QoS ‐Driven Energy‐Efficient Clustering and Routing in Wireless Sensor Networks Using Hybrid ASBO and Multi‐Level Attention Dilated Residual Neural Network Approach

ABSTRACT As Internet of Things (IoT) technologies continue to advance, their adoption in smart cities, healthcare, and smart grids has increased significantly. Wireless sensor networks (WSNs) serve as a key enabling technology for IoT‐based data monitoring and transmission. An IoT‐integrated WSN (IWSN) involves the deployment of numerous sensor nodes in heterogeneous and challenging environments, necessitating efficient and reliable communication mechanisms. A significant issue that requires urgent attention is the potential for security breaches, such as intrusions within WSN traffic. Ineffective intrusion detection can lead to excessive energy consumption by Sensor Nodes (SNs), potentially causing node failures and resulting in diminished network coverage and overall lifespan. Detecting such attacks has led to considerable computational complexity in the existing research. Considering the limited resources of SNs and their deployment in challenging environments, it is essential to design clustering and routing protocols for WSNs that emphasize energy efficiency and security. This study aims to address these issues by developing a clustering and routing protocol that enhances energy efficiency while ensuring robust security and integrating intrusion detection to boost network longevity and data integrity. Initially, clusters have been formed using the fuzzy clustering means (FCM) algorithm. The crested porcupine optimization (CPO) technique is then used to select the optimal cluster heads (CHs). Following the clustering process, an adaptive secretary bird optimization algorithm (ASBO) is used to select the most efficient data transmission routes between the clusters, thereby, the network's energy efficiency is increased. Finally, to enhance the security of clustered WSNs, an advanced intrusion detection system (IDS) based on a multilevel attention dilated residual neural network (MADR‐Net) has been used to detect and mitigate network intrusions. The experimental findings indicate that the proposed method surpasses the existing techniques across various performance metrics. Quality of service (QoS) parameters are measured using a packet delivery ratio (PDR) of 98%, dispersion value of 0.1133, end‐to‐end delay (E2ED) of 45 ms, and energy consumption of 23 J. The MADR‐Net algorithm has outperformed the existing algorithms by achieving 98.5% accuracy on the CICIDS‐2017 dataset and 98.8% accuracy on the NSL‐KDD 2015 dataset.