Leon Stenneth

Transportation mode detection using mobile phones and GIS information

The transportation mode such as walking, cycling or on a train denotes an important characteristic of the mobile users context. In this paper, we propose an approach to inferring a users mode of transportation based on the GPS sensor on her mobile device and knowledge of the underlying transportation network. The transportation network information considered includes real time bus locations, spatial rail and spatial bus stop information. We identify and derive the relevant features related to transportation network information to improve classification effectiveness. This approach can achieve over 93.5% accuracy for inferring various transportation modes including: car, bus, aboveground train, walking, bike, and stationary. Our approach improves the accuracy of detection by 17% in comparison with the GPS only approach, and 9% in comparison with GPS with GIS models. The proposed approach is the first to distinguish between motorized transportation modes such as bus, car and aboveground train with such high accuracy. Additionally, if a user is travelling by bus, we provide further information about which particular bus the user is riding. Five different inference models including Bayesian Net, Decision Tree, Random Forest, Naïve Bayesian and Multilayer Perceptron, are tested in the experiments. The final classification system is deployed and available to the public.

Real-Time Street Parking Availability Estimation

Real-time parking availability information is important in urban areas, and if available could reduce congestion, pollution, and gas consumption. In this paper, we present a software solution called PhonePark for detecting the availability of on-street parking spaces. The solution uses the GPS and/or accelerometer sensors in a travelers mobile phone to automatically detect when and where the traveler parked her car, and when she released a parking slot. PhonePark can also utilize the mobile phones Bluetooth sensor or piggyback on street parking payment transactions for parking activity detection. Thus, the solution considers only mobile phones and does not rely on any external sensors such as cameras, wireless sensors embedded in the pavements, or ultrasonic sensors on vehicles. Further contributions include an algorithm to compute the historical parking availability profile for an arbitrary street block and algorithms to estimate the parking availability in real-time for a given street block. The algorithms are evaluated using real-time and real world street parking data.

PhonePark: Street Parking Using Mobile Phones

Real-time street parking availability information is important in urban areas, and if available could reduce congestion, pollution, and gas consumption. In this paper, an advanced street parking system called PhonePark is presented. Using the GPS, accelerometer, and Bluetooth sensors on a travelers mobile phone, in conjunction with geospatial data, we can automatically detect when and where the traveler parked her car, and when she released a parking slot.

Mobile systems location privacy: “MobiPriv” a robust k anonymous system

With the rapid advancement of positioning and tracking capabilities (mobile phones, on-board navigation systems) location based services are rapidly increasing. Privacy in location based systems is addressed in many papers. Our work is focused on the trusted third party privacy framework that utilizes the concept of k-anonymity with or without l-diversity. In previous anonymization models k may be defined as a personalization parameter of the mobile user or as uniform system parameter for all mobile users . Clearly, k other users may not be available at the time of request in these systems. These requests are discarded because the quality of service (QoS) they require cannot be satisfied. In this paper we introduce a novel suite of algorithms called MobiPriv that guarantees a 100% success rate of processing a mobile request using k-anonymity with diversity considerations. We evaluated our suite of algorithms experimentally against previously proposed anonymization algorithms using real world traffic volume data, real world road network and mobile users generated realistically by a mobile object generator.

Estimating Urban Traffic Congestions with Multi-sourced Data

This paper studies the novel problem of more accurately estimating urban traffic congestions by integrating sparse probe data and traffic related information collected from social media. Limited by the lack of reliability and low sampling frequency of GPS probes, probe data are usually not sufficient for fully estimating traffic conditions of a large arterial network. To address the data sparsity challenge, we extensively collect and model traffic related data from multiple data sources. Besides the GPS probe data, we also extensively collect traffic related tweets that report various traffic events such as congestion, accident, and road construction from both traffic authority accounts and general user accounts from Twitter. To further explore other factors that might affect traffic conditions, we also extract auxiliary information including road congestion correlations, social events, road features, as well as point of interest (POI) for help. To integrate the different types of data coming from different sources, we finally propose a coupled matrix and tensor factorization model to more accurately complete the very sparse traffic congestion matrix by collaboratively factorizing it with other matrices and tensors formed by other data. We evaluate the proposed model on the arterial network of downtown Chicago with 1257 road segments. The results demonstrate the effectiveness and efficiency of the proposed model by comparison with previous approaches.

Automated transportation transfer detection using GPS enabled smartphones

Understanding the mobility of a traveller from mobile sensor data is an important area of work in context aware and ubiquitous computing. Given a multimodal GPS trace, we will identify where in the GPS trace the traveller changed transportation modes. For example, where in the GPS trace the traveller alight a bus and boards a train, or where did the client stop running and start walking. Using data mining schemes to understand mobility data, in conjunction with real world observations, we propose an algorithm to identify mobility transfer points automatically. We compared the proposed algorithm against the state of the art that is used in the previously proposed work. Evaluation on real world data collected from GPS enabled mobile phones indicate that the proposed algorithm is accurate, has a good coverage, and a good asymptotic run time complexity.

An empirical study of data race detector tools

The industry of software applications has been increased significantly because of the high demand of using the software applications. This revolution leads on developing many concurrent software systems. Noticeably, some of these concurrent software systems have falsely report data race condition to one or more of their shared variables. Debugging such concurrent software systems to find the race condition is a challenge, especially for large and complex software systems. Since the race condition concerned mostly ignored in the concurrent software systems, adopting it could help to ensure the efficiency of these software systems. There are few detector tools that have been known in the industry focusing on data race detectors. This paper aims to study those tools. We are going to conduct empirical study of data race using well known tools in order to measure the correctness, performances and effectiveness of those tools in practical by using some benchmarks. Those benchmarks will be tested on each tool and compare it with others to see the similarity and differentiate.

Simultaneously Supporting Privacy and Auditing in Cloud Computing Systems

Over the last few years, cloud services have been steadily gaining traction in their use by commercial and noncommercial entities. As more and more sensitive or valuable processes, business functions and data move into the cloud, the need to improve threat identification and response, via auditing cloud transactions, increases. At the same time, the need for cloud users to protect the security and privacy of their resources has also intensified. In this paper, the problem of simultaneously supporting privacy and auditing in cloud systems is studied. Specifically, the paper discusses the guiding principles, fundamental concepts, and threat models for current cloud computing systems. Finally, we propose infrastructure that exploits a novel thin layer between the client and the cloud service provider to ensure that data storage, operation, and auditing does not reveal sensitive client information.