作者：
BECKER, LOUIS A.SIEGRIST, FRANKLIN I.Louis A. Becker was born in New Rochelle
N.Y. in 1930 receiving his earlier education in the New Rochelle Public Schools. He completed his undergraduate studies at Manhattan College in 1952 receiving his BCE degree during which time he was also engaged in land surveying. Following this he did postgraduate study at Virginia Polytechnic Institute obtaining his MS in 1954. He joined Naval Ship Research and Development Center in 1953 as a Junior Engineer and is currently the Head of the Engineering & Facilities Division Structures Department. His field of specialization is Structural Research and Development. Franklin I. Siegrist was born in Knoxville
Tenn. in 1937 receiving his earlier education in the Public Schools of Erie Pa. He attended Pennsylvania State University graduating in 1962 with a Bachelor of Science degree in Electrical Engineering having prior to that time served four years in the U. S. Navy. He was a Junior Engineer in the AC Spark Plug Division of General Motors from 1962 until 1964 at which time he came to the David Taylor Model Basin as an Electrical Engineer in the Industrial Department. He is currently Supervisory Engineer for Electrical and Electronics Engineering Structures Department Naval Ship Research and Development Center. His field of specialization is Electrical Engineering Control Systems Data Collection Systems Computer Applications to Structural Research and Hydraulic System Design. In the last of these he holds Patent Rights on a “Hydraulic Supercharge and Cooling Circuit” granted in 1970.
Drones have drawn considerable attention as the agents in wireless data collection for agricultural applications, by virtue of their three-dimensional mobility and dominant line-of-sight communication channels. Existi...
详细信息
Drones have drawn considerable attention as the agents in wireless data collection for agricultural applications, by virtue of their three-dimensional mobility and dominant line-of-sight communication channels. Existing works mainly exploit dedicated drones via deployment and maintenance, which is insufficient regarding resource and cost-efficiency. In contrast, leveraging existing delivery drones for the data collection on their way of delivery, called delivery drones’ piggybacking, is a promising solution. For achieving such cost-efficiency, drone scheduling inevitably stands in front, but the delivery missions involved have escalated it to a wholly different and unexplored problem. As an attempt, we first survey 514 delivery workers and conduct field experiments; noticeably, the collection cost, which mostly comes from the energy consumption of drones’ piggybacking, is determined by the decisions on package-route scheduling and data collection time distribution. Based on such findings, we build a new model that jointly optimizes these two decisions to maximize data collection amount, subject to the collection budget and delivery constraints. Further model analysis finds it a Mixed Integer Non-Linear Programming problem, which is NP-hard. The major challenge stems from interdependence entangling the two decisions. For this point, we propose Delta, a \(\frac{1}{9+\delta }\)-approximation delivery drone scheduling algorithm. The key idea is to devise an approximate collection time distribution scheme leveraging energy slicing, which transforms the complex problem with two interdependent variables into a submodular function maximization problem only with one variable. The theoretical proofs and extensive evaluations verify the effectiveness and the near-optimal performance of Delta.
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