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Internet of Things (IoT) encompasses heterogeneous communication devices and wireless technologies to provide ubiquitous access using end‐user devices. It operates on a common website for connecting versatile devices with seamless communication, data access and sharing. Communication through wireless medium requires enormous security features to protect both public and private data across different interconnected resources. Therefore, in this article, a communication‐aware adaptive key management (CAKM) is proposed to ensure two levels of security in IoT communication. The two‐levels of security include both device and message that is facilitated using certificate authority and key management. By exploiting the benefits of hyper elliptic curve cryptography (HECC), the communication‐aware adaptive key management ensures message authentication for the device requests. Initially, the device requests are classified on the basis of its service time and correlated to the time‐to‐live period of the certificate. The proposed method attains low computational time 10.9%, 30.2%, and 54.3%, high security level 10.9%, 30.2%, and 54.3%, high successful message rate 10.9%, 30.2%, and 54.3% and low key agreement time 10.9%, 30.2%, and 54.3% when compared to the three existing methods, such as bilateral generalization inhomogeneous short integer solution (Bi‐GISIS)‐based key management in IoT security (Bi‐GISIS‐IoT), hyper elliptic curve based public key cryptosystem in IoT security (HCCPK‐IoT) and enhanced symmetric key‐based authentication in IoT security (ESKA‐IoT). Finally, the proposed method provides an efficient security with low computational time.

In this manuscript, the combination of IoT and Multilayer Hybrid Dropout Deep-learning Model for waste image categorization is proposed to categorize the wastes as bio waste and non-bio waste. The input captured images are pre-processed and remove noises in the captured images. Under this approach, a Nature inspired Multilayer Hybrid Dropout Deep-learning Model is proposed. Multilayer Hybrid Dropout Deep-learning Model is the consolidation of deep convolutional neural network and Dropout Extreme Learning Machine classifier. Here, deep convolutional neural network is used for feature extraction and Dropout Extreme Learning Machine classifier for categorizing the waste images. To improve the classification accurateness, Horse herd optimization algorithm is used to optimize the parameter of the Dropout Extreme Learning Machine classifier. The objective function is to maximize the accuracy by minimize the computational complexity. The simulation is executed in MATLAB. The proposed Multilayer Hybrid Dropout Deep-learning Model and Horse herd optimization algorithm attains higher accuracy 39.56% and 42.46%, higher Precision 48.74% and 34.56%, higher F-Score 32.5% and 45.34%, higher Sensitivity 24.45% and 34.23%, higher Specificity 31.43% and 21.45%, lower execution time 0.019(s) and 0.014(s) compared with existing waste management and classification using convolutional neural network with hyper parameter of random search optimization algorithm waste management and classification using clustering approach with Ant colony optimization algorithm. Finally, the proposed method categorizes the waste image accurately.

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