Analytics of Things

The analytics of things (also known as AoT) is the analytics layer that occurs with the Internet of Things (IoT) devices and their generated data. IoT is the sharing of network connectivity between all devices connected to a squared grid. IoT devices have sensors that monitor and generate data. AoT is the analysis and transformation of this raw material into information and knowledge to retrieve its business value and related opportunities.
Components
Machine Layer enlists Embedded sensors that act as the fingertips of IoT that measure parameters of the physical world. Local processing and local devices gather sometimes sorted data and transfer it securely to the cloud or a database. Multi-protocol gateway involves proxies of aggregate raw data from multiple types of sensors. A Communication channel can range from wired to wireless communication protocols such as ZigBee (device specific), Bluetooth, Wi-Fi, 3G, 4G, 5G and LPWA networks. A Data Transformation layer ingests data and transforms sensor data from different protocols using protocol adapters and data interpreters into higher level data. Data processing & storage layer enables the storage of massive volumes of data. Popular tools like Hadoop, MongoDB are used for NoSQL queries. Analytics layer involves extracts value from the sourced data. Machine learning algorithms can be run to extract knowledge and insights to ensure optimal business decisions. Tasks can range from Supervised to unsupervised learning. The Application layer, which includes enabled centralized monitoring, is often used in decision-making assistance and BI reports.
Shipping Industry
The shipping industry can present challenges for transporters as ships are inherently confined in the oceans during the transportation. The transporters need to provide products in a timely manner, which without careful planning can be difficult. GPS and IoT technologies help to tackle the supply chain challenges of the shipping industry.
The Swedish telecommunication company Ericsson has developed an IoT platform to this end: The Ericsson maritime ICT Cloud gives stakeholders, producers, and transporters access to advanced real-time data analysis that can transform raw data harnessed via GPS and sensors (in the ships, engines, heavy machinery) into actionable business insight. The Ericsson maritime ICT Cloud monitors the entire supply chain (Ericsson):
*Routes can be optimized taking into account insights based on weather conditions and ocean streams data to reduce fuel consumption and decrease delay in the supply chain.
*Predictive maintenance: constant technical checks by sensors to ensure business continuity. In case a spare part is required ships can directly and automatically communicate with parts manufacturer.
*Reduced insurance premium as insurers will always know that insured vessels are properly maintained.
*The overall supply chain is improved: optimization and monitoring of ship traffic (which docks are saturated or not) less delay with trucks, shorter transit time for cargo. Conclusion: From the raw data harnessed via sensors we are now transforming the maritime transport industry with real-time data, and bring innovation and intelligence to the entire ecosystem.
Wearable Devices and Healthcare
The healthcare industry has been fast to pick up the IoT. It is estimated that by 2020, healthcare’s spending on IoT will reach $1trillion (Kaaproject). Specifically, the wearable device market has a forecasted value for 2018 of $12,642m, of which healthcare and fitness wearables amount to $867m (Statistica). Companies like AngelSensors are working in conjunction with the healthcare sector to draw a greater value from them through targeted analyses. Their latest offering monitors the physical activities and vital signs like heart rate, body temperature, sleep quality, the number of daily steps taken and more. This data can be accessed by the subject and their monitoring physician for evaluation and treatment as needed. The vision is to achieve a preventive care service and personalized wellness (AngelSensor). The quality of health greatly depends on time and fast decision making. Live data is primordial and accurate analysis are key to solving the issues. For example, elderly people can be more susceptible to preventable health issues that may be quickly addressed with active monitoring. Wristbands can monitor their vitals and send the data directly to their physician. On a large scale, whether it is a medical institution, a health center, research center, or insurance provider, gaining access to such data in a live stream allows them to draw meaningful results that can correlate certain activities and situations to a certain period of time. Getting a repetitive indication of similar symptoms can be valuable information for health centers that will warn against a possible epidemic. This data can be a valuable raw material for medical research as well.
Trucking Industry
The truck transportation industry is also being affected by the use of IoT technologies. German truck producer Daimler is using IoT development to make its vehicles more secure and more efficient. Daimler trucks are equipped with 400 types of sensors and cameras capable of using GPS data. The company has built a system to allow semi-autonomous driving and automatic communication between trucks known as The Daimler Highway Pilot System. The connected truck network provides real-time information to all participants in the logistics network. Highly automated driving has numerous advantages: improved safety, efficiency, and ecological sustainability. By using the Daimler Highway Pilot System, trucks in semi-autonomous mode can communicate with each other, to improve the overall supply chain: This allows the trucks to travel in tightly-spaced groups, helping to improve aerodynamics by maximizing the slipstream effect, which reduces fuel consumption and greenhouse emission by up to 7% (Daimler, 2016). Also, space efficiency of the vehicles is greatly enhanced. 3 typical tractor trailer trucks, for example, will occupy a footprint within the roadway of approximately 80 meters. This is compared to similarly-sized trucks that typically occupy 150 meters or more to maintain safety and roadway compliance. Hence, the use of the Daimler Highway Pilot System can allow businesses and roadways to accommodate more vehicles (Daimler, 2016). This results in reduced waiting times while loading and unloading trucks, and the overall supply chain is improved with smoother transport flows The Daimler Highway Pilot System is sufficiently advanced to detect and monitor other roadway vehicles as part of the autonomous driving process. The system also takes into account GPS and map data to adjust the speed/distance between monitored trucks, compensating for continuously changing road conditions.

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