A Big Data Management Architecture for Standardized IoT Based on Smart Scalable SNMP

Zhang, Wentao; Dong, Mianxiong; Ota, Kaoru; Li, Jianhua; Wu, Yang; Wu, Jun

doi:10.1109/icc40277.2020.9149368

Cited by 4 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, in [46], the authors preprocessed data and ran ML algorithms on edge devices, integrating these features within a modular platform with computational cloud capabilities. In [34,47], the authors exploited the computational capabilities of edge devices to run complex algorithms for fault detection, while in [48] the authors used edge computing for anomaly detection.…”

Section: Architectural Perspectivementioning

confidence: 99%

Using Intelligent Edge Devices for Predictive Maintenance on Injection Molds

2023

View full text Add to dashboard Cite

A considerable part of enterprises’ total expenses is dedicated to maintenance interventions. Predictive maintenance (PdM) has appeared as a solution to decrease these costs; however, the necessity of end-to-end solutions in deploying predictive models and the fact that these models are often difficult to interpret by maintenance practitioners hinder the adoption of PdM approaches. In this work, we propose a flexible architecture for PdM to recommend maintenance actions. The proposed architecture is based on containerized microservices on intelligent edge devices together with a hybrid model which fuses generalized fault trees (GFTs) and anomaly detection. Results on injection molds carried out at OLI, a Portuguese company, show that the proposed solution is suitable for deploying predictive models and services such as data preprocessing, sensor management, and data flow control, among others. These services run near the shop floor, allowing for greater flexibility, as they may be remotely managed and customized according to the company’s requirements. The results of the GFT model show an estimated reduction of more than 63% in current maintenance costs, while the distribution of analytics tasks by the edge devices reduces the burden on the network, requiring only 0.2% of the current cloud storage.

show abstract

Section: Architectural Perspectivementioning

confidence: 99%

Using Intelligent Edge Devices for Predictive Maintenance on Injection Molds

2023

View full text Add to dashboard Cite

show abstract

“…Preventing servers from intrusion by malicious network packets has long been part of intrusion prevention systems (IPS) research. Zhang et al (2004) describe the design of an IPS-based on SNMP [18].…”

Section: Intrusion Prevention System Designmentioning

confidence: 99%

PSPS: A Step toward Tamper Resistance against Physical Computer Intrusion

Huang

Chiu

et al. 2022

Sensors

View full text Add to dashboard Cite

Cyberattacks are increasing in both number and severity for private, corporate, and governmental bodies. To prevent these attacks, many intrusion detection systems and intrusion prevention systems provide computer security by monitoring network packets and auditing system records. However, most of these systems only monitor network packets rather than the computer itself, so physical intrusion is also an important security issue. Furthermore, with the rapid progress of the Internet of Things (IoT) technology, security problems of IoT devices are also increasing. Many IoT devices can be disassembled for decompilation, resulting in the theft of sensitive data. To prevent this, physical intrusion detection systems of the IoT should be considered. We here propose a physical security system that can protect data from unauthorized access when the computer chassis is opened or tampered with. Sensor switches monitor the chassis status at all times and upload event logs to a cloud server for remote monitoring. If the system finds that the computer has an abnormal condition, it takes protective measures and notifies the administrator. This system can be extended to IoT devices to protect their data from theft.

show abstract