Paper Info
Title
CEDAR: A Cost-Effective Crowdsensing System for Detecting and Localizing Drones
Abstract
The increasing popularity of drones is bringing many public security and privacy breach issues, such as smuggling, intrusion, and illegal surveillance. Traditional approaches to detecting and localizing drones such as radar and computer vision incur high costs and hence are not desirable for large-scale applications. In this paper, we propose a cost-effective crowdsensing system named CEDAR to achieve such a goal. Specifically, we introduce a novel way of detecting drones by smartphones, exploiting the fact that most drones adopt Wi-Fi for communications with ground control stations. We design an efficient detection algorithm that takes advantage of historical Wi-Fi beacon information and MAC address encoding mechanisms used by drone manufacturers. Using received signal strength, we can also localize the detected drones. Further, to encourage participants' involvement, we design an incentive mechanism based on online auction that guarantees truthfulness and consumer sovereignty. CEDAR can be directly applied to multiple drone scenarios. We implement the system based on Android for the client and Spring, Spring MVC, and Mybatis (SSM) for the centralized platform that supports scalability and hierarchical structure, and enables the coordination between clients and the platform. We perform extensive experiments to validate our analysis. Particularly, the detection rate in the experiments reaches 86.7 percent even without any prior information about drones.
Year
DOI
Venue
2020
10.1109/TMC.2019.2921962
IEEE Transactions on Mobile Computing
Keywords
DocType
Volume
Crowdsensing,drones,detection,cost-effective,multiple drones
Journal
19
Issue
ISSN
Citations 
9
1536-1233
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
guang yang17415.11
Xiufang Shi2819.86
Li Feng3539.79
Shibo He4149478.37
Zhiguo Shi524626.13
Jiming Chen64389238.91