Search

Recently, the revolution of 5G networks has promoted a wide application of wireless technology, and then followed with the prosperity of Location-Based Service (LBS). However, an intricate problem called privacy violation still restricts the development of this type of service. To preserve users' privacy, some privacy preservation strategies had been proposed. As an efficient framework used for protecting personal privacy, users' collaboration is mainly proposed for dealing with the issue of service bottleneck and the issue of attack focus. However, there are still several challenges far from being perfectly solved, such as the collusion attacks as well as the willingness of collaborative users. Therefore, to address these challenges, in this paper, based on the conception of information division and user's incentive, a (t , n) smart contract privacy preservation scheme short for (t , n) SCPPS is proposed. In this scheme, the query information is encrypted and split by (t , n) threshold scheme, so the collaborative users will be difficult to learn anything about the initiator, and the collusion will be difficult to constitute the integral query without t users colluding with each other. On the other hand, the combination of (t , n) threshold scheme and smart contract further provide incentive competition, as just the earliest collaborative users that submit the partition information and get the corresponding feedback will obtain the incentive. At last, the security analyses were given, and then tests of users' granularity and locations difference in the continuous query in the real environment. Furthermore, simulation experiments are conducted to demonstrate the effectiveness of the proposed scheme, and the experimental verification is executed using two different types of datasets. Thus, compared with other similar schemes, the results with brief explanations about reasons can further demonstrate the superiority of the proposed scheme. [ABSTRACT FROM AUTHOR]

Abstract In this paper we study the reachable region queries for objects moving in obstacle space. Loosely speaking, given any one of moving objects and many other information, we are asked to return the whole region in which each location has the non-zero probability to be reachable for the moving object. To solve this problem, this paper presents an efficient algorithm that runs in O (n log n) time and consumes O (n log n) space, where n is the number of vertices of obstacles. Moreover, this paper establishes the lower bound for the proposed problem, showing that our solution essentially is optimal in terms of random access memory (RAM) model. Empirical study based on both real and synthetic datasets demonstrates the effectiveness and efficiency of our solution. [ABSTRACT FROM AUTHOR]

Given a set of weighted objects in a data space, the MaxRS problem in spatial databases studied in a VLDB 2012 paper is to find a location for a rectangular region of a given size such that the weighted sum of all the objects covered by the rectangular region centered at the optimal location is maximized. This problem is useful in lots of location-based service applications, such as finding the location for a new fast food restaurant with a limited delivery range attracting the greatest number of customers. The existing MaxRS problem assumes that the rectangular region is always placed horizontally and is non-rotatable. However, under this assumption, the weighted sum of all the covered objects may not be the greatest when the rectangular region is rotatable. In this paper, we propose a generalized MaxRS problem called rotating MaxRS without this assumption. In rotating MaxRS, the rectangular region is rotatable and can be associated with an inclination angle. The goal of our problem is to find a location and an inclination angle such that the weighted sum of all the objects covered by the rectangular region of a given size centered at this location with this inclination angle is the greatest. We also present an efficient algorithm for the problem. Extensive experiments were conducted to verify the efficiency of our algorithms based on the real and synthetic datasets. The experimental results show that the weighted sum of all the objects in the rotating MaxRS queries can be increased with up to 300% on the synthetic datasets compared with existing non-rotating MaxRS queries, which shows the significance of the new rotating MaxRS queries. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we introduce a novel spatial query called the view field nearest neighbor query. Given the view field and location of a user, the view field nearest neighbor query retrieves a data object that is nearest to the user’s location and falls within the user’s view field. This query can be employed for applications such as augmented reality systems, tour guide systems, and CCTV-based surveillance systems. We propose a view field nearest neighbor query processing method that considers moving data objects (i.e., continuous view field nearest neighbor query processing), where we utilize the grid index. Continuous view field nearest neighbor query processing consists of two phases: (1) initial phase and (2) update phase. The first phase computes the initial result of a view field nearest neighbor query (i.e., snapshot query result) and the second phase continuously updates the result according to the movement of data objects. For the initial phase, we propose two algorithms: Naive Exploration Algorithm and Fan-shaped Exploration Algorithm. For the update phase, we propose the Fan-shaped Monitoring Algorithm to process the moving data objects efficiently. Through extensive experiments, we investigate the performance of our proposed algorithms on synthetic and real data sets. [Copyright &y& Elsevier]

Abstract: In this paper, we explore the problem of the scalable evaluation of continuous range queries (CRQs) over moving objects, each of which continually retrieves the moving objects that are currently within a given query region of interest. Most existing methods assume that moving objects continually communicate with the server to report their current locations and the server continuously updates the results of queries. However, such an assumption degrades the system performance, because the communication cost is huge and the server workload is increased when the number of moving objects and queries is enormous. In this paper, we propose a novel query indexing structure, referred to as the Query Region tree (QR-tree), which allows the server to cooperate with moving objects efficiently by leveraging the available computational resources of the moving objects to improve the overall system performance. In addition, we present another version of the QR-tree, called the Bit-vector Query Region tree (BQR-tree), for the evaluation of CRQs that specify additional non-spatial selections. The BQR-tree stores a summary of the non-spatial information specified by CRQs in the form of bit-vectors. Through a series of comprehensive simulations, we verify the efficiency of the QR-tree and the BQR-tree in terms of the communication cost and server workload. [Copyright &y& Elsevier]

Abstract: With the rapid growth of social media platforms, numerous group commerce websites, which exploit both the advantages of price discounts and experience value, have emerged. Moreover, the popularity of sophisticated mobile devices brings great commercial opportunities for local store to gain publicity. In this research, considering user preference, geographic convenience, and friends’ influence, a group-coupon recommender system is proposed for promoting location-sensitive products. The results of experiments conducted on Facebook indicate that the proposed mechanism could accurately recommend products and satisfactorily provide a companion list of to customers, significantly increasing willingness to purchase by taking advantage of the power of social influence. [Copyright &y& Elsevier]

Abstract: In this paper, we propose an efficient graph-based mining (GBM) algorithm for mining the frequent trajectory patterns in a spatial–temporal database. The proposed method comprises two phases. First, we scan the database once to generate a mapping graph and trajectory information lists (TI-lists). Then, we traverse the mapping graph in a depth-first search manner to mine all frequent trajectory patterns in the database. By using the mapping graph and TI-lists, the GBM algorithm can localize support counting and pattern extension in a small number of TI-lists. Moreover, it utilizes the adjacency property to reduce the search space. Therefore, our proposed method can efficiently mine the frequent trajectory patterns in the database. The experimental results show that it outperforms the Apriori-based and PrefixSpan-based methods by more than one order of magnitude. [Copyright &y& Elsevier]

Abstract: Due to the rapid development in mobile communication technologies, the usage of mobile devices such as cell phone or PDA has increased significantly. As different devices require different applications, various new services are being developed to satisfy the needs. One of the popular services under heavy demand is the location-based service (LBS) that exploits the spatial information of moving objects per temporal changes. In order to support LBS well, in this paper, we investigate how spatio-temporal information of moving objects can be efficiently stored and indexed. In particular, we propose a novel location encoding method based on hierarchical administrative district information. Our proposal is different from conventional approaches where moving objects are often expressed as geometric points in two-dimensional space, (x, y). Instead, in ours, moving objects are encoded as one-dimensional points by both administrative district as well as road information. Our method becomes especially useful for monitoring traffic situation or tracing location of moving objects through approximate spatial queries. [Copyright &y& Elsevier]