Energy efficiency of IoT and edge devices a top research priority – AIOTI
‘Energy efficient intelligent IoT and edge computing systems’ is one of 18 themes for research identified by the European Alliance for IoT and Edge Computing Innovation (AIOTI).
As the number of IoT applications continues to increase along with the trend to move data processing and analysis to the edge, the need for more energy efficient approaches is becoming more and more evident to combat the growing power consumption.
With this need, AIOTI, an industry alliance with the advancement of Europe’s digital and green transformations as its mission, has identified energy efficiency as one of the 18 strategic research and innovation priorities over the period to 2030 that can enable these technologies to evolve into “an integrated digital ecosystem, characterised by distributed architectures and mesh topologies for advancing hyper-automation in all-industrial sectors”.
Specifically AIOTI identifies three research topics – energy harvesting with its potential to remove the dependency on batteries for power and their need for periodic replacement, the energy efficiency of the hardware and the energy efficiency of data processing, particularly AI.
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The next-generation IoT and AI edge solutions should focus on novel energy management techniques to select energy sources, energy harvesting techniques, hardware/software/algorithm optimisation for data sensing, monitoring, filtering, prediction and compression, states AIOTI.
The optimisation for energy efficiency and green IoT requires the use of federation and orchestrations techniques that create dynamic and distributed energy control frameworks for edge IoT applications.
The implementation of energy efficient IoT intelligent search engines, cooling systems, and energy harvesting techniques and renewables must be considered when the hardware/software/algorithm components of the IoT application layer are evaluated.
Suggested short term research priorities include hybrid solutions combining ultra-low power connectivity with energy harvested from ambient RF, thermal, kinetic and solar PV, and system-level optimisation techniques combining lower power consumption and energy harvesting technologies as well as methods and models for data compression and exchange in edge-cloud IoT platforms.
Longer term priorities include interfaces for kinetic energy harvesting, cognitive energy management orchestration and energy efficient data aggregation mechanisms.
6G energy consumption
In a similar vein to edge energy efficiency improvements, continued reductions in energy consumption are a feature of the evolution of the telecoms networks.
The Architecture Working Group of Europe’s 5G Infrastructure Public Private Partnership (5G PPP) in its architecture landscape for the next generation 6G is targeting a more than 90% reduction in the energy consumption per transmitted bit compared with 5G.
This should be achieved primarily at the base station hardware level but also with improved sleep mode and network management with for example AI.
This group also highlights wireless power transfer and energy harvesting among promising techniques to enable sustainable networks.
