Effective Data Aggregation Moel for the Healthcare Data Transmission and Security in Wireless Sensor Network Environment

Abstract

Data aggregation is the process of collecting and combining information from multiple sources to provide a unified view or summary. In various fields such as statistics, economics, and data analysis, aggregating data helps reveal patterns, trends, or general insights that may not be apparent when examining individual data points. Aggregated data can provide a more comprehensive perspective, facilitating decision-making and strategic planning. This paper explores the application of Sugeno Fuzzy Model Monkey Swarm Optimization (SFMMsO) in healthcare, specifically focusing on data aggregation and security within Wireless Sensor Networks (WSN). The study demonstrates SFMMsO’s efficacy in aggregating patient health data, generating comprehensive Aggregated Health Indices. It also highlights SFMMsO’s optimization capabilities, refining decision variables to enhance healthcare algorithm performance. The paper addresses the critical dimension of security, showcasing SFMMsO’s adaptability in optimizing security parameters and assessing its vulnerability to various attack types. The findings underscore SFMMsO’s potential as a robust tool for healthcare applications, emphasizing the need for proactive security measures to fortify its resilience against potential threats. This study contributes valuable insights into the intersection of optimization algorithms, data aggregation, and security in healthcare, paving the way for advancements in utilizing SFMMsO for secure and comprehensive healthcare systems.