User Privacy Leakage in Location-based Mobile Ad Services

The online advertising ecosystem leverages its massive data collection capability to learn the properties of users for targeted ad deliveries. Many Android app developers include ad libraries in their apps as a way of monetization. These ad libraries contain advertisements from the sell-side platforms, which collect an extensive set of sensitive information to provide more relevant advertisements for their customers. Existing efforts have investigated the increasingly pervasive private data collection of mobile ad networks over time. However, there lacks a measurement study to evaluate the scale of privacy leakage of ad networks across different geographical areas. In this work, we present a measurement study of the potential privacy leakage in mobile advertising services conducted across different locations. We develop an automated measurement system to intercept mobile traffic at different locations and perform data analysis to pinpoint data collection behaviors of ad networks at both the app-level and organization-level. With 1,100 popular apps running across 10 different locations, we perform extensive threat assessments for different ad networks. Meanwhile, we explore the ad-blockers’ behavior in the ecosystem of ad networks, and whether those ad-blockers are actually capturing the users’ private data in the meantime of blocking the ads. We find that: the number of location-based ads tends to be positively related to the population density of locations, ad networks collect different types of data across different locations, and ad-blockers can block the private data leakage

Cloud-based or On-device: An Empirical Study of Mobile Deep Inference

Modern mobile applications are benefiting significantly from the advancement in deep learning, e.g., implementing real-time image recognition and conversational system. Given a trained deep learning model, applications usually need to perform a series of matrix operations based on the input data, in order to infer possible output values. Because of computational complexity and size constraints, these trained models are often hosted in the cloud. To utilize these cloud-based models, mobile apps will have to send input data over the network. While cloud-based deep learning can provide reasonable response time for mobile apps, it restricts the use case scenarios, e.g. mobile apps need to have network access. With mobile specific deep learning optimizations, it is now possible to employ on-device inference. However, because mobile hardware, such as GPU and memory size, can be very limited when compared to its desktop counterpart, it is important to understand the feasibility of this new on-device deep learning inference architecture. In this paper, we empirically evaluate the inference performance of three Convolutional Neural Networks (CNNs) using a benchmark Android application we developed. Our measurement and analysis suggest that on-device inference can cost up to two orders of magnitude greater response time and energy when compared to cloud-based inference, and that loading model and computing probability are two performance bottlenecks for on-device deep inferences.Comment: Accepted at The IEEE International Conference on Cloud Engineering (IC2E) conference 201

Network optimization for enhanced resilience of urban heat island measurements

The urban heat island is a well-known phenomenon that impacts a wide variety of city operations. With greater availability of cheap meteorological sensors, it is possible to measure the spatial patterns of urban atmospheric characteristics with greater resolution. To develop robust and resilient networks, recognizing sensors may malfunction, it is important to know when measurement points are providing additional information and also the minimum number of sensors needed to provide spatial information for particular applications. Here we consider the example of temperature data, and the urban heat island, through analysis of a network of sensors in the Tokyo metropolitan area (Extended METROS). The effect of reducing observation points from an existing meteorological measurement network is considered, using random sampling and sampling with clustering. The results indicated the sampling with hierarchical clustering can yield similar temperature patterns with up to a 30% reduction in measurement sites in Tokyo. The methods presented have broader utility in evaluating the robustness and resilience of existing urban temperature networks and in how networks can be enhanced by new mobile and open data sources

Mobile positioning for location dependent services in GSM networks

A feasible Mobile Positioning solution is often sought after by network operators and service providers alike. Location-dependent applications create a new domain of services which might not only be of interest to the next generation of mobile users but also create new potential revenue streams. Applications vary from emergency services and tracking to location-based information services, location-based billing and location-dependent advertising. Due to the shortcomings of location-related information present in GSM networks, and the lack of positioning functionality in most of the commonly sold mobile devices, a straightforward solution for mobile positioning does not currently exist. This research intends to propose cellular positioning methods which do not require any significant changes to the network or the mobile device itself, which are feasible and cost effective, and which provide sufficient accuracy for certain categories of location-based services. These techniques are based on the proper analysis of signal measurement data, probabilistic geometric computation of location areas likely to represent the user’s location, and the correlation of this data with information obtained from path loss models used in the design and planning of a mobile radio network.peer-reviewe

Automatic Recognition of Public Transport Trips from Mobile Device Sensor Data and Transport Infrastructure Information

Automatic detection of public transport (PT) usage has important applications for intelligent transport systems. It is crucial for understanding the commuting habits of passengers at large and over longer periods of time. It also enables compilation of door-to-door trip chains, which in turn can assist public transport providers in improved optimisation of their transport networks. In addition, predictions of future trips based on past activities can be used to assist passengers with targeted information. This article documents a dataset compiled from a day of active commuting by a small group of people using different means of PT in the Helsinki region. Mobility data was collected by two means: (a) manually written details of each PT trip during the day, and (b) measurements using sensors of travellers' mobile devices. The manual log is used to cross-check and verify the results derived from automatic measurements. The mobile client application used for our data collection provides a fully automated measurement service and implements a set of algorithms for decreasing battery consumption. The live locations of some of the public transport vehicles in the region were made available by the local transport provider and sampled with a 30-second interval. The stopping times of local trains at stations during the day were retrieved from the railway operator. The static timetable information of all the PT vehicles operating in the area is made available by the transport provider, and linked to our dataset. The challenge is to correctly detect as many manually logged trips as possible by using the automatically collected data. This paper includes an analysis of challenges due to missing or partially sampled information in the data, and initial results from automatic recognition using a set of algorithms. Improvement of correct recognitions is left as an ongoing challenge.Comment: 22 pages, 7 figures, 10 table

Experimentation and Characterization of Mobile Broadband Networks

The Internet has brought substantial changes to our life as the main tool to access a large variety of services and applications. Internet distributed nature and technological improvements lead to new challenges for researchers, service providers, and network administrators. Internet traffic measurement and analysis is one of the most trivial and powerful tools to study such a complex environment from different aspects. Mobile BroadBand (MBB) networks have become one of the main means to access the Internet. MBB networks are evolving at a rapid pace with technology enhancements that promise drastic improvements in capacity, connectivity, and coverage, i.e., better performance in general. Open experimentation with operational MBB networks in the wild is currently a fundamental requirement of the research community in its endeavor to address the need for innovative solutions for mobile communications. There is a strong need for objective data relating to stability and performance of MBB (e.g., 2G, 3G, 4G, and soon-to-come 5G) networks and for tools that rigorously and scientifically assess their performance. Thus, measuring end user performance in such an environment is a challenge that calls for large-scale measurements and profound analysis of the collected data. The intertwining of technologies, protocols, and setups makes it even more complicated to design scientifically sound and robust measurement campaigns. In such a complex scenario, the randomness of the wireless access channel coupled with the often unknown operator configurations makes this scenario even more challenging. In this thesis, we introduce the MONROE measurement platform: an open access and flexible hardware-based platform for measurements on operational MBB networks. The MONROE platform enables accurate, realistic, and meaningful assessment of the performance and reliability of MBB networks. We detail the challenges we overcame while building and testing the MONROE testbed and argue our design and implementation choices accordingly. Measurements are designed to stress performance of MBB networks at different network layers by proposing scalable experiments and methodologies. We study: (i) Network layer performance, characterizing and possibly estimating the download speed offered by commercial MBB networks; (ii) End users’ Quality of Experience (QoE), specifically targeting the web performance of HTTP1.1/TLS and HTTP2 on various popular web sites; (iii) Implication of roaming in Europe, understanding the roaming ecosystem in Europe after the "Roam like Home" initiative; and (iv) A novel adaptive scheduler family with deadline is proposed for multihomed devices that only require a very coarse knowledge of the wireless bandwidth. Our results comprise different contributions in the scope of each research topic. To put it in a nutshell, we pinpoint the impact of different network configurations that further complicate the picture and hopefully contribute to the debate about performance assessment in MBB networks. The MBB users web performance shows that HTTP1.1/TLS is very similar to HTTP2 in our large-scale measurements. Furthermore, we observe that roaming is well supported for the monitored operators and the operators using the same approach for routing roaming traffic. The proposed adaptive schedulers for content upload in multihomed devices are evaluated in both numerical simulations and real mobile nodes. Simulation results show that the adaptive solutions can effectively leverage the fundamental tradeoff between the upload cost and completion time, despite unpredictable variations in available bandwidth of wireless interfaces. Experiments in the real mobile nodes provided by the MONROE platform confirm the findings