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Smart city article details
| Title | Iot Capabilities Analysis By Using Optimized Machine Learning With Uncertain Traffic Modeling |
|---|---|
| ID_Doc | 33752 |
| Authors | Senthil Kumaran S.; Balakannan S.P. |
| Year | 2023 |
| Published | Journal of Uncertain Systems, 16, 1 |
| DOI | http://dx.doi.org/10.1142/S1752890922420053 |
| Abstract | The Internet of Things (IoT) incorporates software-based physical devices with sensors which require interconnectivity to communicate through the internet and be able to exchange data. IoT traffic can be thought of as the aggregate of packets created by various devices in various contexts, such as smart homes or smart cities. These settings contain several sensors, each of which is dedicated to a certain duty, such as monitoring schemes or gathering cyber-physical information. Thus, unlike internet traffic, which has some human-centric features, IoT traffic and sensor devices consistently perform different tasks and make uncertain quantities of data on a regular basis. The proposed method is based on calculating the entropy values of various uncertain traffic aspects. We describe a novel scheme for identifying IoT devices based on uncertain traffic entropy in this paper. The testbed consists of five popular Smart Home IoT devices with a Raspberry Pi acting as a gateway. Machine learning (ML) algorithms such as support vector machine (SVM), k-nearest neighbor (KNN) and relevance vector machines (RVM) classifier are used to classify the devices which produce IoT traffic based on uncertain entropy significance. The learning rate of the classifier model is improved by using the flower pollination optimization (FPO) technique to increase the classifier performance. The suggested approach minimizes the amount of time spent on optimization operations while maintaining the predictive performance of the induced uncertain models. We work out the entropy standards of traffic characteristics and categorize the uncertain traffic using ML techniques. Our technique successfully identifies devices in a variety of uncertain network situations, with consistent performance in all scenarios. Our technique is also resistant to unpredictability in network behavior, with abnormalities or uncertainties propagating throughout the network. © 2023 World Scientific Publishing Company. |
| Author Keywords | Entropy; Internet of Things; machine learning classifier and optimization; uncertain; uncertain traffic analysis |
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