Location-aware technology spawns numerous unforeseen pervasive applications in a wide range of living, pro- duction, commence, and public services. This article provides an overview of the location, localization, and ...Location-aware technology spawns numerous unforeseen pervasive applications in a wide range of living, pro- duction, commence, and public services. This article provides an overview of the location, localization, and localizability issues of wireless ad-hoc and sensor networks. Making data geographically meaningful, location information is essential for many applications, and it deeply aids a number of network functions, such as network routing, topology control, coverage, boundary detection, clustering, etc. We investigate a large body of existing localization approaches with focuses on error control and network localizability, the two rising aspects that attract significant research interests in recent years. Error control aims to alleviate the negative impact of noisy ranging measurement and the error accumulation effect during coope- rative localization process. Network localizability provides theoretical analysis on the performance of localization approaches, providing guidance on network configuration and adjustment. We emphasize the basic principles of localization to under- stand the state-of-the-art and to address directions of future research in the new and largely open areas of location-aware technologies.展开更多
In cyberspace security,the privacy in location-based services(LBSs) becomes more critical. In previous solutions,a trusted third party(TTP) was usually employed to provide disturbance or obfuscation,but it may become ...In cyberspace security,the privacy in location-based services(LBSs) becomes more critical. In previous solutions,a trusted third party(TTP) was usually employed to provide disturbance or obfuscation,but it may become the single point of failure or service bottleneck. In order to cope with this drawback,we focus on another important class,establishing anonymous group through short-range communication to achieve k-anonymity with collaborative users. Along with the analysis of existing algorithms,we found users in the group must share the same maximum anonymity degree,and they could not ease the process of preservation in a lower one. To cope with this problem,we proposed a random-QBE algorithm to put up with personalized anonymity in user collaboration algorithms,and this algorithm could preserve both query privacy and location privacy. Then we studied the attacks from passive and active adversaries and used entropy to measure user's privacy level. Finally,experimental evaluations further verify its effectiveness and efficiency.展开更多
Location-based social network （LBSN） is at the forefront of emerging trends in social network services （SNS） since the users in LBSN are allowed to ＂check-in＂ the places （locations） when they visit them. The a...Location-based social network （LBSN） is at the forefront of emerging trends in social network services （SNS） since the users in LBSN are allowed to ＂check-in＂ the places （locations） when they visit them. The accurate geographi- cal and temporal information of these check-in actions are provided by the end-user GPS-enabled mobile devices, and recorded by the LBSN system. In this paper, we analyze and mine a big LBSN data, Gowalla, collected by us. First, we investigate the relationship between the spatio-temporal co- occurrences and social ties, and the results show that the co- occurrences are strongly correlative with the social ties. Sec- ond, we present a study of predicting two users whether or not they will meet （co-occur） at a place in a given future time, by exploring their check-in habits. In particular, we first intro- duce two new concepts, bag-of-location and bag-of-time-lag, to characterize user＇s check-in habits. Based on such bag rep- resentations, we define a similarity metric called habits sim- ilarity to measure the similarity between two users＇ check-in habits. Then we propose a machine !earning formula for pre- dicting co-occurrence based on the social ties and habits sim- ilarities. Finally, we conduct extensive experiments on our dataset, and the results demonstrate the effectiveness of the proposed method.展开更多
Recently, as location-based social network(LBSN) rapidly grow, point-of-interest(POI) recommendation has become an important way to help people locate interesting places. Nowadays, there have been deep studies conduct...Recently, as location-based social network(LBSN) rapidly grow, point-of-interest(POI) recommendation has become an important way to help people locate interesting places. Nowadays, there have been deep studies conducted on the geographical and social influence in the point-of-interest recommendation model based on the rating prediction. The fact is, however, relying solely on the rating fails to reflect the user's preferences very accurately, because the users are most concerned with the list of ranked point-of-interests(POIs) on the actual output of recommender systems. In this paper, we propose a co-pairwise ranking model called Geo-Social Bayesian Personalized Ranking model(GSBPR), which is based on the pairwise ranking with the exploiting geo-social correlations by incorporating the method of ranking learning into the process of POI recommendation. In this model, we develop a novel BPR pairwise ranking assumption by injecting users' geo-social preference. Based on this assumption, the POI recommendation model is reformulated by a three-level joint pairwise ranking model. And the experimental results based on real datasets show that the proposed method in this paper enjoys better recommendation performance compared to other state-of-the-art POI recommendation models.展开更多
Location privacy has been a serious concern for mobile users who use location-based services provided by third-party providers via mobile networks. Recently, there have been tremendous efforts on developing new anonym...Location privacy has been a serious concern for mobile users who use location-based services provided by third-party providers via mobile networks. Recently, there have been tremendous efforts on developing new anonymity or obfuscation techniques to protect location privacy of mobile users. Though effective in certain scenarios, these existing techniques usually assume that a user has a constant privacy requirement along spatial and/or temporal dimensions, which may be not true in real-life scenarios. In this paper, we introduce a new location privacy problem： Location-aware Location Privacy Protection （L2P2） problem, where users can define dynamic and diverse privacy requirements for different locations. The goal of the L2P2 problem is to find the smallest cloaking area for each location request so that diverse privacy requirements over spatial and/or temporal dimensions are satisfied for each user. In this paper, we formalize two versions of the L2P2 problem, and propose several efficient heuristics to provide such location-aware location privacy protection for mobile users. Through extensive simulations over large synthetic and real-life datasets, we confirm the effectiveness and efficiency of the proposed L2P2 algorithms.展开更多
Privacy preservation has recently received considerable attention in location-based services （LBSs）. A large number of location cloaking algorithms have been proposed for protecting the location privacy of mobile us...Privacy preservation has recently received considerable attention in location-based services （LBSs）. A large number of location cloaking algorithms have been proposed for protecting the location privacy of mobile users. However, most existing cloaking approaches assume that mobile users are trusted. And exact locations are required to protect location privacy, which is exactly the information mobile users want to hide. In this paper, we propose a p-anti-conspiration privacy model to anonymize over semi-honest users. Further- more, two k＊NNG-based cloaking algorithms, vk＊NNCA and ek＊NNCA, are proposed to protect location privacy without exact locations. The efficiency and effectiveness of the pro- posed algorithms are validated by a series of carefully designed experiments. The experimental results show that the price paid for location privacy protection without exact locations is small.展开更多
Recently, location-based routings in wireless sensor networks (WSNs) are attracting a lot of interest in the research community, especially because of its scalability. In location-based routing, the network size is sc...Recently, location-based routings in wireless sensor networks (WSNs) are attracting a lot of interest in the research community, especially because of its scalability. In location-based routing, the network size is scalable without increasing the signalling overhead as routing decisions are inherently localized. Here, each node is aware of its position in the network through some positioning device like GPS and uses this information in the routing mechanism. In this paper, we first discuss the basics of WSNs including the architecture of the network, energy consumption for the components of a typical sensor node, and draw a detailed picture of classification of location-based routing protocols. Then, we present a systematic and comprehensive taxonomy of location-based routing protocols, mostly for sensor networks. All the schemes are subsequently discussed in depth. Finally, we conclude the paper with some insights on potential research directions for location-based routing in WSNs.展开更多
The widespread use of Location-Based Services （LBSs）, which allows untrusted service providers to collect large quantities of information regarding users＇ locations, has raised serious privacy concerns. In response...The widespread use of Location-Based Services （LBSs）, which allows untrusted service providers to collect large quantities of information regarding users＇ locations, has raised serious privacy concerns. In response to these issues, a variety of LBS Privacy Protection Mechanisms （LPPMs） have been recently proposed. However, evaluating these LPPMs remains problematic because of the absence of a generic adversarial model for most existing privacy metrics. In particular, the relationships between these metrics have not been examined in depth under a common adversarial model, leading to a possible selection of the inappropriate metric, which runs the risk of wrongly evaluating LPPMs. In this paper, we address these issues by proposing a privacy quantification model, which is based on Bayes conditional privacy, to specify a general adversarial model. This model employs a general definition of conditional privacy regarding the adversary＇s estimation error to compare the different LBS privacy metrics. Moreover, we present a theoretical analysis for specifying how to connect our metric with other popular LBS privacy metrics. We show that our privacy quantification model permits interpretation and comparison of various popular LBS privacy metrics under a common perspective. Our results contribute to a better understanding of how privacy properties can be measured, as well as to the better selection of the most appropriate metric for any given LBS application.展开更多
With the booming of the Internet of Things(Io T)and the speedy advancement of Location-Based Social Networks(LBSNs),Point-Of-Interest(POI)recommendation has become a vital strategy for supporting people’s ability to ...With the booming of the Internet of Things(Io T)and the speedy advancement of Location-Based Social Networks(LBSNs),Point-Of-Interest(POI)recommendation has become a vital strategy for supporting people’s ability to mine their POIs.However,classical recommendation models,such as collaborative filtering,are not effective for structuring POI recommendations due to the sparseness of user check-ins.Furthermore,LBSN recommendations are distinct from other recommendation scenarios.With respect to user data,a user’s check-in record sequence requires rich social and geographic information.In this paper,we propose two different neural-network models,structural deep network Graph embedding Neural-network Recommendation system(SG-Neu Rec)and Deepwalk on Graph Neural-network Recommendation system(DG-Neu Rec)to improve POI recommendation.combined with embedding representation from social and geographical graph information(called SG-Neu Rec and DG-Neu Rec).Our model naturally combines the embedding representations of social and geographical graph information with user-POI interaction representation and captures the potential user-POI interactions under the framework of the neural network.Finally,we compare the performances of these two models and analyze the reasons for their differences.Results from comprehensive experiments on two real LBSNs datasets indicate the effective performance of our model.展开更多
With the rapid development of the Internet of Things(IoT),Location-Based Services(LBS)are becoming more and more popular.However,for the users being served,how to protect their location privacy has become a growing co...With the rapid development of the Internet of Things(IoT),Location-Based Services(LBS)are becoming more and more popular.However,for the users being served,how to protect their location privacy has become a growing concern.This has led to great difficulty in establishing trust between the users and the service providers,hindering the development of LBS for more comprehensive functions.In this paper,we first establish a strong identity verification mechanism to ensure the authentication security of the system and then design a new location privacy protection mechanism based on the privacy proximity test problem.This mechanism not only guarantees the confidentiality of the user s information during the subsequent information interaction and dynamic data transmission,but also meets the service provider's requirements for related data.展开更多
The inertial navigation system(INS),which is frequently used in emergency rescue operations and other situations,has the benefits of not relying on infrastructure,high positioning frequency,and strong real-time perfor...The inertial navigation system(INS),which is frequently used in emergency rescue operations and other situations,has the benefits of not relying on infrastructure,high positioning frequency,and strong real-time performance.However,the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with time.This paper aims to enhance the accuracy of zero-velocity interval(ZVI)detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual feet.Aiming at the observational noise problem of low-cost inertial sensors,we utilize a denoising autoencoder to automatically eliminate the inherent noise.Aiming at the problem that inaccurate detection of the ZVI detection results in obvious displacement error,we propose a sample-level ZVI detection algorithm based on the U-Net neural network,which effectively solves the problem of mislabeling caused by sliding windows.Aiming at the problem that Zero-Velocity Update(ZUPT)cannot suppress heading and altitude error,we propose a bipedal INS method based on the equation constraint and ellipsoid constraint,which uses foot-to-foot distance as a new observation to correct heading and altitude error.We conduct extensive and well-designed experiments to evaluate the performance of the proposed method.The experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.展开更多
In mobile social networks,next point-of-interest(POI)recommendation is a very important function that can provide personalized location-based services for mobile users.In this paper,we propose a recurrent neural netwo...In mobile social networks,next point-of-interest(POI)recommendation is a very important function that can provide personalized location-based services for mobile users.In this paper,we propose a recurrent neural network(RNN)-based next POI recommendation approach that considers both the location interests of similar users and contextual information(such as time,current location,and friends’preferences).We develop a spatial-temporal topic model to describe users’location interest,based on which we form comprehensive feature representations of user interests and contextual information.We propose a supervised RNN learning prediction model for next POI recommendation.Experiments based on real-world dataset verify the accuracy and efficiency of the proposed approach,and achieve best F1-score of 0.196754 on the Gowalla dataset and 0.354592 on the Brightkite dataset.展开更多
t LBS （location-based service） is a remarkable outcome of the development from GIS to geospatial information service. Faced by the requirements of geospatial information from the masses and the opportunity provided ...t LBS （location-based service） is a remarkable outcome of the development from GIS to geospatial information service. Faced by the requirements of geospatial information from the masses and the opportunity provided by the next generation lnternet and Web 2.0, a new model of geospatial information service based on DMI （digital measurable image） is presented. First, the con- cept of LBS and the opportunities of Web 2.0 are introduced, then the characteristic of DMI is discussed. Taking the Image City.Wuhan as an example, the function ofgeospatial information service based on DM! is introduced. Finally, the feasibility for its industrialization is discussed.展开更多
Since smartphones embedded with positioning systems and digital maps are widely used,location-based services(LBSs)are rapidly growing in popularity and providing unprecedented convenience in people’s daily lives;howe...Since smartphones embedded with positioning systems and digital maps are widely used,location-based services(LBSs)are rapidly growing in popularity and providing unprecedented convenience in people’s daily lives;however,they also cause great concern about privacy leakage.In particular,location queries can be used to infer users’sensitive private information,such as home addresses,places of work and appointment locations.Hence,many schemes providing query anonymity have been proposed,but they typically ignore the fact that an adversary can infer real locations from the correlations between consecutive locations in a continuous LBS.To address this challenge,a novel dual privacy-preserving scheme(DPPS)is proposed that includes two privacy protection mechanisms.First,to prevent privacy disclosure caused by correlations between locations,a correlation model is proposed based on a hidden Markov model(HMM)to simulate users’mobility and the adversary’s prediction probability.Second,to provide query probability anonymity of each single location,an advanced k-anonymity algorithm is proposed to construct cloaking regions,in which realistic and indistinguishable dummy locations are generated.To validate the effectiveness and efficiency of DPPS,theoretical analysis and experimental verification are further performed on a real-life dataset published by Microsoft,i.e.,GeoLife dataset.展开更多
Mobile location-based services(MLBS)refer to services around geographic location data.Mobile terminals use wireless communication networks(or satellite positioning systems)to obtain users’geographic location coordina...Mobile location-based services(MLBS)refer to services around geographic location data.Mobile terminals use wireless communication networks(or satellite positioning systems)to obtain users’geographic location coordinate information based on spatial databases and integrate with other information to provide users with required location-related services.The development of systems based on MLBS has significance and practical value.In this paper a visualization management information system for personnel in major events based on microservices,namely MEPMIS,is designed and implemented by using MLBS.The system consists of a server and a client app,and it has some functions including map search and query,personnel positioning and scheduling,location management,messaging,and location service.Managers of the events can quickly search and locate the staff on the specific area of the map in real-time,and make broadcasting messages to the staff,and manage the staff.The client app is developed on the Android system,by which staff users can send the positions information to the server timely.The client users can search fuzzily near their peers and list their locations,and also call near peers through sending messages or query the history record of staff locations.In the design of the system,several new proposed techniques,including visual annotation method for overlapping locations,correcting trajectory drift algorithm,microservices-based overall system architecture methodology and other new techniques,which are applied to the implementation of the system.Also,HTML5,JQuery,MLBS APIs(Application Program Interfaces)related programming techniques have been used and combined with loading Ajax asynchronously and Json data encapsulation,map marker optimization techniques,that can improve the positioning accuracy and the performance of the system.The developed system with practical functions can enhance the efficiencies of the organization and management of major events.展开更多
文摘Location-aware technology spawns numerous unforeseen pervasive applications in a wide range of living, pro- duction, commence, and public services. This article provides an overview of the location, localization, and localizability issues of wireless ad-hoc and sensor networks. Making data geographically meaningful, location information is essential for many applications, and it deeply aids a number of network functions, such as network routing, topology control, coverage, boundary detection, clustering, etc. We investigate a large body of existing localization approaches with focuses on error control and network localizability, the two rising aspects that attract significant research interests in recent years. Error control aims to alleviate the negative impact of noisy ranging measurement and the error accumulation effect during coope- rative localization process. Network localizability provides theoretical analysis on the performance of localization approaches, providing guidance on network configuration and adjustment. We emphasize the basic principles of localization to under- stand the state-of-the-art and to address directions of future research in the new and largely open areas of location-aware technologies.
基金supported by the National Natural Science Foundation of China (Grant No.61472097)the Specialized Research Fund for the Doctoral Program of Higher Education(Grant No.20132304110017)+1 种基金the Natural Science Foundation of Heilongjiang Province of China (Grant No.F2015022)the Fujian Provincial Key Laboratory of Network Security and Cryptology Research Fund (Fujian Normal University) (No.15003)
文摘In cyberspace security,the privacy in location-based services(LBSs) becomes more critical. In previous solutions,a trusted third party(TTP) was usually employed to provide disturbance or obfuscation,but it may become the single point of failure or service bottleneck. In order to cope with this drawback,we focus on another important class,establishing anonymous group through short-range communication to achieve k-anonymity with collaborative users. Along with the analysis of existing algorithms,we found users in the group must share the same maximum anonymity degree,and they could not ease the process of preservation in a lower one. To cope with this problem,we proposed a random-QBE algorithm to put up with personalized anonymity in user collaboration algorithms,and this algorithm could preserve both query privacy and location privacy. Then we studied the attacks from passive and active adversaries and used entropy to measure user's privacy level. Finally,experimental evaluations further verify its effectiveness and efficiency.
文摘Location-based social network （LBSN） is at the forefront of emerging trends in social network services （SNS） since the users in LBSN are allowed to ＂check-in＂ the places （locations） when they visit them. The accurate geographi- cal and temporal information of these check-in actions are provided by the end-user GPS-enabled mobile devices, and recorded by the LBSN system. In this paper, we analyze and mine a big LBSN data, Gowalla, collected by us. First, we investigate the relationship between the spatio-temporal co- occurrences and social ties, and the results show that the co- occurrences are strongly correlative with the social ties. Sec- ond, we present a study of predicting two users whether or not they will meet （co-occur） at a place in a given future time, by exploring their check-in habits. In particular, we first intro- duce two new concepts, bag-of-location and bag-of-time-lag, to characterize user＇s check-in habits. Based on such bag rep- resentations, we define a similarity metric called habits sim- ilarity to measure the similarity between two users＇ check-in habits. Then we propose a machine !earning formula for pre- dicting co-occurrence based on the social ties and habits sim- ilarities. Finally, we conduct extensive experiments on our dataset, and the results demonstrate the effectiveness of the proposed method.
基金supported by National Basic Research Program of China (2012CB719905)National Natural Science Funds of China (41201404)Fundamental Research Funds for the Central Universities of China (2042018gf0008)
文摘Recently, as location-based social network(LBSN) rapidly grow, point-of-interest(POI) recommendation has become an important way to help people locate interesting places. Nowadays, there have been deep studies conducted on the geographical and social influence in the point-of-interest recommendation model based on the rating prediction. The fact is, however, relying solely on the rating fails to reflect the user's preferences very accurately, because the users are most concerned with the list of ranked point-of-interests(POIs) on the actual output of recommender systems. In this paper, we propose a co-pairwise ranking model called Geo-Social Bayesian Personalized Ranking model(GSBPR), which is based on the pairwise ranking with the exploiting geo-social correlations by incorporating the method of ranking learning into the process of POI recommendation. In this model, we develop a novel BPR pairwise ranking assumption by injecting users' geo-social preference. Based on this assumption, the POI recommendation model is reformulated by a three-level joint pairwise ranking model. And the experimental results based on real datasets show that the proposed method in this paper enjoys better recommendation performance compared to other state-of-the-art POI recommendation models.
基金supported by the National Natural Science Foundation of China (Nos.61370192,61432015,61428203,and 61572347)the US National Science Foundation (Nos.CNS-1319915 and CNS-1343355)
文摘Location privacy has been a serious concern for mobile users who use location-based services provided by third-party providers via mobile networks. Recently, there have been tremendous efforts on developing new anonymity or obfuscation techniques to protect location privacy of mobile users. Though effective in certain scenarios, these existing techniques usually assume that a user has a constant privacy requirement along spatial and/or temporal dimensions, which may be not true in real-life scenarios. In this paper, we introduce a new location privacy problem： Location-aware Location Privacy Protection （L2P2） problem, where users can define dynamic and diverse privacy requirements for different locations. The goal of the L2P2 problem is to find the smallest cloaking area for each location request so that diverse privacy requirements over spatial and/or temporal dimensions are satisfied for each user. In this paper, we formalize two versions of the L2P2 problem, and propose several efficient heuristics to provide such location-aware location privacy protection for mobile users. Through extensive simulations over large synthetic and real-life datasets, we confirm the effectiveness and efficiency of the proposed L2P2 algorithms.
基金This research was partially supported by the grant from the Hebei Education Department （Q2012131 and SKZD2011113）, and the National Natural Science Foundation of China （Grant No. 61070055）.
文摘Privacy preservation has recently received considerable attention in location-based services （LBSs）. A large number of location cloaking algorithms have been proposed for protecting the location privacy of mobile users. However, most existing cloaking approaches assume that mobile users are trusted. And exact locations are required to protect location privacy, which is exactly the information mobile users want to hide. In this paper, we propose a p-anti-conspiration privacy model to anonymize over semi-honest users. Further- more, two k＊NNG-based cloaking algorithms, vk＊NNCA and ek＊NNCA, are proposed to protect location privacy without exact locations. The efficiency and effectiveness of the pro- posed algorithms are validated by a series of carefully designed experiments. The experimental results show that the price paid for location privacy protection without exact locations is small.
文摘Recently, location-based routings in wireless sensor networks (WSNs) are attracting a lot of interest in the research community, especially because of its scalability. In location-based routing, the network size is scalable without increasing the signalling overhead as routing decisions are inherently localized. Here, each node is aware of its position in the network through some positioning device like GPS and uses this information in the routing mechanism. In this paper, we first discuss the basics of WSNs including the architecture of the network, energy consumption for the components of a typical sensor node, and draw a detailed picture of classification of location-based routing protocols. Then, we present a systematic and comprehensive taxonomy of location-based routing protocols, mostly for sensor networks. All the schemes are subsequently discussed in depth. Finally, we conclude the paper with some insights on potential research directions for location-based routing in WSNs.
基金supported in part by the National Science and Technology Major Project (No. 2012ZX03002001004)the National Natural Science Foundation of China (Nos. 61172090, 61163009, and 61163010)+1 种基金the PhD Programs Foundation of Ministry of Education of China (No. 20120201110013)the Scientific and Technological Project in Shaanxi Province (Nos. 2012K06-30 and 2014JQ8322)
文摘The widespread use of Location-Based Services （LBSs）, which allows untrusted service providers to collect large quantities of information regarding users＇ locations, has raised serious privacy concerns. In response to these issues, a variety of LBS Privacy Protection Mechanisms （LPPMs） have been recently proposed. However, evaluating these LPPMs remains problematic because of the absence of a generic adversarial model for most existing privacy metrics. In particular, the relationships between these metrics have not been examined in depth under a common adversarial model, leading to a possible selection of the inappropriate metric, which runs the risk of wrongly evaluating LPPMs. In this paper, we address these issues by proposing a privacy quantification model, which is based on Bayes conditional privacy, to specify a general adversarial model. This model employs a general definition of conditional privacy regarding the adversary＇s estimation error to compare the different LBS privacy metrics. Moreover, we present a theoretical analysis for specifying how to connect our metric with other popular LBS privacy metrics. We show that our privacy quantification model permits interpretation and comparison of various popular LBS privacy metrics under a common perspective. Our results contribute to a better understanding of how privacy properties can be measured, as well as to the better selection of the most appropriate metric for any given LBS application.
文摘With the booming of the Internet of Things(Io T)and the speedy advancement of Location-Based Social Networks(LBSNs),Point-Of-Interest(POI)recommendation has become a vital strategy for supporting people’s ability to mine their POIs.However,classical recommendation models,such as collaborative filtering,are not effective for structuring POI recommendations due to the sparseness of user check-ins.Furthermore,LBSN recommendations are distinct from other recommendation scenarios.With respect to user data,a user’s check-in record sequence requires rich social and geographic information.In this paper,we propose two different neural-network models,structural deep network Graph embedding Neural-network Recommendation system(SG-Neu Rec)and Deepwalk on Graph Neural-network Recommendation system(DG-Neu Rec)to improve POI recommendation.combined with embedding representation from social and geographical graph information(called SG-Neu Rec and DG-Neu Rec).Our model naturally combines the embedding representations of social and geographical graph information with user-POI interaction representation and captures the potential user-POI interactions under the framework of the neural network.Finally,we compare the performances of these two models and analyze the reasons for their differences.Results from comprehensive experiments on two real LBSNs datasets indicate the effective performance of our model.
基金This work has been partly supported by the National Natural Science Foundation of China under Grant No.61702212the Fundamental Research Funds for the Central Universities under Grand NO.CCNU19TS017.
文摘With the rapid development of the Internet of Things(IoT),Location-Based Services(LBS)are becoming more and more popular.However,for the users being served,how to protect their location privacy has become a growing concern.This has led to great difficulty in establishing trust between the users and the service providers,hindering the development of LBS for more comprehensive functions.In this paper,we first establish a strong identity verification mechanism to ensure the authentication security of the system and then design a new location privacy protection mechanism based on the privacy proximity test problem.This mechanism not only guarantees the confidentiality of the user s information during the subsequent information interaction and dynamic data transmission,but also meets the service provider's requirements for related data.
基金supported in part by National Key Research and Development Program under Grant No.2020YFB1708800China Postdoctoral Science Foundation under Grant No.2021M700385+5 种基金Guang Dong Basic and Applied Basic Research Foundation under Grant No.2021A1515110577Guangdong Key Research and Development Program under Grant No.2020B0101130007Central Guidance on Local Science and Technology Development Fund of Shanxi Province under Grant No.YDZJSX2022B019Fundamental Research Funds for Central Universities under Grant No.FRF-MP-20-37Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities)under Grant No.FRF-IDRY-21-005National Natural Science Foundation of China under Grant No.62002026。
文摘The inertial navigation system(INS),which is frequently used in emergency rescue operations and other situations,has the benefits of not relying on infrastructure,high positioning frequency,and strong real-time performance.However,the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with time.This paper aims to enhance the accuracy of zero-velocity interval(ZVI)detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual feet.Aiming at the observational noise problem of low-cost inertial sensors,we utilize a denoising autoencoder to automatically eliminate the inherent noise.Aiming at the problem that inaccurate detection of the ZVI detection results in obvious displacement error,we propose a sample-level ZVI detection algorithm based on the U-Net neural network,which effectively solves the problem of mislabeling caused by sliding windows.Aiming at the problem that Zero-Velocity Update(ZUPT)cannot suppress heading and altitude error,we propose a bipedal INS method based on the equation constraint and ellipsoid constraint,which uses foot-to-foot distance as a new observation to correct heading and altitude error.We conduct extensive and well-designed experiments to evaluate the performance of the proposed method.The experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.
基金This work was partially supported by the National Key Research and Development Program of China under Grant No.2018YFB1004704the National Natural Science Foundation of China under Grant Nos.61972196,61832008,61832005+1 种基金the Key Research and Development Program of Jiangsu Province of China under Grant No.BE2018116,the open Project from the State Key Laboratory of Smart Grid Protection and Operation Control“Research on Smart Integration of Terminal-Edge-Cloud Techniques for Pervasive Internet of Things”the Collaborative Innovation Center of Novel Software Technology and Industrialization.
文摘In mobile social networks,next point-of-interest(POI)recommendation is a very important function that can provide personalized location-based services for mobile users.In this paper,we propose a recurrent neural network(RNN)-based next POI recommendation approach that considers both the location interests of similar users and contextual information(such as time,current location,and friends’preferences).We develop a spatial-temporal topic model to describe users’location interest,based on which we form comprehensive feature representations of user interests and contextual information.We propose a supervised RNN learning prediction model for next POI recommendation.Experiments based on real-world dataset verify the accuracy and efficiency of the proposed approach,and achieve best F1-score of 0.196754 on the Gowalla dataset and 0.354592 on the Brightkite dataset.
文摘t LBS （location-based service） is a remarkable outcome of the development from GIS to geospatial information service. Faced by the requirements of geospatial information from the masses and the opportunity provided by the next generation lnternet and Web 2.0, a new model of geospatial information service based on DMI （digital measurable image） is presented. First, the con- cept of LBS and the opportunities of Web 2.0 are introduced, then the characteristic of DMI is discussed. Taking the Image City.Wuhan as an example, the function ofgeospatial information service based on DM! is introduced. Finally, the feasibility for its industrialization is discussed.
基金supported by the National Natural Science Foundation of China(Grant No.62172350)the Fundamental Research Funds for the Central Universities(No.21621028)the Innovation Project of GUET Graduate Education(No.2022YCXS083).
文摘Since smartphones embedded with positioning systems and digital maps are widely used,location-based services(LBSs)are rapidly growing in popularity and providing unprecedented convenience in people’s daily lives;however,they also cause great concern about privacy leakage.In particular,location queries can be used to infer users’sensitive private information,such as home addresses,places of work and appointment locations.Hence,many schemes providing query anonymity have been proposed,but they typically ignore the fact that an adversary can infer real locations from the correlations between consecutive locations in a continuous LBS.To address this challenge,a novel dual privacy-preserving scheme(DPPS)is proposed that includes two privacy protection mechanisms.First,to prevent privacy disclosure caused by correlations between locations,a correlation model is proposed based on a hidden Markov model(HMM)to simulate users’mobility and the adversary’s prediction probability.Second,to provide query probability anonymity of each single location,an advanced k-anonymity algorithm is proposed to construct cloaking regions,in which realistic and indistinguishable dummy locations are generated.To validate the effectiveness and efficiency of DPPS,theoretical analysis and experimental verification are further performed on a real-life dataset published by Microsoft,i.e.,GeoLife dataset.
基金The work is supported by the Tianjin Planning Project of Philosophy and Social Science under Grant No.TJGL20-018 for Dr.L.J.Hou of Tianjin Normal University,China。
文摘Mobile location-based services(MLBS)refer to services around geographic location data.Mobile terminals use wireless communication networks(or satellite positioning systems)to obtain users’geographic location coordinate information based on spatial databases and integrate with other information to provide users with required location-related services.The development of systems based on MLBS has significance and practical value.In this paper a visualization management information system for personnel in major events based on microservices,namely MEPMIS,is designed and implemented by using MLBS.The system consists of a server and a client app,and it has some functions including map search and query,personnel positioning and scheduling,location management,messaging,and location service.Managers of the events can quickly search and locate the staff on the specific area of the map in real-time,and make broadcasting messages to the staff,and manage the staff.The client app is developed on the Android system,by which staff users can send the positions information to the server timely.The client users can search fuzzily near their peers and list their locations,and also call near peers through sending messages or query the history record of staff locations.In the design of the system,several new proposed techniques,including visual annotation method for overlapping locations,correcting trajectory drift algorithm,microservices-based overall system architecture methodology and other new techniques,which are applied to the implementation of the system.Also,HTML5,JQuery,MLBS APIs(Application Program Interfaces)related programming techniques have been used and combined with loading Ajax asynchronously and Json data encapsulation,map marker optimization techniques,that can improve the positioning accuracy and the performance of the system.The developed system with practical functions can enhance the efficiencies of the organization and management of major events.
