Internet of Things (IoT) is a network of interconnected devices, such as sensors and smart devices that have processing, sensing, and communication capabilities, as well as can transmit information to each other and a supreme console through the Internet. Network traffic prediction is an important operational and management function for any data network. It has a significant role in today’s increasingly complex and diverse networks. Network traffic prediction is also more important for IoT networks to provide reliable communication. The artificial neural network (ANN) has been successfully applied to traffic prediction. In this paper, we perform the IoT traffic time series prediction using a multistep ahead prediction with Time Series NARX Feedback Neural Networks. The estimation error of a prediction approach has been evaluated using the performance functions MSE, SSE, and MAE, besides, another measure of prediction accuracy the mean absolute percent of error (MAPE).

Smart cities using the Internet of Things (IoT) can operate various IoT systems with better services that provide intelligent and efficient solutions for various aspects of urban life. With the rapidly growing number of IoT systems, the many smart city services, and their various quality of service (QoS) constraints, servers face the challenge of allocating limited resources across all Internet-based applications to achieve an efficient per-formance. The presence of a cloud in the IoT system of a smart city results in high energy con-sumption and delays in the network. Edge computing is based on a cloud computing framework where computation, storage, and network resources are moved close to the data source. The IoT framework is identical to cloud computing. The critical issue in edge computing when executing tasks generated by IoT systems is the efficient use of energy while maintaining delay limitations. In this paper, we study a multicriteria optimization approach for resource allocation with distributed edge computing in IoT-based smart cities. We present a three-layer network architecture for IoT-based smart cities. An edge resource allocation scheme based on an auctionable approach is proposed to ensure efficient resource computation for delay-sensitive tasks.

The next-generation cellular systems, including fifth-generation cellular systems (5G), are empowered with the recent advances in artificial intelligence (AI) and other recent paradigms. The internet of things (IoT) and the tactile internet are paradigms that can be empowered with AI solutions and integrated with 5G systems to deliver novel services that impact the future. Machine learning technologies (ML) can understand examples of nonlinearity from the environment and are suitable for network traffic prediction. Network traffic prediction is one of the most active research areas that integrates AI with information networks. Traffic prediction is an integral approach to ensure security, reliability, and quality of service (QoS) requirements. Nowadays, it can be used in various applications, such as network monitoring, resource management, congestion control, network bandwidth allocation, network intrusion detection, etc. This paper performs time series prediction for IoT and tactile internet delays, using the k-step-ahead prediction approach with nonlinear autoregressive with external input (NARX)-enabled recurrent neural network (RNN). The ML was trained with four different training functions: Bayesian regularization backpropagation (Trainbr), Levenberg–Marquardt backpropagation (Trainlm), conjugate gradient backpropagation with Fletcher–Reeves updates (Traincgf), and the resilient backpropagation algorithm (Trainrp). The accuracy of the predicted delay was measured using three functions based on ML: mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE).

Artificial intelligence (AI) is capable of addressing the complexities and difficulties of fifth-generation (5G) mobile networks and beyond. In this paradigm, it is important to predict network metrics to meet future network requirements. Vehicle-to-everything (V2X) networks are promising wireless communication methods where traffic information exchange in an intelligent transportation system (ITS) still faces challenges, such as V2X communication congestion when many vehicles suddenly appear in an area. In this paper, a deep learning algorithm (DL) based on the unidirectional long short-term memory (LSTM) model is proposed to predict traffic in V2X networks. The prediction problems are studied in different cases depending on the number of packets sent per second. The prediction accuracy is measured in terms of root-mean-square error (RMSE), mean absolute percentage error (MAPE), and processing time.