Leyla Kazemi

GeoCrowd: enabling query answering with spatial crowdsourcing

With the ubiquity of mobile devices, spatial crowdsourcing is emerging as a new platform, enabling spatial tasks (i.e., tasks related to a location) assigned to and performed by human workers. In this paper, for the first time we introduce a taxonomy for spatial crowdsourcing. Subsequently, we focus on one class of this taxonomy, in which workers send their locations to a centralized server and thereafter the server assigns to every worker his nearby tasks with the objective of maximizing the overall number of assigned tasks. We formally define this maximum task assignment (or MTA) problem in spatial crowdsourcing, and identify its challenges. We propose alternative solutions to address these challenges by exploiting the spatial properties of the problem space. Finally, our experimental evaluations on both real-world and synthetic data verify the applicability of our proposed approaches and compare them by measuring both the number of assigned tasks and the travel cost of the workers.

GeoTruCrowd: trustworthy query answering with spatial crowdsourcing

With the abundance and ubiquity of mobile devices, a new class of applications, called spatial crowdsourcing, is emerging, which enables spatial tasks (i.e., tasks related to a location) assigned to and performed by human workers. However, one of the major challenges with spatial crowdsourcing is how to verify the validity of the results provided by workers, when the workers are not trusted equally. To tackle this problem, we assume every worker has a reputation score, which states the probability that the worker performs a task correctly. Moreover, we define a confidence level for every spatial task, which states that the answer to the given spatial task is only accepted if its confidence is higher than a certain threshold. Thus, the problem we are trying to solve is to maximize the number of spatial tasks that are assigned to a set of workers while satisfying the confidence levels of those tasks. Note that a unique aspect of our problem is that the optimal assignment of tasks heavily depends on the geographical locations of workers and tasks. This means that every spatial task should be assigned to enough number of workers such that their aggregate reputation satisfies the confidence of the task. Consequently, an exhaustive approach needs to compute the aggregate reputation score (using a typical decision fusion aggregation mechanism, such as voting) for all possible subsets of the workers, which renders the problem complex (we show it is NP-hard). Subsequently, we propose a number of heuristics and utilizing real-world and synthetic data in extensive sets of experiments we show that we can achieve close to optimal performance with the cost of a greedy approach, by exploiting our problems unique characteristics.

A Server-Assigned Spatial Crowdsourcing Framework

With the popularity of mobile devices, spatial crowdsourcing is rising as a new framework that enables human workers to solve tasks in the physical world. With spatial crowdsourcing, the goal is to crowdsource a set of spatiotemporal tasks (i.e., tasks related to time and location) to a set of workers, which requires the workers to physically travel to those locations in order to perform the tasks. In this article, we focus on one class of spatial crowdsourcing, in which the workers send their locations to the server and thereafter the server assigns to every worker tasks in proximity to the worker’s location with the aim of maximizing the overall number of assigned tasks. We formally define this maximum task assignment (MTA) problem in spatial crowdsourcing, and identify its challenges. We propose alternative solutions to address these challenges by exploiting the spatial properties of the problem space, including the spatial distribution and the travel cost of the workers. MTA is based on the assumptions that all tasks are of the same type and all workers are equally qualified in performing the tasks. Meanwhile, different types of tasks may require workers with various skill sets or expertise. Subsequently, we extend MTA by taking the expertise of the workers into consideration. We refer to this problem as the maximum score assignment (MSA) problem and show its practicality and generality. Extensive experiments with various synthetic and two real-world datasets show the applicability of our proposed framework.

A privacy-aware framework for participatory sensing

With the abundance and ubiquity of mobile devices, a new class of applications is emerging, called participatory sensing (PS), where people can contribute data (e.g., images, video) collected by their mobile devices to central data servers. However, privacy concerns are becoming a major impediment in the success of many participatory sensing systems. While several privacy preserving techniques exist in the context of conventional location-based services, they are not directly applicable to the PS systems because of the extra information that the PS systems can collect from their participants. In this paper, we formally define the problem of privacy in PS systems and identify its unique challenges assuming an un-trusted central data server model. We propose PiRi, a privacy-aware framework for PS systems, which enables participation of the users without compromising their privacy. Our extensive experiments verify the efficiency of our approach.

Processing spatial skyline queries in both vector spaces and spatial network databases

In this article, we first introduce the concept of Spatial Skyline Queries (SSQ). Given a set of data points <i>P</i> and a set of query points <i>Q</i>, each data point has a number of <i>derived spatial</i> attributes each of which is the points distance to a query point. An SSQ retrieves those points of <i>P</i> which are not dominated by any other point in <i>P</i> considering their derived spatial attributes. The main difference with the regular skyline query is that this <i>spatial domination</i> depends on the location of the query points <i>Q</i>. SSQ has application in several domains such as emergency response and online maps. The main intuition and novelty behind our approaches is that we exploit the geometric properties of the SSQ problem space to avoid the exhaustive examination of all the point pairs in <i>P</i> and <i>Q</i>. Consequently, we reduce the complexity of SSQ search from <i>O</i>(|<i>P</i>|²|<i>Q</i>|) to <i>O</i>(|<i>S</i>|²|<i>C</i>| + &sqrt;|<i>P</i>|), where |<i>S</i>| and |<i>C</i>| are the solution size and the number of vertices of the convex hull of <i>Q</i>, respectively. Considering Euclidean distance, we propose two algorithms, B²S² and VS², for static query points and one algorithm, VCS², for streaming <i>Q</i> whose points change location over time (e.g., are mobile). VCS² exploits the pattern of change in <i>Q</i> to avoid unnecessary recomputation of the skyline and hence efficiently perform updates. We also propose two algorithms, SNS² and VSNS², that compute the spatial skyline with respect to the network distance in a spatial network database. Our extensive experiments using real-world datasets verify that both R-tree-based B²S² and Voronoi-based VS² outperform the best competitor approach in terms of both processing time and I/O cost. Furthermore, their output computed based on Euclidean distance is a good approximation of the spatial skyline in network space. For accurate computation of spatial skylines in network space, our experiments showed the superiority of VSNS² over SNS².

Towards preserving privacy in participatory sensing

With the abundance and ubiquity of mobile devices, a new class of applications is emerging, called participatory sensing (PS), where people can contribute data (e.g., images, video) collected by their mobile devices to central data servers. However, privacy concerns are becoming a major impediment in the success of many participatory sensing systems. While several privacy preserving techniques exist in the context of conventional location-based services, they are not directly applicable to the PS systems because of the extra information that the PS systems can collect from their participants. In this paper, we formally define the problem of privacy in PS systems and identify its unique challenges assuming an un-trusted central data server model. We propose PiRi, a privacy-aware framework for PS systems, which enables participation of the users without compromising their privacy.

TAPAS: Trustworthy privacy-aware participatory sensing

With the advent of mobile technology, a new class of applications, called participatory sensing (PS), is emerging, with which the ubiquity of mobile devices is exploited to collect data at scale. However, privacy and trust are the two significant barriers to the success of any PS system. First, the participants may not want to associate themselves with the collected data. Second, the validity of the contributed data is not verified, since the intention of the participants is not always clear. In this paper, we formally define the problem of privacy and trust in PS systems and examine its challenges. We propose a trustworthy privacy-aware framework for PS systems dubbed TAPAS, which enables the participation of the users without compromising their privacy while improving the trustworthiness of the collected data. Our experimental evaluations verify the applicability of our proposed approaches and demonstrate their efficiency.

Optimal traversal planning in road networks with navigational constraints

A frequent query in geospatial planning and decision making domains (e.g., emergency response, data acquisition, street cleaning), is to find an optimal traversal plan (OTP) that traverses an entire area (e.g., a city) by navigating through all its streets. The optimality is defined in terms of the time it takes to complete the traversal. This time depends on the number of times each street segment is traversed as well as the navigation time such as the time spent on changing direction at each intersection. While the problem roots in the classic problems of graph theory, real-world geospatial constraints of road network introduce new application-specific challenges. In this paper, we propose two algorithms to find OTP of a directed road network. Our greedy algorithm employs a classic graph traversal algorithm. During the traversal, it utilizes a set of heuristics at each intersection to minimize the total travel time. Our near-optimal algorithm, however, reduces an OTP problem to an Asymmetric Traveling Salesman Problem (ATSP) by extracting the dual graph of the original network in which each edge is represented by a graph node. Using an approximate solution for ATSP, our algorithm finds a near optimal answer. Our experiments using real-world road networks verify that our near-optimal algorithm outperforms the greedy algorithm in terms of the overall cost of its generated traversal by a factor of two, while its complexity is tolerable in real-world cases.

Efficient approximate visibility query in large dynamic environments

Visibility query is fundamental to many analysis and decision-making tasks in virtual environments. Visibility computation is time complex and the complexity escalates in large and dynamic environments, where the visibility set (i.e., the set of visible objects) of any viewpoint is probe to change at any time. However, exact visibility query is rarely necessary. Besides, it is inefficient, if not infeasible, to obtain the exact result in a dynamic environment. In this paper, we formally define an Approximate Visibility Query (AVQ) as follows: given a viewpoint v, a distance e and a probability p, the answer to an AVQ for the viewpoint v is an approximate visibility set such that its difference with the exact visibility set is guaranteed to be less than e with confidence p. We propose an approach to correctly and efficiently answer AVQ in large and dynamic environments. Our extensive experiments verified the efficiency of our approach.