The rapid growth of ubiquitous mobile smart devices has led to the creation of a new era of mobile crowdsourcing applications, where human workers participate and perform tasks in exchange of a monetary reward. Such crowdsourcing systems can play a vital role during emergency events, where fast and accurate responses are needed. However, a commonly ignored aspect is how the price (i.e. the reward paid to workers) must be set in order for the system to meet two important requirements: (i) to timely receive an adequate number of responses which is crucial during emergencies, and (ii) to meet budget constraints. In the majority of the existing systems, the price per task is set up-front and remains unchanged for all upcoming tasks, leading to either higher monetary cost than necessary or to significantly larger latency than expected. In this work, we provide a formulation based on Kalman Filters that enables the system to estimate the user/worker behavior, i.e., the likelihood over time for a user to provide answers for a specific reward. Specifically, we focus on the problem of developing an adaptive pricing policy to incentivize the users to rapidly provide their responses. Our mechanism can be adjusted dynamically to bridge the gap among the users' behavior and the system's needs so as to maximize the overall utility of the system. We simulate our model and through extensive experimental evaluation we show how our system performs and provides benefits to both the users and the system operator.

Bike-sharing systems have enjoyed tremendous success in many major cities around the world today as a new means of urban public transportation offering a green and facile solution for daily commuters and tourists. One common problem featured in these systems is that the distribution of bikes among stations can be quite uneven, due to topography, rush hours or during the occurrence of major events around the city. This often results in shortage of bikes or bike parking racks. An unbalanced bike system means an unreliable form of transportation and disappointed users. Existing works in the literature are limited as they are not designed to handle fluctuating, high or unpredictable demand during large city events that typically affect multiple stations and require rebalancing in real-time, during the event, to ensure seamless operation. In this work, we present “SmartBIKER”, a cost-effective framework for bike sharing systems focusing on major city events. SmartBIKER models bike demand trends during major events, identifies bike stations with low or high demand using a trend forecasting model and determines a relocation strategy that minimizes the relocation cost while maximizing the utility of the stations. Our experimental evaluation shows that our approach is practical, efficient and outperforms state-of-the-art relocation and prediction schemes.

The wide adoption of Location Based Social Networks along with advances in mobile technology, has brought forth as a core service the analysis of large volumes of location-based data for personalized Point of Interest (POIs) recommendations. The majority of the existing recommendation systems take advantage of Collaborative Filtering, but they fail to exploit the contextual information involved with POI checkins (i.e., POI category, location, or the checkin timestamp). In this paper we propose CoTF, a Context-Aware Point of Interest Recommendation system using Tensor Factorization, that aims at enhancing the user experience by providing personalized context aware POI recommendations. Our approach exploits Category-based context related to checkins without the need of any pre-or post-filtering techniques. Our detailed experimental evaluation using real data from the Foursquare location-based social network illustrates that our approach can efficiently produce personalized recommendations to users, while significantly reducing the training time compared to current state-of-the-art methods.

Recently, the massive prevalence of mobile devices has led to the development of mobile social sensing systems where humans are enlisted to act as social sensors to perform geo-located tasks that require human intelligence or intervention. Social sensing from ubiquitous users can provide significant benefits particularly during crisis management and emergency scenarios. However, an important problem during such emergencies is how to exploit social sensors to accurately determine the location, extent and severity of the event. In this paper we develop a social sensing system that uses humans as social sensors where we apply particle filtering to iteratively determine the spatial areas to be investigated to accurately detect the location and state of the target event. Our experiments illustrate that our approach can accurately identify critical real-world events using feedback from the social sensors.

Recommending nearby Points of Interest (POI) has received growing interest in mobile location-based networks today, where users share content embedded with location information. In this work, we propose a novel caching framework to support personalised proactive caching for mobile location-based social networks. We propose "LOCAI", which uses a probabilistic approach in order to predict the POIs that users will access and retrieve the appropriate data objects that will fulfill user preferences. Our detailed experimental evaluation, using data from the Foursquare location-based social network, illustrates that LOCAI minimizes the user latency to retrieve the data objects they are interested in, is efficient and practical.

With the massive prevalence of smartphones, mobile social sensing systems in which humans acting as social sensors respond to geo-located crowdsourcing tasks, became extremely popular. Such systems can provide significant benefits particularly during crisis management and emergency situations. However, not only querying users can be extremely costly but also human sensors are mobile, subjective and their response delays can highly vary. In this paper we develop a social sensing system that performs sampling on mobile social sensors to achieve accurate and real-time detection of the state of emergency events. Our contributions are two-fold: (i) our approach can capture well emergencies even in large geographical regions, and (ii) our sampling approach considers the individual characteristics of the social sensors to maximize the probability of receiving accurate responses in a timely manner. We provide comprehensive experiments that indicate that our approach accurately identifies critical real-world events, has low overhead and reduces the classification error up to 90% compared to traditional approaches.

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., user's 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.

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%.

Urban traffic gathers increasing interest as cities become bigger, crowded and “smart”. We present a system for het-erogeneous stream processing and crowdsourcing supporting intelligent urban traffic management. Complex events related to traffic congestion (trends) are detected from heterogeneous sources involving fixed sensors mounted on intersections and mobile sensors mounted on public transport vehicles. To deal with data veracity, a crowdsourcing component handles and resolves sensor disagreement. Furthermore, to deal with data sparsity, a traffic modelling component offers information in areas with low sensor coverage. We demonstrate the system with a real-world use-case from Dublin city, Ireland.

With the rapid growth of mobile smartphone users, several commercial mobile companies have exploited crowd sourcing as an effective approach to collect and analyze data, to improve their services. In a crowd sourcing system, "human workers" are enlisted to perform small tasks, that are difficult to be automated, in return for some monetary compensation. This paper presents our crowd sourcing system that seeks to address the challenge of determining the most efficient allocation of tasks to the human crowd. The goal of our algorithm is to efficiently determine the most appropriate set of workers to assign to each incoming task, so that the real-time demands are met and high quality results are returned. We empirically evaluate our approach and show that our system effectively meets the requested demands, has low overhead and can improve the number of tasks processed under the defined constraints over 71% compared to traditional approaches.

Location-based social networks have evolved into powerful tools in recent years. The ability to embed location information in Social Networks such as Facebook, Foursquare and Twitter creates exciting opportunities for users to disseminate and exchange geolocation information in a variety of domains. The problem of exploiting the social ties between the users for maximizing information reach has become a topic of great interest, and many challenges have to be met. In this work we study the problem of efficient information dissemination in location-based social networks under time constraints. The objective is to identify a subset of individuals to propagate the information and make intelligent route selection that can result in maximizing the reach within a time window. Our detailed experimental results illustrate the feasibility and performance of our approach.

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.

In the recent years we have witnessed a proliferation of distributed stream processing systems that need to operate efficiently, even when data bursts occur. Examples include road traffic networks, processing of financial feeds, network monitoring and real-time sensor data analysis systems. An important challenge in managing these systems is effective resource management and meeting the QoS demands of the stream processing applications under different workload conditions, even under bursts. In this paper we present our approach that aims to predict the execution times of the distributed stream processing applications by taking into account the effects of the bursts and what is the typical workload of the stream processing system. Our approach builds application data rate patterns at run-time and predicts the effect of the burst on the performance of the applications, to identify whether there is a need to react on the onset of a burst. Our detailed experimental results over our Synergy middleware illustrate that our approach is practical, depicts good performance and has low resource overhead.

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.

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.

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.

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.