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

Abstract

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 whole IoT module. Hence, the IoT network efficiency rates of the individual must be predicted in an earlier stage to predict the effectiveness of the complete IoT system. The efficiency of each network is forecasted with the help of the constraints such as energy consumption, gathered data size, transmitted data size, mobility, false positive, throughput, packet loss, and delay. The output from each network is considered as input to the Optimized Recurrent Neural Network (ORNN) for predicting the ending superiority of the whole IoT network. Here, the parameter tuning in the RNN is done by the Self Adaptive Honey Badger Algorithm (SA-HBA). The empirical outcomes confirmed that the designed method has forecasted and enhanced the superiority of a whole simulated IoT system in an accurate manner. Throughout the result analysis, the given designed method attains minimum energy consumption rate and also better prediction accuracy.