A novel load balancing algorithm for task allocation in fog-enabled healthcare systems

Timely intervention is critical for saving a patient’s life. To monitor vital signs, various health monitoring sensors are placed on the patient’s body, collecting data at regular intervals. However, the limited computational capacity of these health monitoring sensor nodes prevents them from analyzing the data independently. By integrating sensors with Internet of Things (IoT) nodes, the collected data can be offloaded to nearby fog nodes for processing. A challenge arises when certain fog nodes receive a disproportionate number of healthcare data tasks compared to others, necessitating an efficient load balancing mechanism to process these time-sensitive tasks promptly. In this work, we propose an Intelligent Task Offloading Scheme for Healthcare Data Analysis ( ITOS HDA ). The scheme consists of two key algorithms: (1) a load balancing algorithm that employs the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to allocate offloaded tasks efficiently among fog nodes, and (2) a task execution algorithm that uses the 0/1 Knapsack approach to prioritize tasks based on their deadlines. The performance of ITOS HDA is evaluated against state-of-the-art schemes, demonstrating its superiority in handling varying numbers of fog nodes and offloaded tasks. Experimental results confirm that ITOS HDA outperforms existing algorithms in terms of efficiency and scalability.