In 2020, the IoT industry shifted its focus to adapt during the COVID-19 pandemic, where the top priorities were to keep people safe and healthy and to find better protocols through technology. This COVID-19 pandemic forced to invent new IoT innovations and accelerated the development of new use cases.
Patient monitoring with IoT sensors has the capability to improve patient care quality and reduce healthcare costs. Healthcare providers can use robotics to assist with routine processes, such as patient monitoring or consumables transport.
When combined with IoT, AI can provide greater insight to healthcare use cases. Organizations can aggregate IoT data from disparate sources and apply an AI model to the data to predict machine failures and health threats before they wreak havoc. The COVID-19 pandemic showed many organizations how they can better use IoT devices to improve services and protect every individual.
Through remote monitoring sensors and improved telemedicine, healthcare providers can minimize the number of healthcare workers who must be in contact with an infected patient, while still maintaining patient care. Healthcare providers can also use the technology to perform contact tracing and keep track of individuals who have come into contact with COVID-19.
With the massive amount of IoT-generated data, organizations must turn to machine learning algorithms for real-time data processing. Use cases, such as patient monitoring, autonomous vehicles or predictive maintenance depend on instantaneous actions to keep people safe. Organizations can rely on a variety of open source products to address challenges of IoT deployment without significant upfront costs. Admins can use Apache Kafka or Apache Flink to create real-time data pipelines and Apache Ignite for distributed in-memory computing. Kubernetes simplifies the process to deploy and manage container applications. Distributed computing engines, such as Apache Spark, aggregate and analyze IoT data.