Smart Cities Become Smarter with Edge Computing

Cities and towns are becoming smarter. A smart city is one that uses information and communication technology to improve operational efficiency, share information with the public. and provide a better quality of government service to citizens. Smart cities are proliferating around the world and use cloud computing, sensors, and Big Data to make them smarter.

Edge computing is another key technology for smart cities. Edge computing focuses on bringing computing as close to the source of data as possible in order to reduce transmission delays and the volume of information needed for operations and services as well as to help municipal officials at all levels make more informed decisions. Fewer processes run in a centralized cloud. Moving those processes to locations near the networks edge, such as on a users computer, an IoT device, or an edge server, minimizes the amount of long-distance communication that needs to happen between a client and a server.

Edge computing allows organizations to take the power of the cloud all the way to the network edge, especially to areas where they have not been able to use it before. Agencies can perform data analytics and processing and gain insights at the edge before routing the data back to centralized data centers for further analysis. This capability is especially useful for smart city applications.

There are compelling reasons for smart cities to move data analytics to the edge, where the data are generated and captured, rather than sending everything to faraway corporate and cloud data centers. Many smart city applications require Big Data and data analysis that takes place in near real time as the data are generated. There isnt time to send data to a distant data center for analysis or to store it in the cloud when it might be needed for immediate purposes. Such considerations justify analyzing many types of data at the edge, where the sensors, cameras, or other devices are located and where intelligent systems can take immediate actions based on the results of data analytics. A high percentage of smart cities and government agencies are actively using or exploring edge computing and IoT.

There are many examples of edge and IoT solutions for cities spanning the range of municipal operations, from public safety and security to smart utility metering, traffic management, and parking. For example, the City of St. Petersburg, Florida, uses IoT and edge computing to capture and analyze data on its most dangerous intersection. It installed smart light poles, safety cameras, and environmental sensors and expanded Wi-Fi coverage at the intersection, which enabled the use of data-intensive video analytics and artificial intelligence software. Driver behaviors and activities can now be monitored and tracked automatically and in near real time 24/7/365. Traffic data visualization and insights from external sources are now displayed on a dashboard in a web browser. The system can also correlate multiple datasets to identify near-accidents, types of vehicles, illegal U-turns, jaywalking, and the time it takes pedestrians to cross the street. Correlations with environmental data enable the city to answer questions such as whether pedestrians cross the street more quickly when it rains. In addition, traffic engineers have a better understanding of when, how, and what occurs in the intersection.

Prior to 2019, the City of Syracuse street lighting infrastructure was owned and operated by an electric utility. The City had little visibility when lights were out, and residents had to notify the utility of outages, resulting in more services for affluent neighborhoods. Repairs took weeks or months. The lights were energy inefficient and costly to maintain. In 2019, the City required their streetlights to operate as a municipal service. The City replaced all streetlights with LED lights connected over a Wi-Fi network. Lights are now monitored using an automated, real-time, central dashboard. Outages can be resolved within three days, and lights are fixed within 24 hours of notification. The new lighting infrastructure has dramatically improved service and also provides that service more equitably. The system has also provided more visibility in other infrastructure such as underground power feeds and conduit.

The City of Lima, Ohio, has more than 80 railroad crossings. Vehicles must wait for crossing trains, and rail companies do not publish train schedules because of safety, security, and competitive reasons. In the past, unplanned gridlock for unknown durations inconvenienced citizens, visitors, local businesses, and first responders several times throughout the day. An Intel-sponsored study by Juniper Research found that gridlock costs drivers up to 70 hours per year and that an IoT-enabled intelligent traffic system, safer roads, and frictionless toll and parking payment could save drivers from spending 60 unproductive hours in their cars per year.

The city worked with US Ignite, DriveOhio, and networking solutions provider Spectrum Enterprise to develop a solution that helps mitigate the traffic delays by capturing data about train operations, using predictive analytics, and providing train metrics on a visualization platform. (DriveOhio is an Ohio Department of Transportation initiative to organize and accelerate smart vehicle and connected vehicle projects in the State of Ohio. US Ignite provides technology research to communities on smart city development.) The solution features an IoT system that has 15 multifaceted sensors. Data from the sensors are fed into a local edge computing processing unit that uses artificial intelligence and computer vision technology to detect a train crossing and capture the trains speed, length, and direction of travel. The captured data are sent to a cloud platform via cellular connectivity. The system then uses predictive algorithms to process the sensor data and display in real time the current status of the intersection as well as to predict outcomes, such as when the train will arrive at the next crossing and how long the crossing will be blocked.

1. What problems did the organizations described in this case address by using edge computing, Big Data, and IoT?
2. What people, organization, and technology issues should a municipality address when building a smart city application?
3. How did information technology improve operations and decision making for the organizations described in this case?