Cloud advisory: from learning fast to machine learning

Cloud advisory: from learning fast to machine learning

The market of cloud providers and platforms is developing fast. Cooperation and eventually consolidation are happening continuously with frequent announcements of alliances and/or acquisitions of smaller providers. Trending is cloud providers teaming up to dominate business verticals based on the original characteristics of each supplier.

Consequently, as companies start planning business critical and enterprise-ready solutions it becomes an urgent and complex task to choose the right IoT-cloud. Given each company has its own business, organizational and operational requirements there is numerous criteria that need to be weighed and compared company specific. Business perspectives, infrastructure concerns, legislation demands, flexibility needs, vertical solutions, connectivity and edge integration, tools support, scalability, availability of resources and need for machine learning just to name a few. Unbiased advisory and inspiration on how to select the right platform is often necessary.

Glaze has launched some of Scandinavia’s largest IoT-solutions on the market and we are technology independent and have a collective experience of the 5 layers of the IoT technology stack (hardware, software, communications, cloud platform and cloud applications).

Given that business cases are often developed while IoT-platforms and solutions are deployed we have developed a “learn and fail fast” approach that ensures critical business alignment and IT-competency development, while utilizing our experiences from numerous enterprise IoT cloud projects.

More information:

Partner Jakob Appel, jakob.appel@glaze.dk, +45 26 17 18 58.

Positioning technologies currently applied across industries:

Global Navigational Satellite System: Outdoor positioning requires line-of-sight to satellites, e.g. GPS: the tracking device calculates its position from 4 satellites’ timing signals then transmits to receiving network
–    via local data network, e.g. wifi, proprietary Wide Area Network
–    via public/global data network, e.g. 3G/4G

Active RFID: A local wireless positioning infrastructure built on premises indoor or outdoor calculates the position based on Time of Flight from emitted signal & ID from the tracking device to at least 3 receivers or when passing through a portal. The network is operating in frequency areas such as 2.4 GHz WiFi, 868 MHz, 3.7 GHz (UWB – Ultra Wide Band), the former integrating with existing data network, the latter promising an impressive 0.3 m accuracy. Tracking devices are battery powered.

Passive RFID: Proximity tracking devices are passive tags detected and identified by a reader within close range. Example: Price tags with built-in RFID will set off an alarm if leaving the store. Numerous proprietary systems are on the market. NFC (Near Field Communications) signifies a system where the reader performs the identification by almost touching the tag.

Beacons: Bluetooth Low Energy (BLE) signals sent from a fixed position to a mobile device, which then roughly calculates its proximity based on the fading of the signal strength. For robotic vacuum cleaners an infrared light beacon can be used to guide the vehicle towards the charging station.

Dead Reckoning: Measure via incremental counting of driving wheels’ rotation and steering wheel’s angle. Small variations in sizes of wheel or slip of the surface may introduce an accumulated error, hence this method is often combined with other systems for obtaining an exact re-positioning reset.

Scan and draw map: Laser beam reflections are measured and used for calculating the perimeter of a room and objects. Used for instance when positioning fork-lifts in storage facilities.

Visual recognition: The most advanced degree of vision is required in fully autonomous vehicles using Laser/Radar (Lidar) for recognition of all kinds of object and obstructions. A much simpler method can be used for calculating a position indoor tracking printed 2D barcodes placed at regular intervals in a matrix across the ceiling. An upwards facing camera identifies each pattern and the skewed projection of the viewed angle.

Inertia: A relative movement detection likewise classical gyroscopes in aircrafts now miniaturised to be contained on a chip. From a known starting position and velocity this method measures acceleration as well as rotation in all 3 dimensions which describes any change in movement.

Magnetic field: a digital compass (on chip) can identify the orientation provided no other magnetic signals are causing distortion.

Mix and Improve: Multiple of the listed technologies supplement each other, well-proven or novel, each contributing to precision and robustness of the system. Set a fixpoint via portals or a visual reference to reset dead reckoning & relative movement; supplement satellite signal with known fixpoint: “real time kinematics” refines GPS accuracy to mere centimetres; combine Dead Reckoning and visual recognition of 2D barcodes in the ceiling.