The scale of industrial Internet networks contin-ues to grow, and equipment intelligence continues to improve. Traditional industrial Internet security solutions can no longer meet the requirements of privacy and efficiency. Trust man-agement is an effective way to promote security, efficiency, and scalability in Industrial Internet networks. In this paper, a dy-namic trust management model for the Edge Devices in the Industrial Internet based on the feedback under the edge com-puting architecture is proposed. We divide trust into two parts, direct trust and indirect trust. The model calculates the direct trust between devices based on long-term and short-term trust. Long-term and short-term trust help honest devices restore their trust as soon as possible after experiencing short-term trust decline. They can also prevent dishonest devices from obtaining higher trust due to short-term honest behavior. Be-sides, the model calculates the indirect trust of the edge broker to devices based on the reward-punishment mechanism. The mechanism improves the accumulation speed of the indirect trust of honest devices and reduces the indirect trust of dishon-est devices rapidly. The trust fusion algorithm is then used to aggregate the direct and indirect trust to calculate the compre-hensive trust of the device. Experimental results show that the proposed model outperforms the existing methods in local and global performance in different attack scenarios.

Emerging persistent memory (PM) has the potential to substitute DRAM due to its near-DRAM performance and durability similar to disks. However, hash tables designed for DRAM cannot be directly adopted for PM. Moreover, prior studies on hash tables using Optane DC PM modules (DCPMMs) have shown suboptimal scalability and write performance due to expensive lock-based concurrent control and massive data movement caused by expansion. In this article, we propose an opportunistic lock-free parallel multisplit extendible hashing scheme (PMEH). First, PMEH achieves lock-free operations for evenly distributed data by partitioning the hash table into multiple zones and assigning each zone to one thread. Second, PMEH employs an opportunistic lock-free parallel scheme to effectively handle skewed data distribution, which maximizes the utilization of lock-free operations by enabling dynamic switching between lock-free and locking operations. Finally, PMEH uses multisplit with gradual splitting, instead of 2-split, to reduce the frequency of hash table expansion and, hence, reduce the data movement during expansion. The experimental results under the widely used YCSB workloads demonstrate that PMEH achieves excellent scalability regardless of data distribution. Moreover, PMEH significantly speeds up insertions by$1.44\times $–$15.4\times $, and deletion by$2.04\times $–$18.07\times $compared to other state-of-the-art hashing schemes. In addition, PMEH reduces at least 52% of extra writes while providing instant recovery.

Emerging persistent memory (PM) is a promising technology that provides near-DRAM performance and disk-like durability. However, many data structures designed based on DRAM, such as hash table and B-tree, are sub-optimal for PM. Prior studies have shown that the scalability of hash tables on Intel Optane DC Persistent Memory Modules (DCPMM) degrades significantly due to expensive lock-based concurrency control and massive data movements during rehashing. This paper proposes a lock-free parallel multi-split extendible hashing scheme (PMEH), which eliminates the lock contention overhead, reduces data movements during rehashing, and ensures data consistency. Under the widely used YCSB workloads, the evaluation results show that compared to other state-of-the-art hashing schemes, PMEH is up to 1.38x faster for insertion and up to 1.9x faster for deletion, while reducing 52% extra writes. In addition, PMEH can ensure instant recovery regardless of data size.