The future of smart cities will be built on edge computing infrastructures to maximize data processing. Edge computing is increasingly becoming the solution for managing big data and remote work in smart cities.
As smart cities evolve, a growing amount of attention is being placed on ways to reduce network clutter and latency. Many smart applications require fast or instant response time, such as sprinklers going off when sensors detect fire.
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Gathering and processing data at the same location is one solution to overcoming the complexity and challenges of big data transmission. By keeping computing at network edges, there’s less chance of latency due to shorter data transmission distance. Instead of sending data to the cloud, data is processed near its source, although it requires a certain amount of processing power to be self-reliant.
Self-driving cars generate enormous data when you consider their numerous cameras. These vehicles typically generate up to 1-5 terabytes of data per hour. That’s why they shouldn’t rely on the cloud, which might have 99 percent uptime, but that doesn’t guarantee it will always be on. Edge computing makes autonomous vehicles much safer than if they relied on data transmission to and from the cloud. The longer the distance data must travel to reach its endpoint, the more chances it can slow down from network congestion. Keeping data transmission as lean as possible is the essence of edge computing for smart cities.
Another advantage to edge computing is that it allows you to respond immediately when situations call for quick decision-making. Smart cities are set up for managers and analysts to review data in real time so that swift decisions can be made on issues such as cybersecurity or resource allocation.
Edge computing allows you to perform analytics at the device that’s gathering data. It’s a more convenient solution for systems that generate large volumes of continuous data, such as video surveillance cameras.
Edge Computing Services
Industries that benefit from edge computing services include law enforcement, military, and aerospace. Any IoT device that generates terabytes of data on an hourly basis may be more efficient in an edge computing infrastructure. Here are some of the many services suited for edge computing:
Sensor tracking – Analysts can monitor sensors locally and restrict interaction with the central server to selected applications.
Employee messaging – Edge networking facilitates an alternative to a web connection when integrated with high bandwidth that allows for employee messaging.
LED Streetlights – Not only do LEDs last longer, they use energy more efficiently and cut costs. Due to this level of sustainability, there’s less need to monitor and transmit large amounts of data long distances.
Independent Sensors
The more you use IoT sensors for automated functions, the more potential to limit heavy data transmission. Sensors that have self-regulating capabilities typically provide greater privacy and security benefits. As long as these sensors have adequate computing power, they won’t be affected by network downtime. Eventually, more and more businesses will adopt 5G, which will contribute to more efficient data transmission when large data loads are necessary.
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Edge networks are particularly important for machine learning capabilities. Ideally, the machines themselves contain a wealth of data, but they can also be connected to the internet for access to data from other resources. All IoT sensors need connectivity for updating and potential data sharing. Smart cities will rely more on artificial intelligence as time goes on, which is more powerful the more it draws from diverse sources.
Always On Requirements
In order for smart cities to live up to their name, they must subscribe to an always on policy. Traffic lights, for example, must always be working to ensure public safety. Much of the data generated by traffic lights can be categorized as surplus data that doesn’t require immediate attention. By deploying edge computing, you are freeing up your network while reducing large loads of data transmission.
How Smart Cities are Evolving
Smart cities have grown out of models established by utilities, manufacturers, logistics firms, and local governments. The need to conserve bandwidth has been a top concern, although 5G will provide plenty more room for mass data transmission. Here are some of the key recent developments that have significantly improved smart cities:
- Edge computing with powerful processing is now used for roadways and parking lots.
- Higher quality connectivity with 5G allows for exponentially more IoT devices and data sharing on a network.
- Containerized microservices can now be distributed across multiple clouds.
- Applications are developed at a faster rate via DevSecOps, which has built-in automated security.
Another development embraced by emerging smart cities is the hybrid cloud solution. Red Hat, which was recently acquired by IBM, has collaborated with NVIDIA on a hybrid cloud model that combines edge processing and cloud processing. The NVIDIA EGX platform is useful for both edge computing and transmitting data-intensive content such as graphics.
Red Hat provides open source technologies that allow for blending private and public cloud solutions. It can be used to connect a data center with edge sensors placed in buildings, roads and transit stations.
Red Hat’s suite of customized solutions are practical for facilities that have built infrastructures with multiple clouds and a wealth of edge sites. The developer’s OpenShift software allows smart cities to build useful applications. Red Hat Enterprise Linux allows for customization of edge orientation. Meanwhile, IBM Edge Application Manager allows you to manage workloads that interface with up to 10,000 edge devices.
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Businesses are empowered by Red Hat solutions because they are able to deploy various IoT sensors for different vendors then transmit to other edge devices within close proximity. A rules engine at the sensor determines which data is processed locally and which data is transmitted to the cloud.
Conclusion
Edge computing continues to evolve in the direction of making big data simple for smart cities. User-friendly administrative dashboards and simple seamless ways to collect or process data are helping drive edge computing to the forefront of modern business.