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Information-centric networking (ICN) technology matches many major requirements of vehicular ad hoc networks (VANETs) in terms of its connectionless networking paradigm accordant with the dynamic environments of VANETs and is increasingly being applied to VANETs. However, wireless transmissions of packets in VANETs using ICN mechanisms can lead to broadcast storms and channel contention, severely affecting the performance of data dissemination. At the same time, frequent changes of topology due to driving at high speeds and environmental obstacles can also lead to link interruptions when too few vehicles are involved in data forwarding. Hence, balancing the number of forwarding vehicular nodes and the number of copies of packets that are forwarded is essential for improving the performance of data dissemination in information-centric networking for vehicular ad-hoc networks. In this paper, we propose a context-aware packet-forwarding mechanism for ICN-based VANETs. The relative geographical position of vehicles, the density and relative distribution of vehicles, and the priority of content are considered during the packet forwarding. Simulation results show that the proposed mechanism can improve the performance of data dissemination in ICN-based VANET in terms of a successful data delivery ratio, packet loss rate, bandwidth usage, data response time, and traversed hops.

Information Centric Networking (ICN), a paradigm shift in network design that prioritizes content distribution over host‐centric methods, arises in this dynamic environment. Integrating built‐in network caches into ICN is essential for optimizing content distribution effectiveness, which raises hit rates, boosts throughput, and lowers transmission delays. This paper provides a comprehensive exploration and classification of ICN caching strategies across diverse domains, including the Internet of Things (IoT), Internet of Vehicles (IoV), Mobility ICN, Edge Computing, and Fog Computing. It further offers a detailed analysis of these strategies based on their proposed methods, identifying key trends, strengths, and limitations. Through a balanced performance assessment, the study evaluates these strategies using critical metrics such as cache hit ratio, efficiency, retrieval latency, security, and throughput. Furthermore, the paper highlights open challenges and presents future research directions to advance caching mechanisms, fostering the continued evolution of ICN for scalable and efficient content delivery for users. The graphical illustrates an in‐depth discussion of ICN features, architectures, and naming schemes. It presents the categories of caching, followed by an examination of existing caching strategies, and explores the role of caching in diverse applications, including IoT, IoV, mobility, edge computing, and fog computing. It also conducts a comparative analysis with existing surveys, while Section 7 presents recent research advances on key ICN performance indicators.

The Internet Protocol (IP) architecture could not fully satisfy the Vehicular Ad-hoc Networks (VANETs) needed efficiency, due to their dynamic topology and high mobility. This paper presents a technique that updates the software of Electronic Control Units (ECUs) in vehicles using segmented Over The Air (OTA) platform over Information-Centric Network (ICN) architecture. In VANET, the amount of time for active vehicles’ connectivity varies due to the vehicular network’s dynamic topologies. The importance of Flashing Over The Air (FOTA) has been illustrated as well as the impact of applying the ICN architecture on VANETs. Through our experiments, we apply mobility on vehicles and we compare the introduced FOTA over ICN and the newly introduced segmented OTA platform over ICN.

This research proposes an intelligent classification framework for quality of service (QoS) performance improvement in information-centric networking (ICN). The proposal works towards keyword classification techniques to obtain the most valuable information via suitable content prefixes in ICN. In this study, we have achieved the intelligent function using Artificial Intelligence (AI) implementation. Particularly, to find the most suitable and promising intelligent approach for maintaining QoS matrices, we have evaluated various AI algorithms, including evolutionary algorithms (EA), swarm intelligence (SI), and machine learning (ML) by using the cost function to assess their classification performances. With the goal of enabling a complete ICN prefix classification solution, we also propose a hybrid implementation to optimize classification performances by integration of relevant AI algorithms. This hybrid mechanism searches for a final minimum structure to prevent the local optima from happening. By simulation, the evaluation results show that the proposal outperforms EA and ML in terms of network resource utilization and response delay for QoS performance optimization.

Since the content delivery unit over Information-Centric Networking (ICN) has shifted from files to the segments of a file named chunks, solely either file-level or chunk-level request probability is insufficient for ICN cache management. In this paper, a Request Expectation Index (RXI) based cache replacement algorithm for streaming content delivery is proposed. In this algorithm, RXI is introduced to serve as a fine-grained and unified estimation criteria of possible future request probability for cached chunks. RXI is customized for streaming content delivery by adopting both file-level and chunk-level request probability and considering the dynamically varied request status at each route as well. Compared to prior work, the proposed algorithm evicts the chunk with the minimum expectation of future request to maintain a high cache utilization. Additionally, simulation results demonstrate that the RXI-based algorithm can remarkably enhance the streaming content delivery performance and can be deployed in complex network scenarios. The proposed results validate that, by taking fine-grained request probability and request status into consideration, the customized in-network caching algorithm can improve the ICN streaming content delivery performance by high cache utilization, fast content delivery, and lower network traffic.

The substantial advancements offered by the edge computing has indicated serious evolutionary improvements for the internet of things (IoT) technology. The rigid design philosophy of the traditional network architecture limits its scope to meet future demands. However, information centric networking (ICN) is envisioned as a promising architecture to bridge the huge gaps and maintain IoT networks, mostly referred as ICN-IoT. The edge-enabled ICN-IoT architecture always demands efficient in-network caching techniques for supporting better user’s quality of experience (QoE). In this paper, we propose an enhanced ICN-IoT content caching strategy by enabling artificial intelligence (AI)-based collaborative filtering within the edge cloud to support heterogeneous IoT architecture. This collaborative filtering-based content caching strategy would intelligently cache content on edge nodes for traffic management at cloud databases. The evaluations has been conducted to check the performance of the proposed strategy over various benchmark strategies, such as LCE, LCD, CL4M, and ProbCache. The analytical results demonstrate the better performance of our proposed strategy with average gain of 15% for cache hit ratio, 12% reduction in content retrieval delay, and 28% reduced average hop count in comparison to best considered LCD. We believe that the proposed strategy will contribute an effective solution to the related studies in this domain.

In present days, the utilization of mobile edge computing (MEC) and Internet of Things (IoT) in mobile networks offers a bottleneck in the evolving technological requirements. Wireless Sensors Network (WSN) become an important component of the IoT and is the major source of big data. In IoT enabled WSN, a massive amount of data collection generated from a resource-limited network is a tedious process, posing several challenging issues. Traditional networking protocols offer unfeasible mechanisms for large-scaled networks and might be applied to IoT platform without any modifications. Information-Centric Networking (ICN) is a revolutionary archetype which that can resolve those big data gathering challenges. Employing the ICN architecture for resource-limited WSN enabled IoT networks may additionally enhance the data access mechanism, reliability challenges in case of a mobility event, and maximum delay under multihop communication. In this view, this paper proposes an IoT enabled cluster based routing (CBR) protocol for information centric wireless sensor networks (ICWSN), named CBR-ICWSN. The proposed model undergoes a black widow optimization (BWO) based clustering technique to select the optimal set of cluster heads (CHs) effectively. Besides, the CBR-ICWSN technique involves an oppositional artificial bee colony (OABC) based routing process for optimal selection of paths. A series of simulations take place to verify the performance of the CBR-ICWSN technique and the results are examined under several aspects. The experimental outcome of the CBR-ICWSN technique has outperformed the compared methods interms of network lifetime and energy efficiency.

By enabling name-based routing and ubiquitous in-network caching, Named Data Networking (NDN) is a promising network architecture for sixth generation (6G) edge network infrastructures. However, the performance of content retrieval largely depends on the selected caching strategy, which is implemented in a distributed fashion by each NDN node. Previous research showed the effectiveness of caching decisions based on content popularity and network topology information. This paper presents a new distributed caching strategy for NDN edge networks based on a metric called popularity-aware closeness (PaC), which measures the proximity of the potential cacher to the majority of requesters of a certain content. After identifying the most popular contents, the strategy caches them in the available edge nodes that guarantee the higher PaC. Achieved simulation results show that the proposed strategy outperforms other benchmark schemes, in terms of reduced content retrieval delay and exchanged data traffic.

With the development of new technologies and applications, such as the Internet of Things, smart cities, 5G, and edge computing, traditional Internet Protocol-based (IP-based) networks have been exposed as having many problems. Information-Centric Networking (ICN), Named Data Networking (NDN), and Content-Centric Networking (CCN) are therefore proposed as an alternative for future networks. However, unlike IP-based networks, CCN routing is non-deterministic and difficult to optimize due to frequent in-network caching replacement. This paper presents a novel probe-based routing algorithm that explores real-time in-network caching to ensure the routing table storing the optimal paths to the nearest content provider is up to date. Effective probe-selections, Pending Interest Table (PIT) probe, and Forwarding Information Base (FIB) probe are discussed and analyzed by simulation with different performance measurements. Compared with the basic CCN, in terms of qualitative analysis, the additional computational overhead of our approach is O(NCS + Nrt + NFIB ∗ NSPT) and O(NFIB) on processing interest packets and data packets, respectively. However, in terms of quantitative analysis, our approach reduces the number of timeout interests by 6% and the average response time by 0.6 s. Furthermore, although basic CCN and our approach belong to the same Quality of Service (QoS) category, our approach outperforms basic CCN in terms of real values. Additionally, our probe-based approach performs better than RECIF+PIF and EEGPR. Owing to speedup FIB updating by probes, our approach provides more reliable interest packet routing when accounting for router failures. In summary, the results demonstrate that compared to basic CCN, our probe-based routing approach raises FIB accuracy and reduces network congestion and response time, resulting in efficient routing.

The named data networking (NDN)-based microservice-centric in-network computation poses various challenges in terms of interest aggregation and pending interest table (PIT) lifetime management. A same-named microservice-centric interest packet may have a different number of input parameters with nonidentical input values. In addition, the same-named interest packet with the same number of parameters may have different corresponding parameter values. The vanilla NDN request aggregation (based on the interest name, while ignoring the input parameters count and/or their corresponding values) may result in false aggregation. Moreover, the microservice-centric requested computations may fail to accomplish in the default 4s PIT timer due to the input size. To address these challenges, this paper presents MIA-NDN: microservice-centric interest aggregation in named data networking. We designed microservice-centric interest-naming to enable name-based communication. MIA-NDN develops a robust interest aggregation mechanism that not only performs the interest aggregation based on the interest name but also considers the input parameter counts and their corresponding values in the interest aggregation process to avoid false packet aggregations. A dynamic PIT timer mechanism based on input size was devised that avoids the PIT entry losses if the execution time exceeds the default PIT timer value to avoid computation losses and uphold the application quality of service (QoS). Extensive software-based simulations confirm that the MIA-NDN outperforms the benchmark scheme in terms of microservice-centric interest aggregation, microservice satisfaction rate, and communication overhead.