Network Performance Measurement through Machine to Machine Communication in TeleRobotics System

Lokhande, Meghana; Patil, Dipti D.

doi:10.31803/tg-20210205092413

Cited by 5 publications

(2 citation statements)

References 41 publications

(37 reference statements)

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“…e 5 G network for robotic surgery is discussed in [41]. e M2M network performance in presence of malicious nodes is presented for the telerobotic surgery [42].…”

Section: Related Workmentioning

confidence: 99%

Machine-to-Machine Communication for Device Identification and Classification in Secure Telerobotics Surgery

Lokhande

Patil²,

Patil

et al. 2021

Security and Communication Networks

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The capacity of machine objects to communicate autonomously is seen as the future of the Internet of Things (IoT), but machine-to-machine communication (M2M) is also gaining traction. In everyday life, security, transportation, industry, and healthcare all employ this paradigm. Smart devices have the ability to detect, handle, store, and analyze data, resulting in major network issues such as security and reliability. There are numerous vulnerabilities linked with IoT devices, according to security experts. Prior to performing any activities, it is necessary to identify and classify the device. Device identification and classification in M2M for secure telerobotic surgery are presented in this study. Telerobotics is an important aspect of the telemedicine industry. The major purpose is to provide remote medical care, which eliminates the requirement for both doctors and patients to be in the same location. This paper aims to propose a security and energy-efficient protocol for telerobotic surgeries, which is the primary concern at present. For secure telerobotic surgery, the author presents an Efficient Device type Detection and Classification (EDDC) protocol for device identification and classification in M2M communication. The periodic trust score is calculated using three factors from each sensor node. It demonstrates that the EDDC protocol is more effective and secure in detecting and categorizing rogue devices.

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“…e 5 G network for robotic surgery is discussed in [41]. e M2M network performance in presence of malicious nodes is presented for the telerobotic surgery [42].…”

Section: Related Workmentioning

confidence: 99%

Machine-to-Machine Communication for Device Identification and Classification in Secure Telerobotics Surgery

Lokhande

Patil²,

Patil

et al. 2021

Security and Communication Networks

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“…The CH in the network is then chosen based on the trust-score value. The CH indicates the maximum energy consumption because it must be active at all times during the data transfer process [23,26].…”

Section: Device Classificationmentioning

confidence: 99%

Real-Time Monitoring Systems That Provide M2M Communication between Machines

ZHONG

2024

IEICE Trans. Fundamentals

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Artificial intelligence and the introduction of Internet ofThings technologies have benefited from technological advances and new automated computer system technologies. Eventually, it is now possible to integrate them into a single offline industrial system. This is accomplished through machine-to-machine communication, which eliminates the human factor. The purpose of this article is to examine security systems for machine-to-machine communication systems that rely on identification and authentication algorithms for real-time monitoring. The article investigates security methods for quickly resolving data processing issues by using the Security operations Center's main machine to identify and authenticate devices from 19 different machines. The results indicate that when machines are running offline and performing various tasks, they can be exposed to data leaks and malware attacks by both the individual machine and the system as a whole. The study looks at the operation of 19 computers, 7 of which were subjected to data leakage and malware attacks. AnyLogic software is used to create visual representations of the results using wireless networks and algorithms based on previously processed methods. The W76S is used as a protective element within intelligent sensors due to its built-in memory protection. For 4 machines, the data leakage time with malware attacks was 70 s. For 10 machines, the duration was 150 s with 3 attacks. Machine 15 had the longest attack duration, lasting 190 s and involving 6 malware attacks, while machine 19 had the shortest attack duration, lasting 200 s and involving 7 malware attacks. The highest numbers indicated that attempting to hack a system increased the risk of damaging a device, potentially resulting in the entire system with connected devices failing. Thus, illegal attacks by attackers using malware may be identified over time, and data processing effects can be prevented by intelligent control. The results reveal that applying identification and authentication methods using a protocol increases cyber-physical system security while also allowing real-time monitoring of offline system security.

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