Reviving smart cities with edge computing and 5G

Public safety and crime control

Edge analytics and edge AI enable advanced and secure video, sensor and communication systems to proactively monitor public spaces and law and order. Using edge AI, crimes or other disastrous events can be prevented or de-escalated before they jeopardise public safety. Sensors embedded in critical infrastructure such as bridges and power plants can monitor structural data to identify potential dangers, protecting citizens and the economic well-being of the city. Sensor-equipped drones can monitor vehicular traffic, crowds, construction sites and disaster areas to help monitor conditions continuously and support first responders. Overall, communities benefit from increased trust in law enforcement and disaster management.

Intelligent transport and autonomous intelligent vehicles

Edge analytics will be the key enabler for the connected autonomous driving vehicles revolution. Road vehicles will communicate with each other and with infrastructure and improve overall road safety. It will also lead to reduced traffic congestions and enhanced driver comfort.

Edge computing-based Vehicle-to-Cloud solutions enable edge cloud capabilities for different levels of autonomous driving through different services (e.g. high definition real-time maps, real-time traffic monitoring and alerts). Distributed AI applications in cars will send video data over 5G radio to an edge computing site inside a telco network. Video data will be processed in near real-time by machine learning algorithms at the edge cloud. Results of real-time image processing will be sent to the car, where comparisons with local analysis will be done and a final decision, such as an instruction to the driver, will be made.

Smart healthcare

Healthcare is seeing a huge surge in the number of connected devices. Edge computing and edge analytics can reduce this burden to a great extent. A clinician’s mobile device can capture patient data into a connected analytics platform at the edge in real-time. Patients will no longer need to wait for analysed results, which would significantly reduce their number of visits.

The concept of the collaborative edge will be another enabler wherein geographically dispersed data is fused into a combined and edge device consumable view. For example, deep learning has recently gained relevance in ophthalmology due to its ability to detect clinically significant features for diagnosis and prognosis. This has resulted in various deep learning systems to be embedded within ophthalmic imaging devices, allowing automated image acquisition. The same can be performed at a smartphone-based device level, for instance by using a hi-resolution fundus imaging system attached to a smartphone.

Allowing cities to think for themselves

The transformative potential of edge analytics is nearly limitless. The speed of 5G networks coupled with the fact that local processing can be offloaded to the edge of the network makes the proposition of edge analytics very strong. Reduced latency and connected, intelligent devices speaking to each other make for an invaluable leap from the current paradigm that requires sending large chunks of data back to the cloud for analytical processing and insights. Autonomous decision-making and cognitive intelligence at the devices or the edge network cut down both, processing and decision-making latency. This means that decisions can be made, crucially, in real-time.

Edge analytics in 5G will not be restricted to traditional descriptive analytics and will continue to evolve. This powerful technology has the capacity to learn from context, predict what will happen next, prescribe the next best action or decision, and learn from the past behavioural patterns to take the most optimal decision. For fully autonomous applications, edge analytics will automate the next action in real-time. With the speed of 5G, more information will be collected and processed, and edge analytics generated insights will increasingly drive decision-making, leading to cognitive intelligence applications. In short, the future of smart cities is drawing on 5G and using edge computing to help cities think for themselves.

Co-authored by Manish Sood, Consulting Partner, Analytics & AI Consulting, Wipro and Shamit Bagchi, Data Scientist and AI Consultant, Analytics & AI Consulting, Wipro