Machine learning and data analytics for the IoT

Adi, Erwin; Anwar, Adnan; Baig, Zubair A.; Zeadally, Sherali

doi:10.1007/s00521-020-04874-y

Cited by 192 publications

(79 citation statements)

References 101 publications

(160 reference statements)

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“…In IoV applications, the convergence between machine learning and the Internet of ings promises future progress in efficiency, accuracy, and improved resource management. e use of machine learning with IoV provides high performance in communication and computing to achieve efficient control, management, and decision-making processes [92,97]. ML allows the extraction of big sensory data to get better insights into the range of problems associated with the IoV and the surrounding environment and the ability to make critical operational decisions.…”

Section: Future Directions and Potential Solutionsmentioning

confidence: 99%

“…It also promises soon to upgrade vehicle networks' performance and make them more interactive with other things' Internet applications. Using ML in the IoV enables interaction between the cyber and physical components together and can significantly improve the efficiency and reliability of processes and systems [97]. Moreover, machine learning offers smart solutions to enhance decisionmaking in the event of cyber attacks.…”

Section: Future Directions and Potential Solutionsmentioning

confidence: 99%

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Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Ali

Hasan

Hassan

et al. 2021

Security and Communication Networks

128

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Recently, interest in Internet of Vehicles’ (IoV) technologies has significantly emerged due to the substantial development in the smart automobile industries. Internet of Vehicles’ technology enables vehicles to communicate with public networks and interact with the surrounding environment. It also allows vehicles to exchange and collect information about other vehicles and roads. IoV is introduced to enhance road users’ experience by reducing road congestion, improving traffic management, and ensuring the road safety. The promised applications of smart vehicles and IoV systems face many challenges, such as big data collection in IoV and distribution to attractive vehicles and humans. Another challenge is achieving fast and efficient communication between many different vehicles and smart devices called Vehicle-to-Everything (V2X). One of the vital questions that the researchers need to address is how to effectively handle the privacy of large groups of data and vehicles in IoV systems. Artificial Intelligence technology offers many smart solutions that may help IoV networks address all these questions and issues. Machine learning (ML) is one of the highest efficient AI tools that have been extensively used to resolve all mentioned problematic issues. For example, ML can be used to avoid road accidents by analyzing the driving behavior and environment by sensing data of the surrounding environment. Machine learning mechanisms are characterized by the time change and are critical to channel modeling in-vehicle network scenarios. This paper aims to provide theoretical foundations for machine learning and the leading models and algorithms to resolve IoV applications’ challenges. This paper has conducted a critical review with analytical modeling for offloading mobile edge-computing decisions based on machine learning and Deep Reinforcement Learning (DRL) approaches for the Internet of Vehicles (IoV). The paper has assumed a Secure IoV edge-computing offloading model with various data processing and traffic flow. The proposed analytical model considers the Markov decision process (MDP) and ML in offloading the decision process of different task flows of the IoV network control cycle. In the paper, we focused on buffer and energy aware in ML-enabled Quality of Experience (QoE) optimization, where many recent related research and methods were analyzed, compared, and discussed. The IoV edge computing and fog-based identity authentication and security mechanism were presented as well. Finally, future directions and potential solutions for secure ML IoV and V2X were highlighted.

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Section: Future Directions and Potential Solutionsmentioning

confidence: 99%

Section: Future Directions and Potential Solutionsmentioning

confidence: 99%

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Ali

Hasan

Hassan

et al. 2021

Security and Communication Networks

128

View full text Add to dashboard Cite

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“…This layer passes the data to a Fusion layer which is responsible for gathering data from several sensors, removing redundancy, filling the missing data and improving reliability. The quality of the data is still one of the challenges which data heterogeneity presents in IoT systems [52]. The importance of data quality increases when the goal is to use reduced data in ML models.…”

Section: Data Fusionmentioning

confidence: 99%

Edge Intelligence for Data Handling and Predictive Maintenance in IIOT

Hafeez

McArdle

2021

IEEE Access

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The use of IoT has become pervasive and IoT devices are common in many domains. Industrial IoT (IIoT) utilises IoT devices and sensors to monitor machines and environments to ensure optimal performance of equipment and processes. Predictive Maintenance (PM) which monitors the health of machines to determine the probable failure of components is one IIoT technique which is receiving attention lately. To achieve effective PM, massive amounts of data are collected, processed and ultimately analysed by Machine Learning (ML) algorithms. Traditionally IoT sensors transmit their data readings to the cloud for processing and modelling. Handling and transmitting massive amounts of data between IoT devices and infrastructure has a cost. Edge Computing (EC) in which both sensors and intermediate nodes can process data provides opportunities to reduce data transmission costs and increase processing speed. This article examines IIoT for PM and discusses how and where data can be processed and analysed. Initially, this article presents sampling and data reduction techniques. These techniques allow for a reduction in the amount of data transmitted to the cloud for processing but there are potential accuracy trade-offs when ML algorithms utilise reduced datasets. An alternative approach is to move ML algorithms closer to the data to reduce data transmission. There are three main techniques that utilise the EC paradigm to perform ML and data processing on intermediary nodes. These techniques are categorized according to where data processing occurs: Device and Edge, Edge and Cloud and Device and Cloud (Federated Learning). In addition to exploring traditional approaches, these three state-of-the-art techniques are examined in this article and their benefits and weaknesses are presented. A novel architecture to demonstrate how EC can be utilized both for data reduction and PM in IIoT is also proposed.

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“…Data analysis can be classified in to descriptive analytics, predictive analytics and perspective analytics [4]. Descriptive analytics in IoMT environment gathers data from the IoMT devices and transfers to the cloud, and detailed insights into the past event are predicted using historical data by employing advanced ML techniques.…”

Section: Machine Learning For Data Analyticsmentioning

confidence: 99%