Machine Learning Based Efficient Multi-copy Routing for OppIoT Networks

Srinidhi, N. N.; Sagar, C. S.; Chethan, S; Shreyas, J.; Kumar, Sunil

doi:10.1007/978-981-15-3666-3_24

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…Khattab and Youssry (2020) have examined cutting edge for machine learning inside versatile edge figuring and the advances required in computerizing versatile resource portion, portability displaying security, and energy proficiency for 5G networks. Srinidhi et al (2020) have researched the utilization of calculations that endeavor machine learning in IoT frameworks. They isolated the machine learning-based IoT calculations into those which give productive answers for the IoT fundamental activity challenges, like a limitation, bunching, steering, and information conglomeration, and those which target execution-related difficulties, for example, clog control, flaw location, resource management, and security.…”

Section: Artificial Intelligence For 5gmentioning

confidence: 99%

RETRACTED ARTICLE: 5G standards for the Industry 4.0 enabled communication systems using artificial intelligence: perspective of smart healthcare system

et al. 2022

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The emergence of the Industry 4.0 revolution to upgrade the Internet of Things (IoT) standards provides the prominence outcomes for the future wireless communication systems called 5G. The development of 5G green communication systems suffers from the various challenges to fulfill the requirement of higher user capacity, network speed, minimum cost, and reduced resource consumption. The use of 5G standards for Industry 4.0 applications will increase data rate performance and connected device's reliability. Since the arrival of novel Covid-19 disease, there is a higher demand for smart healthcare systems worldwide. However, designing the 5G communication systems has the research challenges like optimum resource utilization, mobility management, cost-efficiency, interference management, spectral efficiency, etc. The rapid development of Artificial Intelligence (AI) across the different formats brings performance enhancement compared to conventional techniques. Therefore, introducing the AI into 5G standards will optimize the performances further considering the various end-user applications. We first present the survey of the terms like 5G standard, Industry 4.0, and some recent works for future wireless communications. The purpose is to explore the current research problems using the 5G technology. We further propose the novel architecture for smart healthcare systems using the 5G and Industry 4.0 standards. We design and implement that proposed model using the Network Simulator (NS2) to investigate the current 5G methods. The simulation results show that current 5G methods for resource management and interference management suffer from the challenges like performance trade-offs.

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Section: Artificial Intelligence For 5gmentioning

confidence: 99%

RETRACTED ARTICLE: 5G standards for the Industry 4.0 enabled communication systems using artificial intelligence: perspective of smart healthcare system

et al. 2022

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“…The use of this additional security filter added overhead to the proposed system. Srinidhi 18 proposed a dynamic context information‐based multicopy routing protocol for OppIoT, in which RF was used for classifying the nodes as reliable or non‐reliable forwarder based on the present context and location information, for making the routing decision. However, the security aspect was not taken into consideration and the proposed system performed poorly in terms of average buffer time as compared to other multiple copy routing schemes.…”

Section: Related Workmentioning

confidence: 99%

Random forest classifier‐based safe and reliable routing for opportunistic IoT networks

Kandhoul

Dhurandher

Woungang

2020

Int J Communication

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SummaryDesigning a safe and reliable way for communicating the messages among the devices and humans forming the Opportunistic Internet of Things network (OppIoT) has been a challenge since the broadcast mode of message sharing is used. To contribute toward addressing such challenge, this paper proposes a Random Forest Classifier (RFC)‐based safe and reliable routing protocol for OppIoT (called RFCSec) which ensures space efficiency, hash‐based message integrity, and high packet delivery, simultaneously protecting the network against safety threats viz. packet collusion, hypernova, supernova, and wormhole attacks. The proposed RFCSec scheme is composed of two phases. In the first one, the RFC is trained on real data trace, and based on the output of this training, the second phase consists in classifying the encountered nodes of a given node as belonging to one of the output classes of nodes based on their past behavior in the network. This helps in proactively isolating the malicious nodes from participating in the routing process and encourages the participation of the ones with good message forwarding behavior, low packet dropping rate, high buffer availability, and a higher probability of delivering the messages in the past. Simulation results using the ONE simulator show that the proposed RFCSec secure routing scheme is superior to the MLProph, RLProph, and CAML routing protocols, chosen as benchmarks, in terms of legitimate packet delivery, probability of message delivery, count of dropped messages, and latency in packet delivery. The out‐of‐bag error obtained is also minimal

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“…Crop disease is an important factor that affects productivity and is a major concern for Indian framers. Efforts in the application of technological advances to improve crop growth have been made in the field of agriculture however, there still exists a need to address these issues more efficiently in order to grow more healthy crops and improve the overall yield (4)(5)(6) . Automated and technology-enabled farming will draw a large number of people to farming leading to a resurgence in food productivity.…”

Section: Introductionmentioning

confidence: 99%

An artificial intelligence based approach for increasing agricultural yield

Sadashiv¹,

Garg²,

Kumar³

et al. 2021

IJST

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Background/Objectives: Identification of a suitable crop based on soil and climatic condition along with plant disease detection are very much essential as they boost the agricultural yield. Monitoring these parameters is not largely carried out as they are very laborious and require expertise in the field, thus curtails the overall productivity. To address these challenges an artificial intelligence based techniques were applied to predict a suitable crop and also to detect the plant leaf disease at an early stage are been presented. Methods/Statistical analysis: Crop predictions are carried out by leveraging two machine learning approaches, Logistic Regression (LR) and Support Vector Machine (SVM) on the agriculture dataset. Convolution Neural Network (CNN) with ResNet152 architecture is used for the detection of plant disease. An open dataset of 54,306 photos of healthy and diseased crops are considered during the performance evaluation. Findings: Crop predictions based on LR and SVM have achieved an accuracy of 93% and 97% respectively across a class of 13 crops. CNN based model predicts disease among 38 different categories from 14 specific crops with an accuracy of 96%. Novelty/Applications: The proposed approach will be beneficial to farmers to identify suitable crops and have plant leaf disease under control.

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Machine Learning Based Efficient Multi-copy Routing for OppIoT Networks

Cited by 8 publications

References 8 publications

RETRACTED ARTICLE: 5G standards for the Industry 4.0 enabled communication systems using artificial intelligence: perspective of smart healthcare system

RETRACTED ARTICLE: 5G standards for the Industry 4.0 enabled communication systems using artificial intelligence: perspective of smart healthcare system

Random forest classifier‐based safe and reliable routing for opportunistic IoT networks

An artificial intelligence based approach for increasing agricultural yield

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