UTILIZING QUANTUM-ENHANCED DYNAMIC CLUSTERING FOR OPTIMIZED PATH SELECTION IN WBANS FEATURING OPTICAL BIOSENSORS TECHNOLOGY - Poonam Sharma; Kamaljit Singh Saini; Prabhjot Kaur (Proceedings on Engineering Sciences)
A telemedicine system incorporating wearable health monitoring devices, including optical biosensors, is a cutting-edge information technology that may help with the early diagnosis of aberrant disorders and mitigate their harmful repercussions. Optical biosensors present a compelling alternative to traditional analytical methods due to their remarkable precision, sensitivity, compact size, and cost-effectiveness. Wireless technology plays a vital role in the communication and transmission of data. The major challenge in the network design is energy efficiency which can be resolved with efficient path selection. This paper proposed a quantum enhanced Dynamic Cluster-based Optimized Path Selection (DCOPS) approach using Bio-Sensor technology that utilizes two different optimization algorithms for dynamic clustering and path selection. The network is first divided into clusters using the dragonfly optimization algorithm, and then an adaptive cuckoo-based path selection algorithm is proposed. With the natural dynamic behavior of dragonflies and exploration, identifying the connected nodes having low energy becomes easy. Moreover, the node with minimum energy gets selected for cluster head to maintain the network communication. Furthermore, path selection using Cuckoo will find the nodes having minimum energy consumption so that it can transfer the data throughout the communication over the mobile network. This proposed approach is simulated using a Network simulator, and performance is computed based on different network scenarios in terms of total energy consumption (TEC), packet delivery ratio (PDR), and end-to-end delay (E2D). The simulated results are compared with the existing algorithms, and it is found that the performance of the proposed algorithms is improved by 65.6% and 53.3% in terms of TEC, 7.2% and 7.3% in terms of PDR, 33.9% and 45.8% in terms of E2D from ACSO and Dragonfly Optimization respectively. The simulation results elaborate on the performance improvement in each performance metric and prove the effectiveness of the proposed work.