Technical issues addressed in the workshop

In response to the Sustainable Development Goals and the Paris Agreement, which urge countries to reduce carbon emissions by 45% by 2030 and achieve net zero by 2050, wireless technology is expected to play a crucial role in decreasing greenhouse gas (GHG) emissions across various sectors. It facilitates connectivity for digital solutions, reduces energy consumption, minimizes travel and transportation, and ultimately mitigates GHG emissions. Applications include connecting buildings that manage energy efficiently and enabling vehicle communication to lower fuel consumption and improve routing. Beyond traditional remote and mobile work, which diminishes travel emissions, sectors such as agriculture, healthcare, the sharing economy, and smart cities offer substantial potential for further emission reductions.
Conversely, these services and applications contribute to the growing data traffic managed by wireless networks and the increasing demand for distributed computing capabilities, where wireless networks are playing an increasingly crucial role in edge data processing and offloading, resulting in increased energy consumption. Wireless systems are significant consumers of resources, including electricity and raw materials. The energy demand for these networks is considerable. With rising consumer demand, trends toward utilizing data centers for virtualized mobile networks, and the implementation of Artificial Intelligence solutions for network optimization, which are themselves highly energy-intensive, a concerning increase in energy use by wireless networks is projected for 2035. While the edge network, data centers, and core services account for no more than 25% of energy consumption by network operators, the Radio Access Network (RAN) alone represents 73% of this total. Furthermore, since most current energy supplies are derived from the burning of fossil fuels, addressing this increasing energy demand is crucial for the sustainability of future networks.

Topics of interest 

Authors are invited to submit high-quality contributions that explore innovative solutions for creating greener and more sustainable future wireless networks, as well as applications of wireless networks that promote urban sustainability improvement.

Topics of interest of the proposed workshop include, but are not limited to:

• AI/ML (federated or not) for sustainable networks and greener wireless networks 
• AI/ML-aided management of aerial networks to support terrestrial network sustainability
• AI/ML-aided mobile and multi-access edge computing to enhance sustainability
• AI/ML-aided Energy-aware resource allocation and management
• AI/ML-aided Energy-aware routing protocols
• AI/ML-aided Energy-efficient software implementation
• AI/ML-aided Energy harvesting techniques in wireless systems
• AI/ML-aided sustainable physical layer design and medium access control
• AI/ML-aided Reconfigurable Intelligent Surfaces to enhance wireless network sustainability
• AI/ML-aided applications for sustainable and smart cities
• AI/ML-aided integration of renewable energy sources to power wireless networks
• AI/ML-aided Cross-technology cooperation targeting energy saving and sustainability objectives
• Efficient AI/ML-aided energy management in wireless networks
• Energy-saving approaches based on learning for cooperation of wireless networks and smart grids 
• Cross-operator cooperation and network resource sharing to enhance the sustainability of network management, network utilization, or network intelligence
• Sustainable AI/ML computing
• Sustainable integration of AI/ML in network architectures
• Sustainable AI techniques, including for mobile computing and in the compute continuum. 
• Architectural design and system implementation for sustainable wireless networks and sustainable networked computing systems
• Sustainable security & privacy related to the use of AI/ML
• Sustainable AI/ML-aided sensing and monitoring
• Sustainable AI/ML-aided content delivery
• New Key Performance Indicators for energy-aware and sustainability-aware data-driven management of wireless and computational technologies
• New Key Performance Indicators for the evaluation of AI and its sustainability