Enabling Blockchain Applications Over Named Data Networking

The convergence of blockchain, artificial intelligence (AI), and cloud computing is catalyzing a paradigm shift in developing secure, intelligent, and scalable digital infrastructures. This triad of technologies is increasingly utilized to improve performance, transparency, and trust in engineering-driven and socio-technical environments. This study systematically reviews the evolution, integration strategies, and applications of blockchain, AI, and cloud computing in digital ecosystems. The analysis is based on 108 peer-reviewed studies spanning the years 2012 to 2025. A comprehensive literature analysis was conducted to identify trends, synergies, and sector-specific implementations of these systems. The review explores how their integration supports real-world engineering and operational use cases. Blockchain contributes to decentralized architectures, secure data exchange, and identity verification. AI supports adaptive behavior, autonomous decision-making, and predictive analytics. Cloud computing offers the scalable infrastructure necessary for deployment. Key challenges addressed include interoperability, latency, security trade-offs, and resource allocation. Use cases in digital finance, supply chain management, and industrial automation demonstrate the effectiveness of this integration in building resilient, ethically aligned, and high-performance infrastructures. The findings offer valuable insights and technical considerations for engineers and architects seeking to design next-generation cyber-physical systems that are secure, intelligent, and socially responsive. Clinical Trial Number Not applicable. This article accentuates the extensive use of blockchain, cloud computing, and AI principles, emphasizing their crucial significance in the contemporary digital environment and paths in which blockchain technology may be employed to automate financial transactions, enhance the security of personal data and records, facilitate cross-border transactions, verify identities, execute smart contracts, utilize cryptocurrencies, and manage supply chains. This research systematically assesses the development and practical application of blockchain, cloud computing, and artificial intelligence technologies over the past decade. This study primarily focuses on monitoring the progress, practical applications, and benefits of blockchain technology in the context of financial transactions. This article probes the symbiotic relationship between artificial intelligence, cloud and blockchain technology, analyzing the evolution and fundamental concepts of their integration from 2012 to 2025. This paper also presents a detailed analysis of 108 research works acquitted over the past decade. The study provides a comprehensive examination of the current state and fundamental principles related to the integration of artificial intelligence, cloud, and blockchain technology. This paper offers a comprehensive analysis of the major obstacles confronted by blockchain technology, examining its wide-ranging applications over several industries and its socko role in transaction systems and the domain of cryptocurrencies.

Named Data Network (NDN) is a next-generation network architecture that shifts the traditional data communications paradigm Unlike conventional networks that rely on IP addresses, NDN delivers content based on data names rather than specific locations. In NDN, consumers express their requests by sending interest packets containing content names. These names are then propagated through the network nodes, which forward them to the appropriate destinations. The forwarding strategy in an NDN network plays a crucial role in ensuring efficient data delivery. This strategy includes a set of rules that determine the next hop for each interest packet. These rules are designed to optimize the forwarding process, minimizing delays and improving network efficiency. However, if the forwarding strategy is implemented without accurately identifying the appropriate face (i.e., the network interface) to forward interests toward the producer or the nearest cache node, it can lead to significant delays and packet drops. This, in turn, negatively impacts Quality of Service (QoS) parameters and the overall performance of the NDN network. This study applies supervised learning to classify consumer-requested interests to overcome this issue. This technique leverages several related variables to accurately classify these interests. The outcomes of the conducted research demonstrated that raw data from the mini-NDN output can be processed and transformed into a usable dataset. This data is then utilized to train a classification model with supervised learning. In a scenario with 9 NDN nodes and varying numbers of interests, distributed both uniformly and according to Zipf's law, the Random Forest model performs effectively, achieving an accuracy rate of 86.2% with an error rate of 14.8%.

Named Data Networking (NDN) proposes a contentcentric rather than a host-centric approach to data retrieval. Data packets with unique and immutable names are retrieved from a content store (CS) using Interest packets. The current NDN architecture relies on forwarding strategies that are dependent upon on-path caching and is therefore inefficient. This approach reduces data transfer efficiency by ignoring the cached content available on the adjacent off-path routers in the network. In this paper, we propose a novel distributed multipath (D-MP) forwarding strategy and enhancements to the NDN Interest forwarding pipeline. Furthermore, we develop a centralized SDNenabled control for the multipath forwarding strategy (S-MP) that distributes Interests efficiently by using the global knowledge of the NDN network states. We perform extensive evaluations of our proposed methods on an at-scale WAN testbed spanning six geographically separated sites. Our solutions outperform the existing NDN forwarding strategies by a significant margin. We show that the D-MP strategy results in performance gains ranging between 10.4x to 12.5x over the default NDN implementation without in-network caching, and gains of 12.2x to 18.4x with in-network caching. In addition, for the S-MP case, we demonstrate a performance improvement of 10.6x to 12.6x, and 12.9x to 18.5x, for with- and without in-network caching respectively.

Named Data Networking (NDN) is a recently designed Internet architecture that benefits data names instead of locations and creates essential changes in the abstraction of network services from "delivering packets to specific destinations” to "retrieving data with special names" makes. This fundamental change creates new opportunities and intellectual challenges in all areas, especially network routing and communication, communication security, and privacy. The focus of this dissertation is on the forwarding aircraft feature introduced by NDN. Communication in NDN is done by exchanging interest and data packets. Consumers send interest packets to request data, routers send them based on the data prefix name, and manufacturers respond with data packets exactly how interest is received. During this process, routers maintain information about the status of suspended interests. As regards NDN is still in its early stage, none of these strong efficiency features have done systematically designed, measured, or tracked in data retrieval and multi-path detection. This paper tries to improve transport performance by combining label switching with adaptive transport methods. For this purpose, in addition to RTT, another timer called T is used, an RTT factor for interface rating. And uses the BDP to choose the best interface to respond to interest. During this process, response time, throughput, and data recovery time are improved by 12%, 10%, and 5%, respectively.

Named data networking (NDN) and edge cloud computing (ECC) are emerging technologies that are considered as the most representative technologies for the future Internet. Both technologies are the promising enabler for the future Internet such as fifth generation (5G) and beyond which requires fast information response time. We believe that clear benefits can be achieved from the interplay of NDN and ECC and enables future network technology to be much more flexible, secure and efficient. In this paper, therefore, we integrate NDN with ECC in order to achieve fast information response time. Our framework is based on N-Tier architecture and comprises of three main Tiers. The NDN is located at the Tier1 (Things/end devices) and comprises of all the basic functionalities that connect Internet of Things (IoT) devices with Tier 2 (Edge Computing), where we have deployed our Edge node application. The Tier 2 is then further connected with Tier 3 (Cloud Computing), where our Cloud node application is deployed at multiple hops on the Microsoft Azure Cloud machine located in Virginia, WA, USA. We implement an NDN-based ECC framework and the outcomes are evaluated through testbed and simulations in terms of interest aggregation, round trip time (RTT), service lookup time with single query lookup time and with various traffic loads (load-based lookup time) from the IoT devices. Our measurements show that enabling NDN with edge computing is a promising approach to reduce latency and the backbone network traffic and capable of processing large amounts of data quickly and delivering the results to the users in real time.

The Named Data Network (NDN) is a future network architecture to transit conventional host-centric networks into data-centric networks. A routing attack, represented by Interest Flooding Attack (IFA), is a major security concern in NDNs. IFAs seriously harm NDNs by sending excessive fallacious Interest packets to overwhelm the routers to serve legitimate users. It is easy to launch, but difficult to defend, especially when legitimate Interest packets are blended with the IFAs as bIFAs. In this paper, we propose an accurate approach to detect and mitigate IFAs and bIFAs, named Accurate Detection and Mitigation of Blended Interest Flooding Attacks (ADMBIFA). It utilizes fuzzy logic to detect IFAs and bIFAs. And then it identifies malicious Interest prefixes with K-means. Finally, it restrains attackers to mitigate the damage to the networks. The simulation results show that the ADMBIFA is very sensitive to detect attacks with an ability to mitigate IFAs and bIFAs. And it can effectively reduce the negative impacts on NDNs.

The information and communication technology nowadays more than ever depends on the Internet and cloud computing, so that the data centers (DCs) have been converted to a constitutive unit of the cloud computing. A DC is composed of two primary parts: servers and Data Center Networks (DCNs). Robustness and scalability are two major challenges of the DCNs that are expanded based on two strategies, scale-out, and scale-up. This paper is distinctive from the related studies in two aspects. The first one is to simultaneously focus on both the scalability and the robustness challenges of the DCNs. For this purpose, we will concentrate on the comparison of robustness in the scalable models of these networks. The second one is, despite the previous work that only evaluated the DCN robustness under topological changes, we evaluated the robustness and fault tolerance against three types of unexpected changes in topology, traffic, and COI (community of interest) in the present work. Hence, we have chosen the network criticality (NC) as a graph-theoretic metric for analyzing DCN robustness. Afterward, we compare some structural and spectral graph metrics with NC among some well-known DCNs, and their scale-out and scale-up. Our results are useful to select the appropriate scaling strategy with the goal of maximizing the robustness of existing DCNs and provide a guideline for designing the new robust and scalable DCN.

Data center networks, using a reliable, high speed communication network, is a core component of cloud computing. Recently, optical networks using Space Division Multiplexing has been proposed as a solution to the speed bottleneck anticipated in data center networks. To our knowledge the new challenges of designing such communication systems have not been addressed yet. In this paper we have proposed an optimal approach to the problem of developing a path-protection scheme to handle communication requests in data center (DC) networks using elastic optical networking and space division multiplexing. We have formulated our problem as an Integer Linear Program (ILP). We have also proposed a heuristic that can handle problems of practical size. Our simulations explore important features of our approach.

Named data networking (NDN) is a future Internet architecture that directly emphasizes accessible content by assigning each piece of content a unique name. Data transmission in NDN is realized via name-based routing and forwarding. Name-based forwarding information base (FIB) usually has much more and longer prefixes than IP-based ones, and therefore, name-based forwarding brings more challenges on the NDN router in terms of high forwarding throughput, low memory consumption, and fast FIB update. In this paper, we present an index data structure called BFAST for the name-based FIB. BFAST is designed based on a basic hash table, it employs a counting Bloom filter to balance the load among hash table slots, so that the number of items in each non-empty slot is close to 1, leading to low searching time in each slot. Meanwhile, the first-rank-indexed scheme is proposed to effectively reduce the massive memory consumption required by the pointers in all the hash table slots. Evaluation results show that, for the longest prefix match FIB lookup, BFAST achieves a speed of 2.14 MS/S using one thread, and meanwhile, the memory consumption is reasonably low. By leveraging the parallelism of today’s multi-core CPU, BFAST arrives at an FIB lookup speed of 33.64 MS/S using 24 threads, and the latency is around $0.71~\mu s$ .

Objective: This paper explores the use of blockchain technology in agriculture and agricultural products.Research Design & Methods: The article is based on a critical analysis of the literature with a view to understanding the current state of use of blockchain technology in agriculture. It was assumed that blockchain technology is used in the agricultural sector to promote food security, prevent food fraud and verify the origin and authenticity of agricultural products and agricultural inputs.Findings: Blockchain technology improves traceability and transparency, allowing parties within the agricultural value chain to identify faulty or suboptimal processes as well as bad actors. This ensures that ideal conditions are pursued from farm to market. The ability to trace the origin of food products is essential when food safety breaks down. The early identification of the origin of contamination will enable food companies to swing into action quickly to prevent illness and thus save lives. Such a timely response will also help limit food wastage and will save money by containing financial fallout.Implications / Recommendations: Blockchain technology has strong potential for success within the agricultural sector. It can be used to ensure food safety by enabling the source of agricultural products, as well as the source of their potential contamination, to be traced, and the authenticity of farming inputs to be verified. Blockchain can also be employed in the process of disbursing subsidies to farmers to ensure that they benefit from subsidy programmes. Finally, blockchain technology will offer farmers better prices and better payment methods and solve challenges in land title sales and purchase registration.Contribution: Blockchain is a new technology to the agricultural sector, and enormous challenges remain. There is still no established system to regulate blockchain transactions. Nevertheless, the application of blockchain in agriculture holds promising rewards.

Named Data Networking (NDN) is one of the most promising future Internet architectures, which supports communications based on content names but not the content locations. Clients can retrieve content only by sending an Interest packet, which includes the content name. However, independent Interest packets will cause heavy loads on the routers, particularly in the Internet of Things (IoT) scenarios which will induce huge number of small-sized content exchanges. The proposed scheme enables clients to retrieve many different contents by sending an aggregated Interest packet. In addition, the Data packet can also be aggregated by the intermediate routers to improve the efficiency of NDN communications.

A survey on architectures and energy efficiency in Data Center Networks

Named Data Networking (NDN) has emerged as a promising information-centric networking paradigm that addresses the limitations of the traditional IP-based Internet architecture. The core principle of NDN relies on content naming instead of host addressing, to provide efficient, secure, and scalable content delivery. Routing is a critical component of NDN and is responsible for discovering and maintaining optimal paths to named content. This paper presents a comprehensive review of routing techniques in NDN, focusing on the design principles, algorithms, and performance metrics, especially in wired network architecture. We first summarize the NDN architecture and discuss its key components. We then delve into the fundamental routing concepts in NDN and categorize and examine various routing techniques, including link state, distance vector, and centralized approaches based on Software Defined Network. We also summarize the relevant research efforts proposed to address NDN routing challenges by focusing more on wired network architecture. Finally, we identify open research issues and future directions in NDN routing, emphasizing the need for scalable, efficient, and secure routing techniques that can fulfill the growing demands of the modern Internet. In conclusion, this review serves as a valuable reference for researchers and practitioners in NDN, offering a comprehensive understanding of the current state-of-the-art routing techniques, limitations, and potential future advancements.

Chapter 6 - Applications of blockchain in smart cities: detecting fake documents from land records using blockchain technology

The capability of the data center network largely decides the performance of cloud computing. However, the number of servers in the data center network becomes increasingly huge, because of the continuous growth of the application requirements. The performance improvement of cloud computing faces great challenges of how to connect a large number of servers in building a data center network with promising performance. Traditional tree-based data center networks have issues of bandwidth bottleneck, failure of single switch, etc. Recently proposed data center networks such as DCell, FiConn, and BCube, have larger bandwidth and better fault-tolerance with respect to traditional tree-based data center networks. Nonetheless, for DCell and FiConn, the fault-tolerant length of path between servers increases in case of failure of switches; BCube requires higher performance in switches when its scale is enlarged. Based on the above considerations, we propose a new server-centric data center network, called BCDC, based on crossed cube with excellent performance. Then, we study the connectivity of BCDC networks. Furthermore, we propose communication algorithms and fault-tolerant routing algorithm of BCDC networks. Moreover, we analyze the performance and time complexities of the proposed algorithms in BCDC networks. Our research will provide the basis for design and implementation of a new family of data center networks.