Efficient Location-based Services Research Articles

A location-based privacy-preserving oblivious sharing scheme for indoor navigation

In recent years, with the rapid development of wireless communication technology and mobile devices, etc., indoor localization and navigation using WiFi sensing are increasingly being widely employed in life. On this basis, various applications of location-based services have been rapidly developed. However, when users enjoy the location query service of LBS providers, there is a risk of exposing personal information and location privacy simultaneously. In particular, the sensitive information about users based on their location privacy can be inferred by malicious users. Therefore, how to implement location hiding, query privacy protection and query result feedback at the same time is one of the crucial issues of LBS privacy protection. A novel oblivious data sharing scheme employing the designed 1-out-of-n oblivious transfer protocol is proposed to achieve an efficient location-based service for users while effectively hiding location coordinates and protecting the privacy of users and servers. In addition, once a user accesses the same keyword several times, the server is bound to be able to master the accessed frequency of keywords. For this reason, the string length-based transformation algorithm based on private information retrieval is designed and added as a part of OT protocol, which effectively reduces the communication overhead and ensures the privacy of required keywords. The security and performance analysis indicate that the proposed protocol can be instantiated in indoor navigation with high security and efficiency.

Deep Learning-Based Multifloor Indoor Tracking Scheme Using Smartphone Sensors

Having recently become an important research topic, indoor tracking in a multi-floor building delivers comprehensive and efficient location-based services. In this paper, we present a deep learning (DL)-based indoor multi-floor tracking scheme, that is independent of infrastructure and only uses the smartphone as a terminal device to measure and analyze the user’s mobility information. Our method detects the floor transition according to changes in barometer readings. We compiled the time-series barometer data to train the DL model, and applied the data augmentation method to avoid overfitting and data imbalances during model training. Furthermore, we developed a floor decision algorithm to process the DL model’s output and generate the floor detection result. In the proposed scheme, the smartphone’s inertial measurement unit sensors are used to measure the user’s mobility information, and pedestrian dead reckoning (PDR) is exploited to update the user’s 2D location. We implemented the multi-floor tracking by combining the floor detection algorithm with PDR. To avoid the accumulated error problem that commonly arises in the infrastructure-free approach, the calibration nodes (CN) were configured in the floor plan to correct the estimated location by matching the possible CNs during the floor transition. We conducted several experiments in multi-floor buildings to evaluate our scheme’s performance, and found that our floor detection method achieves a 99.6% average floor number accuracy, with all floor transition types (i.e., stairs, elevator) being successfully recognized. Furthermore, we compared localization performance with the conventional methods to validate the effectiveness of our approach.

Computational Complexity Closed-Form Upper Bounds Derivation for Fingerprint-Based Point-of-Interest Recognition Algorithms

Place recognition and user positioning methods are becoming trending topics among the research community thanks to the need of providing cutting-edge solutions to enable innovative and efficient Location-Based Services (LBS) and applications. However, the mobile framework suffers from typical constraints on energy and computational capabilities due to the inherent characteristics of wireless mobile devices. For this reason, in order to be suitable for deploying in such framework, a place recognition algorithm should be thrifty in terms of resource consumption. In this paper we consider six different WiFi-based outdoor positioning approaches and we analytically derive an upper-bound for their computational burden in terms of number of FLoating-point OPerations (FLOPs).

Efficient LBS Security-Preserving Based on NTRU Oblivious Transfer

In vehicular environment, Location Based Services (LBS) are widely deployed to provide a service or a piece of information that is related to the location of a driver. Though a series of technologies, such as anonymity, mix zone or private information retrieval, can be exploited to protect the drivers position information, the data proprietorship is always ignored. In addition, since the embedded systems like Onboard Unit are generally recourse-constrained, efficient schemes are urgent for now. In this paper, we presented a novel oblivious transfer protocol based on Number Theory Research Unit Encryption and structured a security LBS scheme in terms of it. Comparing with Jannati and Bahrak’s protocol, it is concluded that our scheme is more efficient and practical.

Efficient LBS queries with mutual privacy preservation in IoV

Public awareness on privacy stimulates many researches about privacy-preserving location based services (LBS) in terms of providing mutual privacy to both LBS and its users. However, the high latency of privacy preservation in LBS becomes a main obstacle for applying LBS to Internet of Vehicles (IoV). To solve this problem, we propose two privacy-preserving LBS query schemes (kNN and T-kNN) by taking the advance of fog computing and by applying oblivious transfer (OT) and ciphertext-policy attribute based encryption (CP-ABE). Given a query from a vehicle, both schemes return k nearest POIs as response, with the difference that T-kNN supports fine-grained type based POI queries. Based on our proposed oblivious key transfer and privacy-preserving secret key generation, both schemes preserve mutual privacy of both LBS provider and vehicles. Complexity analysis and empirical study show that our approach outperforms the other two state-of-the-art works.

A Secure and Efficient Location-based Service Scheme for Smart Transportation

With the advance of wireless communication techniques and the popularity of embedded devices, Location-Based Service (LBS) has gained a great attention in the Internet-of-Things (IoT) recently. For instance, in smart transportation, reporting all the drivers’ location in a regular frequency is conducive to sense urban traffic congestion. However, it also posts a serious threat to drivers’ privacy because of the exposure to the real-time location. To alleviate this problem, we propose a Secure and Efficient Location-based Service (SELS) scheme for smart transportation in this paper. In the SELS scheme, drivers first utilize smart phones to encrypt their location-based data and then outsource them to a cloud center incessantly. Then they have the ability to call the cloud center to compute the ciphertext of LBS queries, by using a secure homomorphic encryption scheme and group signature scheme. After decrypting the ciphertext, they obtain accurate results of the queries without sacrificing the location privacy. Furthermore, the SELS scheme also supports multi-dimension data by employing a weighted distance algorithm. Finally, the security of the SELS scheme is proved and the cost of computation and communication is analyzed, in order to prove the security and efficiency of the scheme.

Privacy-Preserving Location-Based Service Scheme for Mobile Sensing Data.

With the wide use of mobile sensing application, more and more location-embedded data are collected and stored in mobile clouds, such as iCloud, Samsung cloud, etc. Using these data, the cloud service provider (CSP) can provide location-based service (LBS) for users. However, the mobile cloud is untrustworthy. The privacy concerns force the sensitive locations to be stored on the mobile cloud in an encrypted form. However, this brings a great challenge to utilize these data to provide efficient LBS. To solve this problem, we propose a privacy-preserving LBS scheme for mobile sensing data, based on the RSA (for Rivest, Shamir and Adleman) algorithm and ciphertext policy attribute-based encryption (CP-ABE) scheme. The mobile cloud can perform location distance computing and comparison efficiently for authorized users, without location privacy leakage. In the end, theoretical security analysis and experimental evaluation demonstrate that our scheme is secure against the chosen plaintext attack (CPA) and efficient enough for practical applications in terms of user side computation overhead.

A Caching Strategy for Spatial Queries in Mobile Networks

As a result of the recent developments in mobile terminals such as smartphones and laptop computers, as well as in wireless communication technologies such as GPS, location- based services are coming closer and closer to our daily life. However, the problems associated with the limited resources-such as limited wireless bandwidth, limited battery life, and small memory space-of mobile terminals remain unsolved. Research on cache utilization in mobile terminals, the development of efficient query processing algorithms, and improvement of the index structure in order to support efficient location-based service is geared towards alleviating these problems. In this paper, we propose location- based cache maintenance strategies for wireless broadcast environments in which a mobile client prefetches data that are expected to be used in the near future, and caches and maintains the data at a location close to the client's location. We also propose a hierarchical tree-based privacy approach for supporting anonymous location-based queries in wireless mobile data delivery systems. The results of experiments conducted using our proposed algorithms indicate that they help to reduce communication costs and support rapid spatial query processing.

Prediction of moving object trajectory based on probabilistic suffix tree

In the prediction of moving object trajectory, concerning the low accuracy rate of low order Markov model and the expansion of state space in high order model, a dynamic adaptive Probabilistic Suffix Tree( PST) prediction method based on variable length Markov model was proposed. Firstly, moving object's trajectory path was serialized according to the time;then the probability characteristic of sequence context was trained and calculated from the historical trajectory data of moving objects, the probabilistic suffix tree model based path sequence was constructed, combined with the actual trajectory data,thus the future trajectory information could be predicted dynamically and adaptively. The experimental results show that the highest prediction accuracy was obtained in second order model, with the order of the model increasing, the prediction accuracy was maintained at about 82% and better prediction results were achieved. In the meantime, space complexity was decreased exponentially and storage space was reduced greatly. The proposed method made full use of historical data and current trajectory information to predict the future trajectory, and provided a more flexible and efficient location-based services.

An Efficient Location-Based Service Technology using Self-Powered Smart Tags

LBS(Location-Based Service)의 시장을 확대하고 새로운 부가가치를 창출하기 위해서는 1~2m 이내에 있는 특정 물체나 특정 위치의 존재를 사전정보가 없는 사용자에게 쉽게 알려 줄 수 있는 정밀 위치 측위 기술이 필요하다. 이를 위해 본 논문에서는 스마트 태그 기반 위치 측위 시스템을 제안한다. 제안하는 시스템은 블루투스 기술과 태양전지 기술을 통해 스마트 태그를 구현하고 이를 블루투스 기능이 설치되어 있는 스마트 폰에서 탐색한다. 또한 구현된 스마트 태그는 태양전지 기술을 사용해서 외부 전원 없이 빛만 있으면 자가 전력으로 동작한다. 본 논문에서는 제안하는 스마트 태그의 효용성을 증명하기 위해 실제 구현한 스마트 태그를 이용하여 인식 시간, 인식 성공률, 전력 소모량 등을 측정하였다.

A Dynamic Privacy-Preserving Key Management Scheme for Location-Based Services in VANETs

In this paper, to achieve a vehicle user's privacy preservation while improving the key update efficiency of location-based services (LBSs) in vehicular ad hoc networks (VANETs), we propose a dynamic privacy-preserving key management scheme called DIKE. Specifically, in the proposed DIKE scheme, we first introduce a privacy-preserving authentication technique that not only provides the vehicle user's anonymous authentication but enables double-registration detection as well. We then present efficient LBS session key update procedures: 1) We divide the session of an LBS into several time slots so that each time slot holds a different session key; when no vehicle user departs from the service session, each joined user can use a one-way hash function to autonomously update the new session key for achieving forward secrecy. 2) We also integrate a novel dynamic threshold technique in traditional vehicle-to-vehicle (V-2-V) and vehicle-to-infrastructure (V-2-I) communications to achieve the session key's backward secrecy, i.e., when a vehicle user departs from the service session, more than a threshold number of joined users can cooperatively update the new session key. Performance evaluations via extensive simulations demonstrate the efficiency and effectiveness of the proposed DIKE scheme in terms of low key update delay and fast key update ratio.

An Efficient Adaptive Bitmap-based Selective Tuning Scheme for Spatial Queries in Broadcast Environments

With the advances in wireless communication technology and the advent of smartphones, research on location-based services (LBSs) is being actively carried out. In particular, several spatial index methods have been proposed to provide efficient LBSs. However, finding an optimal indexing method that balances query performance and index size remains a challenge in the case of wireless environments that have limited channel bandwidths and device resources (computational power, memory, and battery power). Thus, mechanisms that make existing spatial indexing techniques more efficient and highly applicable in resource-limited environments should be studied. Bitmap-based Spatial Indexing (BSI) has been designed to support LBSs, especially in wireless broadcast environments. However, the access latency in BSI is extremely large because of the large size of the bitmap, and this may lead to increases in the search time. In this paper, we introduce a Selective Bitmap-based Spatial Indexing (SBSI) technique. Then, we propose an Adaptive Bitmap-based Spatial Indexing (ABSI) to improve the tuning time in the proposed SBSI scheme. The ABSI is applied to the distribution of geographical objects in a grid by using the Hilbert curve (HC). With the information in the ABSI, grid cells that have no objects placed, (i.e., 0-bit information in the spatial bitmap index) are not tuned during a search. This leads to an improvement in the tuning time on the client side. We have carried out a performance evaluation and demonstrated that our SBSI and ABSI techniques outperform the existing bitmap-based DSI (B?DSI) technique.

도로 네트워크 환경에서 센서 네트워크를 이용한 효율적인 분산 브로드캐스트 기법

수많은 이동 노드가 존재하는 유비쿼터스 환경에서 위치 기반 서비스가 중요한 응용 분야로 부상하고 있다. 효율적인 위치 기반 서비스를 제공하기 위해 브로드캐스트을 이용한 다양한 기법들이 연구 되었지만, 대부분 브로드캐스트 인덱스 구축에 대한 연구이고, 브로드캐스트 데이터 전체의 크기를 줄이기 위한 기법은 고려되지 않았다. 이에 본 논문에서는 최근 많은 연구가 이루어지고 있는 센서 네트워크 기반의 객체의 이동 패턴을 고려한 데이터 분산 브로드캐스트 기법을 제안한다. 또한 제안하는 분산 브로드캐스트 기법의 효율성을 위해 도로 네트워크 기반의 센서 클러스터링 기법을 제안한다. 제안하는 기법은 센서노드에 의해 측정 된 객체의 이동 정보를 기반으로 데이터를 최적으로 분산 시킨다. 제안하는 기법의 우수성을 보이기 위해 기존 브로드캐스트 기법과 제안하는 기법을 다양한 환경에서 성능평가를 수행한다. In ubiquitous environments that numerous mobile objects exist, the location-based services have risen as an important application field. For efficient location-based services, various techniques with broadcast schemes have been studied. However, they were mainly concerned with the implementation of a broadcast index and did not consider techniques for reducing the size of the entire broadcast data. Therefore, this paper proposes a data distribution broadcast scheme based on sensor networks that considers the mobile patterns of an object in road network environments. In this paper we also propose a road network based sensor clustering technique for the efficiency of the proposed distributed broadcast scheme. In order to show the superiority of the proposed scheme, we compare it with the existing broadcast scheme in various environments.

Adaptive index management for future location-based queries

Many location-based applications have arisen in various areas including mobile communications, traffic control and military command and control (C2) systems. And one of the important research issue in these areas is tracking and managing moving objects through spatiotemporal indexing for the efficient location-based services. However, managing exact geometric location information is difficult to be achieved due to continual changes of moving objects.Traditionally spatiotemporal index structures focus on optimizing the node accesses during construction and massive updates that do not refer on-line movement updates. In this paper we propose an indexing framework for future location queries based on space partitioning and the dual transformation. Our method provides a constraint database approach for constructing indexes to improve the efficiency of spatiotemporal query answering. In addition, the performance enhancement is achieved by our cost-based dynamic management algorithm that determines the appropriate index reorganization probabilistically induced by various mobility models and query cost functions. This approach can be applied for the predictive range queries on moving object's trajectories specifically in the mobile communication environments. We evaluate our method and compare the performance with the related spatiotemporal index structures in the simulated environments.