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Edge (fog) computing shifts computing applications, data, and services away from centralized servers to the extremes of a network. This enables analytics and knowledge generation to occur at the source of the data.

FOVI is designed to collect sensor data generated on the factory floor, production-equipment logs, production plans and statistics, operator information, and to integrate all this and other related information in the cloud. In this way, it can be used to bring visibility to production facilities, analyze and predict outcomes, and support better decisions for improvements.

Precision farming enables farmers to increase production (yield), lower their operational costs and economize their applications of chemicals and fertilizers. PCM focuses on improving crop yield through the analysis of real-time data from a variety of environmental sensors and other data sources located in commercial crop fields or throughout the enterprise.CHALLENGES / REQUIREMENTS- Present systems are very costly and typically overuse water and fertilizers due to lack of visibility- Many plants flourish in a very narrow soil moisture range- Global population growth is placing pressure on the food supply - Arable land is decreasing due to climate changeSTAKEHOLDERS- Farm owners- Irrigation systems providers- Equipment OEMs- Cloud platform providers- Sensor manufacturersKEY OBJECTIVES- Optimize the use of fertilizer, pesticides, and water to minimize cost and waste- Improve production output by precisely managing plots according to hyper-local requirementsTYPICAL SYSTEM CAPABILITIES- Use of aerial imagery and sensor technologies to collect data on soil humidity, fertilizer and pesticides levels, sunlight, and other meaningful variables- Send data to a central platform for analysis over a near real-time network- Automate activities such as watering schedules based on crop conditions USAGE VIEWPOINT- Feedback from sensors in farm is given to analytical software, which defines levels of water, pesticides and fertilizers to be provided to the cropFUNCTIONAL VIEWPOINT- Sensors: Flow, Chemical- Network: Low-power Wide Area Network (LPWAN), cellular, mesh- Standards: - Augmented Intelligence: Descriptive analytics- Augmented Behavior: Machine-to-machine (M2M) interfaces

Automated Fleet Management solutions to connect vehicles and monitor driver activities, allowing managers to gain an unprecedented level of insight into fleet performance and driver behavior. This enables them to know where vehicles and drivers are at all times, identify potential problems much sooner and mitigate risks before they become larger issues that can jeopardize client satisfaction, impact driver safety or increase costs.

Industrial Internet Consortium members Hewlett-Packard and Real-Time Innovation have joined together on the Edge Intelligence Testbed. The primary objective of the Edge Intelligence Testbed is to significantly accelerate the development of edge architectures and algorithms by removing the barriers that many developers face: access to a wide variety of advanced compute hardware and software configurable to directly resemble state-of-the-art edge systems at very low cost to the tester/developer.

Air quality monitoring (IAQ) is carried out to assess the extent of pollution, ensure compliance with national legislation, evaluate control options, and provide data for air quality modelling. It is particularly important in chemical plants or other facilities with potentially harmful concentrations of pollutants.CHALLENGES / REQUIREMENTS - A system is needed to sense air quality at static and moving locations (on car body) all over the city and feedback the data to the lab over networkSTAKEHOLDERS - City residents- Meteorological labsKEY OBJECTIVES - To have improved air quality in the city- Save environment- To detect air quality at various locations in the city and detect the reasons for its deteriorationTYPICAL SYSTEM CAPABILITIES - A system can detect air quality at stationary locations, as well as it can also be mounted on car body- It continuously sends data to centralized monitoring system, which can log, analyze and predict the air quality of a particular locationUSAGE VIEWPOINT - Sensors are mounted on stationary as well as on moving car body to detect harmful gases and substances in air all across the city- These sensors send real-time information to the central location- Software analytical tools monitor the real-time data and can find the air quality deteriorating premises in the city as per the density variation and trends in the data logFUNCTIONAL VIEWPOINT - Sensors: Chemical- Network: Wide Area Network (WAN) - Standards: - Augmented Intelligence: Predictive Analytics- Augmented Behavior: Machine-to-machine (M2M) interfaces

Indoor Positioning Systems are used to locate persons or objects inside buildings, as opposed to GPS which works outdoors.

Wearable products comprise smart devices equipped with microchips, sensors, and wireless communication capabilities. These devices collect data, track activities, and provide customized experiences to user’s needs and desires.

This testbed seeks to build an Intelligent Urban Water Supply Management Cloud Service that provides a fully integrated multi-function, multi-service, multi-role, multi-tenant solution. The important benefit of building a cloud service for this purpose is to take advantage of economies of scale, avoiding repeatedly building many independent systems across the country offering almost identical functions.

Intrusion Detection System (IDS) are devices or software applications that monitor network or system activities for malicious activities or policy violations and send reports to a management station.

Quality control is a vital aspect of stock control - especially as it may affect the safety of customers or the quality of the finished product.

Early predictions on equipment malfunctions and service maintenance can be automatically scheduled ahead of an actual part failure by installing sensors inside equipment to monitor and send reports.