Cloud Connectivity:
Infrastructure (ex: 6LowPAN, IPv4/IPv6, RPL)
Identification (ex: EPC, uCode, IPv6, URIs)
Comms / Transport (ex: Wifi, Bluetooth, LPWAN)
Discovery (ex: Physical Web, mDNS, DNS-SD)
Data Protocols (ex: MQTT, CoAP, AMQP, Websocket, Node)
Device Management (ex: TR-069, OMA-DM)
Semantic (ex: JSON-LD, Web Thing Model)
Multi-layer Frameworks (ex: Alljoyn, IoTivity, Weave, Homekit)
IoT:
IOTA The Next Generation Blockchain for IoT (ex: Tangle for M2M)
Category: Big Data
Data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy
Production Support with AI
In the industrial sector, AI application is supported by the increasing adoption of devices and sensors connected through the Internet of Things (IoT). Production machines, vehicles, or devices carried by human workers generate enormous amounts of data. AI enables the use of such data for highly value-adding tasks such as predictive maintenance or performance optimization at unprecedented levels of accuracy. Hence, the combination of IoT and AI is expected to kick off the next wave of performance improvements, especially in the industrial sector. AI-equipped controllers are meant to immediately detect signs of equipment irregularity by monitoring the status of equipment and processes and assure product quality.
Business Support with AI
Finance, HR, and IT are key to ensuring a business’ effective operation. They could benefit from AI in form of Expert Systems, IT Agents trained in the specific business environment capable of solving more complex problems. This is achieved with natural language processing and reinforced learning like Google’s AlphaGo.