With the massive amounts of location data being generated every minute, it can be difficult to not only determine what is operationally relevant, but also to access that relevant data as soon as it is produced to analyze it and act on it. This is made more difficult when realizing that digital solutions providers sometimes treat “Big Data,” “Internet of Things,” and “Deep Learning,” as buzzwords instead of touchpoints for technological advancements, forging a gap between the solutions that are needed and what is on the market.
A major source of constant data generation is the Internet of Things, or IoT. Essentially, IoT includes sensors from those as tiny as coffeemaker switches, all the way to highly complex satellite sensors recording data in real time.
With all of the data IoT sensors capture, it can be difficult to discern any real meaning. Today’s data analytics tools have a difficult challenge: they must allow users to find the answers to their operational challenges hidden in the trends of their data, while also discovering deviations from so-called “pattern-of-life” or expected data values, which signal anomalies that must be addressed.
Smart Monitoring Appliance for IoT closes the gap by providing the framework for users to monitor and analyze IoT sensor data, as well as compare it to historical data, for both real-time analytics and trends analysis in 3D. Enhanced with Deep Learning and other Machine Learning capabilities, this digital and interactive analytics solution lets users focus on, and keep track of, how the assets they manage are moving and changing, with the capability to automatically detect those changes.
The solution also offers an unlimited number of integration options for various sensor and data types. When specific occurrences to an asset are detected within the data, notifications can be received through various channels to keep managers informed.
A Closer Look at the Data and What It Reveals
When talking about sensors and data types, specifically space-bound sensors, an interesting combination to consider is a SAR sensor with radar interferometry data processing. With this pairing, surface movements, like in the sample image below, are visible. In the example, which shows infrastructure monitoring, the red dots indicate the biggest surface movement, while the green dots indicate stability.
(Image: Hexagon) Data provided by Airbus Defence and Space GmbH.
Surface movement is dangerous for infrastructure (e.g., a building, if the surface movement is not homogeneous to it). In this case, the red dots indicate a threat that needs to be further validated with on-site sensors and surveying methods. A Smart Monitoring Appliance can add that information to the data delivered from the radar interferometry, enhancing the monitoring capability and closing the “last mile” left open from space surveillance only.
For infrastructure assets, there are a variety of sensors that can and should be monitored. German infrastructure operator Dataport GmbH collaborated with Fujitsu Technologies to create a proof of concept that embraces different sensors in different scenarios. The scenarios span from monitoring rolling shutter gates and temperatures in server rooms to the illegal lifting of manhole covers.
Believe it or not, monitoring manhole covers has become a new and increasingly complex challenge. It is a tremendous safety hazard for drivers and pedestrians if a manhole cover is removed. A missing cover has the potential to cause car accidents, injuries, and even death.
Another threat exists: the possibility that someone might raise manhole covers to destroy the underlying infrastructure. For example, if fiber optic cables are severed, the corresponding effect would be critical to public services, government agencies, and other organizations that rely on telecommunications infrastructure to operate.
Dataport: Employing Smart Monitoring for a Growing Concern
So how does Dataport take a Smart Monitoring Appliance approach to the threat of raising manhole covers? First, they use a browser application for monitoring the infrastructure, as well as monitoring the status of manhole covers (e.g. intact or raised). Second, they utilize a smart client application that performs two essential functions: monitoring management and network tracking.
In the monitoring management function, the monitoring of manhole covers can be altered as necessary. For example, during maintenance work, monitoring can be suspended so that work can be completed without triggering false alarms. This function can be carried out manually by red-lining the corresponding area in the smart client application, or it can be automated if the maintenance system can deliver the corresponding area via web services.
In the case of network tracking, when an alarm occurs, the application can determine a direct evaluation of the affected units based on location data.
In addition, there is a mobile application where local workers can change the status of monitoring if they are granted these rights, or report completion of maintenance.
In Dataport’s proof of concept, another component was added. A WFS service of the Urban Data Hub of the city of Hamburg provides the monitoring system with the boundaries and telephone numbers of the corresponding police stations. In case of an alert, this allows the police to be informed directly and given data such as the exact location, address and phone number of the affected area, as the next figure shows.
(Image: Hexagon.) Sensor data of parking lots as part of Ghent’s Open Data Policy.
For Dataport, the alarm is automated. In the proof of concept, an email alert has been selected, but SMS or other forms of alerting are also supported. An essential characteristic of the infrastructure is that the alarm is triggered directly when a threshold value is violated.
Streamlining Parking Processes with IoT Data for the Public
Transportation and traffic areas require many sensors for floating and stationary traffic. Another example of employing Smart Monitoring Appliances for IoT focuses on parking garage occupancy in the inner-city area of Ghent, Belgium.
(Image: Hexagon) A snapshot of real-time data.
Typical of a weekday during this time, the parking situation is quite relaxed in the snapshot, with a lot of parking spaces available to drivers. In fact, around 64 percent of the parking spaces are available. There is, however, a small roadside obstruction in the city center. This is indicated by the red-lined street segment (using HERE Technologies’ datasets that include the real-time traffic flow).
The traffic and parking data are polled every 60 minutes. This is called a pull method because the sensors do not provide the data independently; rather, a monitoring system requests the data. The selection is made according to different criteria such as criticality (a fire alarm should report independently that an asset is burning instead of waiting to be polled) or bandwidth requirements.
One standout feature of the solution is its ability to provide routing information to the parking lot a user selects. This is an especially beneficial feature due to the underlying traffic data being used to reflect the quickest route to any given parking lot.
In addition, based on historic data of past parking situations, the user can receive information on the likelihood of the parking lot to have the same, more, or less parking availability for a given time of day. In order to do this, long-term and shorter-term data are considered, to reflect seasonal influences like Christmas markets, or shorter-term events like construction hindering reachability, or a change in traffic direction.
Continuing Innovation with Smart Monitoring Solutions
What’s next for Smart Monitoring Appliances and asset tracking? The need to create a digital twin for infrastructure is becoming paramount.
Consider that we live among aging public infrastructure, such as bridges. Understanding the behavior and the dynamics of such infrastructure is critical to prolonging their use. By leveraging a digital twin in concert with sensor data and Smart Monitoring Appliances, infrastructure engineers and transportation planners would be able to test and monitor which elements put additional stress on the infrastructure – aiding in better lifecycle planning and management.