracted a lot of research efforts, however, since multimedia processing requires high performance hardware and software, most literatures in the streaming steganography community focus on simulations due to lack of a practical streaming steganography platform. Towards this issue, we design and develop VISOR, a novel VoIP Steganography Oriented platfoRm. VISOR consists of both hardware(named "VISOR-Key") and software(named "VISORPhone"). In general, VISOR provides an open, high performance and portable platform to the streaming steganography community.

For the three-dimensional surface texture can display the texture information of the object better than the two-dimensional illumination and the view angles; it is widely used in virtual reality and computer games. Photometric Stereo, as one of the effective technologies for capture of three-dimensional surface texture information, has attracted wide attention. Uniform illumination is the essential condition for the capture and reconstruction of three-dimensional surface texture using Photometric Stereo. In practice, non-uniform illumination leads to distorted surface height maps during the capture and reconstruction processes. This paper proposes a simple method to automatic correction of non-uniform illuminate for 3D surface height map reconstruction and to eliminate this kind of distortion and aberration. The experimental result shows the effectiveness of the proposed method.

In networked embedded sensor systems, data fusion is a viable solution to significantly reduce energy consumption while achieving real-time guarantee. Emerging data fusion applications demand efficient task allocation and scheduling techniques. However, existing approaches can not be effectively applied concerning both network topology and wireless communications. In this paper, we formally model TATAS, the topology-aware task allocation and scheduling problem for real-time data fusion applications, and show it is NP-complete. We also propose an efficient three-phase heuristic to solve the TATAS problem. We implement our technique and conduct experiments based on a simulation environment. Experimental results show that, as compared with traditional approaches, our technique can achieve significant energy saving and effectively meet the real-time requirements as well.