Optimizing the network energy of cloud assisted internet of things by using the adaptive neural learning approach in wireless sensor networks

Abstract

Cloud-assisted internet of things (CIoT) is backboned by the wireless sensor network (WSN) architecture. A sensor network is an autonomous self-resource constraint collection of internet of things (IoT) sensor nodes. The nodes communicate in an ad-hoc fashion to transfer cloud information over the virtual environment. Clustering in WSNs helps to improve the quality of the network by controlling energy consumption and improving data gathering accuracy. This improves the service rates of CIoT. Optimizing IoT sensor networks through energy and overhead management requires complex clustering algorithms. A simple clustering scheme cannot achieve the desired performance enhancement during transmission in a virtual environment. This research attempts to propose a reinforcement-based learning technique, adaptive Q-learning (AQL) to improve network performance with minimum energy–overhead tradeoff in a sensor network-aided CIoT. AQL operates in two distinct phases for cluster head selection and forwarder selection. The decision-making system is used to qualify nodes based on their past behavior over transmission. AQL improves both inter- and intra-cluster communication optimization through adaptive forwarder and header selection conditions. The simulation results prove the consistency of the proposed AQL by retaining the live node counts in the network and their persistent energy despite the reduced overheads in the sensor network. With the achievement of constructive features in the sensor networks, the performance of CIoT is considerably improved. The experimental results illustrate the effectiveness of the proposed learning technique by improving network lifetime with a high request–response rate and by minimizing delay, overhead, and request failures.