Verena Kantere, Konstantina Palla, Kostas Patroumpas, T. Sellis
We present a simulation environment that can be employed to study P2P mobile networks that are fast-evolving in both their topology and their content. This simulator implements a proposed P2P architecture based on Mobile Agent and Active Database technology and can be employed in order to build simulated mobile networks that are characterized by a diversity in peer needs, specifications and capabilities.
{"title":"A Simulator for a Mobile Peer-to-Peer Database Environment","authors":"Verena Kantere, Konstantina Palla, Kostas Patroumpas, T. Sellis","doi":"10.1109/MDM.2008.23","DOIUrl":"https://doi.org/10.1109/MDM.2008.23","url":null,"abstract":"We present a simulation environment that can be employed to study P2P mobile networks that are fast-evolving in both their topology and their content. This simulator implements a proposed P2P architecture based on Mobile Agent and Active Database technology and can be employed in order to build simulated mobile networks that are characterized by a diversity in peer needs, specifications and capabilities.","PeriodicalId":365750,"journal":{"name":"The Ninth International Conference on Mobile Data Management (mdm 2008)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123783339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nicola Hönle, M. Großmann, D. Nicklas, B. Mitschang
We present the design and implementation of a component for the preprocessing of position data taken from moving objects. The movement of mobile objects is represented by piece wise functions over time that approximate the real object movement and significantly reduce the initial data volume such that efficient storage and analysis of object trajectories can be achieved. The maximal acceptable deviation - an input parameter of our algorithms - of the approximations also includes the uncertainty of the position sensor measurements. We analyze and compare five different lossy preprocessing methods. Our results clearly indicate that even with simple approaches, a more than sufficient overall performance can be achieved.
{"title":"Preprocessing Position Data of Mobile Objects","authors":"Nicola Hönle, M. Großmann, D. Nicklas, B. Mitschang","doi":"10.1109/MDM.2008.27","DOIUrl":"https://doi.org/10.1109/MDM.2008.27","url":null,"abstract":"We present the design and implementation of a component for the preprocessing of position data taken from moving objects. The movement of mobile objects is represented by piece wise functions over time that approximate the real object movement and significantly reduce the initial data volume such that efficient storage and analysis of object trajectories can be achieved. The maximal acceptable deviation - an input parameter of our algorithms - of the approximations also includes the uncertainty of the position sensor measurements. We analyze and compare five different lossy preprocessing methods. Our results clearly indicate that even with simple approaches, a more than sufficient overall performance can be achieved.","PeriodicalId":365750,"journal":{"name":"The Ninth International Conference on Mobile Data Management (mdm 2008)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123967950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The increasing popularity of GPS device has boosted many Web applications where people can upload, browse and exchange their GPS tracks. In these applications, spatial or temporal search function could provide an effective way for users to retrieve specific GPS tracks they are interested in. However, existing spatial-temporal index for trajectory data has not exploited the characteristic of user behavior in these online GPS track sharing applications. In most cases, when sharing a GPS track, people are more likely to upload GPS data of the near past than the distant past. Thus, the interval between the end time of a GPS track and the time it is uploaded, if viewed as a random variable, has a skewed distribution. In this paper, we first propose a probabilistic model to simulate user behavior of uploading GPS tracks onto an online sharing application. Then we propose a flexible spatio-temporal index scheme, referred to as Compressed Start-End Tree (CSE-tree), for large-scale GPS track retrieval. The CSE-tree combines the advantages of B+ Tree and dynamic array, and maintains different index structure for data with different update frequency. Experiments using synthetic data show that CSE-tree outperforms other schemes in requiring less index size and less update cost while keeping satisfactory retrieval performance.
{"title":"A Flexible Spatio-Temporal Indexing Scheme for Large-Scale GPS Track Retrieval","authors":"Longhao Wang, Yu Zheng, Xing Xie, Wei-Ying Ma","doi":"10.1109/MDM.2008.24","DOIUrl":"https://doi.org/10.1109/MDM.2008.24","url":null,"abstract":"The increasing popularity of GPS device has boosted many Web applications where people can upload, browse and exchange their GPS tracks. In these applications, spatial or temporal search function could provide an effective way for users to retrieve specific GPS tracks they are interested in. However, existing spatial-temporal index for trajectory data has not exploited the characteristic of user behavior in these online GPS track sharing applications. In most cases, when sharing a GPS track, people are more likely to upload GPS data of the near past than the distant past. Thus, the interval between the end time of a GPS track and the time it is uploaded, if viewed as a random variable, has a skewed distribution. In this paper, we first propose a probabilistic model to simulate user behavior of uploading GPS tracks onto an online sharing application. Then we propose a flexible spatio-temporal index scheme, referred to as Compressed Start-End Tree (CSE-tree), for large-scale GPS track retrieval. The CSE-tree combines the advantages of B+ Tree and dynamic array, and maintains different index structure for data with different update frequency. Experiments using synthetic data show that CSE-tree outperforms other schemes in requiring less index size and less update cost while keeping satisfactory retrieval performance.","PeriodicalId":365750,"journal":{"name":"The Ninth International Conference on Mobile Data Management (mdm 2008)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127915777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yu Zheng, Longhao Wang, Ruochi Zhang, Xing Xie, Wei-Ying Ma
The increasing popularity of GPS device has boosted many applications where more and more GPS logs have been accumulating continuously. Managing and understanding the collected GPS data are two important issues for these applications. On one hand, by indexing the increasing GPS data, we can provide effective retrieval method for users to find the corresponding GPS data interests them. On the other hand, by understanding user's GPS data, we are more likely to enable novel services which would stimulate people's passion on contributing GPS data in turn. However, so far, GPS data are still used directly without much understanding. In our project, referred to as GeoLife, we focus on visualization, organization, fast retrieval, and effective understanding of GPS track logs for both personal and public use. It not only provides a powerful platform for people to effectively manage their GPS data but also help them well understand a person's past experience from GPS data.
{"title":"GeoLife: Managing and Understanding Your Past Life over Maps","authors":"Yu Zheng, Longhao Wang, Ruochi Zhang, Xing Xie, Wei-Ying Ma","doi":"10.1109/MDM.2008.20","DOIUrl":"https://doi.org/10.1109/MDM.2008.20","url":null,"abstract":"The increasing popularity of GPS device has boosted many applications where more and more GPS logs have been accumulating continuously. Managing and understanding the collected GPS data are two important issues for these applications. On one hand, by indexing the increasing GPS data, we can provide effective retrieval method for users to find the corresponding GPS data interests them. On the other hand, by understanding user's GPS data, we are more likely to enable novel services which would stimulate people's passion on contributing GPS data in turn. However, so far, GPS data are still used directly without much understanding. In our project, referred to as GeoLife, we focus on visualization, organization, fast retrieval, and effective understanding of GPS track logs for both personal and public use. It not only provides a powerful platform for people to effectively manage their GPS data but also help them well understand a person's past experience from GPS data.","PeriodicalId":365750,"journal":{"name":"The Ninth International Conference on Mobile Data Management (mdm 2008)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123543758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To support context-aware applications, it is beneficial to maintain shared context models that contain different types of information, like mobile objects, stationary objects, or spatially related digital information. This demonstration is about the NexusEditor, a graphical user interface to maintain spatial context models, interactively create queries, send them to a server and visualize the results. The contribution here is to show how schema awareness can improve such a tool: the NexusEditor dynamically parses the underlying data model and provides additional syntactic and semantic checks and short-cuts based on the schema information. Also, it supports export to existing information spaces like GoogleEarth.
{"title":"NexusEditor: A Schema-Aware Graphical User Interface for Managing Spatial Context Models","authors":"D. Nicklas, Carsten Neumann","doi":"10.1109/MDM.2008.39","DOIUrl":"https://doi.org/10.1109/MDM.2008.39","url":null,"abstract":"To support context-aware applications, it is beneficial to maintain shared context models that contain different types of information, like mobile objects, stationary objects, or spatially related digital information. This demonstration is about the NexusEditor, a graphical user interface to maintain spatial context models, interactively create queries, send them to a server and visualize the results. The contribution here is to show how schema awareness can improve such a tool: the NexusEditor dynamically parses the underlying data model and provides additional syntactic and semantic checks and short-cuts based on the schema information. Also, it supports export to existing information spaces like GoogleEarth.","PeriodicalId":365750,"journal":{"name":"The Ninth International Conference on Mobile Data Management (mdm 2008)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125785801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Moving monitoring query on moving objects is an important type of query in location based services. Existing solutions suffer from high communication cost. In this paper, we propose a distributed solution to this problem. Our approach employs two ways of communications, on-demand access and broadcast channel, to reduce communication cost. Two different indexes are proposed and evaluated using simulations. The performance results indicate that our solution achieves from 30% to 60% savings in communications when compared to MobiEyes [2].
{"title":"On Reducing Communication Cost for Distributed Moving Query Monitoring Systems","authors":"Fuyu Liu, K. Hua, Fei Xie","doi":"10.1109/MDM.2008.35","DOIUrl":"https://doi.org/10.1109/MDM.2008.35","url":null,"abstract":"Moving monitoring query on moving objects is an important type of query in location based services. Existing solutions suffer from high communication cost. In this paper, we propose a distributed solution to this problem. Our approach employs two ways of communications, on-demand access and broadcast channel, to reduce communication cost. Two different indexes are proposed and evaluated using simulations. The performance results indicate that our solution achieves from 30% to 60% savings in communications when compared to MobiEyes [2].","PeriodicalId":365750,"journal":{"name":"The Ninth International Conference on Mobile Data Management (mdm 2008)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127216734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary form only given. Wireless sensor networks create an innovative technology that enables users to monitor and study the physical world at an extremely high resolution. Query processing in such ad-hoc environments is a challenging task due to the complexities imposed by the inherent energy and communication constraints. To this end, the research community has proposed to take into account user-defined parameters in order to derive the K most relevant (or Top-K) answers quickly and efficiently A Top-K query returns the subset of most relevant answers, in place of all answers, for two reasons: i) to minimize the cost metric that is associated with the retrieval of all answers; and ii) to improve the recall and the precision of the answer set, such that the user is not overwhelmed with irrelevant results. This tutorial presents the fundamental concepts behind distributed Top- K query processing and the adaptations of these algorithms to distributed and wireless sensor networks. It additionally provides a gentle overview of rudimentary and advanced techniques covering a significant body of research in this domain. The tutorial will start out with an overview of the most influential centralized and middleware Top-K query processing algorithms and then proceed with an elaborate description of distributed Top-K ranking algorithms for one-time top-k queries, continuous top-k queries and approximate top-k queries. Finally, it will provide an outlook to compelling future applications that can be constructed on the foundation of these algorithms. Although the tutorial is specifically geared towards wireless sensor networks, many of the presented ideas find extensions in other mobile environments such as adhoc networks, vehicular networks and the mobile Web.
{"title":"Distributed Top-K Query Processing in Wireless Sensor Networks","authors":"D. Zeinalipour-Yazti, Zografoula Vagena","doi":"10.1109/MDM.2008.43","DOIUrl":"https://doi.org/10.1109/MDM.2008.43","url":null,"abstract":"Summary form only given. Wireless sensor networks create an innovative technology that enables users to monitor and study the physical world at an extremely high resolution. Query processing in such ad-hoc environments is a challenging task due to the complexities imposed by the inherent energy and communication constraints. To this end, the research community has proposed to take into account user-defined parameters in order to derive the K most relevant (or Top-K) answers quickly and efficiently A Top-K query returns the subset of most relevant answers, in place of all answers, for two reasons: i) to minimize the cost metric that is associated with the retrieval of all answers; and ii) to improve the recall and the precision of the answer set, such that the user is not overwhelmed with irrelevant results. This tutorial presents the fundamental concepts behind distributed Top- K query processing and the adaptations of these algorithms to distributed and wireless sensor networks. It additionally provides a gentle overview of rudimentary and advanced techniques covering a significant body of research in this domain. The tutorial will start out with an overview of the most influential centralized and middleware Top-K query processing algorithms and then proceed with an elaborate description of distributed Top-K ranking algorithms for one-time top-k queries, continuous top-k queries and approximate top-k queries. Finally, it will provide an outlook to compelling future applications that can be constructed on the foundation of these algorithms. Although the tutorial is specifically geared towards wireless sensor networks, many of the presented ideas find extensions in other mobile environments such as adhoc networks, vehicular networks and the mobile Web.","PeriodicalId":365750,"journal":{"name":"The Ninth International Conference on Mobile Data Management (mdm 2008)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132611320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
On-demand data broadcast is widely deployed to achieve high scalability in a mobile computing environment. However, traditional on-demand data broadcasting assumes that each time slot includes only one data item. Therefore, the above constraint requires the mobile users to wait until the next broadcast cycle to retrieve a data item if they miss the item in this cycle. The above constraint also limits the number of users that can be served in each time slot. In this paper, we propose a new on-demand data broadcast model with modified network coding. Our approach enables a server to encode multiple data items in each time slot, while each mobile user retrieves the data items by decoding the encoded data items with the locally stored data items. Our approach is different from the traditional network coding because each time slot encodes only a subset of data items, which are decided according to identities of the requested and stored data items of users. The simulation results show that our algorithm can reduce the access time by 66% as compared to the traditional schemes.
{"title":"Multi-data Delivery Based on Network Coding in On-demand Broadcast","authors":"Chung-Hua Chu, De-Nian Yang, Ming-Syan Chen","doi":"10.1109/MDM.2008.25","DOIUrl":"https://doi.org/10.1109/MDM.2008.25","url":null,"abstract":"On-demand data broadcast is widely deployed to achieve high scalability in a mobile computing environment. However, traditional on-demand data broadcasting assumes that each time slot includes only one data item. Therefore, the above constraint requires the mobile users to wait until the next broadcast cycle to retrieve a data item if they miss the item in this cycle. The above constraint also limits the number of users that can be served in each time slot. In this paper, we propose a new on-demand data broadcast model with modified network coding. Our approach enables a server to encode multiple data items in each time slot, while each mobile user retrieves the data items by decoding the encoded data items with the locally stored data items. Our approach is different from the traditional network coding because each time slot encodes only a subset of data items, which are decided according to identities of the requested and stored data items of users. The simulation results show that our algorithm can reduce the access time by 66% as compared to the traditional schemes.","PeriodicalId":365750,"journal":{"name":"The Ninth International Conference on Mobile Data Management (mdm 2008)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115378818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yunjun Gao, Gencai Chen, Qing Li, Baihua Zheng, Chun Xing Li
Given a set of trajectories D, a query object (point or trajectory) q, and a query interval T, a mutual (i.e., symmetric) nearest neighbor (MNN) query over trajectories finds from D within T, the set of trajectories that are among the k1 nearest neighbors (NNs) of q, and meanwhile, have q as one of their k2 NNs. This type of queries considers proximity of q to the trajectories and the proximity of the trajectories to q, which is useful in many applications (e.g., decision making, data mining, pattern recognition, etc.). In this paper, we first formalize MNN query and identify some problem characteristics, and then develop two algorithms to process MNN queries efficiently. In particular, we thoroughly investigate two classes of queries, viz. MNNP and MNNT queries, which are defined w.r.t. stationary query points and moving query trajectories, respectively. Our techniques utilize the advantages of batch processing and reusing technology to reduce the I/O (i.e., number of node/page accesses) and CPU costs significantly. Extensive experiments demonstrate the efficiency and scalability of our proposed algorithms using both real and synthetic datasets.
{"title":"Processing Mutual Nearest Neighbor Queries for Moving Object Trajectories","authors":"Yunjun Gao, Gencai Chen, Qing Li, Baihua Zheng, Chun Xing Li","doi":"10.1109/MDM.2008.17","DOIUrl":"https://doi.org/10.1109/MDM.2008.17","url":null,"abstract":"Given a set of trajectories D, a query object (point or trajectory) q, and a query interval T, a mutual (i.e., symmetric) nearest neighbor (MNN) query over trajectories finds from D within T, the set of trajectories that are among the k1 nearest neighbors (NNs) of q, and meanwhile, have q as one of their k2 NNs. This type of queries considers proximity of q to the trajectories and the proximity of the trajectories to q, which is useful in many applications (e.g., decision making, data mining, pattern recognition, etc.). In this paper, we first formalize MNN query and identify some problem characteristics, and then develop two algorithms to process MNN queries efficiently. In particular, we thoroughly investigate two classes of queries, viz. MNNP and MNNT queries, which are defined w.r.t. stationary query points and moving query trajectories, respectively. Our techniques utilize the advantages of batch processing and reusing technology to reduce the I/O (i.e., number of node/page accesses) and CPU costs significantly. Extensive experiments demonstrate the efficiency and scalability of our proposed algorithms using both real and synthetic datasets.","PeriodicalId":365750,"journal":{"name":"The Ninth International Conference on Mobile Data Management (mdm 2008)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127831039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes an approach for secure data sharing on mobile terminals with members of a particular group. To avoid the data being compromised due to loss or theft, this approach prevents data leakage, while allowing the correct members to recover the data to a new mobile terminal thanks to cooperation between a mobile terminal and a network server. The fundamental concept used to achieve data security involves applying data encryption and secret sharing of the encryption key. In addition, this approach newly introduces a key encapsulation mechanism (KEM) and threshold cryptography. The approach also combines the use of a data protection approach, based on a secret sharing scheme, in order to achieve an efficient data reading process. Once one of the members reads the data, he/she need not use threshold cryptography to reconstruct the encrypted key, but instead uses a secret sharing scheme. This paper confirms the potential of this approach via the prototype implementation onto a mobile phone.
{"title":"Secure Data Sharing in Mobile Environments","authors":"T. Matsunaka, T. Warabino, Y. Kishi","doi":"10.1109/MDM.2008.32","DOIUrl":"https://doi.org/10.1109/MDM.2008.32","url":null,"abstract":"This paper proposes an approach for secure data sharing on mobile terminals with members of a particular group. To avoid the data being compromised due to loss or theft, this approach prevents data leakage, while allowing the correct members to recover the data to a new mobile terminal thanks to cooperation between a mobile terminal and a network server. The fundamental concept used to achieve data security involves applying data encryption and secret sharing of the encryption key. In addition, this approach newly introduces a key encapsulation mechanism (KEM) and threshold cryptography. The approach also combines the use of a data protection approach, based on a secret sharing scheme, in order to achieve an efficient data reading process. Once one of the members reads the data, he/she need not use threshold cryptography to reconstruct the encrypted key, but instead uses a secret sharing scheme. This paper confirms the potential of this approach via the prototype implementation onto a mobile phone.","PeriodicalId":365750,"journal":{"name":"The Ninth International Conference on Mobile Data Management (mdm 2008)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125284085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：