An efficient filter with low memory usage for multimedia data of industrial Internet of Things

Abstract

One of the essential concerns of Internet of Things (IoT) is in industrial systems or data architecture to support the evolutions in transportation and logistics. Considering the Industrial IoT (IIoT) openness, the need for accessibility, availability, and searching of data has rapidly increased. The primary purpose of this research is to propose an Efficient Two-Dimensional Filter (ETDF) to store multimedia data of IIoT applications in a specific format to achieve faster response and dynamic updating. This filter consists of a two-dimensional array and a hash function integrated into a cuckoo filter for efficient use of memory. This study evaluates the scalability of the filter by increasing the number of requests from 10,000 to 100,000. To assess the performance of the proposed filter, we measure the parameters of access time and lookup message latency. The results show that the proposed filter improves the access time by 12%, compared to a Fast Two-Dimensional Filter (FTDF). Moreover, it improves memory usage by 20% compared to FTDF. Experiments indicate a better access time of the proposed filter compared to other filters (i.e., Bloom, quotient, cuckoo, and FTD filters). Insertion and deletion times are essential parameters in comparing filters, so they are also analyzed.

OES-Fed: a federated learning framework in vehicular network based on noise data filtering

“…To face the noise data problem, a reasonable data filter is needed to increase the training efficiency of network models, which is one of the important aspects of network model training. Data filters play an important role when dealing with large-scale datasets with a large number of samples and features ( Toet, 2016 ; Goudarzi & Rahmani, 2021 ; Li, Yang & Wen, 2021 ). We found that the noise data can be filtered from both the current and the historical perspectives of the noise data.…”