Enhancing mobile crowdsensing in Fog-based Internet of Things utilizing Harris hawks optimization

Seyfollahi, Ali; Abeshloo, Hamid; Ghaffari, Ali

doi:10.1007/s12652-021-03344-0

J Ambient Intell Human Comput

2021

DOI: 10.1007/s12652-021-03344-0

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Enhancing mobile crowdsensing in Fog-based Internet of Things utilizing Harris hawks optimization

Ali Seyfollahi

Hamid Abeshloo

Ali Ghaffari

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Cited by 8 publications

(1 citation statement)

References 49 publications

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“…The five levels of cloud computing environment which are hardware / data center, infrastructure, platform, end users and application, are used to illustrate tasks in cloud computing. The network's mathematical model is built to identify the optimization issue and later in order to analyze the usual infrastructure as a service (IaaS) provider's behavior [13]. Here, the method for computing the optimal solution was provided by two objectively optimized solution set cases, which were later contrasted with a linearization method [14,15].…”

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confidence: 99%

An efficient load balancing in cloud computing using hybrid Harris hawks optimization and cuckoo search algorithm

2023

IJATEE

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One of the most common services used by people worldwide is cloud computing. Users get immediate access to a variety of computer components or services, including servers, apps, storage, and network. Task planning is crucial in the cloud environment. Applications for users, or tasks at a particular time, are premeditated for specific properties.The concentration is mostly on minimizing the spectrum of construction and resource utilization.

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mentioning

confidence: 99%

An efficient load balancing in cloud computing using hybrid Harris hawks optimization and cuckoo search algorithm

2023

IJATEE

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Securing internet of things using machine and deep learning methods: a survey

Ghaffari,

Jelodari,

pouralish

et al. 2024

Cluster Comput

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The Internet of Things (IoT) is a vast network of devices with sensors or actuators connected through wired or wireless networks. It has a transformative effect on integrating technology into people’s daily lives. IoT covers essential areas such as smart cities, smart homes, and health-based industries. However, security and privacy challenges arise with the rapid growth of IoT devices and applications. Vulnerabilities such as node spoofing, unauthorized access to data, and cyberattacks such as denial of service (DoS), eavesdropping, and intrusion detection have emerged as significant concerns. Recently, machine learning (ML) and deep learning (DL) methods have significantly progressed and are robust solutions to address these security issues in IoT devices. This paper comprehensively reviews IoT security research focusing on ML/DL approaches. It also categorizes recent studies on security issues based on ML/DL solutions and highlights their opportunities, advantages, and limitations. These insights provide potential directions for future research challenges.

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Task offloading in Internet of Things based on the improved multi-objective aquila optimizer

Nematollahi,

Ghaffari,

Mirzaei

2023

SIViP

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Enhancing mobile crowdsensing in Fog-based Internet of Things utilizing Harris hawks optimization

Cited by 8 publications

References 49 publications

An efficient load balancing in cloud computing using hybrid Harris hawks optimization and cuckoo search algorithm

An efficient load balancing in cloud computing using hybrid Harris hawks optimization and cuckoo search algorithm

Securing internet of things using machine and deep learning methods: a survey

Task offloading in Internet of Things based on the improved multi-objective aquila optimizer

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