全文截稿: 2025-06-01
影响因子: 5.646
CCF分类: 无
中科院JCR分区:
• 大类 : 计算机科学 - 2区
• 小类 : 电信学 - 2区
网址：
https://www.comsoc.org/publications/journals/ieee-tccn/
The advancement of drone technology coupled with the increasing congestion of terrestrial road resources is catalyzing the exploration of low-altitude economy. It denotes the utilization of the airspace up to 3000 meters above ground level, where flying equipment such as unmanned aerial vehicles (UAVs) are employed to foster various applications. These initiatives are designed to exploit the low-altitude range to revolutionize industries such as urban transportation, logistics, agriculture, and tourism, thereby attracting significant attention from various industries across several countries. In comparison to terrestrial networks, the airspace offers greater freedom of movement, which provides more space for multi-UAV path optimization, communication strategy selection, etc. Moreover, unlike the traditional UAV networks, applications within the low-altitude economy framework typically involve massive flying devices acting as air taxis, aerial base stations, and airborne charging stations, each tasked with different missions and having distinct communication needs. This requires advanced capabilities from both drones and low-altitude communication networks.
Artificial general intelligence (AGI), bolstered by diverse AI technologies, such as deep learning models, generative AI models, deep transfer learning techniques, and large language models, possesses capabilities such as autonomous perception, learning, decision-making, execution, and social collaboration. These capabilities enable AGI to independently handle a variety of tasks effectively at or above human level. Within the low-altitude economy, AGI empowers massive UAVs to navigate under varying weather conditions, evade random obstacles like birds, and optimize flight paths, thereby enhancing energy efficiency and promoting sustainability. In low-altitude network communications, AGI enhances the network's cognitive, learning, and decision-making capabilities, playing a crucial role in optimizing network resources, facilitating self-organization and repair, and assessing security vulnerabilities. Despite its promise, using AGI in this context poses significant challenges. These include the training of various robust AGI models that prioritize the security of UAV communications and can autonomously detect and rectify network faults. Moreover, given the highly dynamic nature of low-altitude airspace, developing AGI models capable of robustly handling complex tasks, including the integration of sensor data, real-time processing, and prompt decision-making, also presents difficulties. Therefore, this special issue aims to delve deeply into these challenges and opportunities, inviting contributions on theoretical advancements, technological solutions, and case studies that support various applications within low-altitude airspace via different AI models, to facilitate the development of the low-altitude economy. Potential topics of interest include but are not limited to the following:
- AGI-based integrated communication, sensing, and computing for low-altitude transport networks
