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With the continuous evolution of network attack methods, traditional security mechanisms based solely on data content encryption are no longer sufficient to ensure data security and privacy. Traffic analysis techniques, such as website fingerprinting (WFP) attacks, can effectively classify and extract information without cracking encrypted content, which enables the analysis of user behavior. The splitting of traffic in heterogeneous network resources has emerged as a new defense mechanism to disrupt traffic characteristics, however current research has not achieved a balance between security and efficiency. To address this challenge, we propose a path-weighted random (PWR) scheduling mechanism based on path quality to construct a time-varying path selection probability distribution function, that disrupts traffic characteristics and achieves resistance to traffic analysis. Furthermore, we incorporate order scheduling based on data arrival estimation to balance the transmission efficiency in heterogeneous networks. Our proposed solution was experimentally validated in real-world settings, demonstrating superior performance in terms of both security and efficiency.

Abstract Mechanisms of the proliferation of liver are mainly studied in animal model of liver regeneration after partial hepatectomy (PH). However, the PH model involves complex regeneration mechanisms, including hemodynamic factors, cytokines, growth factors, and metabolites. Among liver metabolites, bile acid (BA) is a key signaling molecule that regulates liver regeneration. This study aimed to establish a new type of rapid liver hyperplasia model induced mainly by bile acid pathway through hepatoenteral circulation with hilar bile duct ligation (HBDL). We first established the HBDL model by ligating the bile duct of all hepatic lobes but the right lateral lobe in rabbits and compared with the PVL model and sham operation group. Changes in the liver lobe and hemodynamics were observed. Liver function and the bile acid level were also analyzed. Then we verified the HBDL model in mice. Liver function and the levels of bile acids and cytokines were tested. The protein and mRNA levels of FXR, FGF15, CYP7A1 and FoxM1b in liver tissue were also analyzed. After hilar ligation of the biliary tract, the unligated liver lobes proliferated significantly. Compared with those in the sham group, the volume and weight of the unligated right lateral lobe of the liver in the HBDL group and the PVL group increased significantly (P

Ubiquitous Internet of Things (IoT) devices have fueled plenty of innovations in the emerging network paradigms. Among them, IoT edge caching has emerged as a promising technique to cope with the explosive growth in network data traffic, with Quality of Service (QoS) improved and energy saved. However, the intrinsic storage limitations of the edge servers poses a critical challenge for the IoT edge caching system. Enabling edge servers to cooperate with each other can provide a potential perspective to improve the edge storage utilization widely discussed. Nevertheless, it also incurs an additional communication overhead, eventually making the caching system more complex. As a result, how to perform an efficient cooperative caching becomes a critical issue. Thus, in this paper, we propose a deep reinforcement learning-based cooperative edge caching approach, which allows the distributed edge servers to learn to cooperate with each other. Specifically, edge servers determine their cache actions based on the local caching state. After that, the centralized remote server evaluates these actions and feeds back the evaluation results to edge servers for subsequent caching actions optimization. We show that, by designing an appropriate reward function, our approach promotes cooperation between edge servers as well as improving the system hit rate. On this basis, we consider a practical and reasonable scenario with inconsistent data item size and propose a novel multi-agent actor-critic caching algorithm. Extensive simulation results demonstrate the performance improvement using our proposed solution over three other caching algorithms.

The existing satellite-terrestrial integrated networks (STINs) suffer from security and privacy concerns due to the limited resources, poor attack resistance and high privacy requirements of satellite networks. Network Intrusion Detection System (NIDS) is intended to provide a high level of protection for modern network environments, but how to implement distributed NIDS on STINs has not been widely discussed. At the same time, satellite networks have always lacked real and effective security data sets as references. To solve these problems, we propose a distributed NIDS using Federal Learning (FL) in STIN to properly allocate resources in each domain to analyze and block malicious traffic, especially distributed denial-of-service (DDoS) attacks. Specifically, we first design a typical STIN topology, on the basis of which we collect and design security data sets adapted to satellite and terrestrial networks in STIN, respectively. To address the problem of poor attack resistance of satellite networks, we propose a satellite network topology optimization algorithm to reduce the difficulty in tracing malicious packets due to frequent link switching. In order to solve the problem of limited resources and high privacy requirements of satellite networks, we propose an algorithm for FL adaptation to STIN, and build a distributed NIDS using FL in STIN. Finally, we deploy the designed distributed NIDS in a prototype system and evaluate our proposed distributed NIDS with a large number of simulations of randomly generated malicious traffic. Related results demonstrate that the performance of our approach is better than traditional deep learning and intrusion detection methods in terms of malicious traffic recognition rate, packet loss rate, and CPU utilization.

The Internet has a significant impact on people’s daily lives and work. Recent studies claim that Artificial Intelligence (AI) has resulted in advances of many scientific and tech- nological fields, that is, AI-based medicine, AI-based trans- portation, and AI-based finance. The era of AI is upon us. As one of the biggest concerns, security is of significance to the development of a sustainable, resilient, and prosperous Internet ecosystem. However, cybersecurity still faces many challenging issues such as intrusion detection, privacy pro- tection, proactive defense, anomalous behaviors, advanced threat detection, and so on. In addition, many threat variations emerge and spread continuously. AI-assisted self-adaptable approaches are expected to deal with these security issues. A joint consideration of the interweaving nature between AI and cybersecurity is a key factor for driving future secure Internet.

The rapid development of intelligent vehicle networking technology has posed new requirements for in-vehicle gateway authentication security in the heterogeneous Internet of Vehicles (IoV). The current research on network layer authentication mechanisms usually relies on PKI infrastructure and interactive key agreement protocols, which have poor support for mobile and multihomed devices. Due to bandwidth and interaction delay overheads, they are not suitable for heterogeneous IoV scenarios with network state fluctuations. In this study, we propose a data-driven noninteractive authentication scheme, a lightweight, stateless scheme supporting mobility and multihoming to meet the lightweight data security requirements of the IoV. Our scheme implements device authentication and noninteractive key agreement through context parameters during data communication. Due to saving the signaling interactive delay and certificate overhead, in the IoV scenario, the proposed scheme reduced the delay by 20.1% and 11.8%, respectively, in the authentication and handover processes and brought higher bandwidth aggregation efficiency.

As information-centric networking (ICN) cache can effectively reduce the requests from customers to producers and improve the efficiency of content acquisition, there are many studies propose to improve system performance of the Internet of Things (IoT) by using the concept of the ICN. In the context of information-centric IoT, the addressing location based on content names and routing transport mechanisms, which presents a high demand for the statistics and prediction of the content popularity. To improve the accuracy of the content popularity prediction, in this paper, we demonstrate a particular analysis of the content popularity and propose a content popularity prediction algorithm based on auto-regressive (AR) model. The algorithm derives regression parameters based on least-squares estimates and predicts future trends of the content popularity through combining various known values in a certain period. The evaluation results show that the proposed algorithm can accurately predict the content popularity of the next time period in information-centric IoT. As a result, the algorithm can increase the cache hit rate in routers, and reduce the network traffic and service access delay effectively to improve the experience of users in various scenarios such as real-time streaming media services.

Network functions virtualization (NFV) is becoming a prevailing design for future Internet by migrating network functions from dedicated hardware appliances to software instances running in virtual computing platforms. NFV resource allocation approaches can dynamically instantiate network functions by using virtualized network functions (VNFs) to satisfy various quality-of-service (QoS) requirements with minimum network costs. The operator can launch a new VNF instance (VNFI) for each VNF required for flows, or assign it to the established VNFI. This makes NFV orchestration (NFVO) even more complicated. In addition, the challenges of developing NFVO scheme include how to manage the dependency between VNFs placement (network-level) and routing of flows (flow-level) through ordered VNFs, and how to efficiently utilize the available network resources. In this paper, ClusVNFI, a hierarchical resource allocation approach based on clustering, is proposed to address these challenges. To be specific, VNFs are proposed to be clustered based on their correlation. Then VNFs belonging to the same cluster are inclined to be deployed on one node to reduce the occupied link bandwidth. Moreover, network nodes are clustered based on the similarity on end-to-end flow latency information. Accordingly, flows in the same cluster intend to share the instantiated VNFIs, aiming at improving VNFI utilization while avoiding path stretch. By capturing the dependency between network-level and flow-level through clustering, ClusVNFI can achieve the tradeoff among multiple objectives including maximizing the number of admitted flows, minimizing path stretch, and improving VNFI utilization. Extensive simulation results show that the proposed ClusVNFI can balance multiple objectives comparing with other typical heuristic algorithms. Moreover, ClusVNFI can reduce network resource occupation effectively, while guaranteeing the average delay and network hops.

5G network is envisioned to provide massive connectivity for a wide range of applications, such as ultra-clear media, internet of vehicles, and smart home. The traditional way of providing security services is difficult to support these new 5G applications flexibly and effectively. In our previous work, we proposed a SFC-based framework that chains security functions in different domains to provide security services on demand. However, creating cross-domain service function chains will inevitably result in the additional network latency. In this paper, we study this problem of minimizing the end-to-end latency when deploying cross-domain service function chains for 5G applications. First, an exact approach, consisting of service chain partition and service subchain embedding, is proposed to derive an optimal solution for cross-domain service function chain placement. Second, we improve the Viterbi algorithm and propose an efficient heuristic approach to derive near-optimal solutions for large networks. We also compare the performance of the proposed exact approach, the proposed heuristic approach, and the simple greedy approach in different scales of network infrastructures. Simulation results are presented to demonstrate the effectiveness of the proposed approaches.

The issues of energy supply had been widely investigated to upgrade the network lifetime, power utilization, and system stability of communication networks. However, the deployable charging solution for massive mobile terminals in 5G is still lack. Although prior studies in wireless rechargeable sensor networks focused on the energy usage effectiveness, the charging time was frequently overlooked in most schemes. In this paper, we propose a novel distributed mobile charging (DMC) algorithm to optimize the charging time and charger quantity. The traditional policies are first analyzed based on the energy source. We noticed that the location of base station is extremely significant for charging performance. Then, the details of the DMC algorithm are illustrated through the main process of energy transfer, formula deductions, and performance optimization. To further promote the network capacity, an improved algorithm named adaptive dynamic energy transfer is proposed by introducing linear node sleeping mechanism. The simulation results demonstrate that the algorithms are able to improve the charging time and charger quantity in multiple scenarios.

To meet the urgent security demands, 5G aims to deploy virtualized and programmable security services and defense potential threats in real time. With the development of network function virtualization and software-defined networking, security functions can be dynamically and flexibly chained to cope with many types of malicious attacks. Although there are a number of studies on security function chaining for the 5G, they primarily focus on the security function composition, rather than rule enforcement. In fact, the misconfiguration of rules for security functions is notably common because of the security function diversity and rule heterogeneity that causes many unexpected and serious problems. Therefore, in this paper, we propose a priority-based anomaly-free mechanism with defined security rule anomalies. To avoid misconfiguration, we also propose and implement a simply configured rule management framework with anomaly resolution. With extensive performance evaluations, we show the availability and efficiency of our proposed mechanism to resolve security rule anomalies.

Network selection is of critical importance for heterogeneous vehicular networks. However, it still faces network congestion and reliability challenges in high-speed vehicular networks, which are caused by users' selfishness and link failures, respectively. To address these challenges, we propose a scheme called enhanced congestion game with link failures (E-CGF) to achieve optimal network selection. In E-CGF, a hidden Markov model is utilized to formulate the link failure probability. Considering link failures, users can use more than one radio network simultaneously to improve throughput performance by redundant transmission, which leads to an increase of transit cost. Accordingly, the goal of E-CGF is to make a compromise between achieved throughput and transit cost. We first prove the existence of Nash equilibrium in E-CGF and construct an efficient algorithm to find the optimal strategy. We then evaluate the effect of different parameters on the utility based on numerical analysis. Finally, we carry out extensive experiments based on real-world traces of link states from high-speed rails and compare with three typical algorithms. The results demonstrate that E-CGF outperforms others by alleviating network congestion and improving transmission reliability with a moderate trade-off between achieved throughput and transit cost.

Flexible and fully transparent thin film transistors (TFT) were fabricated via room temperature processes. The fabricated TFT on the PEN exhibited excellent performance, including a saturation mobility (μsat) of 7.9 cm2/V·s, an Ion/Ioff ratio of 4.58 × 106, a subthreshold swing (SS) of 0.248 V/dec, a transparency of 87.8% at 550 nm, as well as relatively good stability under negative bias stress (NBS) and bending stress, which shows great potential in smart, portable flexible display, and wearable device applications.

Recently, the Internet of Things (IoT) is attracting significant attention from both academia and industry. To connect the huge amount of IoT devices effectively, software-defined networking (SDN) is considered as a promising way because of its centralized network management and programmable routing logic. However, due to the limited resources in both the data plane and the control plane, SDN is vulnerable to the new-flow attack, which can disable the SDN-based IoT by exhausting the switches or the controller. Therefore, in this paper, we propose a smart security mechanism (SSM) to defend against the new-flow attack. The SSM uses the standard southbound and northbound interfaces of SDN, and it includes a low-cost method that monitors the new-flow attack by reusing the asynchronous messages on the control link. The monitor method can differentiate the new-flow attack from the normal flow burst by checking the hit rate of the flow entries. Based on the monitoring result, the SSM uses a dynamic access control method to mitigate the new-flow attack by perceiving the behavior of the security middleware in the IoT. The dynamic access control method can intercept the attack flows at their access switch. Extensive simulations and testbed-based experiments are conducted and the corresponding results verify the feasibility of our claims.

The in-network caching strategy in named data networking can not only reduce the unnecessary fetching of content from the original content server deep in the core network and improve the user response time, but also ease the traffic in the core network. However, challenges exist in in-network caching, such as the distributed locations of storage and relatively small cache space which limit the hit rate, and the cache management introduces further overhead. In this paper, we propose a two-layer hierarchical cluster-based caching solution to improve in-network caching efficiency. A network is grouped into several clusters, then, a cluster-head is nominated for each cluster to make caching decision. The clustering approach offers scalability and permits multiple aspects of inputs to be used for decision making. Our solution jointly considers the location and content popularity for caching. We implement our strategy in ndnSIM and test it on GEANT-based network and AS3967 network. Our simulation results show significant improvement over its peers.

With the emergence of vehicular Internet-of-Things (IoT) applications, it is a significant challenge for vehicular IoT systems to obtain higher throughput in vehicle-to-cloud multipath transmission. Network Coding (NC) has been recognized as a promising paradigm for improving vehicular wireless network throughput by reducing packet loss in transmission. However, existing researches on NC do not consider the influence of the rapid quality change of wireless links on NC schemes, which poses a great challenge to dynamically adjust the coding rate according to the variation of link quality in vehicle-to-cloud multipath transmission in order to avoid consuming unnecessary bandwidth resources and to increase network throughput. Therefore, we propose an Adaptive Network Coding (ANC) scheme brought by the novel integration of the Hidden Markov Model (HMM) into the NC scheme to efficiently adjust the coding rate according to the estimated packet loss rate (PLR). The ANC scheme conquers the rapid change of wireless link quality to obtain the utmost throughput and reduce the packet loss in transmission. In terms of the throughput performance, the simulations and real experiment results show that the ANC scheme outperforms state-of-the-art NC schemes for vehicular wireless multipath transmission in vehicular IoT systems.

Aluminum-doped ZnO (AZO) has huge prospects in the field of conductive electrodes, due to its low price, high transparency, and pro-environment. However, enhancing the conductivity of AZO and realizing ohmic contact between the semiconductor and AZO source/drain (S/D) electrodes without thermal annealing remains a challenge. Here, an approach called pulsed laser deposition (PLD) is reported to improve the comprehensive quality of AZO films due to the high energy of the laser and non-existence of the ion damage. The 80-nm-thick AZO S/D electrodes show remarkable optical properties (transparency: 90.43%, optical band gap: 3.42 eV), good electrical properties (resistivity: 16 × 10−4 Ω·cm, hall mobility: 3.47 cm2/V·s, carrier concentration: 9.77 × 1020 cm−3), and superior surface roughness (Rq = 1.15 nm with scanning area of 5 × 5 μm2). More significantly, their corresponding thin film transistor (TFT) with low contact resistance (RSD = 0.3 MΩ) exhibits excellent performance with a saturation mobility (µsat) of 8.59 cm2/V·s, an Ion/Ioff ratio of 4.13 × 106, a subthreshold swing (SS) of 0.435 V/decade, as well as good stability under PBS/NBS. Furthermore, the average transparency of the unpatterned multi-films composing this transparent TFT can reach 78.5%. The fabrication of this TFT can be suitably transferred to transparent arrays or flexible substrates, which is in line with the trend of display development.

We study the performance of diffusion LMS (Least-Mean-Square) algorithm for distributed parameter estimation problem over sensor networks with quantized data and random topology, where the data are quantized before transmission and the links are interrupted at random times. To achieve unbiased estimation of the unknown parameter, we add dither (small noise) to the sensor states before quantization. We first propose a diffusion LMS algorithm with quantized data and random link failures. We further analyze the stability and convergence of the proposed algorithm and derive the closed-form expressions of the MSD (Mean-Square Deviation) and EMSE (Excess Mean-Square Errors), which characterize the steady-state performance of the proposed algorithm. We show that the convergence of the proposed algorithm is independent of quantized data and random topology. Moreover, the analytical results reveal which factors influence the network performance, and we show that the effect of quantization is the main factor in performance degradation of the proposed algorithm. We finally provide computer simulation results that illustrate the performance of the proposed algorithm and verify the results of the theoretical analysis.

With the prevalence of low-power wireless devices in industrial applications, concerns about timeliness and reliability are bound to continue despite the best efforts of researchers to design Industrial Wireless Sensor Networks (IWSNs) to improve the performance of monitoring and control systems. As mobile devices have a major role to play in industrial production, IWSNs should support mobility. However, research on mobile IWSNs and practical tests have been limited due to the complicated resource scheduling and rescheduling compared with traditional wireless sensor networks. This paper proposes an effective mechanism to guarantee the performance of handoff, including a mobility-aware scheme, temporary connection and quick registration. The main contribution of this paper is that the proposed mechanism is implemented not only in our testbed but in a real industrial environment. The results indicate that our mechanism not only improves the accuracy of handoff triggering, but also solves the problem of ping-pong effect during handoff. Compared with the WirelessHART standard and the RSSI-based approach, our mechanism facilitates real-time communication while being more reliable, which can help end-to-end packet delivery remain an average of 98.5% in the scenario of mobile IWSNs.

The effect of intrinsic stress on the structure and physical properties of silicon-tin-oxide (STO) films have been investigated. Since a state of tensile stress is available in as-deposited films, the value of stress can be exponentially enhanced when the annealing temperature is increased. The tensile stress is able to not only suppress the crystallization and widen the optical band gap of STO films, but also reduce defects of STO films. In this report, the good electrical performance of STO thin-film transistors (TFTs) can be obtained when annealing temperature is 450 °C. This includes a value of saturation mobility that can be reached at 6.7 cm2/Vs, a ratio of Ion/Ioff as 7.34 × 107, a steep sub-threshold swing at 0.625 V/decade, and a low trap density of 7.96 × 1011 eV−1·cm−2, respectively.

There is a tradeoff between routing efficiency and energy equilibrium for sensor nodes in wireless sensor networks (WSNs). Inspired by the large and single-celled amoeboid organism, slime mold Physarum polycephalum , this paper presents a novel Physarum -inspired routing protocol (P-iRP) for WSNs to address the above issue. In P-iRP, a sensor node can choose the proper next hop by using a proposed Physarum -inspired selecting next hop model (P-iSNH), which comprehensively considers the distance, energy residue, and location of the next hop. As a result, the P-iRP can get a rather low algorithm complexity of O ( n ) , which greatly reduces the processing delay and saves the energy of sensors. Moreover, by theoretical analysis, the P-iSNH always has an equilibrium solution for multiple next hop candidates, which is vital factor to the stability of routing protocol. Finally, simulation results show that P-iRP can perform better in many scenarios and achieve the effective tradeoff between routing efficiency and energy equilibrium compared to other famous algorithms.

Recently, vehicular sensor networks (VSNs) have emerged as a new intelligent transport networking paradigm in the Internet of Things. By sensing, collecting, and delivering traffic-related information, VSNs can significantly improve both driving experience and traffic flow control, especially in constrained urban environments. Latest technological advances enable vehicular devices to be equipped with multiple wireless interfaces, which can support cooperative communications for concurrent multipath transfer (CMT) in VSNs. However, path heterogeneity and vehicle mobilitycauseCMT not to achieve the same high transport efficiency recorded in wired nonmobile network environments. This paper proposes a novel vehicular network-based CMT solution (VN-CMT) to address the above issues and improve data delivery efficiency. VN-CMT is based on a CMT disorder analytic model which can effectively and accurately evaluate the degree of out-of-order data. Based on this proposed model, a series of mechanisms are introduced as follows: (1) a packet disorder-reducing retransmission policy to reduce retransmission delay; (2) a path group selection algorithm to find the best path set for data multipath concurrent transfer; and (3) a data scheduling mechanism to distribute data according to each path's capacity. Simulation results show how VN-CMT improves data delivery efficiency in comparison with an existing state-of-the-art solution.

Emergent content-oriented networks prompt Internet service providers (ISPs) to evolve and take major responsibility for content delivery. Numerous content items and varying content popularities motivate interdependence between peering ISPs to elaborate their content caching and sharing strategies. In this paper, we propose the concept of peering for content exchange between interdependent ISPs in content centric Internet to minimize content delivery cost by a proper peering strategy. We model four peering strategic games to formulate four types of peering relationships between ISPs who are characterized by varying degrees of cooperative willingness from egoism to altruism and interconnected as profit-individuals or profit-coalition. Simulation results show the price of anarchy (PoA) and communication cost in the four games to validate that ISPs should decide their peering strategies by balancing intradomain content demand and interdomain peering relations for an optimal cost of content delivery.

Wireless Multimedia Sensor Networks (WMSNs) have gained significant attention with capabilities of retrieving video and audio streams, still images, and scalar sensor data but with challenge of high data loss. As more and more wireless devices are equipped with multiple network interfaces, multimedia delivery over multipath has been cognized as a more promising approach in wireless transmission. In this paper, based on the Concurrent Multipath Transfer (CMT) extension for Stream Control Transport Protocol (SCTP), we propose a novel environment-aware CMT ( e -CMT) to overcome the high data loss challenge, as well as “a hot potato” congestion problem in wireless transmission. The e -CMT provides environment-aware cognitive ability for efficient video delivery with three modules, which are Path Quality-aware Model (PQM) that devotes to estimate path quality and select candidate path using for retransmission, Adaptive Retransmission Trigger (ART) that contributes to cognize packet loss and trigger efficient retransmission behaviors, and Congestion-avoid Data Distributor (CDD) that serves to enable Partial-Reliable retransmission to mitigate congestion condition. We design a close realistic topology to present how the e -CMT outperforms the original CMT for efficient video delivery over multihomed WMSNs.

The content-rich multimedia streaming will be the most attractive services in the next-generation networks. With function of distribute data across multipath end-to-end paths based on SCTP's multihoming feature, concurrent multipath transfer SCTP (CMT-SCTP) has been regarded as the most promising technology for the efficient multimedia streaming transmission. However, the current researches on CMT-SCTP mainly focus on the algorithms related to the data delivery performance while they seldom consider the background traffic factors. Actually, background traffic of realistic network environments has an important impact on the performance of CMT-SCTP. In this paper, we firstly investigate the effect of background traffic on the performance of CMT-SCTP based on a close realistic simulation topology with reasonable background traffic in NS2, and then based on the localness nature of background flow, a further improved retransmission algorithm, named RTX_CSI, is proposed to reach more benefits in terms of average throughput and achieve high users' experience of quality for multimedia streaming services.