Democratization of Intelligent Sensor Network for Low-Connected Remote Healthcare Facilities—A Framework to Improve Population Health & Epidemiological Studies

  • Santosh Kedari
  • Jaya Shankar Vuppalapati
  • Anitha Ilapakurti
  • Chandrasekar VuppalapatiEmail author
  • Sneha Iyer
  • Sharat Kedari
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 69)


Healthcare associated infections (HAI), or infections that are acquired in health-care settings are the most common detrimental events in health-care delivery worldwide. Millions of patients are affected by HAI worldwide each year, leading to high mortality rates and financial losses. Out of every 100 patients that are hospitalized at a particular time, 7 in developed and 10 in developing countries will be affected by at least one HAI. These infections are responsible for approximately 2 million cases and around 80,000 deaths per year in developing countries. The prevalence of HAI in rural areas is more frequent and acute than that of in the urban areas. One chief reason: “Connectivity gap”. Unlike many urban healthcare facilities where the providers usually have Dedicated Internet Access (DIA) with greater bandwidth and better reliability to aid acute services, the healthcare facilities in rural areas have low or no connectivity and are less equipped to prevent containment of HAI. This research paper provides an innovative and low-cost alternative to overcome “Connectivity” obstacle by developing de-centralized intelligent sensor network, based on MQTT, that bring connected intelligence to non-connected healthcare facilities. Thereby overcoming “Connectivity gap” barrier. The paper presents a prototype solution design, its application and a few experimental results.


MQTT MQTT-SN Electronic health records EHR Healthcare associated infections (HAI) Preventive healthcare Supervised machine learning Internet of things IoT Health Real-time stream analytics IoT architecture Sanjeevani electronic health records Association rule mining Naïve bayes classifier And outpatient 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Santosh Kedari
    • 1
  • Jaya Shankar Vuppalapati
    • 1
  • Anitha Ilapakurti
    • 2
  • Chandrasekar Vuppalapati
    • 2
    Email author
  • Sneha Iyer
    • 2
  • Sharat Kedari
    • 2
  1. 1.Hanumayamma Innovation and Technologies Private Limited HIG-IIBaghlingampally, HyderabadIndia
  2. 2.Hanumayamma Innovations and Technologies IncFremontUSA

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