Mohammad Taghi Dabiri;Mazen Hasna;Nizar Zorba;Tamer Khattab
{"title":"Enabling Flexible Arial Backhaul Links for Post Disasters: A Design Using UAV Swarms and Distributed Charging Stations","authors":"Mohammad Taghi Dabiri;Mazen Hasna;Nizar Zorba;Tamer Khattab","doi":"10.1109/OJVT.2024.3365531","DOIUrl":null,"url":null,"abstract":"In this article, our target is to design a permanent long backhaul link using unmanned aerial vehicle (UAV) relays and charge stations (CSs) to transfer data from the nearest core network to disaster area (DA). To this end, we first characterize the communication channel by considering the energy consumption models of the backup UAVs (moving UAVs) and the UAVs in service (hovering UAVs), the position and number of UAVs in service relative to the DA, along with the position of CSs relative to the position of UAVs. Then we define the optimization problem for two different scenarios. First, we design the long backhaul link in such a way that minimizes the implementation cost. In particular, the optimal design includes finding the optimal position for CSs, UAVs in service along with the optimal planning for backup UAVs in such a way as to reduce the implementation cost and guarantee the quality of service of the multi-relay UAV-based wireless backhaul links. The implementation cost is related to the number of CSs, the number of UAVs in service along with the number of backup UAVs. For the second scenario, we assume that the implementation cost is predetermined, and we find the optimal positions for UAVs and CSs along with planning for backup UAVs to minimize the outage probability. By analyzing the effects of optimization parameters, we further propose low complexity sub-optimal algorithms for both scenarios. Then, using simulations, we show that the sub-optimal algorithms achieve a performance that is very close to the optimal solutions.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"384-396"},"PeriodicalIF":5.3000,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10433686","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of Vehicular Technology","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10433686/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
In this article, our target is to design a permanent long backhaul link using unmanned aerial vehicle (UAV) relays and charge stations (CSs) to transfer data from the nearest core network to disaster area (DA). To this end, we first characterize the communication channel by considering the energy consumption models of the backup UAVs (moving UAVs) and the UAVs in service (hovering UAVs), the position and number of UAVs in service relative to the DA, along with the position of CSs relative to the position of UAVs. Then we define the optimization problem for two different scenarios. First, we design the long backhaul link in such a way that minimizes the implementation cost. In particular, the optimal design includes finding the optimal position for CSs, UAVs in service along with the optimal planning for backup UAVs in such a way as to reduce the implementation cost and guarantee the quality of service of the multi-relay UAV-based wireless backhaul links. The implementation cost is related to the number of CSs, the number of UAVs in service along with the number of backup UAVs. For the second scenario, we assume that the implementation cost is predetermined, and we find the optimal positions for UAVs and CSs along with planning for backup UAVs to minimize the outage probability. By analyzing the effects of optimization parameters, we further propose low complexity sub-optimal algorithms for both scenarios. Then, using simulations, we show that the sub-optimal algorithms achieve a performance that is very close to the optimal solutions.