VSmart: DSRC-based Smart Vehicle Testbed
VSmart is a DSRC-enabled smart vehicle testbed established at Cyber-Physical Systems Laboratory (CPS-Lab), McGill University, and is supported by NSERC and General Motors Company. VSmart explores and illustrates the potential to enhance driving safety and traffic efficiency with V2V communications (especially DSRC).
The computing capacity and scale of data centers are increasing to meet the soaring demand for IT applications and services. Mega data center can host thousands of servers and require up to tens of megawatts of electricity. The high power consumption causes two serious consequences. First, data center operators may face millions of dollars annually charged by the electrical grid. Second, the enormous energy consumption can lead to negative environmental impacts. This project focuses on data center sustainability, to cut operational cost and increase renewable integration for data centers.
AppAudit: Detecting Data-leaking Mobile Apps
We design AppAudit, a program analysis framework that checks if an Android application leaks sensitive personal data. AppAudit is designed with minimalism, using least possible memory and least amount of time. Current prototype could vet a real app with 256MB memory
in 5 seconds
on average. AppAudit can be used for three use cases:
- mobile app developers could use AppAudit to check if their apps include any data-leaking libraries or modules
- the app market could use AppAudit to vet newly uploaded apps and remove data-leaking ones
- mobile users could use AppAudit to avoid installing data-leaking apps
AppAudit appears at IEEE Symposium on Security and Privacy (S&P) 2015
EV: Green Charging
The power flow incurred by electric vehicle(EV) and renewable energy are both crucial to the future smart grid. Yet how to integrated them into power systems remains largely unexplored. In the Cyber-Physical Systems Laboratory at McGill University, we are working on developing a series of innovative technology and market strategies to achieve this integration in an efficient, reliable and real-time manner.
A Study of Facebook Likes
This is a project revealing the flaws of the Facebook Like system. These flaws widely exist in most of the websites that take use of the Facebook Like button, and can be exploited by spammers to automatically generate large amount of fake Likes for profits. Meanwhile, these flaws make legitimate users unintentionally generate Facebook Likes against the online contents they don't like or even have negative feelings about. These flaws endanger the Facebook Like ecosystem and the benefits of both legitimate users and advertisers. Many famous websites, such as FoxNews, abcNews, ESPN, HuffingtonPost etc., are victims of these flaws.
We hope this project can help people understand these flaws, and also inspire both the researchers and related companies to address them and eliminate the potential threat.
Apps Drain Battery Because of Memory Leaks
Mobile operating systems embrace new mechanisms
that reduce energy consumption for common usage
scenarios. The background app design is a repre
sentative implemented in all major mobile OSes.
The OS keeps apps that are not currently inter
acting with the user in memory to avoid repeated
app loading. This mechanism improves responsive
ness and reduces the energy consumption when the
user switches apps. However, we demonstrate that
application errors, in particular memory leaks that
cause system memory pressure, can easily cripple
this mechanism. In this paper, we conduct experi
ments on real Android smartphones to 1) evaluate
how the background app design improves respon
siveness and saves energy; 2) characterize memory
leaks in Android apps and outline its energy im
pact; 3) propose design improvements to retrofit the
mechanism against memory leaks.
SafeVChat: Safety and Security in Online Video Chat Systems
Online video chat systems such as Chatroulette have become increasingly popular as a way to meet and converse one-on-one via video and audio with other users online in an open and interactive manner. At the same time, safety and security concerns inherent in such communication have been little explored. Our research group at University of Colorado, Boulder, USA and McGill University, Montreal, Canada seeks to conduct ground-breaking research in the context of an online video chat system.
ISC: Adult Account Detection on Twitter
The widespread of adult content on online social networks (e.g., Twitter) is becoming an emerging yet critical problem. An automatic method to identify accounts spreading sexually explicit content (i.e., adult account) is of significant values in protecting children and improving user experiences. Traditional adult content detection techniques are ill-suited for detecting adult accounts on Twitter due to the diversity and dynamics in Twitter content. In this article, we formulate the adult account detection as a graph based classification problem and demonstrate our detection method on Twitter by using social links between Twitter accounts and entities in tweets. As adult Twitter accounts are mostly connected with normal accounts and post many normal entities, which makes the graph full of noisy links, existing graph based classification techniques cannot work well on such a graph. To address this problem, we propose an iterative social based classifier (ISC), a novel graph based classification technique resistant to the noisy links. Evaluations using large-scale real-world Twitter data show that, by labeling a small number of popular Twitter accounts, ISC can achieve satisfactory performance in adult account detection, significantly outperforming existing techniques.
mZig: Multi-Packet Reception in ZigBee
mZig is a novel physical layer design that enables a receiver to simultaneously decode multiple packets from different transmitters in ZigBee. As a low-power and low-cost wireless protocol, the promising ZigBee has been widely used in sensor networks, cyber-physical systems, and smart buildings. Since ZigBee based networks usually adopt tree or cluster topology, the convergecast scenarios are common in which multiple transmitters need to send packets to one receiver. For example, in a smart home, all appliances report data to one control plane via ZigBee. However, concurrent transmissions lead to the severe collision problem. The conventional ZigBee avoids collisions using backoff time, which introduces additional time overhead. Advanced methods resolve collisions instead of avoidance, in which the state-of-the-art ZigZag resolves one m-packet collision requiring m retransmissions.