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- W4213081318 abstract "Internet of Things (IoT) based devices have proven to be highly useful during this pandemic. From home automation to industrial monitoring, these devices have become an integral part of day-to-day activities. Thus, it is essential that these devices must be highly secure and must possess high Quality of Service (QoS) performance. To integrate these optimizations to IoT devices, a wide variety of options are available; which include application-layer enhancements, network-level patches, and kernel-level modules. Out of these, application-layer enhancements are neither secure nor efficient when large-scale IoT devices are deployed, while network-level patches are focused toward fixing network performance, and have limited node-level enhancement capabilities. Moreover, kernel-level patches have high computational efficiency due to their proximity to IoT BIOS and can be used to trace both node-level and network-level parameters. Thus, in this text recent kernel-level security and QoS models are discussed, and their performance in terms of attack resilience, computational complexity, reaction delay, etc. is evaluated and compared. From the review, it is observed that various soft computing models can be used to further improve the QoS and security performance of IoT networks. These models include but are not limited to fuzzy logic models, neural network models, linear programming models, etc. This evaluation will assist network designers and researchers to select the best possible algorithms for their given network, thereby reducing the time and cost needed for optimum network design. Moreover, this text also recommends various improvements for the compared models, thereby assisting in further optimization of overall IoT performance." @default.
- W4213081318 created "2022-02-24" @default.
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- W4213081318 date "2022-01-01" @default.
- W4213081318 modified "2023-09-27" @default.
- W4213081318 title "Evaluation of Kernel-Level IoT Security and QoS Aware Models from an Empirical Perspective" @default.
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- W4213081318 doi "https://doi.org/10.1007/978-981-16-9154-6_68" @default.
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