IoT based Wireless Networks in Hospitals: Ensuring Seamless Communication in Critical Situations

Madhura K1,*, Rahul Yadav2, Yuvraj Parmar3, Tressa Michael4, Kiran Sanjay Degan5, Prakriti Kapoor6

 

1Department of information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India

2Lead Application Architect, Information Technology, University of Rajasthan, Jaipur, India

3Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh-174103, India

4Asst professor, Department of Electronics and Communication Engineering, Rajagiri School of Engineering and Technology, Kerala, India

5Assistant Professor, Department of Law, Symbiosis Law School, Noida-Symbiosis International (Deemed University), Pune, India

6Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India

Text Box: Abstract The heading "Wireless Networks in Hospitals: Ensuring Seamless Communication in Critical Situations" examines hospital wireless network enhancement. When patient well-being is at stake, this strategy encourages honest conversation. Service quality, resource efficiency, and network security are crucial. These mathematical models increase hospital wireless network stability based on Internet of Thing (IoT). Service management effectiveness influences who gets vital medical information quickly. Information and crucial messages are delivered faster. A mathematical technique considers the relevance and transmission time of each data payload to estimate its priority factor (P(i)). Network performance determines QoS settings. Priority data is transmitted first to ensure quick delivery to the intended recipients. This technology is essential for updating hospital WiFi networks, especially in critical situations where it can transmit accurate and timely information and save lives. WiFi reliability is essential for building operations. Compare failure frequency and MTBF to assess each network point's reliability. An exponential reliability function determines network dependability. The mean time between failures is used. This method maintains network functionality despite its complexity. Determine which pieces are crucial and how they influences network health. This simplifies network backups and maintenance. Load balancing distributes network tasks among entry points. This strategy helps the network function smoothly and minimize congestion during peak demand. The weighted round-robin timing algorithm determines how busy each access point is to send fresh network traffic to the proper areas. By equally distributing load and prioritizing underutilized access points, this method maintains network stability and keeps critical lines available. These three approaches form a full healthcare WiFi network strengthening plan. Mission-critical data is prioritized, the network is more robust, and resources may be allocated quickly. Our solution often outperforms the existing standard in network stability, communication, and cost. Emails: madhura.k@manipal.edu; rahul2706@gmail.com; yuvraj.parmar.orp@chitkara.edu.in; tressamichael6@ gmail.com; kiran.degan@symlaw.edu.in; prakriti.kapoor.orp@chitkara.edu.in  

Received: February 22, 2024 Revised: May 01, 2024 Accepted: July 24, 2024


Keywords: Communication Quality; Cost-Benefit Analysis; Critical Situations; Hospital Wireless Networks; Load Balancing; Network Reliability Enhancement; Internet of Thing (IoT); Quality of Service (QoS) Management; Seamless Communication