Machine Learning (ML) and IoT can Work Together to Improve Lives

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IoT devices are becoming popular nowadays. The widespread use of IoT yields huge amounts of raw data. This data can be effectively processed by using machine learning to derive many useful insights that can become game changers and affect our lives deeply.

The field of machine learning is growing steadily, along with the growth of the IoT. Sensors, nano cameras, and other such IoT elements are now ubiquitous, placed in mobile phones, computers, parking stations, traffic control centres and even in home appliances. There are millions of IoT devices in the world and more are being manufactured every day. They collect huge amounts of data that is fed to machines via the Internet, enabling machines to ‘learn’ from the data and make them more efficient.

In IoT, it is important to note that a single device/element can generate immense amounts of data every second. All this data from IoT is transmitted to servers or gateways to create better machine learning models. Data analytics software can convert this raw data into useful insights so that the machine can be made more intelligent, and perform better with cost-effectiveness and a long life. By the year 2020, the world will have an estimated 20 billion IoT devices. Data collected by these devices mostly pertains to machines. By using this data, machines can learn more effectively and can overcome their own drawbacks.

Now let’s look at how machine learning and IoT can be combined together. Let us suppose that I have some bananas and apples. I have got a sophisticated nano camera and sensors to collect the data from these fruits. If the data collected by these elements is fed to my laptop through the Internet, my laptop will start analysing the information by using sophisticated data analytics software and the cloud platform. Now if my laptop shows graphically how many bananas and apples I have left, it probably means that my machine (laptop) hasn’t learnt enough. On the other had, if my laptop is able to describe graphically how many of these are now ripe enough to be eaten, how many are not quite ripe and how many are very raw, it proves that my machine (laptop) has learned enough and has become more intelligent.

Storing, processing, analysing and being able to ‘reason out’ using IoT data requires numerous computational and financial resources to attain business and machine learning values.

Today an Airbus aircraft is provided with thousands of sensors to measure temperature, speed, fuel consumption, air flow dynamics, mechanisms of working, etc. All this data provided by IoT devices is connected to cloud platforms such as IBM Watson, Microsoft Azure, etc, via the Internet. Using sophisticated data analytics software, useful information is fed back to the machine, i.e., the aircraft. Using this data, the machine can learn very fast to overcome its problems, so that its life span and performance can be greatly enhanced.

Today, the IoT connects several sectors such as manufacturing industries, healthcare, buildings, vehicles, traffic, shopping centres and so on. Data gathered from such diverse domains can certainly make the infrastructure learn meaningfully to work more efficiently.

Giving a new deal to electronic vision
Amazon DeepLens is a wireless-enabled video camera and is integrated with Amazon Cloud. It makes use of the latest AI tools to develop computer vision applications. Using deep learning frameworks such as Caffe, MxNet and Tensorflow, it can develop effective computer vision applications. The device can be effectively connected to Amazon IoT. It can be used to build custom models with Amazon Sage Market. Its efficiency can even be enhanced using Apache MxNet. In fact, Amazon DeepLens can be used in a variety of projects, ranging from safety and education to health and wellness. For example, individuals diagnosed with dementia have difficulty in recognising friends and even family, which can make them disoriented and confused when speaking with loved ones. Amazon DeepLens can greatly assist those who have difficulty in recognising other people.

Why postpone the smart city concept?
Cities today are experiencing unprecedented population growth as more people move to urban areas, and are dealing with several problems such as pollution, surging energy demand, public safety concerns, etc. It is important to remember the lessons from such urban problems. It’s time now to view the smart city concept as an effective way to solve such problems. Smart city projects take advantage of IoT with advanced AI algorithms and machine learning, to relieve pressure on the infrastructure and staff while creating a better environment.

Let us look at the example of smart parking — it effectively solves vehicle parking problems. IoT monitoring today can locate empty parking spaces and quickly direct vehicles to parking spots. Today, up to 30 per cent of traffic congestion is caused by drivers looking for places to park. Not only does the extra traffic clog roadways, it also strains infrastructure and raises carbon emissions.
Today, smart buildings can automate central heating, air conditioning, lighting, elevators, fire-safety systems, the opening of doors, kitchen appliances, etc, using the IoT and machine learning (ML) techniques.

Another important problem faced by smart cities is vehicle platooning (flocking).This situation can be avoided by the construction of automated highways and by building smart cars. IoT and ML together offer better solutions to avoid vehicle platooning. This will result in greater fuel economy, reduced congestion and fewer traffic collisions.

IoT and ML can be effectively implemented in machine prognostics — an engineering discipline that mainly focuses on predicting the time at which a system or component will no longer perform its intended function. So ML with IoT can be effectively implemented in system health management (SHM), e.g., in transportation applications, in vehicle health management (VHM) or engine health management (EHM).

ML and IoT are rapidly attracting the attention of the defence and space sectors. Let’s look at the case of NASA, the US space exploration agency. As a part of a five-node network, Xbee and ZigBee will be used to monitor Exo-Brake devices in space to collect data, which includes three-axis acceleration in addition to temperature and air pressure. This data is relayed to the ground control station via NASA’s Iridium satellite to make the ML of the Exo-Brake instrument more efficient.

Today, drones in military operations are programmed with ML algorithms. This enables them to determine which pieces of data collected by IoT are critical to the mission and which are not. They collect real-time data when in-flight. These drones assess all incoming data and automatically discard irrelevant data, effectively managing data payloads.

In defence systems today, self-healing drones are slowly gaining widespread acceptance. Each drone has its own ML algorithm as it flies on a mission. Using this, a group of drones on a mission can detect when one member of the group has failed, and then communicate with other drones to regroup and continue the military mission without interruption.

In both the lunar and Mars projects, NASA is using hardened sensors that can withstand extreme heat and cold, high radiation levels and other harsh environmental conditions found in space to make the ML algorithm of the Rovers more effective and hence increase their life span and reliability.

In NASA ‘s Lunar Lander project, the energy choice was solar, which is limitless in space. NASA is planning to take advantage of IoT and ML technology in this sector as well.

IoT and ML can boost growth in agriculture
Agriculture is one of the most fundamental human activities. Better technologies mean greater yield. This, in turn, keeps the human race happier and healthier. According to some estimates, worldwide food production will need to increase by 70 per cent by 2050 to keep up with global demand.

Adoption of IoT and ML in the agricultural space is also increasing quickly with the total number of connected devices expected to grow from 30 million in 2015 to 75 million in 2020.

In modern agriculture, all interactions between farmers and agricultural processes are becoming more and more data driven. Even analytical tools are providing the right information at the right time. Slowly but surely, ML is providing the impetus to scale and automate the agricultural sector. It is helping to learn patterns and extract information from large amounts of data, whether structured or unstructured.

ML and IoT ensure better healthcare
Today, intelligent, assisted living environments for home based healthcare for chronic patients are very essential. The environment combines the patient’s clinical history and semantic representation of ICP (individual care process) with the ability to monitor the living conditions using IoT technologies. Thus the Semantic Web of Things (SWOT) and ML algorithms, when combined together, result in LDC (less differentiated caregiver). The resultant integrated healthcare framework can provide significant savings while improving general health.

Machine learning algorithms, techniques and machinery are already present in the market to implement reasonable LDC processes. Thus, this technology is sometimes described as supervised or predictive ML.

IoT in home healthcare systems comprises multi-tier area networks. These consist of body area networks (BAN), the LAN and ultimately the WAN. These also need highly secured hybrid clouds.
IoT devices in home healthcare include nano sensors attached to the skin of the patient’s body to measure blood pressure, sugar levels, the heart beat, etc. This raw data is transmitted to the patient’s database that resides in the highly secured cloud platform. The doctor can access the raw data, previous prescriptions, etc, using sophisticated ML algorithms to recommend specific drugs to patients at remote places if required. Thus, patients at home can be saved from life threatening health conditions such as sudden heart attacks, paralysis, etc.

In this era of communication and connectivity, individuals have multiple technologies to support their day-to-day requirements. In this scenario, IoT together with ML is emerging as a practical solution for problems facing several sectors.

Growth in IoT is fine but just how much of the data collected by IoT devices is actually useful, is the key question. To answer that, efficient data analytics software, open source platforms and cloud technologies should be used. Machine learning and IoT should work towards creating a better technology, which will ensure efficiency and productivity for all sectors.

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