How to achieve secure content distribution and accountability in information-centric networking (ICN) is a crucial problem. Subscribers need to verify whether the data came from a reliable source, rather than from a spoofing adversary. Public key cryptography was introduced to achieve a method of authentication that binds the data packet to its owner. In existing prototypes, PKIs, identity-based signatures (IBSs) and recommendation networks are the common schemes used to ensure the authenticity and availability of public keys. However, CA-based PKIs and KGC-based IBSs have been proven to be weak when it comes to resisting security attacks, with recommendation networks being too complex to deploy. In this respect, we designed a novel distributed authentication model as a secure scheme to support public key cryptography. Our model establishes a decentralized public key infrastructure by combining the smart contracts of blockchain and optimized zero-knowledge proof-verifiable presentations by utilizing the DID project, which realizes the management of public key certificates through blockchain and ensures the authenticity and availability of public keys in decentralized infrastructure. Our scheme fundamentally solves the issues of security and feasibility in existing schemes and provides a more scalable solution with respect to authenticating data sources. An experiment demonstrated that our proposal is 20% faster than the original zero knowledge proof scheme in registration.

Binary bat algorithm (BBA) is a binary version of the bat algorithm (BA). It has been proven that BBA is competitive compared to other binary heuristic algorithms. Since the update processes of velocity in the algorithm are consistent with BA, in some cases, this algorithm also faces the premature convergence problem. This paper proposes an improved binary bat algorithm (IBBA) to solve this problem. To evaluate the performance of IBBA, standard benchmark functions and zero-one knapsack problems have been employed. The numeric results obtained by benchmark functions experiment prove that the proposed approach greatly outperforms the original BBA and binary particle swarm optimization (BPSO). Compared with several other heuristic algorithms on zero-one knapsack problems, it also verifies that the proposed algorithm is more able to avoid local minima.

The major advantage of information-centric networking (ICN) lies in in-network caching. Ubiquitous cache nodes reduce the user’s download latency of content and the drain of network bandwidth, which enables efficient content distribution. Due to the huge cost of updating an entire network infrastructure, it is realistic for ICN to be integrated into an IP network, which poses new challenges to design a cache system and corresponding content router. In this paper, we firstly observed that the behavior pattern of data requests based on a name resolution system (NRS) makes an ICN cache system implicitly form a hierarchical and nested structure. We propose a complete design and an analytical model to characterize an uncooperative hierarchical ICN caching system compatible with IP. Secondly, to facilitate the incremental deployment of an ICN cache system in an IP network, we designed and implemented a cache-supported router with multi-terabyte cache capabilities. Finally, the simulation and measurement results show the accuracy of proposed analytical model, the significant gains on hit ratio, and the access latency of the hierarchical ICN cache system compared with a flat cache system based on naming routing, as well as the high performance of the implemented ICN router.

Artificial bee colony (ABC) algorithm is a popular swarm intelligence based algorithm. Although it has been proven to be competitive to other population-based algorithms, there still exist some problems it cannot solve very well. This paper presents an Enhanced Hybridized Artificial Bee Colony (EHABC) algorithm for optimization problems. The incentive mechanism of EHABC includes enhancing the convergence speed with the information of the global best solution in the onlooker bee phase and enhancing the information exchange between bees by introducing the mutation operator of Genetic Algorithm to ABC in the mutation bee phase. In addition, to enhance the accuracy performance of ABC, the opposition-based learning method is employed to produce the initial population. Experiments are conducted on six standard benchmark functions. The results demonstrate good performance of the enhanced hybridized ABC in solving continuous numerical optimization problems over ABC GABC, HABC and EABC.

With the rapid growth of mass content retrieval on the Internet, Information-Centric Network (ICN) has become one of the hotspots in the field of future network architectures. The in-network cache is an important feature of ICN. For better network performance in ICN, the cache size on each node should be allocated in proportion to its importance. However, in some current studies, the importance of cache nodes is usually determined by their location in the network topology, ignoring their roles in the actual content transmission process. In this paper, we focus on the allocation of cache size for each node within a given total cache space budget. We explore the impact of heterogeneous cache allocation on content dissemination under the same ICN infrastructure and we quantify the importance of nodes from content dissemination and network topology. To this purpose, we implement a hierarchy partitioning method based on content dissemination, then we formulate a set of weight calculation methods for these hierarchies and to provide a per-node cache space allocation to allocate the total cache space budget to each node in the network. The performance of the scheme is evaluated on the Garr topology, and the average hit ratio, latency, and load are compared to show that the proposed scheme has better performance in these aspects than other schemes.

The integration of blockchain with Information-Centric Networking (ICN) enhances content distribution efficiency in areas such as the Internet of Things (IoT) and 5G/6G communications. This integration implies that the network state information of ICN can significantly impact consensus efficiency. However, the Ethereum Casper FFG consensus algorithm overlooks the network heterogeneity among consensus nodes, leading to a potential bottleneck in consensus efficiency, especially when nodes with inferior network quality participate. To address this issue, this paper proposes a multidimensional reputation model based on an ICN-enabled blockchain architecture. The model combines on-chain stake and network contributions to evaluate the reputation of ICN consensus nodes. Furthermore, a reputation-based hybrid consensus mechanism, RepuICN, is introduced, which enhances the network layer of the Casper FFG algorithm. This mechanism selects higher-reputation ICN consensus nodes as proposers for checkpoint blocks, mitigating the impact of network latency fluctuations on block propagation. Additionally, RepuICN improves block propagation efficiency through ICN multicast and caching techniques. Simulation results show that, under identical conditions with a network of 5000 nodes and 2% ICN nodes, RepuICN reduces broadcast latency by 17% for regular blocks and 61.4% for checkpoint blocks and achieves 3.4 times higher transaction throughput than Casper FFG.