Information-Centric Networking Research Articles

Reliable Application Layer Routing Using Decentralized Identifiers

Modern internet of things (IoT) applications can benefit from advanced communication paradigms, including multicast and anycast. Next-generation internet architectures, such as information-centric networking (ICN), promise to support these paradigms, but at the same time they introduce new security challenges. This paper presents a solution that extends an ICN-like architecture based on software-defined networking (SDN) that supports those communication paradigms. Using the proposed solution, the underlying architecture is enhanced with a novel security mechanism that allows content “advertisements” only from authorized endpoints. This mechanism prevents “content pollution”, which is a significant security threat in ICN architectures. The proposed solution is lightweight, and it enables identity sharing as well as secured and controlled identity delegation.

A hybrid caching strategy for information-centric satellite networks based on node classification and popular content awareness

Low Earth Orbit (LEO) satellite networks are inexpensive to deploy and have wide coverage, and are widely available for differentiated content distribution services. With the development of on-board storage and computing capabilities, in-network caching technology in information centric networking (ICN) has proven to be an effective way to increase the throughput and content distribution efficiency of satellite networks. However, traditional caching and distribution schemes, which do not take into account the high-speed motion of satellite nodes and dynamic changes in topology, are not applicable to satellite networks. To address these issues, a hybrid caching strategy for satellite networks based on node classification and popular content awareness (NCPCA) is proposed. Firstly, the time slot partitioning method based on interlayer similarity is proposed to transform the dynamically changing process into a set of time slots with stable topology. Next, the satellite nodes are dynamically divided into two categories by fully considering the changes in connection relationships and interaction order during the spatial–temporal evolution of the satellite nodes. Nodes with satellite topological and functional characteristics are screened out as core nodes with TOPSIS algorithm, and the remaining nodes are regarded as edge nodes. Finally, a probabilistic caching scheme based on content popularity is adopted with core nodes as caching nodes to ensure caching performance and promote the diversity of cached content. Simulation results show that this strategy can effectively improve cache hit rate, reduce user request delay and content fetching hops, and promote the stable operation of satellite networks compared with other caching strategies.

Forwarding and caching in video streaming over ICSDN: A clean-slate publish-subscribe approach

Nowadays, Internet usage has become prevalent, primarily because of high-quality heterogeneous multimedia content expectations from the subscriber (consumer), which puts tremendous pressure on the publisher (producer) in the networks. Information-Centric Networking (ICN) is a future internet architecture that optimizes data resources through content-based forwarding and caching, making it well-suited for multimedia content and video streaming (VS) scenarios. However, real-time data delivery is challenging in the current ICN-based publish–subscribe (pub-sub) mechanism, which pushes the existing pub-sub studies to prioritize more on the forwarding information base (FIB) rather than the pending interest table (PIT). This leads to issues such as inefficient caching and forwarding mechanisms, high overhead, and communication costs. To address these challenges, in this paper, we present a novel forwarding and caching solution named VS-ICSDN, integrating the combined principles of ICN-based pub-sub scheme and software-defined networking (SDN) in order to utilize the network resources more efficiently. We design a clean-slate caching strategy and name-based forwarding method to support both on-path and off-path caching on ICN nodes to coordinate flow entries among the SDN controller and clean-slate ICN nodes to maximize PIT utilization. In addition, the framework allows the content to be stored and searched in chunks with a single request to access the desired content, reducing the communication overhead and significantly improving overall performance. A simulation-based testbed and experimental result analysis validate our proposed work’s effectiveness in ensuring efficient network resource usage with low communication overhead and computational cost compared to other baseline methods.

An Energy Efficient Local Popularity Based Cooperative Caching for Mobile Information Centric Networks

The usage of social media applications such as Youtube, Facebook, and other applications is rapidly increasing with each passing day. These applications are used for uploading informational content such as images, videos, and voices, which results in exponential traffic overhead. Due to these overheads (high bandwidth consumption), the service providers fail to provide high-speed and low latency internet to users around the globe. The current internet cannot cope with such high data traffic due to its fixed infrastructure (host centric-network) degrading the network performance. A new internet paradigm known as Information Centric Networks (ICN) was introduced based on content-oriented addressing. The concept of ICN is to entertain the request locally by neighbor nodes without accessing the source, which will help offload the network’s data traffic. ICN can mitigate traffic overhead and meet future Internet requirements. In this work, we propose a novel decentralized placement scheme named self-organized cooperative caching (SOCC) for mobile ICN to improve the overall network performance through efficient bandwidth utilization and less traffic overhead. The proposed scheme outperforms the state-of-the-art schemes in terms of energy consumption by 55%, average latency, and cache hit rate by a minimum of 35%.

A Cuckoo Filter-Based Name Resolution and Routing Method in Information-Centric Networking

Information-centric networking (ICN) is a new network architecture that routes content based on names to improve transmission performance. Therefore, the efficiency of name resolution and routing becomes a critical issue in ICN. The bloom filter-based routing scheme has gained significant attention for its ability to improve the memory efficiency of routing nodes in the network, but it cannot handle the movement or deletion of content and has a high false positive rate, which increases bandwidth consumption. In this paper, we propose a cuckoo filter-based name resolution and routing method where resolution requests are forwarded through a hierarchical network structure to the node closest to the content copy as much as possible to minimize latency. This method achieves reliable content removal and allows summaries of content to be exchanged between nodes for resolution error correction and information synchronization based on a modified cuckoo filter. The simulation results show that our method can effectively reduce the number of false positives, and it can reduce the additional overhead caused by processing false positives for a large-scale network by 50% compared with the bloom filter-based scheme.

Performance evaluation of route optimization management of producer mobility in information-centric networking

<p class="MDPI17abstract"><span lang="EN-US">Named data networking (NDN) is a network service evolving the Internet's host-based packet delivery model. The idea of NDN is to use named data for routing, which specifies what they are looking for, instead of using location addresses that determine where they expect it to be provided. This architecture is expected to solve many issues that are currently faced by transmission control protocol/internet protocol (TCP/IP) architecture, such as scalability, robustness, mobility, security, and etcetera. One of the problems is about handling producer mobility. Considering the explosion growth rate of Internet connection in public transport vehicles, this is a challenge that needs to be overcome. Therefore, we have proposed a new scheme called route optimization management of producer mobility (ROM-P) with new features such as distributing anchor points and caching by using the same data name and com-paring our previous scheme, efficient producer mobility support (EPMS). This paper shows the analysis result between the ROM-P and EPMS by using simulation. All simulations were conducted using ndnSIM 2.4 NS-3 based. Throughout the simulation ROM-P shows a promising development in better performing compares to EPMS.</span></p>

About The Author

About The Author

A Path Load-Aware Based Caching Strategy for Information-Centric Networking

Ubiquitous in-network caching plays an important role in improving the efficiency of content access and distribution in Information-Centric Networks (ICN). Content placement strategies, which determine the location distribution of content replicas in the network, have a decisive impact on the performance of the cache system. Existing strategies primarily focus on pushing popular content to the network edge, aiming to improve the overall cache hit ratio while neglecting to effectively balance the traffic load between network links; this leads to insufficient utilization of network bandwidth resources and further excessive content delivery time and user QoE degradation. In this paper, a Path Load-Aware Based Caching strategy (PLABC) is proposed, in which content-related information and dynamic network-related information are comprehensively considered to make cache decisions. Specifically, the utility of caching the content at each on-path node is calculated according to the bandwidth consumption savings and the load level of the transmission path, and the node with the greatest utility value is selected as the caching node. Extensive simulations are conducted to compare the performance of PLABC with other state-of-the-art schemes by quantitative analysis. Simulation results validate the PLABC strategy’s effectiveness, especially in balancing link load and reducing content delivery time.

Semantic Based information-centric networking routing algorithms

Semantic Based information-centric networking routing algorithms

A Security Certification Scheme for Information-Centric Networks

Information-Centric Networking is an emerging alternative to host-centric networking designed for large-scale content distribution and stricter privacy requirements. Recent research on Information-Centric Networking focused on the protection of the network from attacks targeting the content delivery protocols, while assuming genuine content can always be retrieved from trustworthy nodes. In this paper, we depart from the assumption of the trustworthiness of network nodes and propose a novel certification methodology for information-centric networks that supports continuous security verification of non-functional properties. Our methodology provides a complete and detailed view of the network security status, increasing the trustworthiness of the network and its services. The proposed approach builds on an enhanced certification model capturing the evolution of the system over time. It also defines certification services that fully integrate with existing networks to collect evidence on the target of certification and carry out the certification process. It finally proposes two certification processes, centralized and decentralized, balancing the impact on the network and the system performance. Efficiency, performance, and soundness of our approach are experimentally evaluated in a simulated Named Data Networking (NDN) network targeting property availability.

On the ICN-IoT with federated learning integration of communication: Concepts, security-privacy issues, applications, and future perspectives

The individual and integration use of the Internet of Things (IoT), Information-Centric Networking (ICN), and Federated Learning (FL) have recently been used in several network-related scenarios and have consequently experienced a growing interest in the research community. Federated learning addresses the privacy and security issues of the IoT data in a decentralized manner. Also, it can be capable of training the multiple learning algorithms through local content except for exchanging data through intelligent Artificial Intelligence (AI)-based algorithms. Moreover, in ICN, the content is retrieved and stored based on the content name rather than the content location address. On the other hand, it is challenging to support the massive IoT devices by the fifth generation (5G) mobile-cellular networks. Therefore, the cellular 6G networks are expected to increase the connection capabilities by 10–100 times over 5G, which necessitates a convergence of Communication, Computing, and Caching (3C). At the same time, the in-network caching capabilities of ICN can be attractive features for IoT networks. IoT aspires to link anybody and/or everything at any time and location. However, integrating IoT with different areas is a new academic topic and is still in its infancy. As a result, this research highlights the potential of ICN for IoTs by conducting an exhaustive literature review. This work provides a comprehensive survey regarding these three recent research trends (i.e., FL, IoT, and ICN) and reviews the related state-of-the-art literature. We first describe the main features of each technology and discuss their most common and used variants. Furthermore, we envision the integration of such technologies to take advantage efficiently. Indeed, we consider their group-wise (FL-ICN-IoT) utilization based on the need for more robust security and privacy. Additionally, we cover the application fields of these technologies both individually and combinedly. Finally, we discuss the open issues of the reviewed research and describe potential directions for future avenues regarding integrating IoT, ICN, and FL technologies.

Design and Implementation of Enhanced Programmable Data Plane Supporting ICN Mobility

Information-centric networking (ICN) separates the identifier and locator of network entities, providing a natural advantage in supporting mobility. To gain the advantage of ICN to support mobility, an urgent challenge is the problem of practical implementation with performance optimization. Software-defined networking (SDN) can be regarded as infrastructure to implement ICN mobility. However, it is difficult for the centralized SDN controller to quickly process mobile signaling. Therefore, this paper proposes enhanced programmable data plane supporting ICN mobility. By offloading mobility-related control plane functions from the controller to the data plane, the data plane can locally process mobile signaling without interacting with the controller. We propose an offloading mechanism for control plane functions, based on a rule table, where the controller authorizes the data plane to process the mobile signaling by loading the programmable rule table to data plane’s control element, and the control element intercepts the mobile signaling, matches the predefined rule table, and executes a series of application logic actions. In addition, we propose an improved SmartSplit algorithm to manage the rule table and speed up packets matching the rule table. Based on Intel’s Data Plane Development Kit (DPDK), we implement the enhanced programmable data plane. Our experimental results prove that the proposed enhanced programmable data plane has a stronger ability to process mobile signaling and reduce latency.

An Information-Centric Network Caching Method Based on Popularity Rating and Topology Weighting

Ubiquitous caching is a feature shared by all proposed information-centric network (ICN) architectures. Prioritising storage resources to popular content in the network is a proven way to guarantee hit rates, reduce the number of hops forwarded, and reduce user request latency. An ideal ICN caching mechanism should make the best use of relevant information such as content information, network state, and user requirements to achieve optimal selection and have the ability to adaptively adjust the decision cache content for dynamic scenarios. Since router nodes have limited cache space, it is then useless to accurately predict the popularity of the content with very low popularity, as this content has no chance of being cached. A more effective approach is to focus on content with high popularity that influences caching decisions. As for different nodes, they have different sets of popular content, and using this property, this paper designs a caching method based on the popularity hierarchy with topological weights. The method considers managing the cached content in nodes with a hierarchy of popularity and improving their distribution in terms of the importance of the nodes’ position in the network. Finally, the scheme is simulated by changing the parameter settings under different actual topologies on the simulation platform to confirm the feasibility of the scheme.

Research on Collaborative Machine English Translation Using the HIC Technology

Due to the rapid development of smart city, Hybrid Information Centric-Networking (HICN) emerges as a promising technology to enable the power of smart city. One of the most important application is the smart English translation, which becomes more and more popular with the process of Internationalization. In this work, we focus on studying the intelligent English translation is smart city using the HICN technology. Particularly, a method using collaborative machine learning and quality estimation technique is proposed, which sets a fixed threshold to filter pseudo-parallel data during unsupervised neural machine translation training. The quality estimation is used to evaluate and screen the pseudo-parallel data with high performance generated during reverse translation training. The results indicate that the proposed method outperforms the state-of-the-art methods.

A Tree Structure Protocol for Hierarchical Deterministic Latency Name Resolution System

Information-centric networking (ICN) shifts the communication model from a host-centric paradigm to an information-centric paradigm, and is promising for solving several problems on today’s Internet. For more efficient information dissemination, most ICN architectures are based on the Identifier/Locator split design. Therefore, how to map an identifier to a routable locator is an important problem for efficient data transmission. Nowadays, many new network services such as industrial control and telemedicine are highly latency-sensitive and require deterministic service response latency. To meet such requirements, name resolution with a deterministic latency guarantee is needed, but less discussed. This paper proposes a tree-based resolution system structure for deterministic latency resolution, which can support the Local Name Mapping Resolution System (LNMRS) in the new ICN network architecture—SEANet—to provide deterministic name resolution service in latency-sensitive scenarios like industrial control and telemedicine. The correctness of such a structure is the key to achieving deterministic latency resolution. To ensure the structure’s correctness in a distributed manner, a tree structure protocol based on delay measurement is also proposed for structure generation and maintenance. Simulation results show that the protocol is effective in generating a correct structure that has good performance in terms of service capability for deterministic name resolution and system scalability.

PbCP: A profit-based cache placement scheme for next-generation IoT-based ICN networks

The large number of connected devices generating a massive amount of data in the emerging technologies such as the Internet of Things (IoT), will surpass the capabilities of the current Internet infrastructure. Therefore, it is essential to design a new communication paradigm to meet the demands and requirements of new applications. Information-Centric Networking (ICN) is a new candidate for future Internet architecture that aims at addressing different challenges in the currently used host-centric Internet. In ICN, the data/content is searched, routed, located, and retrieved through the name of the content rather than the address of data source. ICN also enables in-network caching where the intermediate nodes can cache the content. In-network caching helps in improving the network scalability and reduces the content retrieval delay. To date, multiple caching schemes have been designed for IoT data management in ICN; however, most of them suffer from scalability or ignore constraints on the IoT devices. Moreover, the placement of content in a large-scale environment needs further improvement and optimization since it impacts overall network performance. To fill the gaps, in this paper, we combine various metrics that affect caching performance. We formulate the content placement as an optimization problem and then propose a profit-based caching scheme that harnesses the benefits of the Tabu local search algorithm. The simulation results and comparative analysis show that the proposed strategy outperforms the existing state-of-art schemes, notably in terms of memory resources utilization, data transmission time, content diversity ratio, and cache replacement operations.

SCD2: Secure Content Delivery and Deduplication With Multiple Content Providers in Information Centric Networking

As one of the promising next generation network architectures, information centric networking (ICN) is highly anticipated to improve the bandwidth usage of the Internet and reduce duplicate traffic. Since contents in ICN are disseminated in the whole network, ICN is much more vulnerable and the issue of how to deliver contents securely has been intensively discussed. However, the scalability of the existing schemes is limited. A scalable scheme is expected to be able to achieve fine-grained access control and at the same time also support multiple content providers scenario with efficient key management at user side. Besides, different content providers may publish some identical contents and these contents may be cached in the same intermediate routers, which causes high data redundancy and in turn exerts an adverse impact on the performance of ICN. In this paper, we propose a Secure Content Delivery and Deduplication scheme, called SCD2, to achieve secure and efficient fine-grained access control in ICN with multiple content providers. We first propose a scalable key-policy attribute-based encryption (SKP-ABE) to provide fine-grained access control and allow different attribute authorities to share some public attributes to simplify the key management. Furthermore, based on SKP-ABE, we design a simple but effective mechanism to conduct content deduplication. Finally, we implement a prototype of SCD2 to test its performance and compare it with some existing schemes. The results show that SCD2 has lower storage overhead, a higher degree of deduplication, and better retrieval efficiency.

Global IoT Mobility: A Path Based Forwarding Approach

With the huge proliferation of mobile Internet of Things (IoT) devices such as connected vehicles, drones, and healthcare wearables, IoT networks are promising mobile connectivity capacity far beyond the conventional computing platforms. The success of this service provisioning is highly dependent on the flexibility offered by such enabling technologies to support IoT mobility using different devices and protocol stacks. Many of the connected mobile IoT devices are autonomous, and resource constrained, which poses additional challenges for mobile IoT communication. Therefore, given the unique mobility requirements of IoT devices and applications, many challenges are still to be addressed. This paper presents a global mobility management solution for IoT networks that can handle both micro and macro mobility scenarios. The solution exploits a path-based forwarding fabric together with mechanisms from Information-Centric Networking. The solution is equally suitable for legacy session-based mobile devices and emerging information-based IoT devices such as mobile sensors. Simulation evaluations have shown minimum overhead in terms of packet delivery and signalling costs to support macro mobility handover across different IoT domains.

A Proxy-Authorized Public Auditing Scheme for Cyber-Medical Systems Using AI-IoT

Artificial intelligence based Internet of Things enables autonomous communication among social networks and IoT to leverage the promising solution in the modern paradigms. It can provide an interactive platform across the globe to enrich the quality of networking services to the end users. Of late, the expansion of information-centric networking has brought an incredible technique, known as public auditing scheme, for IoT-enabled sensor technologies. It uses a cloud-based medical cyber-physical system (M-CPS) to rely on cloud computing that ensures fast computing and reliable data storage. Since the medical file is so vital to involve precise diagnoses, data integrity and verification have lately become the data auditing tool. To exploit proxy authorizer and trusted auditor, an identity-based proxy authorized outsourcing with public auditing (ID-PAOPA) is proposed. It uses proxy authorization and verification to upload medical data over cloud-based M-CPS. To substantiate the finding, this article provides security proof based on the EC-DLP assumption. Finally, the performance analysis proves that the proposed ID-PAOPA achieves less computation and auditing timing to fulfill the objectives of cloud-based M-CPS.

Synchronizing tasks for distributed learning in connected and autonomous vehicles

Autonomous driving relies greatly on deep learning to comprehend the surroundings and activities of the road systems. The learning models are traditionally trained off-line and used during driving. However, recent research on federated learning has enabled distributed deep learning for model adaptation with new data inputs from end users. Similarly, the recent research on continual learning has enabled the upgrading of the learned model with newer rounds of training, without losing previously acquired knowledge. For autonomous and/or connected vehicles, these mean it is possible to take new, maybe real time, data inputs from various sensors from multiple vehicles in vicinity, to train and update the preloaded models. The updated models can improve the safety and reduce human involvements when driving through unfamiliar situations. This paper tackles one important issue, namely, the model data dissemination for enabling such a distributed learning. The model data dissemination consists of steps of soliciting workers, transmitting model, and collecting the updates. In a mixed autonomous vehicles and connected vehicles network scenario, communication overhead and network dynamics are the major challenges. We present analyses on critical latency issues pertained to the various aspects of the model data dissemination to gain insights on the feasibility of federated learning for such a scenario. Also, we introduce a communication architecture and a publish-subscribe system for the model data dissemination. Our system is built with an information-centric networking paradigm via a tiered edge network architecture. Such a system organizes the steps of the model data dissemination clearly and manages the dynamics of the participating vehicles easily. The results show that the presented system reduces overall communication overhead and delay. It also provides high resiliency to packet losses in the mixed wireless connected and autonomous vehicular network.