Three Ways Edge Computing Will Affect the Future of Smart Cities

Discussions are occurring in major cities throughout the world between city planners, technologists, and citizens about how to make their city a Smart City. With ambitions for creating a better quality of life for citizens, Smart City planners must tackle a complex landscape of sensors and data processing technologies. Due to the rapid pace of innovation with technologies such as Edge Computing, Machine Learning, and Digital Twins, it’s often hard to keep track of how recent developments will affect the cities of the future.

Edge computing is the processing of data at the “edge” of a network, which can be either embedded on or deployed near field devices. In other words, edge computing is a distributed computing model that leverages the various edge devices on a network, in order to more efficiently compute sensor data as it is created. In this post, the Swim team highlights three Smart Cities use cases that will realize significant benefits from recent developments in edge computing and other related technologies:

Three Ways Edge Computing Will Affect the Future of Smart Cities
  1. Smart Transportation
    According to the ITSA, reducing traffic congestion and delays is the primary motivation for 71% of US states’ smart transportation plans. These smart transportation plans must also take into consideration the “one in five vehicles on the road worldwide [that] will have some form of wireless network connection...amounting to more than 250 million connected vehicles” on the road by the year 2020, as predicted by Gartner. Machine Learning, when deployed on edge devices, will enable connected traffic systems to engage with and react to both manned and autonomous vehicles. Traffic signals will be able to coordinate traffic signals, based on current traffic conditions, to optimize traffic flows and clear paths for emergency response vehicles.
  2. Smart Buildings & Commercial Real Estate
    As of the year 2016, 90% of all commercial real estate data was still unstructured, according to research by Deloitte. However, somewhere within this raw data lies insights that can reduce heating, cooling, and lighting costs, which Gartner has found are responsible for approximately 60% of a building’s energy consumption. With smart homes and smart commercial buildings representing 45% of total connected things in 2015, and estimates from Gartner that this will rise to 81% by 2020, edge computing will enable devices to reduce and transform sensor data locally into structured streams. This will make sensor insights consumable for applications, and greatly reduce the bandwidth required to operate smart building deployments.
  3. Smart Grid & Utilities
    According to Gartner, as of 2017, a total of 380 million connected things were in use to help cities deliver on sustainability and climate change goals. This figure is expected to increase to 1.39 billion units by 2020, making up 20% of all Smart City connected things in use. However, these devices are not enough to tackle the major inefficiencies of energy grids and other utilities around the world. For example, the World Bank estimates that 8.6 trillion gallons a year gets lost through leaks in water infrastructure, enough to fill the Hoover Dam. Digital twin technology, reliant on performing real-time analytics at the edge, will enable utility companies to compose data from millions of smart meters and provide unprecedented resolution into the real-time state of electrical grids, water systems, and other utilities. For example, Frost & Sullivan predicts that digital twins and other technology advancements will will lead to the automation of nearly 150,000 electric utility substations over the next 10 years. This will enable utility companies to better identify inefficiencies, and pass on cost savings and valuable insights to customers. 

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Learn how Swim ESPTM for Smart Cities uses Machine Learning to power analytics at the edge, enabling a resilient and highly-available streaming data analytics engine for Smart Cities.

Posted on Oct 20, 2017 11:24:00 AM by Swim Team in Machine Learning, in Swim Software, in Industrial IOT, in Smart Cities, in Edge Analytics, in Swim AI