SecOFF-FCIoT: Machine learning based secure offloading in Fog-Cloud of things for smart city applications

Alli, Adam A.; Alam, Muhammad

doi:10.1016/j.iot.2019.100070

Cited by 86 publications

(38 citation statements)

References 38 publications

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“…Deep learning is a class of machine learning programming technology that is based around learning from large data sets [154] [155]. The core of deep learning is to get high level interactive features from the raw data.…”

Section: Deep Learningmentioning

confidence: 99%

Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges

et al. 2019

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Cloud computing plays a critical role in modern society and enables a range of applications from infrastructure to social media. Such system must cope with varying load and evolving usage reflecting societies' interaction and dependency on automated computing systems whilst satisfying Quality of Service (QoS) guarantees. Enabling these systems are a cohort of conceptual technologies, synthesised to meet demand of evolving computing applications. In order to understand current and future challenges of such system, there is a need to identify key technologies enabling future applications. In this study, we aim to explore how three emerging paradigms (Blockchain, IoT and Artificial Intelligence) will influence future cloud computing systems. Further, we identify several technologies driving these paradigms and invite international experts to discuss the current status and future directions of cloud computing. Finally, we proposed a conceptual model for cloud futurology to explore the influence of emerging paradigms and technologies on evolution of cloud computing.

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Section: Deep Learningmentioning

confidence: 99%

Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges

et al. 2019

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“…Based on a deep migration learning model, Li et al [36] proposed an intrusion detection algorithm (IDS) that helps identify network anomalies and takes necessary countermeasures to ensure IoT's safe and reliable operation applications. Alli et al [37] proposed a secure calculation download scheme in the Fog-Cloud-IoT environment (SecOFF-FCIoT). Using machine learning strategies, they achieved an efficient and safe download in the Fog-IoT environment.…”

Section: Related Workmentioning

confidence: 99%

Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm

et al. 2021

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In recent years, technological paradigms such as Internet of Things (IoT) and machine learning have become very important due to the benefit that their application represents in various areas of knowledge. It is interesting to note that implementing these two technologies promotes more and better automatic control systems that adjust to each user’s particular preferences in the home automation area. This work presents Smart Home Control, an intelligent platform that offers fully customized automatic control schemes for a home’s domotic devices by obtaining residents’ behavior patterns and applying machine learning to the records of state changes of each device connected to the platform. The platform uses machine learning algorithm C4.5 and the Weka API to identify the behavior patterns necessary to build home devices’ configuration rules. Besides, an experimental case study that validates the platform’s effectiveness is presented, where behavior patterns of smart homes residents were identified according to the IoT devices usage history. The discovery of behavior patterns is essential to improve the automatic configuration schemes of personalization according to the residents’ history of device use.

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“…Performance also changes when the architecture of IoT changes internally. Therefore, fog nodes should also synchronise and arrange the topology and allocate resources accordingly [156]. Figure 3 describes the flow of the proposed FIoTMM system.…”

Section: Concerns Of Fog Computingmentioning

confidence: 99%

Application of IoT‐Fog based real‐time monitoring system for open‐cast mines—A survey

Yadav

Mishra²,

Jayanthu

et al. 2021

IET Wireless Sensor Systems

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Fog computing (FC) archetypes is in focus recently for its potential to utilise resources optimally by the Internet of Things (IoT) network bearing ample end-devices. This enhances the quality of service (QoS) to the proximity of end-users as latency-free processing is obtained in cloud-IoT-based environs. Mining industry will encounter a level-up due to these technological advancements like fog-IoT-based systems. Monitoring mining activities such as blasting, slope monitoring, and miner tracking are time-sensitive. Hence, instant response is demand of extreme mine environment so that life and property are not compromised. New opportunities emerge in mining since the FC approach addresses these demands depending on the demand of the situation/user. The parameters of integrating the state-of-art of fog, IoT, and cloud architectures are provided, and a Fog-IoT mines monitoring (FIoTMM) system for real-time monitoring of open-cast mines is proposed. This work also envisions the applications and future trends of FC concerning IoT and cloud. It emphasises how key features of fog help the system in achieving lower network influx, reduced latency, resource utilisation, and immediate data computation and storage because these services take place closer to point of data generation. This data can be immediately visualised/accessed and used to generate early warning in the mines. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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SecOFF-FCIoT: Machine learning based secure offloading in Fog-Cloud of things for smart city applications

Cited by 86 publications

References 38 publications

Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges

Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges

Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm

Application of IoT‐Fog based real‐time monitoring system for open‐cast mines—A survey

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