인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract In recent years, Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) systems have emerged as innovative solutions for delivering efficient communication and computing services to Internet of Things (IoT) devices. However, the three-dimensional deployment and trajectory decision of UAVs remain challenging due to their highly non-convex and complex process characteristics. Existing methods often face scalability limitations, hindering their applicability to collaborative tasks as the number of UAVs increases. Furthermore, many approaches rely on simplified UAV models, neglecting the complexities of real-world physical dynamics. To address these issues, we propose a joint optimization framework designed to simultaneously minimize real-world UAV system overhead and enhance Air-to-Ground (A2G) communication capabilities. Our approach incorporates a deployment and trajectory design strategy that captures the comprehensive kinematic and dynamic properties of UAVs. In light of the problem’s inherent nonconvex structure and computational intractability, we introduce a collaborative multi-operator Differential Evolution (DE) variant algorithm with a semi-adaptive strategy, termed CSADE. This algorithm utilizes three distinct mutation strategies and integrates an external archiving mechanism to optimize both the number and locations of UAV Task Points (TPs). Additionally, we present an end-to-end dynamic UAV allocation and integrated flight path optimization method to ensure efficient route planning. The proposed method is evaluated through experiments on four data instances and compared with two related algorithms. Results demonstrate that our approach significantly reduces system operating costs while maintaining effectiveness and stability, highlighting its potential for large-scale UAV-assisted MEC applications.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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