As data-driven applications and user demands grow, managing content delivery in Named Data Networking (NDN) has become more challenging. Traditional caching methods struggle to scale in dynamic and decentralized environments where content popularity changes and collaboration among routers is needed. This paper introduces a decentralized collaborative caching framework for NDN, combining Multi-Agent Deep Reinforcement Learning (MADRL) and blockchain technology. MADRL enables routers to autonomously adjust caching strategies based on local states and interactions with neighboring routers, improving cache hit rates and reducing retrieval costs. Blockchain technology ensures fair and transparent rewards through a cryptocurrency-based token system, incentivizing collaboration and minimizing free-riding risks. The framework also integrates Delegated Proof of Stake (DPoS) for efficient, secure validation of caching actions. Simulations demonstrate that the approach significantly enhances caching efficiency, reduces latency, and improves scalability, addressing the challenges of dynamic decentralized environments.

In the Internet of Things (IoT) and Big Data era, large-scale data analysis necessitates rapid and efficient machine learning techniques. Traditional distributed learning methods often rely on a central server and an online privacy model, which poses challenges for scalability and data privacy. We propose a decentralized High-level Proof-of-Work (HPoW) algorithm to address these issues, leveraging blockchain technology to develop a comprehensive predictive model. Our approach ensures participant data privacy through the implementation of a differential privacy scheme and establishes a secure environment against Byzantine attacks. Furthermore, the training of numerous machine learning models in cloud computing environments can lead to data anomalies, adversely affecting resource allocation and overall system performance. We introduce a cloud computing incentive mechanism designed specifically for distributed machine learning frameworks to mitigate these challenges. We present novel methodologies and provide mathematical proof that our blockchain-based approach significantly enhances the establishment of a Nash equilibrium among participating entities. Finally, we present the machine learning service on the blockchain with the cloud service aiding through in-depth experiments to show how well it works and to conclude on a transparent Nash equilibrium value with an incentive value of 14.3 Gwei after a successful session, highlighting our proposed system’s practical viability and performance benefits.

Recently, the adoption of IoT (Internet of Things) and Blockchain has become a hot topic, particularly in areas such as education and industry. IoT involves billions of devices connected worldwide and the management of these devices is largely based on centralized systems. Thus, users will have no choice but to trust these systems. Blockchain (BC), a distributed immutable time-stamped ledger that provides decentralization, immutability, and high security can help solve some of the problems inherent in the IoT landscape. However, integrating blockchain and IoT is not trivial; it comes with some difficulties such as scalability problems, high computational costs, and overheads among others. Therefore, this paper presents a scalable and lightweight Blockchain IoT service system using multi-edge servers that reduces computational overhead by 42% and improves transaction throughput to 658 tps, representing a 2.12-3.76× improvement compared to existing approaches. Our architecture uniquely combines blockchain, group signature, and message authentication code to ensure dependable auditing of users’ access records, anonymous authentication of smart home members, and effective verification of the home management system while maintaining a memory footprint of 4.2 MB, 60-70% smaller than conventional blockchain implementations. Additionally, our solution achieves 21-46% lower communication overhead (240 bytes per transaction) and 55-73% reduced latency (850 ms), demonstrating significant improvements across all performance metrics. The distributed nature of our multi-edge server approach eliminates single points of failure and enables a transaction processing capability that scales linearly with network growth, addressing key limitations in current blockchain-IoT integrations.

Named Data Networking (NDN) has proven to be a suitable candidate for Vehicular Ad-hoc Networks (VANETs) because of its data-centric nature and as a worthy replacement for IP addressing, particularly for those with high mobility like VANETs. This has led to the emergence of Vehicular Named Data Networking (VNDN). With the blockchain’s ability to ensure immutability, transparency, accountability, and trust, the combination of blockchain and NDN in the VANETs environment has the propensity to alleviate many security issues in VANETs. This paper introduces a blockchain-enabled NDN framework that guarantees a trustworthy and secure data-sharing network in VNDN. Moreover, we utilized an effective reputation mechanism to facilitate a trustworthy and honest data provision in our mobility network. We also adopted a collaborative caching mechanism to improve our system performance, as caching is one of the pivotal reasons for VNDN. We simulated our work using SUMO and ndnSIM and tested our framework against other related systems. The findings show that our proposed approach enhances performance depending on the parameters used.

Sharding has the potential to overcome the scalability constraints of monolithic blockchains. However, some challenges are associated with sharding, such as optimizing the placement of transactions into shards to minimize cross-shard transactions, balancing workload as shard capacity increases, and identifying shards that process transactions maliciously. To address these challenges, we propose a hierarchical high-performance blockchain (Hiba) architecture. Hiba leverages inter-shard to facilitate cross-shard consensus, where a pre-selected subset of nodes from both transaction originating and receiving shards collaboratively participate in the validation process. This design ensures the validity of transactions and mitigates double-spending risks across various shards. Simultaneously, it reduces validation costs by eliminating the need for all nodes in both shards to actively participate in the consensus process. Additionally, Hiba implements a novel multi-tiered validation system. Following initial validation at the intra-shard and inter-shard levels, a subset of randomly chosen or suspicion-based transactions undergoes further validation through auxiliary consensus. This auxiliary consensus acts as a secondary validation layer, ensuring the integrity of the intra-shard/inter-shard consensus process. To improve transaction processing efficiency, we implement an optimized workload distribution scheme based on fitness functions to minimize the number of cross-shard transactions. The experimental results demonstrate that Hiba surpasses the existing works regarding throughput and latency.

Named data networking (NDN), which is an implementation of Information-Centric Networking (ICN), has emerged as a promising new direction in networking as a result of the shortcomings of the transmission control protocol/Internet protocol (TCP/IP) architecture. NDN differs from conventional networking protocols since it places more importance on the data than where it originated. Vehicular-named data networking (VNDN) is a game-changing architecture due to its name-based forwarding and in-network caching. VNDN is designed to facilitate effective management of vehicular ad-hoc network (VANET) characteristics such as high mobility, connection intermittency and dynamic topology. However, VNDN’s data verification procedure might lead to lengthy wait times, particularly for mobile and vehicle networks. So, it is sometimes unfit for uses where speed and security are paramount, such as exchanging safety messages. Moreover, the performance of vehicle networks is negatively affected by the longer reaction times brought about by computing-intensive jobs. Therefore, we present a lightweight VNDN-based certificateless signcryption strategy that leverages the hyperelliptic curve cryptosystem’s security hardness and edge computing. Analysis of the scheme’s security and comparisons with similar systems demonstrate its effectiveness. Our suggested approach offers superior security with fewer computing and communication requirements, as verified by the final findings.

Owing to the vast volume of information gathered by computers, data protection and security has become a problem for organizations with the enormous rise in data transmission. Due to many advantages that cloud service providers provide, mainly economic benefits, several data owners outsource their data to cloud repositories. However, data owners do not have full ownership of the data after their data are outsourced. Thus, external data management systems are implemented to manage the data. Several kinds of research refer to the use of encryption techniques to prevent unauthorized access to data. Selective encryption aims at supporting selective and private access to outsourced data. However, the combination of this approach and indexing techniques cause confidentiality violations. In this article, a blockchain-based approach to data access is presented by implementing smart contracts over data access. These executable scripts bind users by stating access policies on the data. Furthermore, we provide a system, where users can offload their computational capabilities due to limitations in their computations. Our systems' computational capabilities outperform that of when users do the computation on their own. The results show a practical approach to data access management using blockchain technology.

In the era of big data, fast and effective machine learning algorithms are urgently required for large-scale data analysis. Data is usually created from several parts and stored in a geographically distributed manner, which has stimulated research in the field of distributed machine learning. The traditional master-level distributed learning algorithm involves the use of a trusted central server and focuses on the online privacy model. On the contrary, the specific linear learning model and security issues are not well understood in this column. We built a decentralized advanced-Proof-of-Work (aPoW) algorithm specifically for learning a general predictive model over the blockchain. In aPoW, we establish the data privacy of the differential privacy based schemes to protect each party and propose a secure domain against potential Byzantine attacks at a reduced rate. We explored a technical module in newsprint to consider a universal learning model (linear or non-linear) to provide a secure, confidential decentralized machine learning system called deepLearning Chain. Finally, we introduce deepLearning Chain on blockchain through comprehensive experiments, demonstrate its performance and effectiveness.