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

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

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

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.

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

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

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.

Federated learning (FL) is a distributed paradigm to support deep neural network (DNN) training while preserving the data owners’ privacy. In this paper, we investigate the resource management problem for FL in distributed industrial Internet of Things (IIoT) networks. Specifically, we introduce a three-layer collaborative architecture to support FL. DNNs are trained locally at the selected IIoT devices, and then the DNN model parameters are aggregated by edge servers every FL epoch or by a cloud server every a few FL epochs to update the global DNN model. To enable efficient FL in the resource-limited IIoT networks, judicious computing and spectrum resource allocation is required for training and transmitting the DNN model parameters. Thus, we formulate a joint device selection and resource allocation problem to minimize the FL evaluating loss while satisfying the strict FL epoch delay and devices’ energy consumption requirements. Since the decisions of device selection and resource allocation are coupled, we transform the joint optimization problem into a Markov decision process and propose a dynamic resource management scheme based on deep reinforcement learning approaches to efficiently facilitate the FL. Simulation results demonstrate that the proposed scheme can effectively improve the FL performance comparing with benchmarks.

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.

Driven by the requirements of space exploration and terrestrial investigation, interplanetary network merging has been widely focused on by academia, industry, and the military. Although system architecture and layered workflow are updated endlessly, the pervasive methodology is still urgently needed to provide a unified solution for heterogeneous elements. In this article, we propose a modeling scheme named generalized logical sphere (GLS) for space-terrestrial networks based on smart collaborative theory. First, the coverage range, transmission mechanism, and practice guideline of integration evolution are fully analyzed via three phases to guarantee the comprehensive combinations. Then the specific approach as well as the advanced characteristics of GLS (i.e., load balancing, smart routing, cognitive healing, and hierarchical shielding) are presented and discussed. Finally, a prototype platform is established to validate the correctness. The results of two scenarios illustrate the functionality and practicability of our model.

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

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

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

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.

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.

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.

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.

Content-Centric Networking is a novel future network architecture that attracts increasing research interests in recent years. In-network caching has been regarded as a prominent feature of Content-Centric Networking since it is able to reduce the network traffic, alleviate the server bottleneck and decrease the user access latency. However, the CCN default caching scheme results in a high caching redundancy, causing an urgent need for an efficient caching scheme. To address this issue, we propose a novel implicit cooperative caching scheme to efficiently reduce the caching redundancy and improve the cache resources utilization. The simulation results show that our design achieves a higher hit ratio and a shorter cache hit distance in comparison with the other typical caching schemes.

End-to-end transmission is an essential pattern in current Internet. It had brought obvious conveniences and potential hazard simultaneously. New network technology provides plenty of opportunities to avoid the harmful influences. After reviewing the identifier-based network, a candidate solution of future network, we proposed a smart collaborative connection management scheme for improving the dependability. Comparing with the conventional approaches, our scheme can protect the hosts via intermediate network equipments. During transmission process, the current usage pattern will not be influenced. Traditional unicast can be easily and smoothly enabled. The details of the scheme design are presented via four perspectives of connection identifier, i.e., communication mode, generation, distribution, and deployment. Then the supporting approaches of multipath transport, service migration, network mobility, P2P distribution, and application layer multicast are carefully investigated. The results of implementation and validation illustrate that the performances of new scheme are acceptable. We expect that this paper could provide a compatible and effective solution for both current and future Internet.

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.

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

In networks with identifier/locator separation, packets from a source to a destination are generally tunneled from the source’s ingress tunnel router (ITR) to the destination’s egress tunnel router (ETR). In addition, the source’s ITR often caches the destination’s identifier-to-locator mapping for a certain duration, called cache timeout, in order to avoid frequently querying mapping servers. When the destination moves from an old ETR to a new one, its identifier-to-locator mapping changes accordingly, which makes the destination’s identifier-to-locator mapping cached at some ITRs outdated. While several approaches have been proposed to address this issue, they either cannot deal with all types of outdated mappings cached at the ITRs or face the triangular routing problem. Accordingly, in this paper, we propose a timer-based pointer forwarding (TBPF) approach that can deal with all types of outdated mappings and avoid the triangular routing problem. We present numerical results to compare TBPF with existing approaches and show that TBPF is a very promising solution for dealing with outdated mappings.

Recently there has been a new emerging trend in integrating Information Centric Networking (ICN) and Software Defined Networking (SDN) together in the future Internet research area. Software defined information centric networking (SD-ICN) may face more serious scalability problem in control plane compared with traditional SDN environment due to new features about in-network caching and content-based communication. In this paper, we propose a scalable area-based hierarchical architecture (SAHA) for intra-domain communication to address the control plane scalability problem in SD-ICN. The SAHA supports scalable awareness of network resources and content resources, as well as guarantees efficient interest matching and resource adaptation. Simulation experiments under OMNET++ show that the proposed SAHA can achieve outstanding scalability in resource awareness and content-based communication. An area-based hierarchical architecture (SAHA) for intra-domain communication in SD-ICN is proposed.The SAHA supports interest matching and resource adaptation.The detailed scalable resource awareness process and content-based communication process are designed.The compatible solution for inter-domain communication is also discussed.

The significant changes enabled by the fog computing had demonstrated that Internet of Things (IoT) urgently needs more evolutional reforms. Limited by the inflexible design philosophy; the traditional structure of a network is hard to meet the latest demands. However, Information-Centric Networking (ICN) is a promising option to bridge and cover these enormous gaps. In this paper, a Smart Collaborative Caching (SCC) scheme is established by leveraging high-level ICN principles for IoT within fog computing paradigm. The proposed solution is supposed to be utilized in resource pooling, content storing, node locating and other related situations. By investigating the available characteristics of ICN, some challenges of such combination are reviewed in depth. The details of building SCC, including basic model and advanced algorithms, are presented based on theoretical analysis and simplified examples. The validation focuses on two typical scenarios: simple status inquiry and complex content sharing. The number of clusters, packet loss probability and other parameters are also considered. The analytical results demonstrate that the performance of our scheme, regarding total packet number and average transmission latency, can outperform that of the original ones. We expect that the SCC will contribute an efficient solution to the related studies.

There were two strategies for the data forwarding in the content-centric networking (CCN): forwarding strategy and routing strategy. Forwarding strategy only considered a separated node rather than the whole network performance, and Interest flooding led to the network overhead and redundancy as well. As for routing strategy in CCN, each node was required to run the protocol. It was a waste of routing cost and unfit for large-scale deployment. This paper presents the super node routing strategy in CCN. Some super nodes selected from the peer nodes in CCN were used to receive the routing information from their slave nodes and compute the face-to-path to establish forwarding information base (FIB). Then FIB was sent to slave nodes to control and manage the slave nodes. The theoretical analysis showed that the super node routing strategy possessed robustness and scalability, achieved load balancing, reduced the redundancy and improved the network performance. In three topologies, three experiments were carried out to test the super node routing strategy. Network performance results showed that the proposed strategy had a shorter delay, lower CPU utilization and less redundancy compared with CCN.

Information-Centric Networking (ICN) is a novel networking paradigm that attracts increasing research interests in recent years. In-network caching has been viewed as an attractive feature of ICN because it can reduce network traffic, alleviate server bottleneck, and reduce the user access latencies. Because of this, the network community has proposed many in-network caching mechanisms that aim at optimizing various performance metrics such as cache hit ratio and cache hit distance. In this survey, we present a comprehensive overview of the recently proposed in-network caching mechanisms for ICN. For each caching mechanism, we describe it in detail, present examples to illustrate how it works, and analyze its possible benefits and drawbacks. We also compare some typical in-network caching mechanisms through extensive simulations and discuss the remaining research challenges.

Proxy Mobile IPv6 (PMIPv6) is proposed as a promising network-based mobility management protocol, which does not need any participation of mobile nodes. PMIPv6 does not support the multicast well and most of the current research concentrates on the mobile multicast receiver. However, the mobile multicast sender is also very important and challenging, which has not been addressed well. Therefore, in this paper we propose two efficient PMIPv6 based mobile multicast sender support schemes which are PMIP bidirectional tunneling (PMIP-BT) and PMIP direct routing (PMIP-DR). In the PMIP-BT, the multicast traffic can be delivered through the PMIPv6 bidirectional tunnel, while, in the PMIP-DR, the multicast data can be transmitted via an optimized direct multicast routing. Both of them can support the multicast sender mobility transparently enabled in the PMIPv6 networks. We evaluate the performance of the proposed schemes by theoretical analysis, and the numerical results show that the proposed schemes have a better performance in terms of the signaling cost than the current schemes. Meanwhile, the proposed schemes are also implemented on the test bed, and the experimental results not only verify the validity and feasibility of our proposed schemes, but also conclude the different scenarios to which they are applicable.

Stream control transport protocol (SCTP)-based concurrent multipath transfer (CMT) can help multi-homed devices to increase their throughput by making use of parallel transmissions over multiple paths and bandwidth aggregation. However, if CMT cannot identify wireless error, it cannot really achieve the desired performance. Furthermore, if CMT only utilizes all available paths for data delivery, it will undoubtedly degrade application-level performance since the asymmetric paths may involve large quality differences. This paper proposes a novel cross-layer quality-of-service (QoS)-aware adaptive CMT (CMT-CQA) with the following aims: (i) to provide an adaptive ‘CMT-to-partial CMT’ adjustment strategy for efficient bandwidth aggregation by jointly considering transport layer QoS, MAC layer QoS, and path history information; (ii) to address an enhanced congestion window (cwnd) fast recovery mechanism to reduce bursty transmission in multi-homed wireless network environments where fail-over occurs frequently; and (iii) to introduce a proper multimedia transmission behavior to improve users' quality of experience (QoE) for multimedia streaming service. Simulation results show that CMT-CQA outperforms the existing CMT solutions in terms of performance and QoS. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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

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

Due to the dramatic development of mobile devices and technologies, wireless networks have become a convenient and popular means of accessing the Internet for the public. However, the existing wireless networking techniques still face several fundamental inherent problems, such as network scalability, flexibility, and interoperability. As a result, future wireless communication networking, such as 5G, is expected to address these problems. In this article, we present a hierarchical identifier network, a novel and practical hierarchical network architecture based on the idea of network/user separation and mapping. We performed extensive experiments in a realworld high-mobility scenario to evaluate the proposed wireless network architecture. The results demonstrate that the hierarchical identifier network achieves better performance on scalability, flexibility, and interoperability compared to its existing counterparts.

Vehicular networking is promising to improve traffic efficiency and driving safety, as well as travel experience. However, the traditional network employs a highly coupled design, which is quite limited in its ability to satisfy various challenging vehicular demands. Recently, new studies focus on how to design software defined vehicular networks smartly to meet various vehicular demands. In this article, we investigate a new smart identifier networking (SINET) paradigm and propose a SINET customized solution enabling crowd collaborations for software defined vehicular networks (SINET-V). In particular, through crowd sensing, network function slices are well organized with a group of function-similar components. Different function slices are further driven to serve various applications by using crowd collaborations. We clearly illustrate how SINET-V works and also analyze its potential advantages in several special vehicular instances. Experimental results show that SINET-V has great potential to promote powerful vehicular networks.

Named Data Networking (NDN) adopts onedimensional hierarchical naming and lookup operations based on longest prefix matching. On the other hand, users may express their requests with multi-dimensional attributes in order to meet the intrinsic spatial- temporal nature of the data existing in IoT applications. To bridge the gap between multi- dimensional user requests and one-dimensional hierarchical names in NDN applications, a novel middleware supporting multi-dimensional naming is designed to support efficient Interest expressions through name translation and optimization, as well as keep original NDN operations unchanged. Simulation experiments show that the proposed solution can improve the content retrieval efficiency and outperform the other baseline schemes in terms of the number of Interest packets and content retrieval time.

In the Locator/ID separation protocol (LISP) network, mapping caches are usually used in Ingress tunnel routers to cache recently used identifier-to-locator mappings of remote hosts. When the original mapping in the Egress tunnel router changes, it introduces the problem of cache consistency. In general, the mapping cache supports weak cache consistency by using the time-to-live (TTL) mechanism. However, a stale mapping could lead the packets to a wrong destination. In this paper, we first evaluate the performance of TTL by trace-driven simulation and the results indicate that maintaining strong mapping cache consistency has become an indispensable mechanism in the LISP network. Then we design the lease algorithm to realize the strong consistency and propose analytical models to decide the lease duration based on the state space overhead and the control message overhead, respectively. We present numerical results to estimate the mapping change frequency, to show the impact of different parameters on the lease duration and to explore the relationship between the state space and control message overhead. Finally, we do the trace-driven simulation to compare the lease algorithm to polling-every-time and invalidation in terms of the state space and control messages. The lease algorithm with an appropriate lease duration can balance the state space and the control message overhead while providing strong mapping cache consistency.

According to the important methodology of convex optimization theory, the energy-efficient and scalability problems of modern data centers are studied. Then a novel virtual machine (VM) placement scheme is proposed for solving these problems in large scale. Firstly, by referring the definition of VM placement fairness and utility function, the basic algorithm of VM placement which fulfills server constraints of physical machines is discussed. Then, we abstract the VM placement as an optimization problem which considers the inherent dependencies and traffic between VMs. By given the structural differences of recently proposed data center architectures, we further investigate a comparative analysis on the impact of the network architectures, server constraints and application dependencies on the potential performance gain of optimization-based VM placement. Comparing with the existing schemes, the performance improvements are illustrated from multiple perspectives, such as reducing the number of physical machines deployment, decreasing communication cost between VMs, improving energy-efficient and scalability of data centers.

Employing multiple paths for achieving high capability and robustness has some obvious benefits. New wireless technologies are giving more Internet access modes for notebooks and smart phones. However, most multipath protocols may not get acceptable throughput if reliable transmission is guaranteed. For some cases, the situation may even worse than using single path only. The main reason has strong relationship with the principal of multipath transmission. Motivated by these facts, specific analytical mechanisms are proposed to analyze the potential problems which may lead to serious performance decrease. Following that, we investigate how to use multiple paths legitimately when network environments are fluctuating. In our simulation, topologies for multiple paths and single path are set up for evaluating our analytical methods. Some distinguished scenarios are chosen from different perspectives. The results have revealed some throughput critical conditions and could be helpful in designing scheduling schemes for multipath protocols.

SUMMARY In traditional Internet architectures, current centralized mobility management schemes face scalability issues because of the creation of network bottlenecks and a single mobility agent of failures. To address these issues, we propose a distributed mobility management scheme in locator/identifier separation networks (DMMLIS). We divide the network into many domains according to the area of autonomous systems. Each domain consists of a mapping server and several tunnel routers (xTRs). The mapping server stores global mappings between endpoint identifier (EID) prefixes and autonomous system numbers (ASNs) to lookup the mobile nodes' (MNs') home domain. Meanwhile, the mapping server also contains ASN-to-xTR mappings so that it can forward Map-Register and Map-Request messages to any xTR of the MN's home domain, thus enhancing reliability. In addition, the xTRs in each domain constitute one-hop distributed hash table ring. Moreover, any xTR in the MN's home domain may be the home agent of MNs, and the MNs' EID-to-RLOC (routing locator) mapping is stored in two xTRs using two hash functions. In this way, it not only supports quick lookup but also improves scalability and survivability. To evaluate the survivability and the efficiency of DMMLIS, we analyze the service blocking probability, the system response time, and the total protocol cost. We also present numerical results to demonstrate the performance of the proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.

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

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

Due to the widespread research on Software Defined Networks (SDNs), its security has received much attention recently. But most of those attempts consider SDN security from the OpenFlow perspective. To the best of our knowledge, none so far has paid attention to the security analysis and modeling of Forwarding and Control planes Separation Network Structure (FCSNS) in SDN. Therefore, this paper provides a different approach to network security based on Petri net and Attack tree models. Our objective is to analyze the FCSNS security via the combination of model and state. This method represents the network structure and state transferring by way of Petri net. In addition, it introduces the security analysis method of STRIDE to build up the Attack tree model. Finally, we analyze FCSNS via the combination of Petri net and Attack tree model and present the results. Our results are very promising in using such models to achieve such security objectives.

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

In order to provide emerging services and improve network performance, SINET is evolving to become more intelligent, dynamic and adaptive. In this chapter, we will give in-depth descriptions and discussions of the evolutionary architecture of SINET.

As introduced previously, SINET supports two AID formats, namely aggregate AID and flat AID. Similarly, two mapping systems corresponding to the two different AIDs are presented in this chapter: the DHT-based mapping system (DHT-MAP) and the hierarchical mapping system (HMS). DHT-MAP and HMS are both overlay systems built to perform the mapping between AIDs and RIDs.

To provide seamless mobility support for the User in NDN (Named Data Networking) based cloud storage service, we propose a proxy based mobility support approach, called PMNDN. In this approach, we first introduce an ID that is structurally similar to the content name, to maintain the routing scalability of NDN architecture, and improve the addressing efficiency of INTEREST packet for NDN-based cloud storage service. Then, we deploy a new functional entity named Proxy, which is responsible for maintaining the reach-ability state of the User. We also extend the basic functionalities of the NDN Access Routers, such as tracking the mobility status of the User and initiating the handoff related signaling to the Proxy. With that, the User avoids participation in mobility signaling exchange. Finally, we construct an elastic 3-layer network analysis model, and compare the handoff performance of the mobility support approaches with that in NDN architecture and IPv6 architecture via analytical and simulation investigation. Our results indicate that not only PMNDN has lower handoff cost, it also has shorter handoff latency and less packet losses during the handoff process.

Communication architecture is a crucial component in smart grid. Most of the previous researches have been focused on the traditional Internet and proposed numerous evolutionary designs. However, the traditional network architecture has been reported with multiple inherent shortcomings, which bring unprecedented challenges for the Smart Grid. Moreover, the smart network architecture for the future Smart Grid is still unexplored. In this context, this paper proposes a clean-slate communication approach to boost the development of smart grid in the respective of Smart Identifier Network (SINET), named SI4SG. It also designs the service resolution mechanism and the ns-3 based simulating tool for the proposed communication architecture.

With the rapid increase in low cost sensor devices, internet of things (IoT) has become more and more popular. Wireless sensor network (WSN) is commonly recognized as the core technology of IoT, which has become a hot research area in both academic and industry recently. Because of unreliable and unpredictable behaviors of WSN, intelligent routing selection is crucial to WSN. To provide good quality of service (QoS) guarantee and reduce energy consuming, some routing protocols in WSN consider link-quality estimation on the routing selection. Most of them consider little with the stability of link quality and the weights of varying. This paper proposed a novel routing selection algorithm in WSN, called integrated link quality estimation-based routing protocol (I-LQER). I-LQER firstly analyzed the energy consuming model, added the different weights for the elements in link-quality estimation, and then established a metric for the stability of link quality. I-LQER considered both the link quality and the stability of link quality to support better QoS. For evaluating the performance of I-LQER, we made extensive simulation experiments in network simulator-2 (NS-2). The simulation results show that I-LQER gives better performance in terms of end-to-end delay, scalability and energy consumption, compared with the link quality estimation-based routing (LQER).