Machine Learning in the Internet of Things Applications

Isn’t it fascinating when you take a traditional device like a watch & you add computing, connectivity and a layer of intelligence to it, you end up transforming the user experience?

What is the Internet of Things? We could get physical devices and connect them in a smarter way to the internet to transform the user experience that is called the Internet of Things. There is a whole range of possibilities you can imagine where you can use IoT, the most common things that you can talk about is smarter home. Imagine driving in your garage door, lighting your home and turning on music all working together to create a better home. A farmer managing an entire farm by a smartphone. The security camera, the irrigation equipment, the temperature sensors all of them can be connected so that it can work better together. In the city public transportation system, you could manage traffic easily, bus schedules, parking spots, maintenance and you can create a much better experience for people living in the city. First, you need to make sure that you’re well aware of what Machine Learning is?

Bringing the Internet of Things & ML to Healthcare

Healthcare institutions today are increasingly adopting technology that offers diverse remote monitoring, healthcare delivery, and in-home diagnostics capabilities, which leverage Machine Learning and the Internet of Things. Smart healthcare systems today, for instance, incorporate round-the-clock real-time data streaming, medication reminders, and real-time medical diagnosis, all of which reduce the need for hospital visits. The IoT – also commonly known in the healthcare industry as the Internet of Medical Things (IoMT) – consists of any and all medical devices, patient monitoring tools, wearables, and other sensors that can send signals to other devices via the internet. These tools generate massive amounts of data that must be stored, integrated, and analyzed in order to generate actionable insights for chronic disease management and acute patient care needs.

Predicting patient deterioration or infection in the inpatient setting requires continuous feedback from bedside devices, while home monitoring tools such as Bluetooth-enabled blood pressure cuff, scales, and pill bottles can keep patients adherent to chronic disease management protocols outside of the clinic. Sensors, Machine Learning, Big Data Analytics, and Blockchain are vital technologies for IoMT as they provide multiple benefits to patients and facilities.

Agriculture Industry Embracing the Internet of Things

We all know how mobile phone communications are impacting agricultural communities. IoT will further extend and amplify this impact. With IoT, farmers can monitor and manage every tiny detail — from the moisture, nutrient and acidity contents of soil and individual plants to the presence of pests and local weather patterns to the health and well-being and minute-to-minute movements of cattle. And as these elements become connected, farmers stand to gain huge insights from Big Data Analytics that enable better decisions.

IoT and remote sensing have helped unearth new innovations. For example, GroundCover uses farm imagery to map and predict potato yields; its CanopyCheck app adds geolocation information to farmers using smartphones. Monsanto’s Integrated Farming System (IFS) uses science-based analytics tools such as FieldScript for advice on planting, precision seeding, and genetic gain. GrowSafe Systems uses sensors and analytics to track the movement and health of cattle and help farmers deal with disease detection and prevention.

IoT in agriculture can unleash a range of benefits. It can increase the productivity of agricultural workers by automating processes such as switching on remote farming equipment, which also reduces travel costs. More responsive health care services can also be provided for livestock, and their health indicators can be tracked via wireless sensors. IoT, along with analytics, can reveal new insights into the seasonal planning of crops, and thus increase food safety and mitigate crop failure risks.

IoT Applications in Transportation

As the Internet of Things (IoT) continues to connect the world around us, some of the most exciting digital transformations are emerging in the transportation industry. Already, we see cities and states piloting and deploying IoT-enabled technologies that connect transportation infrastructures, Mass transit systems, roads and highways, signage and street lights to vehicles and everything in between. Together, these connections promise to create streamlined, frictionless transportation, while increasing safety and sustainability.

Artificial Intelligence and Machine learning will become much more relevant in the transportation sector in the future, enabling more automated, predictive analytics, and therefore, better decision-making. AI and ML will increase roads and highways safety and efficiency by making it possible to predict when to deploy emergency response vehicles, tow trucks, snowplows and more. For example, by aggregating and analyzing current and historic weather, microclimate and traffic data, transportation agencies can preemptively deploy salt trucks to roadways that often ice over, just before they begin to freeze. Subsequently, they can predict when fog is likely to appear on hyper-local sections of roadways and warn drivers.

These types of predictive decisions, powered by AI and ML, enable transportation to move with fewer disruptions, keep costs down and ensure safer travel.