A Survey on IOT Based on Renewable Energy for Efficient Energy Conservation Using Machine Learning Approaches

Batcha, R. Rahin; Geetha, M. Kalaiselvi

doi:10.1109/icetce48199.2020.9091737

Cited by 6 publications

(1 citation statement)

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“…According to statistics [1], the number of devices connected to the internet will keep increasing to reach 19.1 billion devices by the year 2025. IoT technology plays a vital role in improving services provided in several domains such as energy, transportation, food production, water supply, health care, education, and other crucial services [2,3,4,5]. IoT Technology has the capability of solving several issues related to smart grids, warming systems, saving water, increasing smart farms, and are considered to be the main part when it comes to smart cars.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Model for Predicting Consumers’ Interests of IoT Recommendation System

Noor¹,

Almars²,

Atlam³

et al. 2022

IJACSA

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This electronic the Internet of Things (IoT) technology has contributed to several domains such as health, energy, education, transportation, industry, and other domains. However, with the increased number of IoT solutions worldwide, IoT consumers find it difficult to choose the technology that suits their needs. This article describes the design and implementation of an IoT recommendation system based on consumer interests. In particular, the knowledge-based IoT recommendation system exploits a Service Oriented Architecture (SOA) where IoT device and service providers use a registry to advertise their products. Moreover, the proposed model uses a Long Short-term Memory (LSTM) deep learning technique to predict the consumer's interest based on the consumer's data. Then the recommendation system do the mapping between the consumers and the related IoT devices based on the consumer interests. The proposed Knowledge-based IoT recommendation system has been validated using a real-world IoT dataset collected from Twitter Application Programming Interface (API) that include more than 15,791 tweets. Overall the results of our experiment are promising in terms of precision and recall. Furthermore, the proposed model achieved the highest accuracy score compared with other state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%