Hybrid Routing Techniques for Location Privacy in IoT-Enabled Wireless Sensor Healthcare Networks

Ensuring patient privacy is critical for healthcare systems reliant on the Internet of Things. While prior work has addressed patient privacy extensively, a comprehensive end-to-end strategy protecting patients’ location and data privacy under the assumption that system entities are untrusted has yet to be developed. In response, this paper proposes a novel hybrid Deep Q-learning Neural Network (DQ-NN) technique tailored to Source Location Privacy (SLP) integrated with Blockchain technology for routing in Internet of Things (IoT)-enabled Wireless Sensor Networks (WSNs). To overcome the shortcomings of the existing systems, the study offers extremely random routing paths between the source and sink nodes. The plan uses strategically selected phantom nodes to transmit packets to the sink node randomly, ensuring the paths are incredibly confusing to the enemy. To attain maximum privacy and optimize energy consumption, the suggested method employs a hybrid DQNN model to select optimal paths among multiple paths generated. Blockchain integration adds an extra layer of security to the routing network. The results of the simulation show that the proposed scheme outperforms other schemes by offering a more extended safety period, optimized energy, and enhanced privacy for the healthcare environment.