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 / 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.

Sensor Endpoints: The originators of IoT data, sensor endpoints are often overlooked.  The same sensor microcontrollers used to handle basic tasks, if optimally programmed, can 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.


Overlooked Processing - While modest by today's standards, modern 32-bit sensor SoCs still have much algorithm potential.  Many such vendors' chips cite performance benchmarks exceeding PCs of the mid '90s.

More Security/Privacy - Processing raw image and audio data locally for only the events of interest avoids having to send raw feeds over networks where intercepts and data loss fears can raise real security and privacy objections.

Smart Sensing for Battery Powered Devices - While counter intuitive, by adding rich sensing algorithms to smart sensors the need for continuously transmitting raw data can be removed lowering both consumed battery power and bandwidth needed over constrained wireless LAN/PAN and LP-WAN networks.

Full Exploitation - The major obstacle to employing smart sensors remains the lack of automated ML algorithm development tools for these SoCs.  This leaves the challenging work of creating optimized algorithms to the few capable of doing this programming manually.  It's for this reason that many so-called "smart" IoT endpoint devices (i.e. the "things") are merely connected devices that inefficiently stream lots of redundant raw data and noise and leave the job of intelligent sensor data processing to be performed elsewhere in the network.