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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 for vehicular networks to ensure that network access is only given to authorized users. Among authentication solutions for vehicular networks, group signature not only offers authentication services, but also provides conditional privacy preservation. However, the current group signature approach for authentication in vehicular networks exhibits time-consuming signature verification and poor scalability. To overcome these shortcomings, we propose a flexible and efficient delay-aware authentication scheme (FEDAS) by utilizing edge computing paradigm. In the proposed architecture, we design the authentication group maintaining mechanism and develop the collaborative CRL management method. Moreover, we propose transition zone to solve the reliable authentication problem in border area of the group. To implement the proposed architecture, we propose a model for calculating the length of local CRL, which establishes the relationship between the size of a sub-area and the length of local CRL. And we also design a method for area division based on the length of local CRL, which provides division principle for our authentication scheme. We conduct extensive simulations to verify the effectiveness of our proposed scheme.

Performing deep neural network (DNN) inference in real time requires excessive network resources, which poses a big challenge to the resource-limited industrial Internet of things (IIoT) networks. To address the challenge, in this paper, we introduce an end-edge-cloud orchestration architecture, in which the inference task assignment and DNN model placement are flexibly coordinated. Specifically, the DNN models, trained and pre-stored in the cloud, are properly placed at the end and edge to perform DNN inference. To achieve efficient DNN inference, a multi-dimensional resource management problem is formulated to maximize the average inference accuracy while satisfying the strict delay requirements of inference tasks. Due to the mix-integer decision variables, it is difficult to solve the formulated problem directly. Thus, we transform the formulated problem into a Markov decision process which can be solved efficiently. Furthermore, a deep reinforcement learning based resource management scheme is proposed to make real-time optimal resource allocation decisions. Simulation results are provided to demonstrate that the proposed scheme can efficiently allocate the available spectrum, caching, and computing resources, and improve average inference accuracy by 31.4 $\%$ compared with the deep deterministic policy gradient benchmark.

Cholangiojejunostomy (CJ) is a popular operation; however, no specific anastomotic device is available. A novel magnamosis device for CJ was developed in 2017; here, we evaluated the feasibility and safety of the device. Between January 2017 and December 2019, 23 patients who underwent CJ using a novel magnamosis device were enrolled. For the CJ: the parent magnet was placed in the proximal duct, and the purse–string suture was tightened over the rod of the parent magnet. The magnamosis device was introduced into the jejunum, and the mandrel penetrated the jejunum at the anastomotic site, before insertion into the rod of the parent magnet. After rotating the knob, the distance between two magnets was shortened enough to achieve coupling. Sixteen patients (69.6%) underwent open CJ, while 7 (30.4%) underwent laparoscopic CJ; 21 patients (91.3%) underwent choledochojejunostomy, and 2 (8.7%) underwent right or left hepatic duct jejunostomy. The mean time for completion of CJ was 9.2±2.5 min; it was significantly shorter for open CJ than for the laparoscopic way (8±1.2 min vs. 11.8±2.5 min, P

Research about online failure prediction of rail vehicle core components (such as wheels, bearings, and bogies) based on big data and artificial intelligence (AI) has become popular in view of its role of improving rail vehicle operation safety. The recent vibration energy harvesting sensor network relieves sensor nodes' dependence on wired power, which provides a green and low-cost way of collecting data from rail vehicle core components. However, the integration of an energy harvesting sensor network and AI to provide online failure prediction for rail vehicle components still faces several challenges, such as weak energy harvesting power and unstable vehicle-ground communication data rate. In this article, EH-Edge, an energy harvesting-driven cloud-edge-end device collaborative Internet of Things (IoT) platform, is proposed to efficiently integrate energy harvesting and AI to solve these challenges. A two-level collaborative AI failure prediction is proposed and deployed in the EH-Edge platform to reduce energy consumption in terms of sensor node, amount of data upload, and time delay of failure prediction. Detailed software and hardware designs and real-world data sets are also published.

Coronavirus disease (COVID-19) has presented extremely harsh requirements on deterministic transmission capability within different communication networks. However, most existing solutions mainly focus on one specific homogeneous scenario, such as pure IP routers or classical medium access control bridges, which leads to many tough challenges in flexibility and scalability. In this article, we propose a framework for heterogeneous deterministic networks (HDNs) to offer low latency and high reliability in tackling epidemics based on smart collaborative theory. Various wired and wireless connecting patterns are supported by utilizing a hybrid mode for COVID-19 relevant operations. Components generated from deterministic networking, time-sensitive networks, and 5G systems have been integrated to achieve corresponding features. The pervasive function translators are available according to virtualization technologies. The protocol-independent and segment-based concepts have been well considered and successfully implemented during our design process. The validations executed on top of the prototype and simulation platform illustrate the advantages of HDN. We aim to enable HDN as an evolutional structure for information exchange in eHealth and other vertical industries.

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%.

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

Despite the emerging network technologies such as 5G, edge computing, and cloud computing growing vigorously, it is difficult for terrestrial networks to provide quality services for IoV and other daily communications at the same time. With the advancement of satellite communication technology, more and more people are paying attention to the integration of satellite networks and ground networks. In this article, we propose a space-ground integrated information network based Internet of Vehicles (SGIIN-IoV) architecture supporting multilink access, which provides a good example for the application of the SGIIN in the IoV. In SGIIN-IoV, high delay-tolerant data will be transmitted through satellite links, avoiding using ultra low-latency links. Afterward, we propose a multilayer resource awareness (MLRA) mechanism to achieve full-dimensional monitoring in SGIIN-IoV, which consists of an intravehicle service layer, a link detection layer, and a global monitor layer. Further, we design an adaptive multilink data transmission (AMDT) mechanism to improve utilization of heterogeneous links in SGIIN-IoV. Finally, we verify the two mechanisms, MLRA and AMDT, and the results show that the proposed design can provide higher reliability for data transmission in time.

Long identifier brings low packet forwarding efficiency in Internet of Things (IoT), whereas short identifier may suffer from the exhaustion of identifier space. Compared with fixed-length identifiers ( e.g. , IPv4 and IPv6), flexible identifiers are expected for balancing the packet processing efficiency with the various IoT scales. However, it is challenging to make IoT support the flexible identifier-based forwarding. In this letter, we firstly proposed a novel variable-length identifier (VLI) solution for interconnecting IoT networks. In particular, a VLI datagram header is designed to effectively support a flexible identifier field. Following a basic VLI header, one (multiple) extension header(s) can be added if require. Each extension header includes a fixed-size identifier field. According to the combination of multiple identifier fields, the variable-length identifier can be easily achieved, resolved and supported by the IoT nodes in a flexible way. Experimental results show that VLI can decrease the processing delay effectively.

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

Static assignment of IP addresses or identifiers can be exploited by an adversary to attack a network. However, existing dynamic IP address assignment approaches suffer from two limitations, namely: participation of terminals in the assignment and inadequate network server management. Thus, in this paper, we propose an Overall-transparent Dynamic Identifier-mapping Mechanism (ODIM) to manage the identifier of network nodes to defend against scanning and worm propagation in the Smart Identifier NETwork (SINET). We establish the selection and allocation constraints, and present selection and allocation algorithms to determine the constraints. The non-repetition probability and cover cycle allow us to evaluate the defense efficiency against scanning. We propose the probability for routing identifiers and derive the defense efficiency of ODIM against worm propagation. Simulation results and theoretical analysis show that the proposed method effectively reduces the detection probability of Routing IDentifiers (RIDs) and thus improves defense capabilities against worm propagation.

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

The 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.

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

For real-time reporting of power supply interruption on LV users, there essentially need the availabilities of both the back-up supply for communication module in smart meter and the communication channel between smart meter and main station. This paper studies a real-time reporting solution which embeds super capacitor as the back-up supply of communication module in smart meter and uses micro-power communication scheme for LV users. Field tests of 30 LV distribution areas in Baoji Power Supply Branch verifies the proposed solution. The application prospects of the proposed solution are further discussed, such as interruption diagnosis and fault analysis, under urban and rural LV scenarios.

This paper analyzes the electricity transaction of distributed generation (DG) prosumers under different settlement modes in China. In general, DG prosumers have dual-directional power exchange with power grid and thus participate in both the purchase and sale of electricity, involving in the processes of metering, settlement, and billing. For DG prosumers, this paper presents several metering models, settlement mechanisms, electricity price of feed-in and off-grid. Furthermore, the economic benefits under different settlement modes are compared, including the ‘surplus generation’ and ‘total generation’ feed-in modes in China, and the net energy mode abroad, for DG prosumers, distribution system operator and governmental decision maker. The future challenges about DG prosumer development are discussed.

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

Efficient transmission control is an intricate issue in vehicular networks due to the inherent topology dynamics and unreliable link conditions. Leveraging flexible management in software-defined vehicular networks, we propose a software-defined adaptive transmission control protocol (SATCP) for selecting various transmission control policies to adapt to the time-varying vehicular environment. We formulate the optimal transmission control problem, and develop a practical heuristic algorithm for adaptive transmission control. NS-3 based preliminary simulations are conducted to verify that SATCP achieves an improvement by 27.8% than TcpHtcp, and by 54.4% than TcpVegas in terms of average network throughput.

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

Background Magnetic compression anastomosis is a feasible and effective method for bilioenteric anastomosis (BEA) in animal model. The objective of the present study was to report our initial clinical experience in laparoscopically magnetic compression bilioenteric anastomosis (LMC-BEA). Methods Patients with obstructive jaundice who were candidates for LMC-BEA were prospectively enrolled from 2013 to 2015. All the procedures were performed laparoscopically. A mother magnet and drainage tube were placed in the proximal bile duct and tightened by a purse suture after dissection of the common bile duct. The drainage tube was introduced into the jejunal lumen at the anastomotic site and guided a daughter magnet to approximate the mother magnet. The two magnets mated at the anastomotic site. All the patients were routinely followed up for magnets discharge till the end of the study. Results In total, four patients with malignant obstructive jaundice and one patient with benign biliary stricture were included. The median age was 70 y (range, 49-74 y). The median time for LMC-BEA was 12 min (range, 8-15 min). A complete anastomosis was confirmed after a median time of 21 d (range, 5-25 d) postoperatively by cholangiography via drainage tube. The magnets were expulsed around 41 d after surgery (range, 12-47 d) postoperatively. With a median follow-up of 313 d (range, 223-1042 d), no complications associated with magnetic anastomosis was documented, such as bile leakage or anastomotic stricture. Conclusions Magnetic compression is a promising alternate method for laparoscopic BEA. Among the five patients undergoing LMC-BEA, no one developed anastomotic complications.

BACKGROUND The purpose of this study was to develop a remote-controlled injection device for T-tube cholangiography to avoid occupational exposure. MATERIAL AND METHODS The remote-controlled injection device has 3 major components: an injection pump, a pressure sensor, and a wireless remote-control panel. The feasibility and effectiveness of this device for T-tube cholangiography was evaluated in ex vivo porcine livers using a laparoscopic training platform and in in vivo canine experiments. RESULTS The contrast dye was successfully injected into the biliary tracts of the ex vivo porcine liver and canines by the designed device. The X-ray images clearly showed the anatomical structure of the bile ducts. No obvious adverse reaction was observed in the dogs during or after the procedure. All steps were controlled remotely, avoiding ionizing radiation exposure to the surgeons. CONCLUSIONS This novel remote-controlled injection device for T-tube cholangiography can assist operators in completing cholangiography remotely and protecting them from occupational exposure.

© 2019 Elsevier Ltd Stateful forwarding plane is fully considered as a novel forwarding paradigm, which is proven to be beneficial to delivery efficiency and resilient to certain types of attacks. However, this fresh attempt also introduces “varietal” Denial-of-Service attack due to complicated forwarding state operations, which may cause long-term memory exhaustion of forwarding nodes, especially for resource-limited IoT nodes. This new distributed exhaustion attack is extremely hidden and there is currently no effective defense against it. In this paper, we first establish a game model to analyze the attack benefit between attacker and defender. To further make the defender obtain more utility, it is significative to make the defender manage expired state-entries during stateful forwarding. To this end, we propose an enhanced distributed low-rate attack mitigating (eDLAM) mechanism. Particularly, eDLAM maintains a lightweight malicious request table (MRT), which is very small, to offload burden of practical forwarding state table. When a packet request is matched in MRT, it will be marked and dropped directly without any impact on forwarding state table. Based on this, eDLAM adopts an optimal threshold update method for MRT to achieve a maximum defender utility. We evaluate the eDLAM performance in terms of false negatives rate (FNR) and false positives rate (FPR). Extensive experimental results show that eDLAM can reduce FNR by 10.5% and FPR by 44% on average compared with state-of-the-art mechanisms.

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.

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.

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

As the demand of mobile services for high bandwidth grows, aggregation of various wireless network resources in hybrid communication system becomes a trend. Network coding has been widely studied to solve the packet reordering, low aggregation efficiency problems which are brought by network heterogeneity. Even if the determination of coding scale is the core issue of network coding, current research can not adapt to heterogeneous wireless channels, resulting in low bandwidth aggregation and decoding efficiency. Therefore, we propose a cross-layer network coding scheme called Delay Determined Group Size (DDGS) scheme, which adaptively adjusts the coding scale to solve the problem of overall performance degradation caused by heterogeneous characteristics of wireless channels. It can get the utmost out of the performance improvement brought by network coding, to better avoid packet reordering as well as reduce the receiving delay at the receiving end. The simulation results show that DDGS is significantly superior to network coding schemes in existing state-of-the-art solutions. This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant 2020YJS019, in part by the National Natural Science Foundation of China (NSFC) under Grant 61872029, and in part by the Beijing Municipal Natural Science Foundation under Grant 4182048.

Due to improving computational capacity of supercomputers, transmitting encrypted packets via one single network path is vulnerable to brute-force attacks. The versatile attackers secretly eavesdrop all the packets, classify packets into different streams, performs an exhaustive search for the decryption key, and extract sensitive personal information from the streams. However, new Internet Protocol (IP) brings great opportunities and challenges for preventing eavesdropping attacks. In this paper, we propose a Programming Protocol-independent Packet Processors (P4) based Network Immune Scheme (P4NIS) against the eavesdropping attacks. Specifically, P4NIS is equipped with three lines of defense to improve the network immunity. The first line is promiscuous forwarding by splitting all the traffic packets in different network paths disorderly. Complementally, the second line encrypts transmission port fields of the packets using diverse encryption algorithms. The encryption could distribute traffic packets from one stream into different streams, and disturb eavesdroppers to classify them correctly. Besides, P4NIS inherits the advantages from the existing encryption-based countermeasures which is the third line of defense. 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 significantly, and increase transmission throughput by 31.7% compared with state-of-the-art mechanisms.

Background Laparoscopic pancreatoduodenectomy (LPD) is a minimally invasive technique widely developed in the last few decades. Although magnetic compression anastomosis (magnamosis) is used during cholangiojejunostomy, its applicability in LPD has not yet been reported. Herein, we evaluated the feasibility and effectiveness of magnamosis in LPD. Methods Between January 2018 and December 2019, seven patients who underwent laparoscopic magnetic compression choledochojejunostomy (LMC-CJ) or laparoscopic magnetic compression pancreatojejunostomy (LMC-PJ) in LPD were enrolled. After LPD, a parent magnet with or without a drainage tube was placed in the proximal bile duct and pancreatic duct of each patient. Daughter magnets were introduced to couple with the parent magnets at the desired sites. A close postoperative surveillance of magnet movements was performed. Various relevant data were collected, and all patients were followed up until February 2020. Results LPD was successfully completed in all seven patients, of which seven underwent LMC-CJ and two received LMC-PJ. The median time needed for completion of LMC-CJ was 11 min (range, 8-16). The cost time for the two cases of LMC-PJ was 12 and 15 min, respectively. After a median time of 50 d (range, 40-170) postoperation, all magnets were expelled. No leakages of LMC-CJ or LMC-PJ were observed after operation. After a median follow-up period of 11 mo (range, 4-18), there was no incidence of anastomotic stricture.

Vehicle-to-cloud is a network access model that aims to share computing resources for vehicles. In vehicle-to-cloud communication, reliable links are critical. Through multipath transmission, comprehensive utilization of heterogeneous wireless networks between the vehicle and the cloud server can effectively improve the reliability and efficiency of transmission. A large number of studies have pointed out that a receive butter can greatly improve the performance of multipath transmission in vehicle-to-cloud communication. However, most of these studies, which use receive buffers to improve multipath transmission performance, do not explicitly specify the design details of the receive butter. A classic design idea is to use a fixed length receive butter. However, this idea makes it difficult to determine the appropriate butter length in a complex vehicle-to-cloud communication scenario. This paper proposes a Dynamic Time-Threshold (DTT) receive buffer. The DTT receive buffer takes into account the instability of vehicular wireless networks, and dynamically adjusts the time-threshold so that the packets in the receive butter do not affect the overall communication performance due to premature departure or excessive waiting. We verified the performance of the DTT receive butter through a large number of simulations and real tests. By comparison, the performance of DTT receive butter is significantly better than the fixed length receive butter. This work was supported in part by NSFC under grant No. 61872029, the Beijing Municipal Natural Science Foundation 4182048, and the Fundamental Research Funds for the Central Universities (Grant no. 2018YJS010).

In order to alleviate the impact of network congestion on the spatial network running traditional contact graph routing (CGR) algorithm and DTN protocol, we propose a flow intelligent control method based on deep convolutional neural network (CNN). The method includes two stages of offline learning and online prediction to intelligently predict the traffic congestion trend of the spatial network. A CGR update mechanism is also proposed to intelligently update the CGR to select a better contact path and achieve a higher congestion avoidance rate. The proposed method is evaluated in the prototype system. The experimental results show that it is superior to the existing CGR algorithm in terms of transmission delay, receiver throughput and packet loss probability.

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

VCC can bring many benefits to intelligent transportation systems. Meanwhile, HSR, an increasingly efficient means of transportation, faces several challenges in terms of high-frequency handover at speeds over 300 km/h. This includes large volumes of data of different types and different degrees of importance. Therefore, a secure and comprehensive cloud computing solution is attractive to improve the safety and efficiency of intelligent HSR. In this article, we present a novel and practical SVCC-HSR based on our long-term research and practice in this field. SVCC-HSR not only considers the various technical features of vehicular cloud computing, but also addresses several special demands in the HSR context. We perform extensive experiments using various scenarios, including frequent handover scenarios in high-speed trains running at 300 km/h with large-volume data transmission scenarios in locomotive depots. The real-world experimental results demonstrate that SVCC-HSR achieves better performance in fast authentication, hierarchical attribute-based data encryption, and transmission efficiency compared to its counterparts.

The current Internet architecture was designed on the basis of a triple bindings model, which could meet the needs of the early Internet. However, with the tremendous growth of the Internet and the continuous innovations of applications, the triple bindings (namely, control/data binding, resource/ location binding and user/network binding) have given rise to many problems for the Internet, such as scalability, mobility, and security. In order to solve these problems, many network techniques have been developed. Besides, novel future Internet architectures including Information-Centric Networks and Software-Defined Networks have emerged. However, these architectures only partially decouple the triple bindings. To completely separate the triple bindings, Smart Collaborative Identifier Networks (SINET) is proposed. There have been many research efforts on SINET, which built the foundation of SINET being a promising future Internet. In this paper, we comprehensively introduce SINET and these related research works. We start by introducing the motivation for SINET, explain its fundamental concepts and how it can make a difference. Next, we present in details the design principles of every layer in SINET, explaining how it can decouple the triple bindings and solve the problems of the current Internet. Finally, we discuss the state-of-the-art challenges in SINET. We investigate the ongoing research works and commercial products in SINET, with aspects such as scalability, mobility, security and reliability.

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

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

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.

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.

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

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.

AIM To explore the possible relationship between fecal microbial communities and non-anastomotic stricture (NAS) after liver transplantation (LT). METHODS A total of 30 subjects including 10 patients with NAS, 10 patients with no complications after LT, and 10 non-LT healthy individuals were enrolled. Fecal microbial communities were assessed by the 16S rRNA gene sequencing technology. RESULTS Different from the uncomplicated and healthy groups, unbalanced fecal bacterium ratio existed in patients with NAS after LT. The results showed that NAS patients were associated with a decrease of Firmicutes and Bacteroidetes and an increase of Proteobacteria at the phylum level, with the proportion-ratio imbalance between potential pathogenic families including Enterococcaceae, Streptococcaceae, Enterobacteriaceae, Pseudomonadaceae and dominant families including Bacteroidaceae. CONCLUSION The compositional shifts of the increase of potential pathogenic bacteria as well as the decrease of dominant bacteria might contribute to the incidence of NAS.

5G is increasingly becoming a prominent technology promoting the development of mobile networks. Meanwhile, the ever increasing demands for vehicular networks are driven by a variety of vehicular services and application scenarios. Therefore, a new architectural design, which can harness the benefits of 5G for vehicular networks, can take a solid step toward increasing bandwidth and improving reliability for vehicular communications. In this article, we focus on the innovations of a novel and practical 5G-enabled smart collaborative vehicular network (SCVN) architecture, based on our long-Term research and practice in this field. SCVN not only considers the various technical features of a 5G network, but also includes different mobile scenarios of vehicular networks. We have performed extensive experiments in various scenarios, including high-density vehicles moving at low or high speed across dense cells, to evaluate the performance of the proposed architecture. The real-world experimental results demonstrate that SCVN achieves better performance in throughput, reliability, and handover latency compared to its counterparts.

As satellite networks have played an indispensable role in many fields, how to integrate them with terrestrial networks (e.g., the Internet) has attracted significant attention in academia. However, it is challenging to efficiently build such an integrated network, since terrestrial networks are facing a number of serious problems, and since they do not provide good support for heterogeneous network convergence. In this article, we propose a flexible network architecture, HetNet, for efficient integration of heterogeneous satellite-terrestrial networks. Specifically, the HetNet synthesizes Locator/ID split and Information-Centric Networking to establish a general network architecture. In this way, it is able to achieve heterogeneous network convergence, routing scalability alleviation, mobility support, traffic engineering, and efficient content delivery. Moreover, the HetNet can further improve its network elasticity by using the techniques of Software-Defined Networking and Network Functions Virtualization. In addition, to evaluate the HetNet performance, we build a proof-of-concept prototype system and conduct extensive experiments. The results confirm the feasibility of the HetNet and its advantages.

Recently, Information-centric networking (ICN) has flourished research interest. One common and important feature of ICN is that has built-in caching capability, which means that every node can cache content along the content delivery path. To improve the transmission efficiency of content dissemination, we propose an efficient caching scheme for ICN, which is named as NCPP-based caching scheme. In our proposed caching scheme, we use the definition of node-content pass probability (NCPP), which taking both node utilization ratio and popularity of content into consideration, to decide whether the node needs to cache the content. In addition, the reasonable resource allocation can also improve the performance of ICN. Therefore, we propose a NUR-based resource allocation scheme, which uses the definition of node utilization radio (NUR). We compare the server hit ratio of random caching scheme and the proposed caching scheme by simulation. Our results show that our proposed scheme has smaller server hit ratio than random caching scheme. Furthermore, our proposed caching scheme depends on the number of requests.

AIM To compare the effect of University of Wisconsin (UW) solution with or without metformin, an AMP-activated protein kinase (AMPK) activator, for preserving standard and marginal liver grafts of young and aged rats ex vivo by hypothermic machine perfusion (HMP). METHODS Eighteen young (4 mo old) and 18 aged (17 mo old) healthy male SD rats were selected and randomly divided into three groups: control group, UW solution perfusion group (UWP), and UW solution with metformin perfusion group (MUWP). Aspartate aminotransferase (AST), alanine aminotransferase (ALT), lactate dehydrogenase (LDH), interleukin-18 (IL-18), and tumor necrosis factor-alpha (TNF-α) in the perfused liquid were tested. The expression levels of AMPK and endothelial nitric oxide synthase (eNOS) in liver sinusoidal endothelial cells were also examined. Additionally, microscopic evaluation of the harvested perfused liver tissue samples was done. RESULTS AST, ALT, LDH, IL-18 and TNF-α levels in the young and aged liver-perfused liquid were, respectively, significantly lower in the MUWP group than in the UWP group (P < 0.05), but no significant differences were found between the young and aged MUWP groups. Metformin increased the expression of AMPK and eNOS protein levels, and promoted the extracellular release of nitric oxide through activation of the AMPK-eNOS mediated pathway. Histological examination revealed that in the MUWP group, the extent of liver cells and tissue damage was significantly reduced compared with the UWP group. CONCLUSION The addition of metformin to the UW preservative solution for ex vivo HMP can reduce rat liver injury during cold ischemia, with significant protective effects on livers, especially of aged rats.

With continuous increase in the number of its users and applications, the Internet has been gradually encountering the issues of security, mobility, scalability. Therefore, many organizations around the world are engaged in research that aims to overcome these drawbacks. We first describe the main problems faced by the Internet, review some related research, and analyze some promising technologies, such as SDN, NFV, NS, and ICN. Then, we highlight the research work on SINET, which allows to integrate a variety of advanced network technologies and adapt to different application environments dynamically. This design conforms to the development trend of the future Internet.

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.

The emerging future network designs such as Smart Identifier Network (SINET) and Information-Centric Networking (ICN) can provide efficient content delivery via its in-path caching. However, it is not an optimal way to cache contents at all intermediate routers for that current technology is not yet ready to support an Internet scale deployment. Most of the existing works select cache location based on the important of single node rather than that of entire cache group, which may result in inefficient problem caused by reduplicative impertinences. In this work, we study the intelligent cache location selection algorithm with an objective to maximize cache delivery performance while minimize the number of caching nodes. We first investigate the recent work in term of content location selection schemes, and formulate this problem as finding the prominent group with the highest Group Betweenness Centrality (GBC). We then propose a GBC-based in-path caching function group selection algorithm to select the caching nodes in SINET. We evaluate the performance of proposed algorithm through simulations and compare it with others. The final results show that GBC-based algorithm can provide better performance in term of average hop of content delivery. Using our finding, the network operators could deploy cache easily.