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.
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.
A structured advisory process — from use case definition and platform evaluation to deployment architecture and ongoing optimization.
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.
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.
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.
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.
The dimensions that determine whether an IoT deployment delivers lasting operational value — and the questions RLM helps you answer before any commitment.
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.
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.
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.
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.
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."
"We needed smart metering and energy management across our campus portfolio. RLM mapped the vendor landscape, ran the evaluation, and we're now hitting our ESG targets ahead of schedule."
Talk to an RLM advisor who specializes in enterprise IoT deployments. Independent guidance from platform selection through operational deployment.