Performance Evaluation of Distance-Based Random Mean Shift Clustering and Humpback Whale Optimization Algorithm for Sink Node Placement in WBAN

Wireless Body Area Networks (WBANs) have transformed human life, bringing about notable advancements in healthcare, fitness, entertainment, and sports applications. Sink node placement in WBANs has an impact on network connectivity, power efficiency, and overall network performance. Designing the placement of sinks in Wireless Body Area Networks (WBANs) poses two significant challenges: ensuring energy efficiency and establishing robust connectivity. To track a patient’s vital signs, sensor nodes of a WBAN are implanted in various locations throughout the patient. Additionally, a sink node receives the physiological data sent by the WBAN sensor nodes. As a result, choosing the best position for the central hub node turns out to be essential for minimizing node energy consumption during data transmission. Considering the hardness of the problem, this paper presents Distance-based random mean shift clustering(D-RMS). In D-RMS we apply: a) Random coordinator placement and b) Mean shift clustering algorithm to compute optimal position and compare it with the state-of-the-art approaches i.e.; Humpback Whale Optimization Algorithm (HWOA) for sink node placement. The best position of coordinator placement using D-RMS and HWOA is (97.9175, 115.76) and (97,100). The results demonstrate that the suggested approach (D-RMS) is more stable and has lower localization errors than earlier ones. Overall, the residual energy of D-RMS is 92% and HWOA is 90%. Moreover, the Average localization error (ALE) of D-RMS and HWOA is 0.568 m and 0.619 m.