A REAL TIME ADAPTIVE WIRELESS SENSOR NETWORK TRUST MODEL USING ARTIFICIAL NEURAL NETWORKS

Abstract

The rapid progress in wireless sensor networks (WSNs) has created new opportunities for real-time monitoring and data collection in different fields, such as environmental monitoring, healthcare, and industrial automation. Nevertheless, the dependability and protection of data in these networks are crucial considerations. This paper introduces an analytical research study on a trust model for a wireless sensor network that can adapt in real-time. The trust model utilises artificial neural networks (ANNs). The main goal of this research is to improve the reliability of data sent through WSNs by utilising the predictive and adaptive capacities of ANNs.

Wireless Sensor Networks (WSNs) are intrinsically susceptible to a range of security risks, such as data manipulation, node compromise, and unauthorised data retrieval. Conventional trust models in Wireless Sensor Networks (WSNs) frequently depend on unchanging measurements and pre-established regulations, which may not adequately tackle the ever-changing and developing security obstacles. In order to address these constraints, this study presents a dynamic trust model that adjusts to immediate modifications in the network environment. The model's adaptability is achieved by incorporating Artificial Neural Networks (ANNs), which can learn from past data and make predictions on the reliability of network nodes.

The trust model being suggested integrates various metrics to assess the reliability of sensor nodes. These elements encompass data integrity, node conduct, communication patterns, and environmental conditions. Artificial neural networks (ANNs) are used to analyse these factors and calculate a trust score for each node. The methodology is meant to iteratively update the trust scores using up-to-date data, enabling prompt identification of suspicious activities and malevolent nodes. The efficacy and performance of the adaptive trust model are evaluated by implementing and testing it in a simulated Wireless Sensor Network (WSN) environment.

The research findings suggest that the trust assessments in WSNs are much more accurate and reliable when using the ANN-based trust model, as compared to traditional approaches. The model's adaptability enables it to promptly react to network changes, thereby bolstering the system's security and stability. The model also exhibits resilience against a wide range of attacks, such as Sybil attacks, wormhole attacks, and data falsification attacks.

The adaptive trust model not only improves security but also aids in the effective management of network resources. The model enhances routing decisions, minimises energy usage, and prolongs the network's lifespan by precisely identifying reliable nodes. By integrating artificial neural networks (ANNs), the model becomes capable of effectively managing extensive amounts of data and intricate network situations, thereby making it well-suited for use in various wireless sensor network (WSN) applications.

This work also investigates the possibility of expanding the adaptive trust model to include additional machine learning techniques, such as reinforcement learning and deep learning, in order to improve its prediction powers and adaptability. The report ends by examining the practical consequences of the research results and potential future paths for creating more advanced trust models for WSNs.