Ioannis Boutsis

Privacy preservation for participatory sensing data

Over the recent years, the proliferation of mobile networking and the increasing capabilities of smartphone devices have led to the development of “Participatory Sensing” applications, where users actively participate in data collection and sharing in a wide range of application domains from entertainment, to transportation, to environmental monitoring. One important challenge in these settings is privacy preservation for participatory sensing data, such as user trajectories. This paper develops a participatory sensing system for Android smartphones and proposes an efficient approach for privacy preservation which enables users to disclose their trajectory information without compromising their privacy. Existing approaches for privacy-aware data sharing operate under the assumption that user data is maintained in a centralized database. In our work we assume that user data is generated and stored locally on the individual smartphone devices. Our technique is distributed and low cost. We present detailed experimental results to illustrate that our approach is practical, efficient and with low overhead.

Crowdsourcing under Real-Time Constraints

In the recent years we are experiencing the rapid growth of crowdsourcing systems, in which “human workers” are enlisted to perform tasks more effectively than computers, and get compensated for the work they provide. The common belief is that the wisdom of the “human crowd” can greatly complement many computer tasks which are assigned to machines. A significant challenge facing these systems is determining the most efficient allocation of tasks to workers to achieve successful completion of the tasks under real-time constraints. This paper presents REACT, a crowdsourcing system that seeks to address this challenge and proposes algorithms that aim to stimulate user participation and handle dynamic task assignment and execution in the crowdsourcing system. The goal is to determine the most appropriate workers to assign incoming tasks, in such a way so that the realtime demands are met and high quality results are returned. We empirically evaluate our approach and show that REACT meets the requested real-time demands, achieves good accuracy, is efficient, and improves the amount of successful tasks that meet their deadlines up to 61% compared to traditional approaches like AMT.

Misco: A System for Data Analysis Applications on Networks of Smartphones Using MapReduce

The recent years have seen a proliferation of community sensing or participatory sensing paradigms, where individuals rely on the use of smart and powerful mobile devices to collect, store and analyze data from everyday life. Due to this massive collection of the data, a key challenge to all such developments, is to provide a simple but efficient way to facilitate the programming of distributed applications on the embedded devices. We will demonstrate a novel system that provides a principled approach to developing distributed data clustering applications on networks of smartphones and other mobile devices. The system comprises three components: (a) a distributed framework, implemented on mobile phones that eases the programmability and deployment of applications on the devices using simple programming primitives, (b) a data gathering component that tracks the movement of wireless device users and collects sensor data (i.e., GPS and accelerometer sensor data), and (c) a distributed data clustering algorithm that allows users to combine their individual data, that is distributed and energy efficient. Using a road traffic monitoring application we demonstrate how MISCO can efficiently identify anomalies in the road surface conditions and illustrate that our system is practical and has low energy and resource overhead.

RADAR: Adaptive Rate Allocation in Distributed Stream Processing Systems under Bursty Workloads

In the recent years we have witnessed a proliferation of distributed stream processing systems that need to operate under bursty workloads. Examples include road traffic control, processing of financial feeds, network monitoring and real-time sensor data analysis systems. Meeting the QoS requirements of the stream processing systems under burstiness is a challenging process. In this paper we present our approach for adaptive rate allocation within the distributed stream processing system to meet the end-to-end execution time and rate demands of the applications. Our algorithm determines the rates of the application components at runtime, with respect to the QoS constraints, to compensate for delays experienced by the components or to react to sudden bursts of load. Our technique is distributed and low-cost. Our detailed experimental results over our Synergy middleware illustrate that our approach is practical, depicts good performance and has low resource overhead.

Intelligent Urban Data Monitoring for Smart Cities

Urban data management is already an essential element of modern cities. The authorities can build on the variety of automatically generated information and develop intelligent services that improve citizens daily life, save environmental resources or aid in coping with emergencies. From a data mining perspective, urban data introduce a lot of challenges. Data volume, velocity and veracity are some obvious obstacles. However, there are even more issues of equal importance like data quality, resilience, privacy and security. In this paper we describe the development of a set of techniques and frameworks that aim at effective and efficient urban data management in real settings. To do this, we collaborated with the city of Dublin and worked on real problems and data. Our solutions were integrated in a system that was evaluated and is currently utilized by the city.

Efficient event detection by exploiting crowds

Encouraging users to participate in community-based sensing and collection for the purpose of identifying events of interest for the community has found important applications in the recent years in a wide variety of domains including entertainment, transportation and environmental monitoring. One important challenge in these settings is how significant events can be detected by exploiting the data sensed, gathered and shared by the crowd, while respecting the resource costs. In this paper we investigate the use of dynamic clustering and sampling techniques that allow us to significantly reduce utilization costs by clustering low-level streams of events based on their geo-spatial locations and then selectively retrieving the ones that depict the highest interest. Our experimental results illustrate that our approach is practical, efficient and depicts good performance.

Location privacy for crowdsourcing applications

This paper contributes to mobile crowdsourcing applications by developing a privacy preserving framework that enables users to contribute content to the community while controlling their privacy exposure. One fundamental challenge in such applications is how to preserve user privacy, as participants may end up revealing a great deal of user-identified, geo-located data, which can easily unfold user trajectories or sensitive locations (e.g., users home or work location). In this paper we develop PROMPT, a highly efficient privacy preserving framework that runs locally on mobile devices. PROMPT relies on a novel geometric approximation approach to preserve user privacy, by evaluating the privacy exposure of users before sharing their geo-located data. Our detailed experimental evaluation using real-world datasets illustrates that our approach is effective, practical and has low overhead on smartphones.

Heterogeneous Stream Processing and Crowdsourcing for Traffic Monitoring: Highlights

We give an overview of an intelligent urban traffic management system. Complex events related to congestions are detected from heterogeneous sources involving fixed sensors mounted on intersections and mobile sensors mounted on public transport vehicles. To deal with data veracity, sensor disagreements are resolved by crowdsourcing. To deal with data sparsity, a traffic model offers information in areas with low sensor coverage. We apply the system to a real-world use-case.

Personalized Event Recommendations Using Social Networks

In recent years we have observed a significant increase in the popularity of location-based social networks for exchanging news and experiences, sharing location information, or publishing real world events. One important challenge in such networks is to understand human crowd mobility behavior based on user social activities and interactions. In this paper we introduce PRESENT, our middleware that utilizes a Mixed Markov Model to extract the behavioral patterns of the users in social groups, to make personalized event recommendations. Our detailed experimental evaluation, using data from the Meet up location-based social network, illustrates that our approach is efficient, practical and achieves an average prediction for the user attendance of over 73%.

Mobile Stream Sampling under Time Constraints

The proliferation of mobile networking and the increasing capabilities of smartphone devices in the recent years have resulted in transforming mobile smartphone devices into ubiquitous sensing platforms. In this new class of “Community-based Participatory Sensing” systems, users actively participate in the data collection and sharing for the benefit of the community, in a wide range of application areas from entertainment, to transportation, to environmental monitoring. These approaches, however, generate large amounts of transient data streams, leading to real-time computational challenges. In this paper we propose sampling algorithms on streams of mobile data generated by ubiquitous sensing devices that need to be processed under time constraints. In our approach users participate in the system by sensing and sharing streams of data. The system then uses a sampling mechanism to select a subset of data streams that preserves the characteristics of the stream data and provides the highest “information gain” to the system, given the real-time, budget and resource constraints. Detailed experimental results illustrate that our approach is practical, efficient and depicts good performance.