Optimized RNN-based performance prediction of IoT and WSN-oriented smart city application using improved honey badger algorithm

[Display omitted] •To present a new deep learning model called Optimized Recurrent Neural Network (O-RNN)-based performance prediction model of Internet of Things (IoT) regarding smart city application using Self Adaptive Honey Badger Algorithm (SA-HBA) for maximizing the performance of the IoT networks using various networks like Wireless Sensor Network (WSN), Radio Frequency Identification (RFID), Mobile Ad-hoc NETworks (MANET), Wireless Body Area Networks (WBAN), and Vehicular Ad-hoc NETwork (VANET).•To construct a new IoT performance prediction model using various networks like VANET, WBAN, MANET, RFID and WSN by suggesting a new SA-HBA to maximize the convergence rate of IoT network for smart city application.•To evaluate the efficiency of IoT networks by comparing with diverse heuristic and conventional prediction approaches regarding various performance measures like energy consumption, throughput, and packet loss ratio. Internet of Things (IoT) has been widely utilized as the major significant element of Information and Communications Technology (ICT) for feasible smart cities due to the capability of IoT for supporting sustainability in numerous domains. There is a need for fault avoidance via the continuous and dynamic utilization of network behavior for achieving the necessary quality of the IoT communication system that allows us to get sustainable enhancement in smart cities regarding IoT communication systems. While considering the IoT-assisted network, the basic part of the IoT model is considered a Wireless Sensor Network (WSN), especially for data management that consists of various sensor nodes in the smart city area. Moreover, every node in a WSN is considerably utilized for a specific reason and thus, every node is handled by a battery that increases the consumption of energy in the entire smart city scheme for processing the data regarding communication. On the other hand, there exist various limitations like scalability, communication latency, centralization, privacy, security, etc. Thus, this paper plans to develop the IoT and WSN-based smart city application using novel intelligent techniques, aiming for the optimal performance classification of the network. The infrastructure of IoT will consist of WSN, Vehicular Ad-hoc NETwork (VANET), Mobile Ad-hoc NETworks (MANET), Radio Frequency Identification (RFID), and Wireless Body Area Networks (WBAN). Here, the efficiency of the whole IoT system is acquired from the efficiency of the who