In this paper, we clarify the importance of performance evaluation using a plurality of smartphones in a positioning system based on radio waves. Specifically, in a positioning system using bluetooth low energy, the positioning performance of two types of positioning algorithms is performed using a plurality of smartphones. As a result, we confirmed that the fingerprint algorithm does not always provide sufficient positioning performance. It depends on the model of the smartphone used. On the other hand, the hybrid algorithm that the authors have already proposed is robust in the difference of the received signal characteristics of the smartphone. Consequently, we spotlighted that the use of multiple devices is essential for providing high-quality location-based services in real environments in the performance evaluation of radio wave-based positioning systems using smartphones.

Location-based services (LBSs) are useful for many applications in internet of things(IoT). However, LBSs has raised serious concerns about users' location privacy. In this paper, we propose a new location privacy attack in LBSs called hidden location inference attack, in which the adversary infers users' hidden locations based on the users' check-in histories. We discover three factors that influence individual check-in behaviors: geographic information, human mobility patterns and user preferences. We first separately evaluate the effects of each of these three factors on users' check-in behaviors. Next, we propose a novel algorithm that integrates the above heterogeneous factors and captures the probability of hidden location privacy leakage. Then, we design a novel privacy alert framework to warn users when their sharing behavior does not match their sharing rules. Finally, we use our experimental results to demonstrate the validity and practicality of the proposed strategy.

Given a set of positive-weighted points and a query rectangle r (specified by a client) of given extents, the goal of a maximizing range sum (MaxRS) query is to find the optimal location of r such that the total weights of all points covered by r is maximized. In this paper, we address the problem of processing MaxRS queries over road network databases and propose two new external memory methods. Through a set of simulations, we evaluate the performance of the proposed methods.

User context and user location in particular play an important role in location-based services (LBS). The location can be determined by various positioning methods. These are typically evaluated with average positioning error or percentile values, which are not the most suitable metrics for evaluation of how a positioning method functions in the semantic space. Therefore, we propose a new method for evaluation of positioning accuracy in the semantic space. We focus on two types of semantic user locations that are widely available in urban areas: the street address and the categories of the surrounding points of interest (POIs). We demonstrate its use on ten different positioning methods: a standalone satellite navigation device, GPS module on a smartphone, two versions of Foursquare positioning service, Google positioning service, a positioning service of the local mobile operator, and four other possible variants of mobile operator-based positioning methods. The evaluation suggests that approach with the street addresses is more promising approach due to either sparse or unevenly distributed POIs. Furthermore, some of the positioning methods that are less accurate in Euclidean space, such as a combination of the GPS data with the mobile operator-based method that relies on the propagation models, performed comparably well in the semantic space as the methods that are using more accurate technologies, such as Google and Foursquare.

The continuous advances in sensing and positioning technologies have resulted in a dramatic increase in popularity of Location-Based Services (LBS). Nevertheless, the LBS can lead to user privacy breach due to sharing location information with potentially malicious services. A high degree of location privacy preservation for LBS is extremely required. In this paper, a clustering K-anonymity scheme for location privacy preservation (namely CK) is proposed. The CK scheme does not rely on a trusted third party to anonymize the location information of users. In CK scheme, the whole area that all the users reside is divided into clusters recursively in order to get cloaked area. The exact location information of the user is replaced by the cloaked spatial temporal boundary (STB) including K users. The user can adjust the resolution of location information with spatial or temporal constraints to meet his personalized privacy requirement. The experimental results show that CK can provide stringent privacy guarantees, strong robustness and high QoS (Quality of Service).

It is observed, surprisingly, that existing nearest neighbor search methods in wireless data broadcast may not work effectively on mobile clients with very limited memory space. To resolve this problem, a novel method for nearest neighbor search is introduced in the context of a representative of indexes, the grid-partition index, in wireless data broadcast. In the proposed scheme, a mobile client performs the nearest neighbor search by making a sequential access to index packets according to their broadcast order over a wireless channel. The performance evaluation demonstrates that our approach substantially outperforms limited memory versions of existing methods in terms of access time, while retaining a good energy conservation.

We present a model checking approach to the rationale, implementation, and applications of a query language for location-based services. Such query mechanisms are necessary so that users, objects, and/or services can effectively benefit from the location-awareness of their surrounding environment. The underlying data model is founded on a symbolic model of space organized in a tree structure. Once extended to a semantic model for modal logic, we regard location query processing as a model checking problem, and thus define location queries as hybrid logic-based formulas. Our approach is unique to existing research because it explores the connection between location models and query processing in ubiquitous computing systems, relies on a sound theoretical basis, and provides modal logic-based query mechanisms for expressive searches over a decentralized data structure. A prototype implementation is also presented and will be discussed.

With the rapid adoption of mobile devices and location based services (LBS), applications provide with nearby information like recommending sightseeing resort are becoming more and more popular. In the mean time, traffic congestion in cities led to the development of mobile public transportation systems. In such applications, mobile devices need to communicate with servers via wireless communications and servers should process queries from tons of devices. However, because users can not neglect the payment for the wireless communications and server capacities are limited, decreasing the communications made between central servers and devices and reducing the burden on servers are quite demanding. Therefore, in this paper, we propose a cost-effective intelligent public transportation system, ACE-INPUTS, which utilizes a mobile device to retrieve the bus routes to reach a destination from the current location at the lowest wireless communication cost. To accomplish this task, ACE-INPUTS maintains a small amount of information on bus stops and bus routes in a mobile device and runs a heuristic routing algorithm based on such information. Only when a user asks more accurate route information or calls for a "leave later query", ACE-INPUTS entrusts the task to a server into which real-time traffic and bus location information is being collected. By separating the roles into mobile devices and servers, ACE-INPUTS is able to provide bus routes at the lowest wireless communication cost and reduces burden on servers. Experimental results have revealed that ACE-INPUTS is effective and scalable in most experimental settings.

We present a world model for location-aware and user-aware services in ubiquitous computing environments. It can be dynamically organized like a tree based on geographical containment, such as in a user-room-floor-building hierarchy and each node in the tree can be constructed as an executable software component. The model is unique to existing approaches because it enables location-aware services to be managed without databases, can be managed by multiple computers, and provides a unified view of the locations of not only physical entities and spaces, including users and objects, but also computing devices and services. A prototype implementation of this approach was constructed on a Java-based mobile agent system. This paper presents the rationale, design, implementation, and applications of the prototype system.