IoT Edge Computing — Intelligence Where the Data Is Generated
IoT edge computing processes sensor data locally at or near the device — enabling real-time decisions that cloud round-trip latency can't support, reducing bandwidth costs by transmitting processed insights rather than raw data, maintaining operation during cloud connectivity interruptions, and protecting sensitive data that regulations or security policies prohibit from leaving the premises.
What RLM Delivers on IoT Edge Computing
The promise of IoT cloud analytics — send all data to the cloud, analyze centrally — breaks down when latency requirements demand millisecond response times, bandwidth costs make raw data transmission uneconomical, or data sovereignty requires local processing. Edge computing brings cloud-like processing capabilities to the data source. RLM advises on edge computing architecture, platform selection, and the hybrid edge-cloud design that balances local processing with cloud analytics.
How We Approach IoT Edge Computing
What we look at on IoT Edge Computing — the criteria that predict outcomes rather than the ones that demo well.
Edge Computing Requirements Assessment
We assess the edge computing requirements driving your IoT architecture — documenting latency-sensitive use cases, bandwidth-constrained sites, data sovereignty requirements, and the offline operation scenarios where local processing is essential.
Edge Platform Evaluation
We evaluate edge computing platforms — AWS Greengrass, Azure IoT Edge, Google Cloud IoT Edge, NVIDIA Jetson, Dell Edge Gateway, industrial edge computers — against your processing requirements, device ecosystem, connectivity, and the management model for distributed edge infrastructure.
Edge-Cloud Architecture Design
We design the edge-cloud architecture — defining which processing occurs at the edge (real-time control, local filtering, anomaly detection) vs. in the cloud (historical analytics, model training, fleet-wide aggregation), and the synchronization protocol that keeps edge and cloud in alignment.
Edge ML & AI Design
We design machine learning model deployment at the edge — model selection for edge constraints (size, inference speed, power consumption), MLOps pipeline for model updates to distributed edge devices, and the performance monitoring that detects model drift.
IoT Edge Computing Evaluation Criteria
What we look at on IoT Edge Computing — the criteria that predict outcomes rather than the ones that demo well.
Edge Hardware Selection
Edge computing hardware ranges from small microcontrollers to industrial servers. Evaluate hardware selection against your processing requirements, operating environment (temperature range, vibration, enclosure requirements), and the hardware lifecycle management overhead of distributed edge infrastructure.
Connectivity Dependency
Edge computing reduces cloud dependency for real-time processing but still requires connectivity for model updates, monitoring, and cloud synchronization. Evaluate the offline operation window and the data buffering capacity that bridges connectivity interruptions.
Security at the Edge
Edge devices deployed in physically accessible locations are vulnerable to physical tampering. Evaluate hardware security (TPM, secure boot, encrypted storage) and the remote attestation capabilities that detect edge device compromise.
MLOps for Distributed Edge
Updating ML models on thousands of distributed edge devices requires automated deployment pipelines. Evaluate the MLOps capabilities for edge model management — canary deployments, rollback, and performance monitoring that validates model updates before fleet-wide deployment.
Total Cost of Edge Infrastructure
Edge infrastructure costs include hardware, installation, connectivity, management software, and maintenance. Build a per-site cost model before comparing edge processing costs against cloud alternatives — for some use cases, bandwidth costs savings justify edge investment; for others, they don't.
"RLM helped us select and deploy an IoT platform across 28 facilities in under six months. Their vendor-neutral approach saved us from a costly mistake with our initial shortlist."
Every engagement is measured against the baseline we establish at the start — not against a vendor’s projection.
Where This Matters Most
Sector-specific considerations we see repeatedly in iot engagements.
A Sample of the IoT Providers We Evaluate
RLM is vendor neutral. These are among 600+ providers in our evaluation set — inclusion here is not an endorsement, and we are paid by the provider you choose, not by any provider in particular. How that works →
Ready to Move on IoT Edge Computing?
Talk to an RLM advisor who specializes in enterprise IoT deployments. Independent guidance from platform selection through operational deployment.