2019 20th IEEE International Conference on Mobile Data Management (MDM)最新文献_第10页

A Practical Delivery Route Planning System 一种实用的送货路线规划系统

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-37

Asger Gitz-Johansen, Mikkel Elkjaer Holm, Laurids Vinther Kirkeby, Dan Kristiansen, Alexander Stoica Ostenfeld, M. K. Schou, Bin Yang

引用次数: 1

Prevalent Co-Visiting Patterns Mining from Location-Based Social Networks 基于位置的社交网络中常见的共同访问模式挖掘

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00123

Xiaoxuan Wang, Lizhen Wang, Peizhong Yang

{"title":"Prevalent Co-Visiting Patterns Mining from Location-Based Social Networks","authors":"Xiaoxuan Wang, Lizhen Wang, Peizhong Yang","doi":"10.1109/MDM.2019.00123","DOIUrl":"https://doi.org/10.1109/MDM.2019.00123","url":null,"abstract":"Spatial co-location mining is a key problem in urban planning and marketing. Current spatial co-location mining methods ignore the people who are related to the co-location patterns' instances, which results that the mining results are hard to explain and understand by the users. In this paper, we combine the theories of co-location mining and social networks analysis to mine a kind of special co-location patterns: Co-visiting patterns, which consider spatial information and social information at the same time. A co-visiting pattern is also a spatial feature set, whose instances are always visited by the similar users and located in a nearby region. We propose some new measures, including the user similarity, the weight of neighborhood relationship of two visited spatial instances, and the prevalent degree of a co-visiting pattern. In addition, we also explore the properties of the co-visiting patterns in this paper, and present an efficient algorithm. Finally, experiments and a detailed analysis are given at the end of this paper. Experimental results show that the rationality of co-visiting pattern, and the effectiveness and stability of the mining algorithm.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130103144","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}

引用次数: 3

Mobile Video Multipath Concurrent Transmission Over Heterogeneous Wireless Networks Based on ACO 基于蚁群算法的异构无线网络移动视频多径并发传输

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00116

S. Zhang, Feng Chen, Wen-Kang Jia, H. Jiang

{"title":"Mobile Video Multipath Concurrent Transmission Over Heterogeneous Wireless Networks Based on ACO","authors":"S. Zhang, Feng Chen, Wen-Kang Jia, H. Jiang","doi":"10.1109/MDM.2019.00116","DOIUrl":"https://doi.org/10.1109/MDM.2019.00116","url":null,"abstract":"In the past few years, data traffic generated by video applications has become the dominant traffic on the internet due to the rapid development of wireless network and mobile terminal. However, using a single wireless network for video transmission with limited bandwidth is facing great challenges under the increasing video quality requirement. Fortunately, mobile terminal equipped with multiple network interfaces can transmit data over heterogeneous wireless networks concurrently to achieve higher bandwidth. In order to make effective use of heterogeneous access network resources, this paper develops a heuristic flow splitting algorithm based on ACO (Ant Colony Optimization) algorithm. Firstly, a system model with the objective that minimizes the multipath video transmission distortion is proposed under the end-to-end delay constraint. Secondly, to solve the problem based on heuristic ACO, the relevant models such as pheromone model and reliability model are presented, and a heuristic ACO algorithm is proposed. Finally, the proposed algorithm is tested and evaluated in the real-world platform. The evaluation results show that the proposed algorithm performs better than the referenced traditional algorithm in terms of PSNR and end-to-end frame delay.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123365967","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}

引用次数: 0

Toward System-Optimal Route Guidance 面向系统最优路由引导

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-70

R. Fitzgerald, F. Kashani

{"title":"Toward System-Optimal Route Guidance","authors":"R. Fitzgerald, F. Kashani","doi":"10.1109/MDM.2019.00-70","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-70","url":null,"abstract":"The existing online mapping systems process many user route queries simultaneously, yet solve each independently, using typical route guidance solutions. These route recommendations are presented as optimal, but often this is not truly the case, due to the effects of competition users experience over the resulting experienced routes, a phenomenon referred to in Game Theory as a Nash Equilibrium. Additionally, route plans of this nature can result in poor utilization of the road network from a system-optimizing perspective as well. In this paper, we introduce an enhanced approach for route guidance, motivated by the relevance of a system optimal equilibrium strategy, while also maintaining some fairness to the individual. With this approach the objective is to optimize the global road network utilization (as measured by, e.g., mobility, or global emissions) by selecting from a set of generally fair user route alternatives in a batch setting. For the first time, we present an approximate, anytime algorithm based on Monte Carlo Tree Search and Eppstein's Top-K Shortest Paths algorithm to solve this complex dual optimization problem in real-time. This approach attempts to identify and avoid the potentially harmful network effects of sub-optimal route combinations. Experiments show that mobility optimization over real road networks of Rye and Golden, Colorado in a microscopic traffic simulation with a network congestion-minimizing objective can achieve considerable mobility improvement for users, as observed by their effective travel time improvement up to 12% with some consideration of route fairness.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"459 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131856013","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}

引用次数: 2

MODELHealth: An Innovative Software Platform for Machine Learning in Healthcare Leveraging Indoor Localization Services MODELHealth:利用室内定位服务的医疗保健机器学习创新软件平台

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.000-5

Athanasios Anastasiou, Stavros Pitoglou, Thelma Androutsou, Evaggelos Kostalas, G. Matsopoulos, D. Koutsouris

引用次数: 1

Attention Based Stack ResNet for Citywide Traffic Accident Prediction 基于注意力的城市交通事故预测堆栈ResNet

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-27

Zhengyang Zhou

引用次数: 10

Lighthouse: Enabling Landmark-Based Accurate and Robust Next Generation Indoor LBSs on a Worldwide Scale 灯塔:在全球范围内实现基于地标的精确和稳健的下一代室内lbs

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-79

M. Youssef, P. Robertson, Heba Abdelnasir, Maria Garcia Puyol, Etienne Le Grand, L. Bruno

{"title":"Lighthouse: Enabling Landmark-Based Accurate and Robust Next Generation Indoor LBSs on a Worldwide Scale","authors":"M. Youssef, P. Robertson, Heba Abdelnasir, Maria Garcia Puyol, Etienne Le Grand, L. Bruno","doi":"10.1109/MDM.2019.00-79","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-79","url":null,"abstract":"A WiFi-based landmark (LM) is a unique point in the physical space that has a repeatable and identifiable WiFi signature as sensed by a mobile device. We present Lighthouse, a new class of WiFi landmarks based on concepts from computational geometry theory that can be leveraged to provide worldwide robust and accurate location based services (LBSs). The proposed Lighthouse landmarks have the nice properties of being abundant in space, hardware-and carry position-independent, can be computed efficiently from a single scan, are confined to a small area of space, do not restrict the user movement path, and do not require any calibration. We show that the positioning error of the Lighthouse landmarks is bounded and present the different extensions that allow it to handle practical situations including the noisy wireless channel, different AP transmit powers, obstacles in the environment, among others. We further present efficient algorithms for extracting them from WiFi scans. We have implemented and evaluated Lighthouse using thousands of surveys collected from different cities worldwide over a six months period. Our results show that Lighthouse's landmarks are one order of magnitude more frequent in the environment compared to the other state-of-the-art WiFi-based landmarks. In addition, the median accuracy of determining the LMs location is less than 3.6 meters. This accuracy is robust over time, different phones hardware, phone carrying positions, and parameters configurations; highlighting the promise of Lighthouse landmarks for enabling the next generation LBSs on a worldwide scale.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124505436","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}

引用次数: 4

Mining Spatial Co-Location Patterns Based on Overlap Maximal Clique Partitioning 基于重叠最大团块划分的空间共位模式挖掘

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00007

Vanha Tran, Lizhen Wang, Lihua Zhou

{"title":"Mining Spatial Co-Location Patterns Based on Overlap Maximal Clique Partitioning","authors":"Vanha Tran, Lizhen Wang, Lihua Zhou","doi":"10.1109/MDM.2019.00007","DOIUrl":"https://doi.org/10.1109/MDM.2019.00007","url":null,"abstract":"Spatial co-location patterns are groups of spatial features whose instances are frequently located together in spatial proximity. Most existing algorithms of discovering spatial co-location patterns are based on the candidate-test model, which is computationally expensive. When the user adjusts the participation index (PI) threshold, these algorithms have to be re-executed from the size 2 co-location patterns. In this paper, we propose a novel spatial instance partition method for mining co-location patterns which called overlap maximal clique partitioning algorithm (OMCP). The OMCP co-location mining algorithm divides instances of an input spatial dataset into a set of overlap maximal cliques. Table instances of all colocation patterns are collected by the overlap maximal cliques. Prevalent co-location patterns are directly calculated without generating the candidate patterns. The OMCP algorithm only needs to execute once to get the PI of all patterns, without reexecuting when the PI threshold is adjusted. Our algorithm is performed on both synthetic and real-world datasets to demonstrate that the OMCP algorithm improvements in efficiency of co-location pattern mining.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117110198","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}

引用次数: 2

Scalable and Accurate Estimation of Room-Level People Counts from Multi-Modal Fusion of Perimeter Sensors and WiFi Trajectories 基于周界传感器和WiFi轨迹多模态融合的可扩展和精确估计房间级人数

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-76

Fisayo Caleb Sangogboye, M. Kjærgaard

{"title":"Scalable and Accurate Estimation of Room-Level People Counts from Multi-Modal Fusion of Perimeter Sensors and WiFi Trajectories","authors":"Fisayo Caleb Sangogboye, M. Kjærgaard","doi":"10.1109/MDM.2019.00-76","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-76","url":null,"abstract":"Estimating the number of people in rooms and zones within commercial buildings are gaining enormous attention for facilitating various domain applications. However, the deployment of state-of-art counting sensors such as camera technologies can be economically in-viable for individual rooms or zones in large commercial and public buildings. Such sensors are also known to be highly intrusive within building deployments. In this paper, we propose a multi-modal fusion method that leverages the accuracy of camera technologies for estimating building-level counts and the non-intrusive and scalability of wireless fidelity (WiFi) trajectory data to estimate room-level counts. This multi-modal fusion method disaggregates the obtained building-level counts by applying a series of data cleaning methods and a two-step probabilistic method. We evaluate the disaggregation method with datasets from a large teaching building, and we benchmark its performance with a state-of-art estimation algorithm and count estimates from raw WiFi trajectories. The obtained evaluation results highlight that the disaggregation algorithm outperforms other estimation methods by a minimum ratio of 35% for all room cases using the Normalized Root Mean Squared Error (NRMSE) metric.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129400800","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}

引用次数: 5

Road Intersection Detection Based on Direction Ratio Statistics Analysis 基于方向比统计分析的道路交叉口检测

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-46

Min Pu, Jiali Mao, Yuntao Du, Yibin Shen, Cheqing Jin

引用次数: 5