Energy-Efficient Cloud Service Selection and Recommendation Based on QoS for Sustainable Smart Cities

Sirohi, Preeti; Al‐Wesabi, Fahd N.; Alshahrani, Haya Mesfer; Maheshwari, Piyush; Agarwal, Amit; Dewangan, Bhupesh Kumar; Hilal, Anwer Mustafa; Choudhury, Tanupriya

doi:10.3390/app11209394

Cited by 26 publications

(9 citation statements)

References 60 publications

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“…DATA MINING: The process of discovering hidden patterns, trends, and relationships in large amounts of data [65]. It uses techniques and algorithms to analyse large data sets so that valuable information can be discovered to make strategic decisions and improve an organisation's performance [10,40,52,65].…”

Section: Analysis Of the Structure And Evolution Of Recommender Syste...mentioning

confidence: 99%

Emerging Perspectives on the Application of Recommender Systems in Smart Cities

Andrade-Ruiz,

Carrasco,

Porcel

et al. 2024

Electronics

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Smart cities represent the convergence of information and communication technologies (ICT) with urban management to improve the quality of life of city dwellers. In this context, recommender systems, tools that offer personalised suggestions to city dwellers, have emerged as key contributors to this convergence. Their successful application in various areas of city life and their ability to process massive amounts of data generated in urban environments has expedited their status as a crucial technology in the evolution of city planning. Our methodology included reviewing the Web of Science database, resulting in 130 articles that, filtered for relevancy, were reduced to 86. The first stage consisted of carrying out a bibliometric analysis with the objective of analysing structural aspects with the SciMAT tool. Secondly, a systematic literature review was undertaken using the PRISMA 2020 statement. The results illustrated the different processes by which recommendations are filtered in areas such as tourism, health, mobility, and transport. This research is seen as a significant breakthrough that can drive the evolution and efficiency of smart cities, establishing a solid framework for future research in this dynamic field.

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Section: Analysis Of the Structure And Evolution Of Recommender Syste...mentioning

confidence: 99%

Emerging Perspectives on the Application of Recommender Systems in Smart Cities

Andrade-Ruiz,

Carrasco,

Porcel

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Recent crowd density estimation methodologies are primarily dependent on regression or identification. Detection approaches can be implemented in cases that have a minimum number of persons and no occlusion-like detectors depending on closer frames [11,12]. Other methodologies that depend on regression are of two categories.…”

Section: Introductionmentioning

confidence: 99%

Deep Transfer Learning Enabled Intelligent Object Detection for Crowd Density Analysis on Video Surveillance Systems

et al. 2022

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Object detection is a computer vision based technique which is used to detect instances of semantic objects of a particular class in digital images and videos. Crowd density analysis is one of the commonly utilized applications of object detection. Since crowd density classification techniques face challenges like non-uniform density, occlusion, inter-scene, and intra-scene deviations, convolutional neural network (CNN) models are useful. This paper presents a Metaheuristics with Deep Transfer Learning Enabled Intelligent Crowd Density Detection and Classification (MDTL-ICDDC) model for video surveillance systems. The proposed MDTL-ICDDC technique mostly concentrates on the effective identification and classification of crowd density on video surveillance systems. In order to achieve this, the MDTL-ICDDC model primarily leverages a Salp Swarm Algorithm (SSA) with NASNetLarge model as a feature extraction in which the hyperparameter tuning process is performed by the SSA. Furthermore, a weighted extreme learning machine (WELM) method was utilized for crowd density and classification process. Finally, the krill swarm algorithm (KSA) is applied for an effective parameter optimization process and thereby improves the classification results. The experimental validation of the MDTL-ICDDC approach was carried out with a benchmark dataset, and the outcomes are examined under several aspects. The experimental values indicated that the MDTL-ICDDC system has accomplished enhanced performance over other models such as Gabor, BoW-SRP, Bow-LBP, GLCM-SVM, GoogleNet, and VGGNet.

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“…The intruder might alter this information, resulting in the misguidance of the receivers. However, IoD access management is a significant variable, and security problems regarding authorization and accessibilities must be emphasized [8,9]. This applies to the data in transfer, which should be protected for confidentiality, authenticity, and integrity.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Search-and-Rescue Optimization-Enabled Secure Route Planning Scheme for Internet of Drones Environment

et al. 2022

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The Internet of Drones (IoD) is greatly developed and promotes many civil applications. However, it can still be prone to several security problems which threaten public safety. The issue of security poses further problems upon linking the IoD to the Internet, as its data stream is exposed to attack. For secure communication between drones, an effective route planning scheme with a major intention of accomplishing security is needed. With this aim, this study develops an enhanced search-and-rescue optimization-enabled secure route planning (ESRO-SRP) scheme for the IoD environment. The presented ESRO-SRP technique mainly aims to derive a set of optimal routes to the destination. In addition, the ESRO-SRP algorithm is derived by the integration of the quasi-oppositional-based learning (QOBL) concept with the conventional SRO algorithm. Moreover, the presented ESRO-SRP technique derived a fitness function encompassing different input parameters such as residual energy, distance, and degree of trust. The experimental validation of the ESRO-SRP technique is carried out under several aspects, and the results demonstrated the enhancements of the ESRO-SRP model over recent approaches. The ESRO-SRP model has provided an increased packet delivery ratio (PDR) of 86%, whereas the BRUe-IoE, ORP-FANET, UAVe-WSN, and TR-UAV Swarm approaches have accomplished a minimal PDR of 79.60%, 73.60%, 67.60%, and 63.20%, respectively.

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Energy-Efficient Cloud Service Selection and Recommendation Based on QoS for Sustainable Smart Cities

Cited by 26 publications

References 60 publications

Emerging Perspectives on the Application of Recommender Systems in Smart Cities

Emerging Perspectives on the Application of Recommender Systems in Smart Cities

Deep Transfer Learning Enabled Intelligent Object Detection for Crowd Density Analysis on Video Surveillance Systems

Enhanced Search-and-Rescue Optimization-Enabled Secure Route Planning Scheme for Internet of Drones Environment

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