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Dear Colleagues,

Renewable energies have been widely used on smart grids due to significant advantages such as security, reliability, sustainability, and resiliency. Monitoring multiple generation units requires smart sensors and Internet of Things (IoT) devices. However, ensuring the security and reliability of smart grids in the presence of a high penetration of smart sensors is challenging. We also need advanced techniques, e.g., machine/deep learning, to not only evaluate the system in terms of reliability and security, but also to prevent any cyber-attacks within the system. To this end, the main goal of this Special Issue is to investigate the resiliency and reliability of smart grids considering the high penetration of smart sensors. Moreover, we are eager to receive high-quality research that uses advanced techniques and technologies, e.g., blockchain, IoT, and machine/deep learning, to provide higher flexibility, reliability, sustainability, and resiliency in smart sensor-based smart grids. Hence, topics of interest include, but are not limited to:

Smart sensor applications for smart grid reliability and resiliency;
Smart sensor real-time data processing using advanced machine/deep learning techniques;
Blockchain-enabled smart sensors to mitigate fraud and increase smart grid security;
Smart sensor-enabled IoT for real-time monitoring and protection of smart grids;
Analysis and design of smart sensor-based smart grids;
Data mining for modeling and visualizing a smart grid security problem;
Smart sensor-based decision-making and problem-solving networks in smart grids;
Smart sensor-based security issues and solutions for smart grid networks;
Automatic learning techniques in smart grid security systems and smart grid networks.

Dr. Amin Sahba
Guest Editor

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Keywords

machine learning
IoT
smart sensors
smart grids
cybersecurity

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Research

13 pages, 1422 KiB

Open AccessArticle

Intelligent Sensors for POI Recommendation Model Using Deep Learning in Location-Based Social Network Big Data

by Wanjun Chang, Dong Sun and Qidong Du

Sensors 2023, 23(2), 850; https://doi.org/10.3390/s23020850 - 11 Jan 2023

Cited by 7 | Viewed by 2437

Abstract

Aiming at the problem that the existing Point of Interest (POI) recommendation model in social network big data is difficult to extract deep feature information, a POI recommendation model based on deep learning in social networks and big data is proposed in this article. The input data are all gathered through intelligent sensors to apply some raw data pre-processing tasks and thus reduce the computational burden on the model. First, a POI static feature extraction method based on symmetric matrix decomposition is designed to capture the geographical location and POI category features in Location-Based Social Networking (LBSN). Second, the improved Continuous Bags-of-Words (CBOW) model is used to extract the semantic features in the user comment information, and realize the implicit vector representation of POI in geographic, category, semantic and temporal feature space. Finally, by adaptively selecting relevant check-in activities from the check-in history to learn and change user preferences, the Geographical-Spatiotemporal Gated Recurrent Unit Network (GSGRUN) can distinguish the user preferences of different check-in. Experiments show that when the length of the recommendation list is 15, the precision of the proposed algorithm on the loc-Gowalla data set is 0.0686, the recall is 0.0769, and the precision on the loc-Brightkite data set is 0.0659, the recall is 0.0835, both of which are better than the comparative recommendation methods. Therefore, compared with the comparison methods, the proposed method can significantly improve the performance of the POI recommendation system. Full article

(This article belongs to the Special Issue Smart Sensor Applications for Resilient and Reliable Smart Grids)

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