{"title":"Drones, Smartphones and Sensors to Face Natural Disasters","authors":"Milan Erdelj, E. Natalizio","doi":"10.1145/3213526.3213541","DOIUrl":null,"url":null,"abstract":"Many efforts are being done in order to recognize and forecast the occurrence of a natural disaster, in order to react in an efficient manner to the disaster in course of happening, and to quickly and efficiently assess the damage, fix and restore normal state [2–6]. Large-scale natural disasters test the most fundamental human instinct of survival by inflicting massive, and often unpredictable loss to life and property. Various types of natural disasters have been classified in [1] according to the technology that can be used to respond to them: geophysical (earthquake, tsunami, volcano, landslide, avalanche), hydrological (flash-floods, debris flow, floods), climatological (extreme temperature, drought, wildfire) andmeteorological (tropical storm, hurricane, sandstorm, heavy rainfall), among others, have caused losses of many lives in addition to increase in material losses in the order of 100% – 150% over the period of last 30 years [7]. Acknowledging the need for bolstering disaster resilience, this paper contributes a vision of leveraging the latest advances in wireless sensor network (WSN) technology and unmanned aerial vehicles (UAVs) to enhance the ability of network-assisted disaster prediction, assessment and response. Around 47% of the overall losses and 45% of the insured losses derived from inland flooding that occurred in Europe, Canada, Asia and Australia. Altogether, at around US$ 45bn, losses from natural catastrophes were below the average amount for the past ten years (US$ 85bn). Insured losses totaled approximately US$ 13bn. Thus, in this paper, we will focus our attention on inland flooding events, and a special emphasis will be given at mobility schemes for UAVs coverage [9].","PeriodicalId":237910,"journal":{"name":"Proceedings of the 4th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 4th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3213526.3213541","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
Many efforts are being done in order to recognize and forecast the occurrence of a natural disaster, in order to react in an efficient manner to the disaster in course of happening, and to quickly and efficiently assess the damage, fix and restore normal state [2–6]. Large-scale natural disasters test the most fundamental human instinct of survival by inflicting massive, and often unpredictable loss to life and property. Various types of natural disasters have been classified in [1] according to the technology that can be used to respond to them: geophysical (earthquake, tsunami, volcano, landslide, avalanche), hydrological (flash-floods, debris flow, floods), climatological (extreme temperature, drought, wildfire) andmeteorological (tropical storm, hurricane, sandstorm, heavy rainfall), among others, have caused losses of many lives in addition to increase in material losses in the order of 100% – 150% over the period of last 30 years [7]. Acknowledging the need for bolstering disaster resilience, this paper contributes a vision of leveraging the latest advances in wireless sensor network (WSN) technology and unmanned aerial vehicles (UAVs) to enhance the ability of network-assisted disaster prediction, assessment and response. Around 47% of the overall losses and 45% of the insured losses derived from inland flooding that occurred in Europe, Canada, Asia and Australia. Altogether, at around US$ 45bn, losses from natural catastrophes were below the average amount for the past ten years (US$ 85bn). Insured losses totaled approximately US$ 13bn. Thus, in this paper, we will focus our attention on inland flooding events, and a special emphasis will be given at mobility schemes for UAVs coverage [9].