Quantum key distribution networks (QKDNs) offer a promising platform for information-theoretically secure (IT-secure) communication between two or more nodes, representing an increasingly practical communication platform. Despite their promise, these systems continue to encounter significant challenges due to their restricted key-generation rate. Therefore, the efficient and effective utilization of key resources is pivotal to enhancing IT-secure communication. In this study, we propose an in-network coded–cache–aware multipath-transport mechanism called CodCam-TP that exploits information-centric networking (ICN) technologies. CodCam-TP facilitates efficient and effective IT-secure communication through in-network coding and caching, multicast, and multipath communication. Furthermore, by exploiting the pull-based hop-by-hop communication model, CodCam-TP leverages an in-band network telemetry technique to select a path based on the forwarding capacity of each available path, thus improving throughput. Additionally, we conduct several performance evaluations for CodCam-TP using Cefore, an open-source software for driving ICN-based communication, demonstrating improved secure-key-utilization efficiency and data-delivery effectiveness compared with those realized by an existing scheme.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.