3rd International Conference on Bioinformatics and computational studies (ICBCS 2025)

Accepted Papers

IOT-d Riven Smart Healthcare S Ystems : a Ddressing S Ecurity, Interoperability, and Sustainable Implementation

Farheen Fathima A. H., Independent Researcher, Former Associate Software Developer, EMIS Health, Chennai, India

ABSTRACT

IoT-driven smart healthcare systems have the potential to revolutionize patient care through real time monitoring, personalized treatment, and improved operational efficiency. However, critical research gaps remain in security, interoperability, and sustainable implementation. This paper identifies insufficient exploration of AI-driven data optimization, rural connectivity frameworks, and longitudinal device performance as key barriers to scalable adoption. Empirical evidence from recent trials indicates a 58% failure rate in emergency alert synchronization when integrating legacy systems and a 41% breach reduction achieved by employing zero-trust security architectures. To address these issues, we propose adopting standardized communication protocols, edge computing frameworks, and robust zero-trust security architectures. This paper further outlines a comprehensive methodology to evaluate the current state of IoT healthcare, identifies primary research gaps, and offers targeted solutions to overcome implementation challenges.

Keywords

IoT healthcare, interoperability, edge computing, cybersecurity, rural connectivity, AI-driven optimization, biosensors.

Cost-effective Bioinformatics using Serverless Cloud Architectures

Olivier Gatete, Senior Lecturer, Texila American University, Zambia

ABSTRACT

Cloud computing has become essential in bioinformatics due to its scalability and flexibility. This study explores the use of serverless architectures to reduce the cost and complexity of genomic data analysis. We design and implement a serverless pipeline using AWS Lambda to execute key bioinformatics tasks—quality control, alignment, and variant calling—triggered through object storage events and orchestrated with AWS Step Functions. Performance and cost metrics are compared against a traditional virtual machine (VM)-based setup using whole-exome sequencing data. Results show that serverless architectures can significantly lower operational costs and simplify deployment without sacrificing performance for moderately sized datasets. This research highlights the potential of serverless computing as a cost-effective, scalable solution for bioinformatics workflows in both research and clinical settings.

Keywords

Serverless Computing, Bioinformatics Pipeline, Cloud Architecture, Genomic Data Analysis, Cost Optimization