This paper explores the feasibility of tracking motion and activities of humans using visible light. Shadows created by casting visible light on humans and objects are sensed using sensors that are embedded along with the light sources. Existing Visible Light Sensing (VLS) techniques require either light sensors deployed on the floor or a person carrying a device. Our approach seeks to measure light reflected off the floor to achieve an entirely device-free and light-source based system. We co-locate photosensors with LED light sources to observe the changes in light level occurring on the floor.
- EyeLight: Light-based Occupancy Estimation and Activity Recognition from Shadows on the Floor Mohamed Ibrahim, Viet Nguyen, Siddharth Rupavatharam, Minitha Jawahar, Marco Gruteser, and Richard Howard IEEE INFOCOM 2018. [PDF] [BibTex]
- Visible Light based Activity Sensing using Ceiling Photosensors Mohamed Ibrahim, Viet Nguyen, Siddharth Rupavatharam, Minitha Jawahar, Marco Gruteser, and Richard Howard ACM VLCS 2016 at MobiCom. Best Paper Award [PDF] [BibTex]
This work introduces a mobile sensing technique to detect a nearby active television, the channel it is tuned to, and whether it is receiving this channel over the air. This technique can find applications in tracking TV viewership, second screen services and advertising, as well as improving the efficiency of TV whitespace spectrum usage. The technique uses a three-stage detection process. It first uses a probabilistic model on audio recordings from mobile phones to detect likely TV sounds in the area. It then correlates the recording with known TV channel audio to identify the channel and improve detection robustness. Finally, it applies a latency analysis to determine whether programming is received over-the-air or through alternate means such as cable or satellite TV.
- Over-The-Air TV Detection using Mobile Devices Mohamed Ibrahim, Marco Gruteser, Khaled A. Harras, and Moustafa Youssef IEEE ICCCN 2017. Invited Paper [PDF] [BibTex]
- Toward dynamic real-time geo-location databases for TV white spaces. Ahmed Saeed, Mohamed Ibrahim, Khaled A. Harras, and Moustafa Youssef IEEE Network, vol. 29, no. 5, pp. 76-82, September-October 2015. [PDF] [BibTex]
- Unconventional TV Detection using Mobile Devices Mohamed Ibrahim, Ahmed Saeed, Khaled A. Harras and Moustafa Youssef IARIA UBICOMM 2013- Work-in-progress paper. [PDF] [BibTex]
Building testbeds for Cognitive Radio Networks (CRNs) is one of the main challenges that can affect the wide deployability of such networks. In this project we develop a framework that facilitates the development of cost-efficient large-scale CRNs routing protocols testbeds. The framework allows the designers to focus on the CRNs routing protocols by abstracting the PHY and MAC layers while providing the necessary cross layer functionalities. CogFrame, our developed framework, allows the development of testbeds that work with standard computers and WiFi cards to reduce the cost while allowing integrating with other special hardware for more flexibility. It also provides different modules for implementing and emulating complex scenarios such as regulatory authority policies, mobility management, topology management.
- Primary User-aware Optimal Discovery Routing for Cognitive Radio Networks. Arsany Guirguis, Fadel Digham, Karim G Seddik, Mohamed Ibrahim, Khaled A. Harras, and Moustafa Youssef IEEE Transactions on Mobile Computing, 2018 [PDF] [BibTex]
- Primary User Aware k-hop Routing for Cognitive Radio Networks Arsany Guirguis, Mohamed Ibrahim, Karim G Seddik, Khaled A. Harras, Fadel Digham, and Moustafa IEEE Globecom 2015. [PDF] [BibTex]
- Routing metrics of cognitive radio networks: A survey. Moustafa Youssef, Mohamed Ibrahim, Mohamed Abdelatif, Lin Chen, and Athanasios V. Vasilakos IEEE Communications Surveys & Tutorials 16.1 (2014): 92-109. [PDF] [BibTex]
- A Low-Cost Large Scale Framework for Cognitive Radio Routing Protocols Testing Ahmed Saeed, Mohamed Ibrahim, Khaled A. Harras and Moustafa Youssef IEEE ICC 2013. [PDF] [BibTex]
Context-aware applications have been gaining huge interest in the last few years. With cell phones becoming ubiquitous computing devices, cell phone localization has become an important research problem. In this project, we present CellSense, a probabilistic RSSI-based fingerprinting location determination system for GSM phones. We discuss the challenges of implementing a probabilistic fingerprinting localization technique in GSM networks and present the details of the CellSense system and how it addresses these challenges. We then extend the proposed system using a hybrid technique that combines probabilistic and deterministic estimation to achieve both high accuracy and low computational overhead.
- Enabling Wide Deployment of GSM Localization over Heterogeneous Phones Mohamed Ibrahim, and Moustafa Youssef IEEE ICC 2013. [PDF] [BibTex]
- CellSense: An accurate energy-efficient GSM positioning system. Mohamed Ibrahim, and Moustafa Youssef IEEE Transactions on Vehicular Technology 61.1 (2012): 286-296. [PDF] [BibTex]
- A Hidden Markov Model for Localization Using Low-End GSM Cell Phones Mohamed Ibrahim, and Moustafa Youssef IEEE ICC 2011. [PDF] [BibTex]
- CellSense: A Probabilistic RSSI-based GSM Positioning System Mohamed Ibrahim, and Moustafa Youssef IEEE Globecom 2010. [PDF] [BibTex]