Abstract

The ability of Wireless Sensor Networks (WSNs) to efficiently monitor and collect data from difficult and distant places has made them a notable technical solution for a variety of applications. It is crucial to optimize energy usage and secure data integrity in order to put these technologies to reality with the Internet of Things (IoT). Maintaining a balance between energy economy, flexibility to changing network circumstances, and security is a common difficulty for contemporary routing systems. Because of this, the performance of these protocols drops and they become vulnerable. Specifically, for WSNs, this research presents HGL-ASRP, a state-of-the-art Resilient and Adaptive Secure Routing Protocol. By integrating the Quantum Assisted Grey Wolf Optimizer (GWO) with the Lightning Search Algorithm (LSA), this protocol overcomes the challenges faced by previous routing protocols in the Internet of Things (IoT) setting. Hybrid GWO takes cues from the social behavior of grey wolves and uses them to solve optimization problems very well. As a result of this hybrid GWO and hypercube sampling, grey wolves can only go to a better area to live. With hypercube sampling, the HGL-ASRP method performs better in a number of areas, including speed (90.9 kbps), end-to-end delay (88.8 ms), packet delivery ratio (91.2%), energy efficiency (92.09%), network lifetime (163.63 hours), and security (87.59%). The HGL-ASRP system ensures continuous data transmission even in demanding environments by successfully managing node failures and changing topologies.