By Shikhar Kwatra, Utpal Mangla, Luca Marchi
The sheer volume of information being processed and generated by Internet of things enabled sensors has been burgeoning tremendously in the past few years. As Moore’s law is still valid providing an indication of number of transistors in an integrated circuit doubling roughly every two years, growing ability of edge devices to handle vast amounts of data with complex designs is also expanding on a large scale.
Most current IT architectures are unable to compete with the growing data volume and maintain the processing power to understand, analyze, process and generate outcomes from the data in a quick and transparent fashion. That is where IoT solutions meets Edge Computing.
Transmitting the data to a centralized location across multiple hoops, for instance, from the remote sensors to the cloud involves additional transport delays, security concerns and processing latency. The ability of Edge architectures to move the necessary centralized framework directly to the edge in a distributed fashion in order to make the data to be processed at the originating source has been a great improvement in the IoT-Edge industry.
As IoT projects scale with Edge computing, the ability to efficiently deploy IoT projects, reduce security to the IoT network, and adding of complex processing with inclusion of machine learning and deep learning frameworks have been made possible to the IoT network.
International Data Corporation’s (IDC) Worldwide Edge Spending Guide estimates that spending on edge computing will reach $24 billion in 2021 in Europe. It expects spending to continue to experience solid growth through 2025, driven by its role in bringing computing resources closer to where the data is created, dramatically reducing time to value, and enabling business processes, decisions, and intelligence outside of the core IT environment. [1]
Sixty percent of organizations surveyed in a recent study conducted by RightScale agree that the holy grail of cost-saving hides in cloud computing initiatives. Edge AI, in contrast, eliminates the exorbitant expenses incurred on the AI or machine learning processes carried out on cloud-based data centers. [2]
Edge is now widely being applied in a cross-engagement matrix across various sectors:
- Industrial – IoT devices and sensors where data loss is unacceptable
- Telecommunication – Virtual and Software Defined solutions involving ease of virtualization, network security, network management etc.
- Network Cloud – Hybrid and Multi-cloud frameworks with peering endpoint connections, provisioning of public and private services requiring uptime, security and scalability
- Consumer & Retail framework – Inclusion of Personal Engagement devices, for instance, wearables, speakers, AR/VR, TV, radios, smart phones etc. wherein data security is essential for sensitive information handling
Since not all the edge devices are powerful or capable of handling complex processing tasks while maintaining an effective security software, such tasks are usually pushed to the edge server or gateway. In that sense, the gateway becomes the communications hub performing critical network functions inclusive of but not limited to data accumulation, sensor data processing, sensor protocol translation and understanding prior to the gateway forwarding the data to the on-premise network or cloud.
From Autonomous vehicles running different Edge AI solutions to actuators with WIFI or Zigbee with the ability to connect to the network, with increased complexity of fusion of different protocols, different edge capabilities will continue to surface in coming years.
References:
[1] https://www.idc.com/getdoc.jsp?containerId=prEUR148081021
[2] https://www.itbusinessedge.com/data-center/developments-edge-ai/
