Abstract:Internet of Things (IoT) represents a new generation of information and communication technology for anyone, anytime and anywhere. Cloud service‐based IoT applications significantly increase latency and network utilization. The fog environment is closer to the user to perform computing, communication, and storage tasks on network edge devices. Therefore, it can greatly reduce the latency of real‐time applications. It is an essential feature of fog computing and its most important advantage compared to cloud co… Show more
“…The authors stated the importance of Recommender Systems with Semi-supervised Learning using machine learning but didn't work on e-learning applications for smart tutoring recommendation models [ 33 ]. In this work, authors implemented a cost-efficient model with IoT for the best approach to IoT applications based on QoS with the help of a whale optimization algorithm to produce good throughput and energy consumption [ 34 , 36 ]. In this work, the authors implemented a smart library recommendation model educational organization but did not work on the e-learning tutoring model.…”
“…The authors stated the importance of Recommender Systems with Semi-supervised Learning using machine learning but didn't work on e-learning applications for smart tutoring recommendation models [ 33 ]. In this work, authors implemented a cost-efficient model with IoT for the best approach to IoT applications based on QoS with the help of a whale optimization algorithm to produce good throughput and energy consumption [ 34 , 36 ]. In this work, the authors implemented a smart library recommendation model educational organization but did not work on the e-learning tutoring model.…”
“…IoT is not restricted to a particular sector; it finds diverse applications in different sectors from smart homes, social media, smart cities, and industry. Industrial IoT(IIoT) is envisioned as the key player for the successful realization of industry 4.0 1,2 . With the advancement in related fields (wireless communication, device technologies, etc.)…”
Section: Introductionmentioning
confidence: 99%
“…Industrial IoT(IIoT) is envisioned as the key player for the successful realization of industry 4.0. 1,2 With the advancement in related fields (wireless communication, device technologies, etc.) and the increase in the number of IoT devices deployed, the concept gathers broader industry momentum.…”
The most well-known sort of remote Internet connection is wireless local area networks (WLANs) due to its unsophisticated operation and deployment. Subsequently, the quantity of gadgets getting to the Internet through WLANs, for example, PCs, cell phones, or wearables, is expanding radically at the equivalent time that applications' throughput necessities do. To provide wireless networks with supplementary spectral resources, the researchers are considering the aggregation of frequency spectrums in licensed, unlicensed, and shared access (SA) bands. Channel aggregation/channel bonding (CA/CB) techniques accumulate quite a few channels together as one channel for the purpose of achieving better bandwidth utilization. In this study, we focus on reliable CA/CB techniques in different wireless networks. CA/CB procedures are utilized for empowering higher information rates by transmitting in more extensive channels, accordingly expanding range proficiency with the assured secure channel for communication. We also discuss the spectral scarcity issues in today's wireless IoT network. This paper presents an extensive survey on CA/CB procedures and methods, issues and challenges, and open research areas related to IoT devices. We analyze the performance of channel CA/CB strategies in the different wireless networks too.
Fog computing (FC) is a promising paradigm to use as an efficient architecture for the Internet of Things applications. Proximity, low latency, flexible resource power, and distributed structure of this architecture are some benefits of it. A huge number of generated data and their requisites to real-time process causes fog nodes offload number of tasks to the others that make trust issues. Here, each clients prefers to offload task to a trusted server, also each server tends to service the trusted clients. This may takes a long especially when we want to consume less energy. In order to encounter this problem, in this paper, we propose a two-way trust management strategy based on Bayesian learning automata. The proposed approach outperforms the other state-of-the-art approaches in terms of the energy consumption, network usage, latency, response time, and trust value.
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