ETD system

Electronic theses and dissertations repository


Tesi etd-09072016-114921

Thesis type
Tesi di laurea magistrale
A NetLogo simulation tool for UAV-based secure location verification in crowd sensing
Corso di studi
relatore Prof. Dini, Gianluca
relatore Ing. Perazzo, Pericle
Parole chiave
  • secure location verification.
  • VRP
  • crowd sensing
  • UAV
Data inizio appello
Riassunto analitico
In the last decade, Unmanned Aerial Vehicle (UAV) production and interest is observing a continuous growth that appears not to decline. Meanwhile, thanks to the increase in the use of personal mobile devices and their onboard sensors, which is becoming more and more widespread, a new data collection technique, named crowd sensing, has emerged.
Unfortunately, security remains a relevant issue, chiefly the integrity, i.e. the assurance that the information reported is trustworthy and accurate, still remains unsolved. The information the participant declares could be inaccurate or even counterfeit, due to flaws or fraud. Current literature shows no efficient solutions to the security problem, hence the arising need to point in this direction.
The idea of this thesis came from the merging of the aforementioned mobile technologies. The aim is to fill the security gap in the crowd sensing process through UAVs employment, to prove trustworthiness and accuracy of sensorial data.
The project presumes UAVs expedition in swarms where the data is originated, the authenticity of which could be promptly and directly verified thanks to the onboard sensors and, possibly, through interaction with other close sensors.
Through the deployment of a simulator, written in the NetLogo language, it has been possible to reproduce a crowd sensing system and investigate the trustworthiness gap.
We proposed and compared two different decision criteria to reveal attacks, named Dictatorship and Majority, both based on distance evaluation through radio frequency communication with the participant. In Dictatorship, it is sufficient that one UAV detects an inconsistency to warn an attack. In Majority, the half plus one of UAVs must detect an inconsistency in order to warn an attack. With regard to that, Dictatorship criterion showed certainly a better performance than Majority one.
We further focused on participants' waiting time reduction acting on the algorithms to schedule swarms missions. A First Come First Served (FCFS)-like routine and an Insertion heuristic have been deployed. Since there are no statistical differences between the two for the tests we performed, the former scheduling algorithm is preferable due to its deterministic nature.