Wireless Location-based Services Research Articles

Location Privacy Protection: A Power Allocation Approach

High-precision location information formulates the basis of many modern wireless location-based services (LBSs). However, location privacy becomes an important issue along with the advantages that LBS offer. In this paper, we first show that a novel eavesdropper is able to perform position estimation of the agent by purely overhearing the measurement signals between anchors and the agent, using a time difference of arrival way. Optimal power allocation-based approaches are presented to protect the location privacy against the eavesdropper. Then, high-accuracy approximation algorithms are also presented to solve the essential non-convex optimization problems. According to the numeric results, we can see that the presented power allocation strategies are able to prevent the eavesdropper from performing accurate position estimation of the agent. We can also see that there exists an optimal tradeoff between the system localization and privacy protection, which can be achieved by the presented frameworks.

Internet of Things for a Smart Transportation System

Today, school buses transport millions of students to and from schools. Therefore, safety of school students is still a hot topic and the most imperative issue. The evolution of wireless location-based services has created consumer requests for availability of global positioning systems (GPS) in urban and indoor environments. Nowadays, there is a requirement to deliver a system for the school bus, that monitors its location and speed. This work investigates two different ways to track the vehicle through ordinary GPS, Assisted-GPS (AGPS). A-GPS improves startup performance (time-to-first-fix (TTFF)), of a GPS satellite-based positioning system in the downtown of big cities depending on 4G/LTE cellular networks. The proposed system is basically built using Raspberry Pi (3) with 4G model shield and important telematics technologies like Representational state transfer (RESTful API) which is the vital key for the Internet of Things (IoT) field.

TPP: Trajectory Privacy Preservation Against Tensor Voting Based Inference Attacks

The popularity of mobile devices with global positioning system (GPS) has boosted various wireless location-based services (LBSs). Certain honest-but-curious or even dishonest LBS servers may learn the users’ trajectories from location trace files, and the users’ privacy can be compromised. In this paper, we propose a quantitative approach to model trajectory inference attacks via tensor voting, which can be widely applied in computer vision and machine learning as a perceptual organization. To counter the tensor voting based attacks, we propose a novel trajectory privacy preservation TPP scheme, in which LBS users will intentionally generate dummy trajectories to obfuscate LBS servers. Meanwhile, the LBS users have the option to disclose their trajectories to trustworthy parties (e.g., users’ parents) by sending those parties a few more encrypted locations. Considering the power constraint of hand-held mobile devices, we mathematically formulate the trajectory privacy preservation problem into a mixed integer linear programming optimization problem and propose the algorithms for optimizing solutions. Through simulations and analysis, we show that the proposed scheme can effectively preserve LBS users’ trajectory privacy against tensor voting-based inference attacks with limited power consumption.

Grid-partition index: a hybrid method for nearest-neighbor queries in wireless location-based services

Traditional nearest-neighbor (NN) search is based on two basic indexing approaches: object-based indexing and solution-based indexing. The former is constructed based on the locations of data objects: using some distance heuristics on object locations. The latter is built on a precomputed solution space. Thus, NN queries can be reduced to and processed as simple point queries in this solution space. Both approaches exhibit some disadvantages, especially when employed for wireless data broadcast in mobile computing environments.In this paper, we introduce a new index method, called the grid-partition index, to support NN search in both on-demand access and periodic broadcast modes of mobile computing. The grid-partition index is constructed based on the Voronoi diagram, i.e., the solution space of NN queries. However, it has two distinctive characteristics. First, it divides the solution space into grid cells such that a query point can be efficiently mapped into a grid cell around which the nearest object is located. This significantly reduces the search space. Second, the grid-partition index stores the objects that are potential NNs of any query falling within the cell. The storage of objects, instead of the Voronoi cells, makes the grid-partition index a hybrid of the solution-based and object-based approaches. As a result, it achieves a much more compact representation than the pure solution-based approach and avoids backtracked traversals required in the typical object-based approach, thus realizing the advantages of both approaches.We develop an incremental construction algorithm to address the issue of object update. In addition, we present a cost model to approximate the search cost of different grid partitioning schemes. The performances of the grid-partition index and existing indexes are evaluated using both synthetic and real data. The results show that, overall, the grid-partition index significantly outperforms object-based indexes and solution-based indexes. Furthermore, we extend the grid-partition index to support continuous-nearest-neighbor search. Both algorithms and experimental results are presented.

The D-tree: an index structure for planar point queries in location-based wireless services

Location-based services (LBSs), considered as a killer application in the wireless data market, provide information based on locations specified in the queries. In this paper, we examine the indexing issue for querying location-dependent data in wireless LBSs; in particular, we focus on an important class of queries, planar point queries. To address the issues of responsiveness, energy consumption, and bandwidth contention in wireless communications, an index has to minimize the search time and maintain a small storage overhead. It is shown that the traditional point-location algorithms and spatial index structures fail to achieve either objective or both. This paper proposes a new index structure, called D-tree, which indexes spatial regions based on the divisions that form the boundaries of the regions. We describe how to construct a binary D-tree index, how to process queries based on the D-tree, and how to page the binary D-tree. Moreover, two parameterized methods for partitioning the original space, called fixed grid assignment (FGA) and adaptive grid assignment (AGA), are proposed to enhance the D-tree. The performance of the D-tree is evaluated using both synthetic and real data sets. Experimental results show that the proposed D-tree outperforms the well-known indexes such as the R/sup */-tree, and that both the FGA and AGA approaches can achieve different performance trade-offs between the index search time and storage overhead by fine-tuning their algorithmic parameters.

Algorithms for GPS operation indoors and downtown

The proliferation of mobile devices and the emergence of wireless location-based services has generated consumer demand for availability of GPS in urban and indoor environments. This demand calls for enhanced GPS algorithms that accommodate high degrees of signal attenuation and multipath effects unique to the "urban channel." This paper overviews the market for location-based services and discusses algorithmic innovations that address challenges posed by urban environments.