Mitigate Inter-WBAN Interference in Body-to-Body Network to Restrain Epidemic Spread

Rapid growth of wireless technology and machine learning fueled demand for ubiquitous healthcare service in India. This necessity is served by an emerging technology called wireless body area network (WBAN). This facilitates an individual to be aware of his health status anywhere anytime without any assistance. In case of any emergency, this network is capable to initialize other automated systems. During an epidemic, if we can early detect a susceptible individual, spread of the disease can be curbed. In our paper, early detection is achieved using multiple cooperating WBANs that leads to a network called Body-to-Body Network (BBN). We have also proposed quarantine strategies by minimizing contact between different staged WBANs based on their health status. An unsupervised learning algorithm is used to efficiently divide the area into non-overlapping clusters minimizing inter-WBAN interference. We have considered two test case scenarios based on how the WBANs are distributed in BBN architecture. OMNet++-based simulator Castalia $$ 3.2 is used to evaluate routing protocol in BBN network. Performance of our system is assessed based on network parameters like Packet Delivery Ratio (PDR). Results ensure that our method guarantees low epidemic spread of disease in enclosed area by enhancing throughput and minimizing interference of our stable system.