https://scholar.dgist.ac.kr/handle/20.500.11750/13855 2026-04-04T12:02:49Z https://scholar.dgist.ac.kr/handle/20.500.11750/59962 Title: Landing-Aware Multi-Drone Routing in Last-Mile Delivery Services Author(s): Kwon, JiHyun; Chen, Yi-Ying; Lee, GaHyun; Lin, Chung-Wei; Kim, BaekGyu Abstract: We propose a framework to compute the optimal routes for multi-drones to minimize the delivery time in the last-mile delivery service. We mainly focus on a notion of the landing exclusion zone that appears during the landing phase; an area around the drop-off site is blocked until a drop-off is completed. Such zones affect the delivery time as other drones need to detour or hover around the site unnecessarily. We formulate the Mixed-Integer Linear Programming (MILP) problem by explicitly modeling the landing phase. Then, we present the heuristic algorithm that iteratively solves a sequence of single-drone delivery problems according to the delivery priorities. A delivery priority is determined according to the spatiotemporal occupancy that quantifies the significance of the size of the landing exclusion zone and its blocking period. We designed the experiment for 48 urban delivery scenarios with varying density and distribution of delivery destinations, departure points, and order quantities. Our experiment results show that the heuristic computes the routes significantly faster than the original MILP, and the delivery time is 5% higher from the optimal solution (lower-bound), and 60% lower from the general requirement of a single package per round-trip (upper-bound). 2025-10-18T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/57931 Title: Preliminary Design of Hybrid Simulation Framework for Workload Analysis in Automotive Edge Computing Author(s): Kwon, JiHyun; Park, SangEun; Chwa, Hoon Sung; Kwak, Jeongho; Choi, Ji-Woong; Kim, BaekGyu Abstract: Many vehicular tasks shall be completed in a timely manner as they move. In automotive edge computing, a vehicle can offload such tasks to an edge server in proximity to process them faster than using the cloud computing model. However, such an edge server has limited communication coverage and relatively smaller computing power compared to the general cloud server, it is important to distribute the offloaded workloads appropriately. One needs to understand such workload patterns and quantitatively analyze them for such workload distribution. We propose a preliminary hybrid simulation framework that generates compute workloads via a combination of the virtual vehicular traffic and the physical edge-server platforms. © 2024 IEEE. 2024-10-15T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/57273 Title: Landing-Type Aware Multi-Drone Route Generation for Last-Mile Delivery Service Author(s): Kwon, JiHyun; Kim, BaekGyu; Chen, Yi-Ying; Lin, Chung-Wei Abstract: We consider the problem of generating delivery routes for multiple drones in the last-mile delivery service. In particular, the landing type - how a parcel is to be dropped off from a drone - is explicitly modeled in terms of the landing area and the landing time, which was not considered in other drone delivery works. A Mixed Integer Linear Programming (MILP) problem is formulated to optimize the delivery route for each drone by minimizing the total delivery completion time. Our preliminary result shows that landing types affect the total delivery completion time significantly, even with a small number of drones. Therefore, it is necessary to explicitly consider the characteristics of landing types for more realistic delivery route generation of a large number of drones. © 2024 IEEE. 2024-05-12T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/57270 Title: Cooperative Network-Computation Load Balancing Simulator for Vehicular Edge Computing Author(s): Song, Juho; Kim, BaekGyu; Kwak, Jeongho; Choi, Ji-Woong; Chwa, Hoon Sung Abstract: To enhance the performance of autonomous driving, recent studies have been incorporating various tasks that require increasingly more computation. As computational demands increase, it is often difficult to achieve timely execution with the limited performance of onboard computing units alone. To address this issue, Vehicle Edge Computing (VEC), which offloads computational workloads to the edge and retrieves the results back to the vehicle, is gaining significant attention. To achieve efficient offloaded analytics via VEC, it is crucial to comprehensively consider both of the computing and network conditions of the V2X systems, as well as the vehicle energy consumption and timely execution. However, current studies have not sufficiently addressed the comprehensive modeling of computational and network loads in these V2X systems. To deal with this, we propose a Cooperative Network-Computation Load Balancing Simulator for VEC. © 2024 IEEE. 2024-08-20T15:00:00Z