A Survey on Security for IoT via Machine Learning

Gupta, Shaurya; Vyas, Sonali; Sharma, Kanta Prasad

doi:10.1109/iccsea49143.2020.9132898

Cited by 18 publications

(15 citation statements)

References 9 publications

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“…In agriculture, machine learning allows for more accurate disease diagnosis while conserving energy and avoiding false data. Farmers can upload field images captured by intelligent systems like satellites, unmanned aerial vehicles (UAVs), land-based rovers, smart phones, and tools such as the Climate Field View platform, which can identify potential farm issues and recommend a management plan [48][49][50][51][52].…”

Section: Recommendation System For Renting and Sharingmentioning

confidence: 99%

Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Rakhra

Bhargava

et al. 2022

Journal of Food Quality

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Several industries have recently seen the replacement of human labor by automated machinery and equipment. Across the globe, farmers’ attitudes on the use of technology in agriculture are divergent. However, although some people are excited and ready to embrace technology, others are cautious and wary of trying new technologies for the first time. The third category is particularly prevalent in underdeveloped nations such as India, owing to a lack of competence, a lack of effective translation, and most crucially, a lack of financial resources. It is fruitless for the government to attempt to resolve these difficulties due to the fact that they do not take into consideration the changing circumstances and input needs of each agricultural group. Smart Tillage is a cutting-edge framework that was developed to solve the challenges listed above. In India, a decision-based smart engine for the rental and sharing of tools and equipment has been developed, which leverages machine learning methods to proceed towards a selection of tools and equipment. The option is entirely reliant on a variety of input variables, including crop kind, harvest time/month, crop equipment needed, harvest type, and the amount of money available for rental. Additionally, an ideal recommendation engine driven by content and collaborative-based filtering will provide the farmer’s requirements depending on their requirements. In terms of escalation, the proposals would be cost-effective and excellent since they would need little changes in training, technique improvements, and resource management via a new rent-share model similar to that used by Uber. In this work, demand and supply algorithms are used to define market equilibrium, and the results are shown in graphs. This includes discussion of a variety of demand and supply parameters, their impact on market equilibrium prices and quantities, and their effect on shifting demand and supply curves. The many sorts of elasticities (demand, cross-price, supply, income, and so on) are examined, as well as the ramifications for pricing systems that may result from these elasticities.

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Section: Recommendation System For Renting and Sharingmentioning

confidence: 99%

Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Rakhra

Bhargava

et al. 2022

Journal of Food Quality

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“…Machine learning is becoming more efficient and accurate because of deep learning algorithms. Using automated machine learning, one may reduce the need for ML expertise while also automating the ML workflow with greater precision [33,34].…”

Section: E Real Purpose Of Smartmentioning

confidence: 99%

Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment

Rakhra

Sanober

Naveed

et al. 2022

Journal of Food Quality

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Farmers’ physical labor and debt are reduced as a result of agricultural automation, which emphasizes efficient and effective use of various machines in farming operations with the purpose of reducing physical labor and debt. It is a revolutionary idea in agriculture to create custom hiring centers, which are intended to make it easier for like-minded farmers to embrace technology/machinery for enhanced resource management practices. The study in question examines the significance of tool renting and sharing in the workplace. Rental and sharing equipment are two approaches that might be used to enable farmers to borrow equipment at a cheaper cost than they would otherwise have to pay for it. The following is a manual pilot study of 562 farmers in India to address the numerous challenges farmers face when looking for tools and equipment, as well as to determine their strong interest in the process of renting and sharing equipment. The study was conducted to address the numerous challenges farmers face when looking for tools and equipment and to determine their strong interest in the process of renting and sharing equipment. Farmers are divided into three groups according to the results of this poll: small, moderate, and large. Training and testing splits were used on the same data set in order to get a better understanding of the target variables. The data set for the survey was standardized in order to remove ambiguity. In this research, three different machine learning models were utilized: nearest neighbors, logistic regression, and decision trees. K-nearest neighbors was the most often used model, followed by logistic regression and decision trees. In order to get the best possible result, a comparison of the aforementioned algorithm models was carried out, which revealed that the decision tree is the better model among the others in this regard. Because the decision tree model is completely reliant on a large number of input factors, such as the kind of crop, the time/month of harvest, and the type of equipment necessary for the crops, it has the potential to have a social and economic impact on farmers and their livelihoods.

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“…Selvakanmani and Sumathi [20] have proposed the blockchain and smart contract-based framework for cloudedge computing in the healthcare system, thus providing data privacy, security, latency, cost-effective storage, and data availability. Computational and Mathematical Methods in Medicine After closer analysis of the related work and other models [4,[20][21][22][23], we have found that most of the security networks are centralized in nature which rely on thirdparty sources for security and data exchange. The healthcare system is spread across heterogeneous platforms and devices, which enforces the developer to think about a decentralized framework for easy data exchange with complete security and privacy.…”

Section: Related Workmentioning

confidence: 99%

Blockchain and IPFS Integrated Framework in Bilevel Fog-Cloud Network for Security and Privacy of IoMT Devices

Mehbodniya

Neware

Vyas

et al. 2021

Computational and Mathematical Methods in Medicine

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Internet of Medical Things (IoMT) has emerged as an integral part of the smart health monitoring system in the present world. The smart health monitoring deals with not only for emergency and hospital services but also for maintaining a healthy lifestyle. The industry 5.0 and 5/6G has allowed the development of cost-efficient sensors and devices which can collect a wide range of human biological data and transfer it through wireless network communication in real time. This led to real-time monitoring of patient data through multiple IoMT devices from remote locations. The IoMT network registers a large number of patients and devices every day, along with the generation of huge amount of big data or health data. This patient data should retain data privacy and data security on the IoMT network to avoid any misuse. To attain such data security and privacy of the patient and IoMT devices, a three-level/tier network integrated with blockchain and interplanetary file system (IPFS) has been proposed. The proposed network is making the best use of IPFS and blockchain technology for security and data exchange in a three-level healthcare network. The present framework has been evaluated for various network activities for validating the scalability of the network. The network was found to be efficient in handling complex data with the capability of scalability.

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A Survey on Security for IoT via Machine Learning

Cited by 18 publications

References 9 publications

Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment

Blockchain and IPFS Integrated Framework in Bilevel Fog-Cloud Network for Security and Privacy of IoMT Devices

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