WG-SLSQP: Weighted Geometric Based Sequential Least Square Programming for Sink Node Placement in WBAN

Abstract

Wireless Body Area Networks (WBANs) have significantly transformed human life, particularly in sectors such as healthcare, fitness, entertainment, and sports. The strategic placement of sink nodes in WBANs plays a pivotal role in influencing network connectivity, power efficiency, and overall network performance. In the context of designing WBAN sink placement, the primary challenges revolve around ensuring energy efficiency and robust connectivity. For monitoring a patient’s vital signs, sensor nodes are implanted at various locations within the patient’s body, which transmit physiological data to a central hub known as a sink node. The selection of the optimal position for the sink node is crucial in minimizing node energy consumption during data transmission. Addressing the complexity of the problem, this paper introduces four approaches; (i) Weighted geometric-based sequential least square programming (WG-SLSQP). WG-SLSQP incorporates three key approaches: a) Geometric median b) Weighted average technique and c)Sequential least square programming technique, (ii) Humpback Whale Optimization Algorithm (HWOA), (iii) Distance-based random mean shift (D-RMS), and (iv) Voronoi-based Positioning (VP) for sink node placement. WG-SLSQP demonstrates greater stability and lower localization errors as compared to D-RMS, HWOA and VP. The residual energy of WG-SLSQP is 93.6%, D-RMS is measured at 92%, while VP and HWOA exhibit values of 90.4% and 90% respectively. Moreover, the Average Localization Error (ALE) for WG-SLSQP is 0.500 m, D-RMS is 0.568 m, whereas VP and HWOA have ALE values of 0.60 m and 0.619 m, respectively. The results indicate that the suggested WG-SLSQP approach outperforms its predecessors.

Keywords

• Wireless body area network
• Humpback Whale Optimization Algorithm (HWOA)
• D-RMS
• node placement
• D-RMS shift clustering