Using Unmanned Aerial Vehicles for Wireless Localization in Search and Rescue

This thesis presents how unmanned aerial vehicles (UAVs) can successfully assist in search and rescue (SAR) operations using wireless localization. The zone-grid to partition to capture/detect WiFi probe requests follows the concepts found in Search Theory Method. The UAV has attached a sensor, e.g., WiFi sniffer, to capture/detect the WiFi probes from victims or lost people’s smartphones. Applying the Random-Forest based machine learning algorithm, an estimation of the user\u27s location is determined with a 81.8% accuracy. UAV technology has shown limitations in the navigational performance and limited flight time. Procedures to optimize these limitations are presented. Additionally, how the UAV is maneuvered during flight is analyzed, considering different SAR flight patterns and Li-Po battery consumption rates of the UAV. Results show that controlling the UAV by remote-controll detected the most probes, but it is less power efficient compared to control it autonomously

People Counting and occupancy Monitoring using WiFi Probe Requests and Unmanned Aerial Vehicles

Smart phones have become an important part of our daily lives due to their capabilities of accessing the web using WiFi and mobile data networks. These WiFi equipment are constantly sending out packets referred as probe requests, which can be tracked using wireless sniffers. In this thesis, first we investigate capturing of WiFi probe request packets using the help of WiFi Pineapple devices, and analyze how we can use signal strength information of probe request data for indoor occupancy monitoring. Applications of such occupancy monitoring into building surveillance and building energy management are also discussed. After completing the initial test indoors, research was moved to outdoor monitoring with the help of unmanned aerial vehicles (UAVs) flying in various trajectories and capturing probe request messages. The information captured from the probe requests is used to identify and localize WiFi users with a single UAV, which can be instrumental in search and rescue applications. Finally, we study in detail various security, privacy, and public safety issues related to drones equipped with wireless communications capabilities

Autonomous flying WiFi access point

Unmanned aerial vehicles (UAVs), aka drones, are widely used civil and commercial applications. A promising one is to use the drones as relying nodes to extend the wireless coverage. However, existing solutions only focus on deploying them to predefined locations. After that, they either remain stationary or only move in predefined trajectories throughout the whole deployment. In the open outdoor scenarios such as search and rescue or large music events, etc., users can move and cluster dynamically. As a result, network demand will change constantly over time and hence will require the drones to adapt dynamically. In this paper, we present a proof of concept implementation of an UAV access point (AP) which can dynamically reposition itself depends on the users movement on the ground. Our solution is to continuously keeping track of the received signal strength from the user devices for estimating the distance between users devices and the drone, followed by trilateration to localise them. This process is challenging because our on-site measurements show that the heterogeneity of user devices means that change of their signal strengths reacts very differently to the change of distance to the drone AP. Our initial results demonstrate that our drone is able to effectively localise users and autonomously moving to a position closer to them

Building up knowledge through passive WiFi probes

Inexpensive WiFi-capable hardware can be nowadays easily used to capture traffic from end users and extract knowledge. Such knowledge can be leveraged to support advanced services like user profiling, device classification. We review here the main building blocks to develop a system based on passive WiFi monitors, that is, cheap and viable sniffers which collect data from end devices even without an explicit association to any Wi-Fi network. We provide an overview of the services which can be enabled by such approach with three practical scenarios: user localization, user profiling and device classification. We evaluate the performance of each one of the three scenarios and highlight the challenges and threats for the aforementioned systems

Counting and locating people in outdoor environments: a comparative experimental study using WiFi-based passive methods

WiFi-based passive methods are becoming a common tool to count, estimate, and/or locate people. One area of applicability is the development of intelligent control system for traffic management in urban areas, so that these systems are able to take into account not only vehicles’ behaviors but also pedestrians’, as important actors in the road scenario. In this work, we present the performance evaluation in terms of accuracy of a WiFi-based passive method used to identify pedestrians, classify them as moving pedestrians or static pedestrians, and for the latter, to locate them in a traffic intersection. The proposed algorithm is implemented in a low-cost development board and tested through several experiments in a real outdoor scenario. Our proposal is compared with several classic Machine Learning (ML) algorithms, specifically with Binary Logistic Regression, Support Vector Classification, Gaussian Naive Bayes, Random Forest, and k-Nearest Neighbors. Results show that despite the simplicity of our method, the outcomes are similar or better than most of the ML techniques, without the expected complexity or computational requirements that the latter required

Robust, Resilient Networked Communication in Challenged Environments

In challenged environments, digital communication infrastructure may be difficult or even impossible to access. This is especially true in rural and developing regions, as well as in any region during a time of political or environmental crisis. We advance the state of the art in wireless networking and security to design networks and applications that rapidly assess changing networking conditions to restore communication and provide local situational awareness. This dissertation examines new systems for responding to current and emerging needs for wireless networks. This work looks across the wireless ecosystem of widely deployed standards. We develop new tools to improve network assessment and to provide robust and reliable network communication. By incorporating new technological breakthroughs, such as the wide commercial success of Unmanned Aircraft Systems (UAS), we introduce novel methods and systems for existing wireless standards for these challenged networks. We assess how existing technologies and standards function in difficult environments: lacking end-end Internet connectivity, experiencing overload or other resource constraints, and operating in three dimensional space. Through this lens, we demonstrate how to optimize networks to serve marginalized communities outside of first world urban cities and make our networks resilient to natural and political crisis that threaten communication

Development of a Drone-Mounted Wireless Attack Platform

The commercial drone market has grown rapidly due to the increasing utility and capabilities of drones. This new found popularity has made it possible for inexpensive drones capable of impressive carry capacities and flight times to reach the consumer market. These new features also offer an invaluable resource to wireless hackers. Capitalizing on their mobility, a wireless hacker can equip a drone with hacking tools to surpass physical security (e.g. fences) with relative ease and reach wireless networks. This research seeks to experimentally evaluate the ability of a drone-mounted wireless attack platform equipped with a directional antenna to conduct wireless attacks effectively at distances greater than 800 meters. To test this hypothesis, the “skypie v2” prototype conducts computer network attacks against a target network and captured data is used to evaluate the effectiveness of the platform. Results showed that capture of a WPA2 handshake was possible at a RSSI of -72 dBm or 2400 meters from a network located in a open field. Additionally, nmap scans were conducted with a RSSI value of -74 dBm or nearly 3000 meters from the target network

Passive Radiolocation of IEEE 802.11 Emitters using Directional Antennae

Low-cost commodity hardware and cheaper, more capable consumer-grade drones make the threat of home-made, inexpensive drone-mounted wireless attack platforms (DWAPs) greater than ever. Fences and physical security do little to impede a drone from approaching private, commercial, or government wireless access points (WAPs) and conducting wireless attacks. At the same time, unmanned aerial vehicles (UAVs) present a valuable tool for network defenders conducting site surveys and emulating threats. These platforms present near-term dangers and opportunities for corporations and governments. Despite the vast leaps in technology these capabilities represent, UAVs are noisy and consequently difficult to conceal as they approach a potential target; stealth is a valuable asset to an attacker. Using a directional antenna instead of the typical omnidirectional antenna would significantly increase the distance from which a DWAP may conduct attacks and would improve their stealthiness and overall effectiveness. This research seeks to investigate the possibility of using directional antennae on DWAPs by resolving issues inhibiting directional antennae use on consumer and hobbyist drone platforms. This research presents the hypothesis that a DWAP equipped with a directional antenna can predict bearings and locations of WAPs within an acceptable margin of error