The world is getting busier and more crowded each year. Due to this fact resources such as public transport, available energy, and usable space are becoming congested and require vast amounts of logistical support. As of February 2018, nearly 95% of Americans own a mobile cell phone according to the Pew Research Center. These devices are consistently broadcasting their presents to other devices. By leveraging this data to provide occupational awareness of high traffic areas such as public transit stops, buildings, etc logistic efforts can be streamline to best suit the dynamics of the population. With the rise of The Internet of Things, a scalable low-cost occupancy monitoring system can be deployed to collect this broadcasted data and present it to logistics in real time. Simple IoT devices such as the Raspberry Pi, wireless cards capable of passive monitoring, and the utilization of specialized software can provide this capability. Additionally, this combination of hardware and software can be integrated in a way to be as simple as a typical plug and play set up making system deployment quick and easy. This effort details the development and integration work done to deliver a working product acting as a foundation to build upon. Machine learning algorithms such as k-Nearest-Neighbors were also developed to estimate a mobile device's approximate location inside a building.

Tools that automate processes are always sough after across the entire IT field. This project's aim was to build and evaluate a semi-automated reconnaissance tool based on a Raspberry Pi 4, for use in penetration testing and/or digital forensics. The software is written in Python 3 and utilizes Scapy, PyQt5 and the Aircrack-ng suite along with other pre-existing tools. The device is targeted against wireless networks and its main purpose is to capture what is known as the WPA handshake and thereby crack Wi-Fi passwords. Upon achieving this, the program shall then connect to the cracked network, start packet sniffing and perform a host discovery and scan for open ports. The final product underwent three tests and passed them all, except the step involving port scanning - most likely due to hardware and/or operating system faults, since other devices are able to perform these operations. The main functionalities of this device and software are to: identify and assess nearby network access points, perform deauthentication attacks, capture network traffic (including WPA handshakes), crack Wi-Fi passwords, connect to cracked networks and finally to perform host discovery and port scanning. All of these steps shall be executed automatically after selecting the target networks and pressing the start button. Based on the test results it can be stated that this device is well suited for practical use within cyber security and digital forensics. However, due to the Raspberry Pi's limited computing power users may be advised to outsource the cracking process to a more powerful machine, for the purpose of productivity and time efficiency.