Search

Summary: Industrial internet‐of‐things (IIoT) cloud computing gives users with the ease of outsourcing calculations and carrying the risk of privacy disclosure. To solve this issue, secure high‐order clustering algorithm by fast search (SHOCFS) technique is introduced recently. The major factors of SHOCFS algorithm are speed improvement and detecting optimal density peaks. The optimal selection of density points in the SHOCFS algorithm still becomes an important issue to be considered. The swallow swarm optimization (SSO) is introduced in the proposed work for choosing optimal density peaks in clustering algorithm. This paper motivates to design a new secure cloud service system for cloud in IIoT environment. The secure high‐order optimal density selection (SHODS3O‐CFS) approach is proposed in hybrid cloud environment. The major contribution of the work is to develop an optimization‐based secure clustering algorithm for finding the best density points in clustering algorithm. It may reduce the overlapping peak points in the cluster and increase the security of system in hybrid cloud. The experimental results of the proposed algorithm and existing methods are measured via clustering metrics such as accuracy, clustering center quality, and speed ratio. The metrics like clustering center quality, Rand index (RI), encryption time (ms), and speedup ratio have been used to measure the results of the proposed system and existing methods. From the results, it is concluded that the proposed work has achieved higher average RI of 93.4%, which is 29.68% and 17.4% higher when compared with the privacy preservation high‐order clustering algorithm by fast search (PPHOCFS) and SHOCFS methods. [ABSTRACT FROM AUTHOR]

Summary: In edge computing, how to save energy among sustainable edge nodes is a hot topic. ON/OFF switching of edge nodes as a key point is efficient but still suffers from the long round‐trip time problem because of its centralized control manner. Especially in the wireless network, service coverage is proved to be NP‐Complete. To this end, we propose a Distributed Swarm intelligence‐based Energy‐saving algorithm (DSE). In DSE, pheromone and residual energy are used to calculate the wake‐up probability. Through the wake‐up probability, the edge node can be activated periodically and efficiently. In order to balance the energy in the whole system that contains massive edge nodes, we further use a correction factor, that is, DSE+, to adjust the wake‐up probability of the nodes. The proposed methods allow for distributed implementation without requiring a centralized control by the coordinator, and the pheromone accumulated temporally and spatially. In addition, they do not require node localization. Experiments show that both DSE and DSE+ can work as expected, and DSE+ with the correction factor improves the lifetime of the whole system at least 12.6% compared with the DSE without the correction factor. [ABSTRACT FROM AUTHOR]

Summary: Cognitive radio network (CRN) has occupied rich awareness on amazing potentiality of spectrum utilization for the secondary users. In CRN, effective spectrum management has received more attention by increasing number of communication devices and several techniques have been evolved in recent years. However, efficient spectrum sensing with minimum delay reducing number of spectrum handoffs and maximum throughputs are some of the major challenges to be addressed. In this proposed work, discrete Hughes‐Hartogs optimal and dual vector spectrum management (DHHO‐DVSM) in CRN are used for effective utilization of channel selection. The expected maximization Q channel selection is designed to obtain an efficient idle channels for improved transmission of the secondary user. The optimal channel is attained by the number of idle channels maintained from the Q channel selection list. Based on each SU requests, discrete Hughes‐Hartogs particle swarm optimal channel selection is proposed to find the best channel by computing the fitness function for discrete factor. Finally, dual constraints‐based vector spectrum handoff model is proposed to produce the hyper plane based on the probability conditional factors with the objective of minimizing the number of handoffs for secondary users. The simulation result shows that the proposed method has transparent edges over conventional channel selection models. In addition, results are verified to learn the optimal channel selection with minimal sensing delay, increasing throughput and minimal handoffs. The proposed algorithm DHHO‐DVSM is 22.8% comparatively better than E‐CRNs and 41% improvement than SpecPSO. [ABSTRACT FROM AUTHOR]