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MANAGING CONSULTANT

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CLOUD ARCHITECT CONSULTANT

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IOT CLOUD DEVELOPER

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SALES EXECUTIVE

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Services

Beacon Tower
IoT Cloud Platform
Industrial Edge Computing
Cloud Architecture and Business Integration
Project Execution

CONTACT

Danmark: +45 81 71 09 20
Christians Brygge 28
1559 Copenhagen
Sverige: +46 705 087 070
Hyllie Boulevard 39
215 37 Malmö
info@glazeholding.com

© Glaze. All Rights Reserved.

Beacon Tower is Glaze’s Industrial IoT Cloud Platform that can act as either a stepping stone (Platform-as-a-Service, PaaS) or as an out-of-the-box solution (Software-as-a-Service, SaaS) for collection of IoT-data.

Beacon Tower resides in Microsoft Azure and is designed as a customisable and cost-effective IIoT Cloud Platform that helps simplify deploying, managing, operating, and capturing insights from internet-of things (IoT)-enabled devices. Our customers have the full ownership of their data.

When running it as a PaaS we utilise the design and can run it on our customers’ Azure tenant and customise it fully to their requirements.

Beacon Tower connects to all sensors, PLC, DCS, SCADA, ERP, Historians and MES to gain maximum automation flexibility and ​prevent vendor lock-in.

For more information visit www.beacontower.io 

EDGE COMPUTING CATEGORIES AND QUESTIONS

Device:
o    Sensors
o    Internet connectivity
o    Battery consumption
o    Field Gateway
o    Communication protocols (HTTP, AMQP, MQTT, Gateway)
o    Format of the telegrams sent to the cloud (JSON, Avro, etc.)

Data:
o    Number of devices & number of signals
o    Amount of data to transfer per day
– Event-based or batched or mix
– Transfer rate (every second, minute, hour)
o    Device timestamps
– Synchronized timestamps with cloud or not
– Local buffering on device, late and/or repeated data
o    Any time-critical notifications / alarms
– Latency expectations for non-time critical data
– Alarms generated by device and/or by cloud platform
o    Cloud-to-device messages & commands
o    Analytics
– Results from time-series data / Streaming analytics
– Analytics workflows on data, machine learning etc.
– Edge analytics / intelligence

Cost expectations:
o    Retention periods (for reporting purposes)
o    Aggregation of data, possibilities for cost saving

External integrations:
o    Reference data / online data

Administration, rights and access:
o    Requirements for multi-tenancy (segregated owners)
o    Owners/tenants and operators/technicians
o    Administrating access to data, auditing use
o    API management, consumption of data, 3rd party integrators

Operation:
o    KPI measurements for device
o    KPI measurements for cloud platform
o    Requirements on operators and SLA’s

User-interfaces and functions:
o    Operators/technicians
o    Customers/end-users