IoT Edge Devices: The Next Frontier for Distributed AI
Best-in-class IoT Solutions Distribute Data Processing Where It Makes Most Sense
Leading IoT application providers know the best user experiences, most secure applications, and greatest product performance require data analytics to be partitioned intelligently so processing tasks run where they yield maximum overall benefit.
Cloud: Ideal for associating and analyzing broadly disparate data sources using the power and resources of high performance servers. The value of cloud analytics comes from drawing non-obvious inferences and correlations with deep learning AI and data mining techniques and putting these insights to work to improve business operations.
Edge Servers and Gateways: As local data aggregation and compute nodes, edge servers and gateways can perform local rules processing more efficiently than concentrating all compute within the data center. Fog computing architecture extends portions of cloud computing to the local environment accelerating insights where such devices are practical to move portions of the workload closer to the source.
IoT Edge Devices: The originators of IoT data, IoT edge sensor endpoints are the next frontier of distributed AI. The same sensor microcontrollers used to handle raw data ingest and connectivity, can also contribute significantly to IoT data processing as true smart sensors. Fully exploited, they can greatly improve overall IoT system performance, offer better data security/privacy, and simplify user deployments by allowing battery powered rich sensing over wireless networks. Additionally, smart sensors fill a crucial need for offline tolerant applications where real-time standalone operation allows local feedback and control in mission critical, safety, and mobile devices.
Common Challenges
Creating IoT Edge Device algorithms that can fit within resourced constrained MCUs is very challenging. Tools to automate IoT Edge AI algorithm development are only just appearing in the market. To date, most implementations involve hand-coding with carefully tailored embedded code created by skilled teams of data scientists and firmware engineers. For all but the highest volume applications, this effort and time involved is often impractical.
SensiML Solution
Creating IoT Edge Device algorithms that can fit within resourced constrained MCUs is very challenging. Tools to automate IoT Edge AI algorithm development are only just appearing in the market. To date, most implementations involve hand-coding with carefully tailored embedded code created by skilled teams of data scientists and firmware engineers. For all but the highest volume applications, this effort and time involved is often impractical.Cloud-Centric loT Analytics
An adequate solution so long as analysis needs are not real-time, sensors are simple binary switches or slow varying signals, and/or network bandwidth is plentiful.
Disadvantages:
- Excessive network traffic with rich sensors and many endpoints.
- Latency is too great for many time critical applications
- No insight if network fails or throughput is sufficiently impaired.
- Security and data privacy require end-to-end protection
IoT Edge AI Sensor Analytics
SensiML enables a superior architecture for networked IoT applications with clear advantages over centralized analytics. Distribution of raw data processing allows much greater responsiveness, fault tolerance, and network efficiency.
Advantages:
- Sensor need only send useful insight data, not voluminous raw signal data.
- Less network traffic supports cellular IoT networks for remote endpoints
- Zero network latency with local AI insight at endpoint
- No network dependence with local AI insight at endpoint
- Partitioned security/data privacy with local AI pre-processing and filtering
