Explaining Edge Intelligence, Its Main Benefits, and Best Practices.
Intelligent Edge refers to processes in which data is collected, analyzed and insights delivered close to where it is captured in a network. As of today, smart edge applications promise real-time insights by analyzing data at the edge itself. This heralds a new technology that is incredibly exciting: Leading Artificial Intelligence(AI)/Machine Learning(ML) technologies, the cloud and hardware developments come together.
This new inter-discipline, Edge AI or Edge Intelligence, is rightly receiving a lot of buzz today. Edge Intelligence enables machines to make decisions on the locally harvested data, instead of sending it to a centralized cloud or on-prem server. The ability to deploy AI and ML on the intelligent edge is game-changing because it can perform on the collected data and make decisions before data is sent to the cloud.
Edge Computing Market is expected to grow globally to USD 19.4 billion by the end of the year 2023, at 17.9% CAGR during the forecast period 2017-2023. (Source: ABNEWSWIRE)
Therefore, Edge Intelligence can be more simply understood as a ‘decentralized cloud’. The internet is marrying the physical world at the edge and this spawns new and exciting opportunities in tech such as the Internet of Things (IoT).
Edge Intelligence paves the way for the new generation of smart machines along with highly compelling use cases – wide-ranging applications such as real-time and predictive healthcare, fraud detection and prevention in finance, autonomous car systems, intelligent devices tracking, and real-time routing decisions for cab companies.
Cutting-edge intelligence is helping businesses understand their customers more deeply and engage with them more authentically, in real-time at stores. However, businesses are grappling with certain questions in order to make this vision a reality: How to apply the right technological solutions for different business scenarios and how to ensure the security of data.
How Many Connected Devices or IoT Are There Currently?
Today, mobile computing and IoT devices have proliferated, and billions of mobile and IoT devices have become connected to the Internet, generating zillions of bytes of data at the network edge. The number of devices connected is estimated to grow to 75.44 billion by 2025. With the rise of data collected and emerging uses of data, the urgent need is to deploy AI models to the network edge, thereby fully unleashing the potential of the edge analytics.
Edge Computing is pushing computing tasks and services from the centralized cloud to the network edge. However, the significant challenge is that managing this massive amount of data and deploying the analytics capabilities could cripple the existing IT infrastructure.
Edge Intelligence requires the collected data to be quickly processed and executed. Intelligent edge uses edge devices such as sensors, UAVs, autonomous cars, GPS receivers, navigation systems, streamers, which collect, generate, analyze and communicate data insights in near real-time.
Challenges remain in implementing the solutions due to the complexity of the ecosystem and lack of interoperability.
Powered by real-time databases and AI, the intelligent edge can provide real-time insights for the improvement of many industries.
What are the main benefits of implementing Edge Intelligence?
- Transferring such vast volumes of data that IoT devices generate, across large geographical areas, would take up a long time, can be highly expensive and leads to data privacy concerns. Edge provides the capability to perform analytics, distribute and federate big data, instead of shipping it to a centralized location. Edge intelligence is helping businesses to manage and analyze data anywhere – with responses to queries in seconds.
- Edge intelligence for telecommunication services has multiple use cases such as subscriber analytics leading to maximizing customer lifetime values- providing increased network monetization, seamless customer experience, customized product bundles, preventing churn, and allowing for wiser capital outlays.
- SaaS services are able to develop personalized, data-driven experiences improving the adoption of application and creating higher levels of user engagement and customer satisfaction.
- IoT allows manufacturers to create automated, real-time monitoring and derive insights for predictive maintenance and better manufacturing uptime – leading to improved operational efficiency and profits.
- Government agencies can improve their operations, use location-based data to assist with criminal investigations and make intelligent capital allocation decisions.
Primary Components To Make Edge Intelligence Successful
Edge intelligence can be achieved through combined innovations in software and hardware. IoT devices continually generate tremendous amounts of data. For example, autonomous vehicles are estimated to generate data around 4 TB/day.
Processing this massive amount of data and deploying the AI/ML models to derive intelligent insights will require significant upgrades and improvements to the existing hardware. Deploying the AI/ML models will require huge computing power with edge computing pushing the cloud services to the edge of the network.
In order to successfully deploy intelligent edge, following components need to exist in the device:
Connectivity—The devices must be able to connect to networks that enable data exchange such as the Internet or an internal decentralized network.
Computing—The devices must be equipped with internal computing resources such as processing chips, enabling processing and analyzing data in near real-time.
Controllability—The devices must be capable of using databases to implement decisions such as controlling devices, making instantaneous changes, and instigating actions across networks.
Autonomy—The devices must have autonomous computing processes and capabilities that are enabled by edge databases and which won’t require assistance for monitoring, managing, and transferring data.
These require developments in the following key areas of:
- Hardware with Partitioned Edge Compute – Hardware with partitioned edge computing is providing the desired levels of connectivity, sensing, data aggregation – making up the IoT stack.
- Seamless Cloud Connectivity – Systems with edge intelligence will need to be connected to the cloud to leverage storage and compute resources on edge.
- Remote Device Management – Remote device management will be needed for security, management automation, edge intelligence, and API Integration.
Application of Edge Intelligence is promising cutting edge business transformations. Developers and systems integrators need to prepare the foundation for the change for their businesses by including key enabling ingredients.
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