Adaptive Reinforcement learning with Dij-Huff Method to Secure Optimal Route in Smart Healthcare System

Abstract

The Wireless Sensor Network (WSN) is a multi-hop wireless network that contains multiple sensor nodes agreed in a self-organized mode. The significant advancement in the healthcare, the security of the medical data became huge disputes for healthcare services. Intrusion Detection System increasingly demands automatic and intelligent intrusion detection approaches to handle threats caused by a growing number of attackers in the WSN environment. Reinforcement Learning is a fundamental approach to improving routing efficiency. This approach introduces Adaptive Reinforcement learning with Dij-Huff Method (ARDM) for secure optimal route in WSN. Adaptive Reinforcement Learning (ARL) is a machine learning technique that selects the best forwarder node. The node energy, node Received Signal Strength, and node delay parameters determine the forwarder nodes in the WSN. Furthermore, the Dij-Huff Procedure (DHP) is a mixture of Dijkstra’s algorithm and Huffman coding. Dijkstra’s algorithm is applied to discover the sensor nodes with the highest energy and the best possible distance route. Huffman coding computes the binary hop count to offer each hop security. The simulation results and their comparison with conventional protocol demonstrate that the proposed scheme has a better detection ratio, a better throughput, and increased energy efficiency.