IoT architecture is an intelligent green city when it incorporates information systems and Artificial Intelligence (AI) to maximize the utilization of resources, upgrade public services, and produce cities very efficient but also environment-friendly. There remain poor feature selection and extraction, unbalanced load, Quality of Service (QoS) issues, and energy consumption by blockchain which prevents effective implementation. This research solves these issues through a multi-step approach. First, we build a smart IoT network that gets the IoT device data from our CICIDS2017 dataset, preprocessed with Normalization to standardize the data. Next, Wrapper-based Feature Selection is applied to identify the most significant features used by the Autoencoder's deeper Feature Extraction to improve model performance. A QoS scheme based on Software Defined Networking (SDN) will dynamically distribute loads with low latency to balance loads and achieve QoS. We further utilize a stateless Q-learning algorithm to avoid congestion in IoT device data distribution. We then use the Hyperledger Fabric for efficient blockchain in combination with Linear Network Coding (LNC) to save energy on the system. To detect cyber-attacks, we adopt a Quasi-Recurrent Neural Network (QRNN) that is Bio-optimized to enhance detection while minimizing false positive responses. Finally, we evaluate the performance of the proposed system on the following metrics: Response Time (ms), Throughput (Mbps), Attack Detection (%), False Positive Rate (%), and Energy Consumption (%). This approach is implemented through NS-3.35 using Python, providing a solid framework for comparative studies and the promotion of sustainable operations of a smart city.
Rista et al. (2026) studied this question.