To realize effective caching in a resource-constrained hardware router such as field-programmable gate array (FPGA), we propose a mechanism for protecting popular content in a lightweight FIFO-based cache on an FPGA-based ICN router. Our FPGA-based ICN router system comprises an interface that retrieves popular-content names from an external data analytics function and makes them keep in the cache on the FPGA-based router. We implement the proposed architecture, demonstrating that our implementation timely maintains popular content in the cache and minimizes the interruption of data forwarding of the FPGA-based ICN router.

Modern artificial intelligence (AI) workloads are characterized by increasing complexity and large datasets with multi-billions of parameters, which place unique demands on the computational and communication infrastructure. These requirements expose the limit of server-centric AI systems in scalability and responsiveness, particularly for tasks operating on high-volume network data flows. While serverless architectures offer promising alternatives through on-demand execution and adaptive scaling, they often struggle with compute-intensive and latency-sensitive AI workloads. To address these challenges, we propose a novel adaptive in-network execution framework for distributed AI inference tasks that adopts information-centric networking (ICN) principles to enable data-centric orchestration and seamless service discovery for latency-sensitive intelligent services. Our proposal treats AI tasks as named invocable functions, which allow computational requests (e.g., object detection, anomaly analysis) to be seamlessly routed, resolved, and executed on intermediate network nodes based on intent-aware resolution and in-network availability. The experimental results show that the proposed system achieves a minimum task completion latency and maintains a success rate of over 96% across all tasks.

The resolution of network intents to network service definitions is a key operation of intent-based networking. In this paper, we study the resolution of multi-domain network intents and propose EDAIR, an efficient distributed architecture that greatly improves accuracy of network intent resolution with little impact on performance. While previous state-of-the-art systems for multi-domain intent resolution consist on the sequential interrogation of all domains involved in the intent and the aggregation of their answers to construct a final network service definition, EDAIR defines an intelligent agent to represent each domain and constructs a distributed multi-agent system based on artificial intelligence. Only the required agents will be involved in intent resolution and the process stops as soon as a proper solution is achieved. We evaluated EDAIR to show that it is 48% more accurate and only increases the time needed to resolve intents by 38%, denoting its efficiency in the distributed operation.

Network virtualization offers enormous flexibility to construct network services tailored to the needs of network tenants and users. Network services can be implemented through composition of virtual network functions, which can be deployed in multiple sites, forming multi-site network services. However, network virtualization further induces the increasing complexity of the network, which, in turn, increases the difficulty of error avoidance in management operations. Thus, network management must be automated to tackle such complexity and eliminate errors. In this paper, we propose the intelligent network service automation system (INSA), and we demonstrate its high efficiency and accuracy in automated network service construction and management. INSA is designed to transparently orchestrate all elements of the management system using a control plane based on publish/subscribe messaging, known as a management service bus. This enables INSA to make highly accurate decisions using vast telemetry information without overloading the control plane. Additionally, INSA integrates key enablers such as efficient and scalable control plane networking, telemetry compression, distributed processing, hidden fault detection, and autonomic resource control. We evaluated INSA both theoretically and through a prototype implementation. The former proves our claims regarding the efficiency improvement provided by compression and distribution. The latter showcases INSA management accuracy and performance, and it also demonstrates how INSA manages multi-site network services. The evaluation results show that, in comparison with the state-of-the-art alternatives, INSA uses 15–36% less CPU time, possesses 80% higher scalability, and improves accuracy by 33%.

The continuous growth of the Internet of Things (IoT) and its expanding network drives an exponential increase in data generation, which necessitates robust and efficient processing solutions for latency-sensitive applications. While local processing on resource-constrained devices can be inefficient, offloading computations to third parties introduces significant security and privacy risks. Homomorphic encryption (HE) offers a powerful solution for secure computations on encrypted data without compromising confidentiality. However, the computational intensity of HE results in high latency, limiting its applicability in real-time decision-making scenarios. To overcome these challenges, we propose ShieldDINC, a distributed in-network computation scheme designed for efficiency and privacy protection using localized edge processing to optimize secure implementations with HE. In addition, it leverages the efficient data delivery and caching capabilities inspired by an information-centric networking (ICN) approach. ShieldDINC ensures that sensitive data is securely offloaded and computed at the nearest edge router, which caches results for potential reuse, reducing retrieval time and repetitive computations. Extensive simulations demonstrate that ShieldDINC achieves a 30% latency reduction and a 20% computational efficiency improvement for real-time intensive computations compared to state-of-the-art alternatives while promising robust security and privacy.

Network virtualization has become a central aspect of 6G networks, which enable the composition of virtual network functions (VNFs), which can be instantiated at single or multiple sites to create highly flexible virtual network services. As a result of the increased complexity imposed on network components, network service automation systems (NSASs) are expected to become key technology enablers for 6G, whose implementation requirements on the control plane that are not met by current approaches based on a central message broker. We propose to meet these requirements by extending NSAS with an efficient and scalable control plane network based on information-centric networking (ICN), named iCPN, which uses names instead of addresses for information transmission and exploits in-network cache. We evaluated an iCPN prototype, demonstrating that it uses 50% less bandwidth and 15-36% less computation time than common alternatives.

Workload prediction is the key to improving the quality of service (QoS) of various user applications running in autonomous large-scale networks. Machine learning (ML) technologies contribute to workload prediction because they provide fully automated data analytics. However, no ML algorithm can simultaneously provide perfect, timely, and generalized solutions at the same time. In this paper, we propose an adaptive optimized ML algorithm selection and installation (OMASI) method for workload prediction and predictive autoscaling to maintain the QoS of user applications. With OMASI, a central repository server collects information about the analytical objectives and optimization preferences of multiple distributively deployed ML engines and provides them with the best-suited ML algorithms for their data-analytics. We implemented and demonstrated OMASI on an experimental in-network computing testbed and evaluated the workload prediction accuracy of several available ML algorithms in terms of content downloading latency and throughput. The results show that OMASI can maintain the QoS of user applications, such as latency and throughput, by reducing the CPU usage prediction error by up to 86%.

Information-centric networking (ICN), originally innovated for efficient content dissemination, has recently been studied to provide end users with in-network computing services. However, existing schemes with the passive communication style suffer from the problem of a larger number of request messages (interests) and consequently, higher latency. This is because of the insufficient interplay between data transport and construction of appropriate chains of the required in-network functions. In this study, we introduced an efficient data transport scheme in cooperation with establishing a path in which the required in-network functions are well chained. The proposed data transport manipulates data retrieval and function calls in the network by exploiting the features of interest message types that provide efficient data transport in ICN-based in-network computing. Using an actual implementation, we demonstrated that the proposed scheme reduces the interest traffic overhead and latency for completing a task compared to a conventional scheme for ICN-based in-network computing.

Information-centric networking (ICN) brings in-network caching as a significant characteristic along with its novel network architecture. In-network caching serves as an efficient content distribution strategy in which intermediate routers store and redistribute data to reduce network latency, congestion, and retrieval time. Meanwhile, it decouples contents from the publisher, and content integrity and confidentiality turn out to be problematic. In this study, we propose a mechanism named “in-network secure content management” (ISCM) to facilitate content integrity and authenticity in ICN by exploiting identity-based cryptography (IBC) for content signature verification. In addition, it guarantees content confidentiality by applying a hybrid encryption-based access control mechanism to prevent unauthorized content access and tampering attacks. Evaluation results reveal that ISCM offers secure content retrieval in ICN with a scalable key distribution without significant computational overhead and communication delay.

Caches are instrumental in reducing network and server loads that are typically deployed at the network edge to shorten the response time. Cache optimization for modern delay-sensitive applications is not only concerned with maximizing the cache hit ratio but is also related to minimizing the cache response time. In this paper, we revisit collapsed-forwarding caches and propose COLFT, which is a cache-friendly transport equipped with a cache admission filter that takes into account the download time between the cache and content server. To analyze the performance of COLFT, we developed an in-depth analysis of the cache system that characterizes its behavior, either with or without collapsed forwarding-enhanced cache admission, capturing the effect of the bandwidth share in the transport layer. A numerical evaluation agrees well with the analytical results and shows a significant improvement of COLFT in both the hit rate and response time in realistic scenarios when the transport-dependent download time is considered.

A network digital twin (NDT) is a virtual object that mirrors a network system (NS) so that a control and management system (CMS) may identify present and potential failures using artificial intelligence methods, resulting in enhanced NS resilience. In this study, we propose a method of enhancing an NDT by generating and transmitting telemetry knowledge objects (TKOs) instead of raw monitoring data. TKOs enforce data correction, provide rich information, and are inherently adaptable. We also apply the telemetry knowledge distributed processing (TKDP) approach to the CMS so that each NS element processes its own measurements to generate and transmit TKOs, thus avoiding the transmission of meaningless, wrongly formed, or incomplete data. Moreover, distributing the processing capacity allows the target NDTs to reflect more complex NS states than when using centralized processing. To maximize the impact of TKOs and TKDPs, we followed the current standardization specifications for network telemetry and NDT construction. Finally, we demonstrated the benefits of TKOs and TKDPs by evaluating the fidelity of an NDT that matches a typical scenario. We showed that the resultant NDT fidelity is much higher when using TKO and TKDP than when relying on current alternatives.

In today’s healthcare landscape, emerging technologies serve as crucial foundations, and the integration of blockchain technology into this rapidly evolving digital framework is invaluable. However, the healthcare industry has long struggled with managing the immense and ever-expanding amount of big data electronic health records (EHRs) collected from various sources, including the Internet of Things (IoT), wearable devices, and mobile applications. The storage of all these big data EHRs on the chain creates blockchain bloat, leading to slow transaction speed and high storage costs. More importantly, it incurs security and privacy leakage problems while sharing patients’ sensitive data transparently with a wide range of users in a distributed network. To address these challenges, we propose a secure and privacy-preserving data management system for distributed off-chain networks called PrivOff based on blockchain technology, which stores big EHRs separately in the decentralized file system with efficient access control enforcement and flexible revocation to prevent data breaches. In addition, patients can securely share data without revealing their unique identities to ensure privacy. The evaluation results and security analysis reveal that PrivOff can notably reduce the blockchain storage burden while offering data security, patient privacy, and high data availability.

Off-chain storage is utilized to reduce on-chain storage costs, and further enhance the scalability of blockchain technology. For such mechanisms, transaction data of large size is stored in external centralized databases or distributed peer-to-peer storage, instead of blockchain nodes themselves. However, in emerging blockchain application areas, such as healthcare and the Internet of Things (IoT), off-chain data should be located close to the users with the right privileges, yet it is currently challenging to locate data close to such users and limit data transfers accordingly. To meet these challenges, we design a user-centric in-network caching mechanism for off-chain storage (UCINC) with information-centric networking (ICN) approach to regulate the data caching to the off-chain storage in the network where users are located. With UCINC, data is cached at off-chain storage based on location attributes, retrieved through the interest/data ICN paradigm, and users’ access privileges are determined based on their attributes including locations. We furthermore conduct simulation experiments to confirm that the proposed UCINC achieves higher download performance and traffic efficiency, compared with the existing off-chain storage mechanisms.

With the advent of massive Internet of Things (IoT) applications, the demand for efficient and secure data communication has been increased. Quantum key distribution (QKD) and information-centric networking (ICN) can be considered promising approaches. However, because these approaches have been developed independently thus far, they impose problems in effectively meeting demand. In this paper, we propose a system architecture that combines the two networking technologies such that each problem can be compensated for in a sophisticated manner. Through comprehensive simulations, we demonstrate that the streamlined system brings about a significant synergistic effect especially in terms of the utilization efficiency of secure keys and the effectiveness of content delivery.

Smart contract (SC) is a user-defined program code over blockchain, which holds the characteristics of immutability and auditability without requiring trust third party and is crucial to provide in-network computation for green applications. Herein we investigate the interactions among the SCs, where the functions defined in one SC are called by another one. The existing work, Ethereum name service (ENS), provides a method to map human-readable names to machine-readable addresses. However, it still suffers the problems induced from the ossification of SC, such as no mechanism to call functions in a non-existing SC, to update SC, to acquire the SC information (e.g. contract application binary interface and address), and to achieve access control. To solve these problems, we propose a flexible smart contract interaction framework with access control (FSCC), where SC name and information are separately stored at blockchain and the distributed off-chain storage to reduce on-chain storage overhead. With the FSCC, interactions with non-existing SC, update of SC, and access control of SC can be achieved. Furthermore, performance evaluations show that on-chain storage overhead can be greatly reduced with keeping the communication delay at a low level.

Service Function Chaining (SFC) has been proposed as an enhanced Software-Defined Networking/Network Function Virtualization (SDN/NFV) technology to accommodate the network service requirements of various applications. In particular, the provisioning of network service functions, such as in-network caching, transcoding, and multicasting, improves the Quality of Experience (QoE) of video streaming services and reduces the traffic load. However, these functionalities require high computational resources of the network nodes, and over-provisioning the computational resources causes another problem. In this paper, we propose an elastic Function Offloading Network (FON) for SFC. FON adopts a highly scalable functional call using Information-Centric Networking (ICN) technology and provides valuable functions for common SFC networks. We experimented with FON implementation running on a real SFC network and evaluated the throughput, traffic load, and QoE of the streaming applications. The results show that FON significantly outperforms an existing SFC framework, which does not have any offloading functionality.

In this paper we propose a mechanism to automate the management of large networks based on the autonomic network management scheme. It incorporates a novel embedding algorithm that is 3 to 6 times faster than related work in finding out optimal embedding a virtual network (VN) or network slice (NS) to a substrate network (SN). It therefore finds out a configuration that assigns the elements of the VN/NS to the elements of the SN. The main novelty of this algorithm is the application of the knowledge from the VN/NS to get a configuration by improving a previous configuration, so less computing power is required than related work. The reasoning process is designed to consider the traffic of the network together with node load, so the resulting configuration optimizes the overall usage of the SN. Finally, the algorithm and heuristic also target the minimization of the number of operations required to obtain a new configuration from the current configuration, thus avoid the addition, removal, or movement of elements as much as possible. We demonstrate these claims by evaluating a proof-of-concept implementation of our solution and comparing it with the previous solutions.

There has been a considerable increase in the demand for high quality mobile video streaming services, while at the same time, the video traffic volume is expected to grow exponentially. Consequently, maintaining high quality of experience (QoE) and saving network resources are becoming crucial challenges to solve. In this paper, we propose a name-based mobile streaming scheme that allows efficient video content delivery by exploiting a smart pulling mechanism designed for information-centric networks (ICNs). The proposed mechanism enables fast packet loss recovery by leveraging in-network caching and coding. Through an experimental evaluation of our mechanism over an open wireless testbed and the Internet, we demonstrate that the proposed scheme leads to higher QoE levels than classical ICN and TCP-based streaming mechanisms.

We revisit route caching in information-centric networking and propose SmartFIB, a tandem route storage design for dealing with the name forwarding table scalability problem. The proposed route storage not only can quickly adapt to traffic pattern to retain the most frequently referred name prefixes but also is hardware-friendly thanks to its exceptionally low replacement rate. The low replacement, moreover, reduces route lookup rates in settings where centralized name resolution service or scoped route discovery is deployed. Our scheme is lightweight, can enhance route hit rate of small-size fast-memory forwarding information base (FIB), and most remarkably is able to reduce route replacement multiple folds. Evaluated in real-topology networks under a wide variety of parameter settings, the proposed route storage can increase hit rate by 27% compared with a conventional route cache especially with a tiny FIB size, four orders of magnitude smaller than the name prefix space, and more importantly, it can lower route replacement more than 7 times. The results suggest that our proposed scheme is a viable technique to complement route caching in dealing with the name forwarding table scalability problem.

Applications of Information-Centric Networking (ICN) technology to future internet of things (IoT) and distributed edge/fog computing are widely discussed in various research committees. In this paper, we demonstrate a real-time video streaming scenario using CeforeSim, an NS-3 based ICN simulator. CeforeSim is based on Cefore, an open-source implementation of ICN, which is compliant with the CCNx packet format standardized by the IRTF ICN Research Group (ICNRG). The virtual interfaces provisioned in CeforeSim expedite seamless interaction between the simulated nodes and physical nodes that run the Cefore applications, thereby affording performance evaluations in various scenarios, such as handover of mobile nodes, large-scale sensor networks, and distributed edge/fog computing with the real environments.

Network softwarization is a transformation trend for networks to converge in programmable service platforms able to host different requirements from multiple tenants. The concept of network slicing plays a key role in this trend. It offers isolated logical networks to different tenants over the same underlying infrastructure. Network slices hosting missioncritical services require fast reactions to changes in environment, adapting their capacity as needed to avoid service disruption and performance degradation. This represents a key challenge for the management plane, traditionally under the direct operation of human administrators, unable to provide a nimble enough response rate. To support tenants in driving lifecycle management operations over their network slices in a timely manner, datadriven, closed-loop mechanisms must be designed. In this paper we introduce a solution that takes advantage of events occurring outside the boundaries of a tenant-managed network slice to guide those adaptations. We finally demonstrate the feasibility of the proposal and describe an experimentation scenario to exploit such results.

Wireless big data raises the demands on the networking schemes to support the efficient group data sharing over heterogeneous wireless technologies, which take many-to-many data delivery as the foundation. Information-centric networking (ICN) approach is a promising networking technology to support big data delivery, which has the potential to establish the harmony between networking and wireless big data sharing. However, the existing ICN schemes have not carefully addressed the many-to-many communications. To address this issue, we propose an efficient and secure many-to-many wireless big data delivery scheme (MWBS) to provide group-based data dissemination and retrieval with name-integrated forwarding. In MWBS, a bi-directional tree is securely constructed for each group through the procedures of group initiation, join, leave, publication, and multi-level inter-zone routing. Especially, Designated Forwarding and Cacheable Nodes (DFCNs) are introduced to act as the roots for the construction of such bi-directional trees. The implementation details of MWBS are provided for function verifications. To effectively deploy MWBS, we investigate the impacts to the MWBS performance from the number and locations of DFCNs, which show that the optimized number of DFCNs can reduce the total traffic cost and the DFCN close to users is preferred to be selected for a group. Finally, simulations are performed to evaluate the performance of MWBS, which show that MWBS can reduce the control packet overhead and the state storage overhead compared to the existing ICN schemes.

Blockchain is a distributed ledger, characterized by immutability, anonymity and auditability without requiring trust third party. To provide data exchanges to form such ledger, blockchain network enables the dissemination of transactions and blocks to reach the consensus, which mainly consists of attachment strategy and communication strategy. Currently, it is implemented with peer-to-peer overlay network, which, however, suffers from the intrinsic problem of mismatching between traffic flows and underlying network topology. To solve this problem, we employ information-centric networking (ICN) approach to design a decentralized information-centric blockchain network (DIBN), where categories are named to enable the traffic to be decentralized and an any-to-all category dissemination structure (CDS) is established among all the blockchain nodes (BNs) for each category. For the CDS, one BN can efficiently send data to all other BNs aligning the traffic with the underlying network, which overcomes the problem of mismatch. The performance analysis shows that the proposed DIBN can greatly reduce the average path length for data dissemination in blockchain.

In this paper, we introduce “CeforeSim”, which is an ns-3-based network simulator for Information-Centric Networking (ICN). CeforeSim is compliant with an ICN software platform known as Cefore. This simulator supports the CCNx 1.0 messages specified in the IRTF and inherits the unique features of Cefore such as the transport protocol for real-time video streaming, cache storage separation from the forwarding engine, network measurement using CCNinfo defined in the IRTF, and so on. As one of the example features, we focuses on the CeforeSim function for real-time video streaming and show the low-overhead multicast streaming compared to the conventional ICN approach.

Information-centric networking (ICN) is a promising paradigm to support in-network big data sharing. However, it suffers from the problem of the segmented cached chunks resulting in low throughput and the large Interest packet overhead (IPO). The existing work cannot address these problems well, largely due to their insufficient considerations on the interplay between caching and data transport mechanisms. Through our experiments, we observe that ICN data chunks are cached in a distributed manner. Based on these observations, we propose consecutive data chunk caching (ConCaching) and adaptive data chunk retrieval (ACUR) to bridge the gap between caching and transport, where intermediate nodes on transmission path only cache the consecutive data chunks, while users can adjust the range of requested data chunks to maximize the throughput. Through the intensive simulations, we show that the proposed mechanisms can achieve better performance, in terms of higher throughput and substantial reduction in IPO compared with the existing pipeline mechanism in ICN.

In an Information-Centric Internet of Things (ICIoT) environment for big data sharing, IoT data can be cached throughout the network. Such distributed data caching poses a challenge on flexible authorization and identity verification. For fine-grained data access authorization in a distributed manner, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has been identified as a promising approach. However in the existing CP-ABE based scheme, each publisher would need to retrieve the attributes from the centralized server for encrypting data, resulting in high communication overhead. Moreover, valid authorization period and distributed authentication are still not addressed and seamlessly incorporated. In this paper, we propose a Verifiable and Flexible Data Sharing (VFDS) mechanism for ICIoT, which exploits CP-ABE for authorization and Identity-Based Signature (IBS) for the distributed verification of the identities. In VFDS, publishers retrieve the attributes from the nearby cache holders. In addition, the Attribute Manifest (AM) and the Automatic Attribute Update (AAU) realize efficient attribute updates within the distributed caches to achieve valid authorization period. Meanwhile, VFDS provides the public parameters of IBS in local domain, which enables the efficient identity verifications. Our system evaluations show that the VFDS can achieve lower bandwidth cost compared to the existing schemes for both authentication and flexible authorization.

New applications where anyone can broadcast video are becoming very popular on smartphones. With the advent of high definition video, ISP providers may take the opportunity to propose new high quality broadcast services to their clients. Because of its centralized control plane, Software Defined Networking (SDN) seems an ideal way to deploy such a service in a flexible and bandwidth-efficient way. But deploying large scale multicast services on SDN requires smart group membership management and a bandwidth reservation mechanism to support QoS guarantees that should neither waste bandwidth nor impact too severely best effort traffic. In this paper, we propose a Network Function Virtualization based solution for Software Defined ISP networks to implement scalable multicast group management. Then, we propose the Lazy Load balancing Multicast (L2BM) routing algorithm for sharing the network capacity in a friendly way between guaranteed-bandwidth multicast traffic and best-effort traffic. Our implementation of the framework made on Floodlight controllers and Open vSwitches is used to study the performance of L2BM.

Owing to the rapid growth in high-quality video streaming over the Internet, preserving high-level robustness against data loss and low latency, while maintaining higher data transmission rates, is becoming an increasingly important issue for high-quality real-time delay-sensitive streaming. In this paper, we propose a low latency, low loss streaming mechanism, L4C2, specialized for high-quality delay-sensitive streaming. With L4C2, nodes in the network estimate the acceptable delay and packet loss probability in their uplinks, aiming at retrieving lost data packets from in-network cache and/or coded data packets using in-network coding within an acceptable delay, by extending the Content-Centric Networking (CCN) approach. Further, L4C2 naturally provides multiple paths and multicast technologies to efficiently utilize network resources while sharing network resources fairly with competing data flows by adjusting the video quality when necessary. We validate through comprehensive simulations that L4C2 achieves a high success probability of data transmission considering the acceptable one-way delay, and higher QoE while suppressing the interest and redundant data traffic than the proposed multipath congestion control mechanism in CCN.

Recently, as the development of content-centric networking and Telco-CDNs, inter-domain cache sharing has attracted increased attention. With inter-domain cache sharing, ISPs can further reduce transit cost and improve the quality-of-service (QoS) by accessing their neighbors' caching devices. Currently, cache sharing is limited to free-peering ISPs. Enabling eyeball ISPs to share caches with their transit providers has substantial benefits, but both technical and economic challenges must be addressed. In this study, we consider the inter-domain cache-sharing problem as a double-sided market. In this market, eyeball ISPs receive reimbursement from transit providers for sharing their local caches, while transit ISPs obtain content from the caches of eyeball ISPs at lower cost than by obtaining content from upper tier ISPs. We propose an efficient cooperative mechanism based on Nash bargaining solution to address resource allocation issues in the double-sided market. The proposed mechanism can satisfy both the technical and economic properties desired.

In Information-Centric Internet of Things (ICIoT), Internet of Things (IoT) data can be cached throughout a network for close data copy retrievals. Such a distributed data caching environment, however, poses a challenge to flexible authorization in the network. To address this challenge, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has been identified as a promising approach. However, in the existing CP-ABE scheme, publishers need to retrieve attributes from a centralized server for encrypting data, which leads to high communication overhead. To solve this problem, we incorporate CP-ABE and propose a novel Distributed Publisher-Driven secure data sharing for ICIoT (DPD-ICIoT) to enable only authorized users to retrieve IoT data from distributed cache. In DPD-ICIoT, newly introduced attribute manifest is cached in the network, through which publishers can retrieve the attributes from nearby copy holders instead of a centralized attribute server. In addition, a key chain mechanism is utilized for efficient cryptographic operations, and an automatic attribute self-update mechanism is proposed to enable fast updates of attributes without querying centralized servers. According to the performance evaluation, DPD-ICIoT achieves lower bandwidth cost compared to the existing CP-ABE scheme.

New applications where anyone can broadcast high quality video are becoming very popular. Internet services providers (ISPs) may take the opportunity to propose new high quality multicast services to their clients. Because of its centralized control plane, software defined networking (SDN) enables the deployment of such a service in a flexible and bandwidth-efficient way. But deploying large-scale multicast services on SDN requires smart group membership management and a bandwidth reservation mechanism with QoS guarantees that should neither waste bandwidth nor impact too severely best effort traffic. In this paper, we propose: 1) a scalable multicast group management mechanism based on a network function virtualization approach for software defined ISP networks to implement and deploy multicast services on the network edge and 2) the lazy load balancing multicast (L2BM) routing algorithm for sharing the core network capacity in a friendly way between guaranteed-bandwidth multicast traffic and best-effort traffic and that does not require costly real-time monitoring of link utilization. We have implemented the mechanism and algorithm, and evaluated them both in a simulator and a testbed. In the testbed, we experimented the group management at the edge and L2BM in the core with an open vSwitch-based QoS framework and evaluated the performance of L2BM with an exhaustive set of experiments on various realistic scenarios. The results show that L2BM outperforms other state-of-the art algorithms by being less aggressive with best-effort traffic and accepting about 5%-15% more guaranteed-bandwidth multicast join requests.

Content-Centric Networking (CCN) or Named-Data Networking (NDN) has been proposed as an alternative to the existing Internet architecture. In this paper, we propose Content Name-based Real-time Streaming (NRTS) to enhance the basic concept and implementation of CCN in order to improve streaming quality. NRTS utilizes “symbolic interest,” which eliminates segment identification, and “content-name-based streaming,” which enables per-content (instead of per-packet) streaming and multicasting using a rate control scheme based on network conditions. NRTS is more suitable for high-performance delay-sensitive real-time applications than the original CCN architecture. It avoids the generation of bursty interests and the need for complex operations such as setting the pipeline size. The results from the experiments conducted using simulations and testbeds show that NRTS attains higher throughput and scalability than the original CCN architecture.

Several types of AL-FEC (Application-Level FEC) codes for the Packet Erasure Channel exist. Random Linear Codes (RLC), where redundancy packets consist of random linear combinations of source packets over a certain finite field, are a simple yet efficient coding technique, for instance massively used for Network Coding applications. However the price to pay is a high encoding and decoding complexity, especially when working on GF(28), which seriously limits the number of packets in the encoding window. On the opposite, structured block codes have been designed for situations where the set of source packets is known in advance, for instance with file transfer applications. Here the encoding and decoding complexity is controlled, even for huge block sizes, thanks to the sparse nature of the code and advanced decoding techniques that exploit this sparseness (e.g., Structured Gaussian Elimination). But their design also prevents their use in convolutional use-cases featuring an encoding window that slides over a continuous set of incoming packets. In this work we try to bridge the gap between these two code classes, bringing some structure to RLC codes in order to enlarge the use-cases where they can be efficiently used: in convolutional mode (as any RLC code), but also in block mode with either tiny, medium or large block sizes. We also demonstrate how to design compact signaling for these codes (for encoder/decoder synchronization), which is an essential practical aspect.

In-network caching is a core technology for saving network resources and improving the response time in Information-/Content-Centric Networking (ICN/CCN). In an ordinary ICN/CCN, content name based routing is used to find cached content in the network. Although there have been several studies on effective and efficient cache retrieval from in-network caches, we propose a novel content hunting scheme, named Local Tree Hunting (LTH) from the point of view to approach the ideal performance. LTH hunts for the closest node to the request node among the nodes with disseminated cache content. It autonomously adjusts the hunting area by changing the size of the hunting tree depending on the degree to which the content has been disseminated through the network. The proposed scheme was verified in a performance analysis; it was shown to have almost the same level of performance as the case of finding the true closest node. System architecture and operations based on the proposed scheme are also described.

The Internet of Things (IoT) is emerging as one of the major trends for the next evolution of the Internet, where billions of physical objects or things (including but not limited to humans) will be connected over the Internet, and a vast amount of information data will be shared among them. However, the current Internet was built on a host-centric communication model, which was primarily designed for meeting the demand of pair-wise peer-to-peer communications and cannot well accommodate various advanced data-centric services boosted by the IoT in which users care about content and are oblivious to locations where the content is stored. In this paper, we propose a novel architecture for the future Internet based on information-centric networking (ICN), which is called DataClouds, to better accommodate data-centric services. Different from existing ICN-based architectures, we take the sharing nature of data-centric services under the IoT into consideration and introduce logically and physically formed communities as the basic building blocks to construct the network so that data could be more efficiently shared and disseminated among interested users. We also elaborate on several fundamental design challenges for the Internet under this new architecture and show that DataClouds could offer more efficient and flexible solutions than traditional ICN-based architectures.

The accommodation of growing tussles among different communities and the efficient and robust information dissemination in cyberspace have become crucial challenges for future network design, while the current Internet is ossified into the principle of end-to-end communications. To satisfy these challenges, in this paper we devise a community-oriented route coordination (CORIN) system using information-centric networking approach to naturally and efficiently provide community-based information dissemination and retrieval with name-integrated forwarding. The proposed CORIN modularizes users into communities, lets them express their interests and choices, and enables information objects to be searchable and retrievable in community units. We conduct performance analysis, which shows that CORIN can greatly reduce the control packet overhead compared with PURSUIT for community communication service provision.

Real-time streaming applications typically require minimizing packet loss and transmission delay so as to keep the best possible playback quality. From this point of view, IP datagram losses (e.g. caused by a congested router, or caused by a short term fading problem with wireless transmissions) have major negative impacts. Although Application Layer Forward Error Correction (AL-FEC) is a useful technique for protecting against packet loss, the playback quality is largely sensitive to the AL-FEC code/codec features and the way they are used. In this work, we consider three FEC schemes for the erasure channel: 2D parity check codes, Reed-Solomon over GF(28) codes, and LDPC-Staircase codes, all of them being currently standardized within IETF. We have integrated these FEC schemes in the FECFRAME framework, a framework that is also being standardized at IETF, and whose goal is to integrate AL-FEC schemes in real-time protocol stacks in a simple and flexible way. Then we modified the Digital Video Transport System (DVTS) high-performance real-time video streaming application so that it can benefit from FECFRAME in order to recover from transmission impairments. We then carried out several performance evaluations in order to identify, for a given loss rate, the optimal configuration in which DVTS performs the best.

For higher streaming quality, a data sender adjusts the data transmission rate according to the network condition between the sender and receiver. The sender and the receiver exchange information about the network condition to decide the appropriate transmission rate. However, monitoring each data flow in real time is difficult, and controlling the sender to adjust the best quality for each receiver in a heterogeneous environment is a real challenge. In this paper, we study packet loss patterns and FEC recovery rate upon data transmission in a congested network, and define an adaptive rate control mechanism that dynamically adjusts the transmission rate and the FEC encoding rate. We then show our adaptive DV Transmission System (DVTS) that supports appropriate rate control for provisioning multimedia streaming with the best possible quality, and evaluate the system on top of our testbed network.

In a mobile ad-hoc network (MANET) architecture, there is no pre-existing fixed network infrastructure, and a mobile node in this network sends data packets to a destination node directly or through its neighbor nodes. This situation is of potential security concern since the neighbor nodes cannot be always trusted. In this paper, we design a group member authentication protocol used in a MANET. It aims to allow a set of nodes to legitimately participate in group communication and then distribute a secret group key to the approved nodes to establish secure communication with group members. Our protocol provides knowledge-based group member authentication, which recognizes a list of secret group keys held in a mobile node as the node's group membership. It employs zero knowledge proof and threshold cryptography. We then introduce our actual implementation and evaluate the behavior to ensure its successful deployment