A Zero-input Cloud-based Application to Measure and Map Road Conditions in Pakistan
We propose to design and develop a smartphone app that detects when it finds itself on a road and measures shocks and vibrations in order
to A) map and measure the state of disrepair of roads, B) map the locations of potholes and C) map the locations of speed bumps. The app will not require any input from its
user and report measurements to a cloud hosted application which stores data from cellphones
and jointly processes the data from all users to obtain a better and more complete estimate of
road conditions. This map will be made publicly available via a website that will allow citizens
and municipal authorities alike to spot potholes and road segments in need of repair, as well as
imbalances in infrastructure maintenance efforts across cities. If the municipality has data of
approved and legally constructed speed bumps, it is able to identify all others as illegally constructed.
Funding Agency: Microsoft Research
Amount: 40,000 Dollars
Detecting Covert links in Instant Messaging Networks using Flow level log data
AN-DASH group is studying an attack on relayed instant messaging (IM) traffic that allows an
attacker to infer who’s talking to whom with high accuracy. This attack only requires collection of packet header
traces between users and IM servers for a short time period, where each packet in the trace goes from a
user to an IM server or vice versa. The packet header traces contain information such as timestamps,
IP addresses, and port numbers of users and IM servers. Note that packet payloads are encrypted by most
IM services; therefore, they cannot be used to infer which users are talking to each other.
The specific goal of this attack is to accurately identify a candidate set of top-k users with whom a
given user possibly talked to, while using only the information available in packet header traces. It
is technically challenging to perform this attack in real-world IM networks due to simultaneous
one-to-many communication among users, unequal transmission delays, and out of order or duplicate packets.
Funding Agency: National ICT R&D Fund, Pakistan
Amount: 9.86 million rupees
Performance Analysis of OpenFlow enabled routers over OF@TEIN Testbed
Software-Defined Networking (SDN), a new networking paradigm, provides a solution by smartly managing and configuring the network elements. It makes the network programmable by separating the control plane of the network from the data plane. This leaves behind data plane having only switches with packet forwarding capabilities. The centralized control plane consists of a southbound Application Programming Interface (API), for communication with the networks hardware, and a northbound API, for communication with applications of the network. OpenFlow is the main southbound API, promoted by the Open Networking Foundation (ONF). It aims to provide interoperability between networking equipments of different vendors, as previously only proprietary attempts has been made in this regard. ANDASH research focuses over the performance analysis of Software Defined Networking. Performance enhancement techniques are being devised for the current state-of-the-art architecture. Real world experiments are being performed over OF@TEIN testbed comprising of nodes situated in 8+ countries.
Collaborators: Higher Education Commission (Pakistan), Gwangju University (Korea), Pakistan Education Research Network (Pakistan)
Physical Activity Detection Using Smartphone Sensors
In this research we hope to explore a broad group of disciplines--transportation and physical planning, engineering, mobile sensing, data mining, geography, public health, nutrition, etc. — which will converge to provide an improved approach to community design to promote higher level of physical activity. There has been considerable interest in activity inference based on context, activity and surrounding using mobile sensing platform to develop social network, gaming and healthcare based applications. The objectives of our research are three-fold, (1) Develop classifiers that leverage data obtained from cellphone sensors (accelerometer, gyroscope and GPS sensors) to infer the physical activity performed by the phone’s owner/ carrier. (2) Convert the information about physical activity level to alternative forms, e.g. carbon footprint, calorie burn rate etc. (3) Collect and analyze the collective information of users’ physical activity levels to measure trends in various communities, localities and demographics and use this information to inform city and environmental planning. Further enhance the smartphone app to provide proactive information to users to encourage them to adopt healthier living habits.
Funding Agency: Higher Education Commission (HEC), Pakistan
- Flowing Data
- TNT - The Network Thinkers
- Jure Leskovec
- Knowledge Discover Lab
- CASOS Datasets
- UCSB CURRENT Lab : Datasets
- Stanford Large Dataset Collection
- UC Davis RUbiNet Lab : Datasets
- Online Social Networks Research@Max Planck Inst (2007)
- Online Social Networks Research@Max Planck Inst (2008)