Nearest surrounder searching in mobile computing environments

Chung, Jaehwa; Jang, Hong Jun; Jung, Kyoung Ho; Jung, Sung-No

doi:10.1002/dac.2409

Cited by 6 publications

(10 citation statements)

References 25 publications

(62 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Anonymity techniques have been intensively studied for the purpose of preserving users' privacy such as identities (e.g., IP addresses), interests, and location information [17][18][19][20][21][22][23][24]. Amongst these techniques, onion routing [17] is a representative for online connections anonymity by which the encrypted message is repeatedly decrypted by the 'onion routers' before reaching its destinations so as to make it difficult for an eavesdropper to trace a user's online activities.…”

Section: Related Workmentioning

confidence: 99%

X‐Region: A framework for location privacy preservation in mobile peer‐to‐peer networks

Che

Hong

et al. 2013

Int J Communication

View full text Add to dashboard Cite

SUMMARYWhile enjoying various LBS (location-based services), users also face the threats of location privacy disclosure. This is because even if the communications between users and LBS providers can be encrypted and anonymized, the sensitive information inside LBS queries may disclose the exact location or even the identity of a user. The existing research on location privacy preservation in mobile peer-to-peer (P2P) networks assumed that users trust each other and directly share location information with each other. Nonetheless, this assumption is not practical for most of the mobile P2P scenarios, for example, an adversary can pretend to be a normal user and collect the locations of other users. Aiming at this issue, this paper presents x-region as a solution to preserve the location privacy in a mobile P2P environment where no trust relationships are assumed amongst mobile users. The main idea is to allow users to share a blurred region known as x-region instead of their exact locations so that one cannot distinguish any user from others inside the region. We propose a theoretical metric for measuring the anonymity property of x-region, together with three algorithms for generating an x-region, namely, benchmark algorithm, weighted expanding algorithm, and aggressive weighted expanding algorithm. These algorithms achieve the anonymity and QoS requirements with different strategies. Our experiments verify the performance of the algorithms against three key metrics.

show abstract

Section: Related Workmentioning

confidence: 99%

X‐Region: A framework for location privacy preservation in mobile peer‐to‐peer networks

Che

Hong

et al. 2013

Int J Communication

View full text Add to dashboard Cite

show abstract

“…Recall when we do the cell partitioning, we used two variables p l and d l . Cell B is given the range (15+1, 15 C 1 C 4 3 2 1) = (16,19), meaning cell B can accommodate four smallest cells in Figure 4. For cell A in Figure 4, its value of p l is one, while cells B, C, D, and E all have their p l equal to two.…”

Section: Partitioning the Road Sensor Network Into Numbered Grid Cellsmentioning

confidence: 99%

A novel distributed air index for efficient spatial query processing in road sensor networks on the air

Shu

Zhu

et al. 2016

Int J Communication

View full text Add to dashboard Cite

Recent development of wireless technology and smart mobile devices has spurred intense research efforts to address spatial queries. In particular, an increasing interest for tackling spatial query processing in broadcast environments has been observed. To the best of our knowledge, most of the existing work on this problem has assumed an Euclidean space. However, for real applications, query clients move within a road sensor network, where the distance between a data object and a query is determined by the connectivity of the road sensor network. This paper explores the problem of spatial query processing in road sensor networks by means of wireless data broadcast. We present an efficient method to partition the recordkeeping information about the underlying road sensor network and its associated objects, by which we develop a fully distributed air index, called integrated exponential index, based on an extended version of the Hilbert curve. We also propose efficient client-side algorithms to facilitate the processing of several kinds of spatial queries, including kNN query, CkNN query, and range query. Finally, extensive simulation experiments have been conducted to demonstrate the strengths of our proposed techniques. access for relatively heavily loaded systems. A year and a half ago, soccer fans from around the world arrived in Brasilia to watch the 2014 FIFA World Cup. After a game, many people would like to locate good nearby restaurants. Because there could be tons of people doing the search at the same time, an ideal solution in this case would be to provide spatial query processing using data broadcast.Earlier approaches to designing broadcast-based spatial query processing methods [2-7] had assumed that MCs and searched objects were located in an Euclidean space. In real-life situations, however, MCs normally move along a road sensor network, and the Euclidean distance may not properly reflect real road sensor network distance [8]. Thus, the existing spatial query processing methods proposed for Euclidean space are inapplicable to process spatial queries in road sensor networks. Recently, researchers have begun to zero in on the problem. Li et al. [9] addressed the issue of processing CNN queries in road networks on the air and proposed an network Voronoi diagrams (NVDs)-based distributed air index to support query processing. One key limitation of the proposed method is that it processes CkNN queries with k D 1. Sun et al. [10] presented an air index called network partition index (NPI) to support spatial query processing in road networks on the air and proposed efficient algorithms to process different spatial queries such as kNN query, range query, and CNN query. NPI is, however, not a fully distributed structure, and thus, each query client will have to wait for the arrival of the index part followed by the whole index before starting its query processing, which could lead to long access latency and tuning time. In this paper, we aim to overcome these limitations and propose a fully distributed air ...

show abstract

“…Chung. J, et al, [3] proposed a server-client framework for NS query that tracks the NS results of a query trajectory. In framework, once a server processes a full NS query processing, a client caches the NS result.…”

Section: Related Workmentioning

confidence: 99%

“…Formally, for a given query Q and dataset R, NS(Q) returns a set of tuples, , where i≠j. Although the NS query provides a wide spectrum of applications, such as the augmented reality services on mobile devices, it is restricted to be implemented as the real services since the existing work [1][2][3] on NS queries considers only two dimensional spaces. Figure 1 illustrates an example of NS query in 2D spaces and 3D spaces.…”

Section: Introductionmentioning

confidence: 99%