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Location, location, location! It turns out that mantra is not just for the real estate market. Location is a critical aspect of fog computing as well.
Cisco introduced the notion of fog computing about two and a half years ago. (See Cisco unveils ‘fog computing’ to bridge clouds and the Internet of Things.) This distributed computing architecture addresses the challenge of backhauling a lot of raw data generated in the field –say from thousands or millions of IoT devices – to the cloud for analysis.
Fog computing, also called edge computing, has some of the data processing and analysis take place at the edge location, close to the devices that are generating the data. The edge nodes have sufficient processing capabilities to capture, distil and analyze data and send only the most relevant information up to the cloud where further action can take place; for example, sending an alert that a piece of equipment in the field is about to fail.
It’s easy to conceive how this works when the edge node is in a permanent location, say on a factory floor, and it can communicate with the corresponding cloud component over Wi-Fi with relative stability. But what if the location of the edge node is always changing because it is, quite literally, in motion? For example, consider the edge node being on a bus, on a megaship full of shipping containers, or on a first responder vehicle like a police car or ambulance.
Unlike in real estate, a questionable location doesn’t have to be a show stopper. LILEE Systems has recently announced a fog computing platform to address the broad needs of mobile deployments in distributed enterprises.
Since its founding in 2009, LILEE has focused on the challenges of enterprise organizations deploying and managing equipment and keeping people, machines and IoT devices connected to the corporate network as they became more mobile. The company honed its fog computing solutions on the railroad industry, where trains and working crews present a mobile component but there is also equipment on the wayside that is doing sensor monitoring of the track conditions. LILEE has put network equipment on trains and on the wayside, and has software management tools to manage M2M communications and look for alerts and conduct analytics.
The company says it does business with five of the seven Class I railroads in the U.S., and is now branching out to other markets that can benefit from having fog nodes at the edge. Specifically, the company is targeting markets such as freight and supply chain; industrial, with remote facilities and monitoring needs; smart cities, including distributed traffic lights and video surveillance; first responders such as police, ambulance and fire trucks; retail where there is a need for point-of-sale backup, digital displays and other in-store services; commercial fleets with a vast mobile workforce; and education, both at schools and in buses.
In mid September, LILEE announced a series of hardened fog computing gateway devices and a cloud management platform designed to make it easier for distributed enterprises to deploy and manage their nodes at the edge.
The LILEE TransAir STS product series is a “5 in 1” gateway—basically an industrial PC with routing capabilities. The platform is comprised of cloud management, communication, and fog computing capabilities, along with application interfaces and sensors. In designing this product, LILEE says it considered the needs of the IT group to have the gateway device be as easy as possible to set up and configure so that remote deployment doesn’t require significant IT skills.
LILEE provides cellular and Wi-Fi router capability in the box that enables connection to the cloud. As the device is powered up, it connects to LILEE’s backend cloud-based management offering, T-cloud, to get its configuration and provisioning information. Once the device identifies itself, the applications it needs can be downloaded. For example, if this edge device is going into a coffee shop, it might need applications for point-of-sale, digital signage and customer Wi-Fi. All of that can come straight from the cloud platform, run on the local platform, and be managed from the cloud.
The gateway supports a variety of local interfaces, including Ethernet, serial port, USB port, HDMI for display, and more. It also supports sensors such as OBD for vehicle analytics, gyroscope and accelerometer for anything that is changing location, and digital I/O for any sort of binary emergency or panic switches.
Software that runs on the fog computing’s industrial PC can enable the applications to interact through customized interfaces as well as through the various sensors, and all of that information can be analyzed on that fog computing engine locally or up in the cloud. The real benefit is being able to distribute that analysis between the IT department that is looking at that from the cloud or from the branch side that is actually managing their own environment locally.
The value of this solution can be better understood through a couple of use cases.
A long-haul bus company has a video system installed to monitor the passengers and make sure everyone is sitting calmly. The bus is moving so the only way to get that information to the cloud is over LTE. It’s too expensive to funnel all that video straight to the cloud for analysis, so an on-board fog platform does the video analysis locally. Most of the data is going to be benign as people sit quietly, but if some passengers get into an argument and cause a stir that might give the driver some concern, the analytics software on the fog node sends up an alert and starts sending the video stream back to the bus company’s cloud instance. There it can be permanently recorded in the event that an incident ensues.
In another bussing scenario, there’s a company that provides private bussing services to corporate clients who have employees that commute long distances. The passengers are all picked up in one location and may spend two or more hours on the bus daily to get to and from a work location. The bus company has a fog computing node on the vehicle that enables the corporate client’s business applications for these workers, so they can use a VPN to get to email, videoconferences, etc. Again, bandwidth matters, so some of the processing is offloaded to the local node before being passed off to the cloud.
In a first responder situation, there is a mission critical aspect of video surveillance, tracking license plates, sending a patient’s vital signs to a hospital, or detecting where all the firefighters are in a fire. These kinds of things can be core applications that are deployed locally to the police, fire or EMT vehicles. In addition, the vehicles themselves may need to be monitored to make sure they are running well. LILEE’s solution provides connectivity to the OBD2 port which monitors and manages all the sensors across the vehicle. Relevant data can be sent to the cloud as needed, say to notify a dispatch group of dangerous situations in the field.
As mobility increases and IoT proliferates, distributed enterprises will be challenged to support applications in their remote locations. LILEE’s fog computing platform looks like a good way to support them.