A Survey on Various Machine Learning Models in IOT Applications

Porkodi, V.; Yuvaraj, D.; Khan, Juveriya; Karuppusamy, S. Anbu; Goel, Pallavi; Sivaram, M.

doi:10.1109/iccit-144147971.2020.9213819

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…V. Porkodi, S. Anbu Karuppusamy, D. Yuvaraj, Pallavi Murghai Goel, Juveriya Khan, and M. Sivaram [27] IoT consists of many devices that handle many connections and the transmission of vast amounts of information. Smart cities are regarded as one of the most important applications of IoT in the areas of urban planning, and mobility.…”

Section: Sudiantomentioning

confidence: 99%

Role of Machine Learning and Internet of Thing in Agriculture- A Survey

Jha

Yadav

Gupta

2022

2022 IEEE International Conference on Current Development in Engineering and Technology (CCET)

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Agriculture is the backbone of the economy and a source of jobs for developing nations like India. For human life, food is a necessity that can be fulfilled only through farming. Agricultural activities are usually classified into three broad categories: post-harvest, harvest, and pre-harvest. Farmers are doing their best to produce grain, but there is grain loss at the time of various post-harvest activities like cutting, storage, etc. Various studies have already been conducted in the fields of preharvest, harvest, and food quality. But in the field of postharvest, there is lots of scope for work. For food sustainability reduction in post-harvest activities is necessary. This paper presents a broad survey of the post-harvest activities, Internet of Things (IoT) and machine learning (ML) applications in the agricultural domain to mitigate the post-harvest issue. Applying machine learning (ML) and the internet of things (IoT) in agriculture allows more efficient and accurate monitoring of grain so food loss problem can be solved in effective manner.

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Section: Sudiantomentioning

confidence: 99%

Role of Machine Learning and Internet of Thing in Agriculture- A Survey

Jha

Yadav

Gupta

2022

2022 IEEE International Conference on Current Development in Engineering and Technology (CCET)

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“…Complex tasks such as inference, situation judgment, and big data processing are processed by sending data to the server. In addition, applications that need to process large numbers of data such as artificial intelligence (AI) and healthcare have been increasing recently [3,4]; however, large numbers of data were transmitted to the server due to the lack of computing power at the edge. Due to these changes, delays due to network congestion, bandwidth limitations, and server workload limitations are occurring [5,6].…”

Section: Introductionmentioning

confidence: 99%

An Efficient On-Demand Hardware Replacement Platform for Metamorphic Functional Processing in Edge-Centric IoT Applications

Moon

Park

2021

Electronics

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The paradigm of Internet-of-things (IoT) systems is changing from a cloud-based system to an edge-based system. These changes were able to solve the delay caused by the rapid concentration of data in the communication network, the delay caused by the lack of server computing capacity, and the security issues that occur in the data communication process. However, edge-based IoT systems performance was insufficient to process large numbers of data due to limited power supply, fixed hardware functions, and limited hardware resources. To improve their performance, application-specific hardware can be installed in edge devices, but performance cannot be improved except for specific applications due to a fixed function of an application-specific hardware. This paper introduces an edge-centric metamorphic IoT (mIoT) platform that can use various hardware modules through on-demand partial reconfiguration, despite the limited hardware resources of edge devices. In addition, this paper introduces an RISC-V based metamorphic IoT processor (mIoTP) with reconfigurable peripheral modules. We experimented to prove that the proposed structure can reduce the server access of edges and can be applied to a large-scale IoT system. Experiments were conducted in a single-edge environment and a large-scale environment combining one physical edge and 99 virtual edges. According to the experimental results, the edge-centric mIoT platform that executes the reconfiguration prediction algorithm at the edge was able to reduce the number of server accesses by up to 82.2% compared to our previous study in which the prediction process was executed at the server. Furthermore, we confirmed that there is no additional reconfiguration time overhead even for the large IoT systems.

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