Distance Energy-Efficient Soft Computing Model for Data Forwarding in Healthcare Sensor Network

Abstract

Data forwarding is a crucial process in computer networking and telecommunications. It involves the transmission of data packets from a source to a destination within a network. The forwarding process is fundamental for enabling communication between devices on different segments of a network and plays a vital role in maintaining the overall performance and reliability of the network infrastructure. This paper presents the development and evaluation of the Threshold Congestion Data Forwarding Soft Computing (TCDF-SC) algorithm, designed for healthcare sensor networks. The algorithm combines threshold-based congestion management with soft computing techniques to enhance data forwarding efficiency in dynamic healthcare environments. Through extensive simulations, TCDF-SC demonstrates scalability, achieving high total packets forwarded and maintaining a reliable packet delivery ratio as the network scales. The algorithm minimizes average transmission delay and optimizes energy consumption, ensuring timely and energy-efficient data transmission. Comparative analysis against existing protocols, LEACH and BCP, highlights TCDF-SC’s superior performance across key metrics, positioning it as a promising solution for healthcare sensor networks. This research contributes to the advancement of data transmission optimization in healthcare, addressing critical requirements of reliability, energy efficiency, and adaptability in dynamic healthcare settings. Further validation and real-world experimentation will enhance the algorithm’s applicability in diverse healthcare scenarios, fostering advancements in patient monitoring and healthcare applications.