Maximum Data-Resolution Efficiency for Fog-Computing Supported Spatial Big Data Processing in Disaster Scenarios

Spatial big data analysis is very important in disaster scenarios to understand distribution patterns of situations, e.g., people's movements, people's requirements, resource shortage situations, and so on. In a general case, spatial big data is generated from distributed sensing devices and analyzed in a centralized way, e.g., a cloud center with high-performance computing resources. However, data transmission from sensing devices to cloud centers always takes a long time, especially in disaster scenarios with an unstable network. Fog computing is a promising technique to solve the above problem by offloading data processing tasks from the cloud to nearby computation devices. But data resolution also decreases after local processing in the fog nodes. It is necessary to investigate the optimal task distribution solutions to efficiently use computation resources in the fog layer. In this paper, we take the above research problem, and study fog-computing supported spatial big data processing. We analyze the process for spatial clustering, which is a typical category for spatial data analysis, and propose an architecture to integrate data processing into fog computing. We formalize a problem to maximize the data-resolution efficiency by considering data resolution and delay. We further propose core algorithms to enable spatial clustering in a fog-computing environment and implement the above algorithms in a real system. We have performed both simulations and experiments on a real Twitter dataset collected when Kumamoto-city suffered an earthquake. Through the simulations and the experiments, we have determined that the proposed solution significantly outperforms the other solutions.

[1]  Satoshi Kotabe,et al.  Bringing movable and deployable networks to disaster areas: development and field test of MDRU , 2016, IEEE Network.

[2]  Barton P. Miller,et al.  Data Reduction and Partitioning in an Extreme Scale GPU-Based Clustering Algorithm , 2017 .

[3]  Karen Spärck Jones.  A statistical interpretation of term specificity and its application in retrieval , 2021, J. Documentation.

[4]  Xuegang Wang,et al.  Spatial Clustering Algorithm Based on Hierarchical-Partition Tree , 2010, J. Digit. Content Technol. its Appl..

[5]  Di Ma,et al.  MR-DBSCAN: An Efficient Parallel Density-Based Clustering Algorithm Using MapReduce , 2011, 2011 IEEE 17th International Conference on Parallel and Distributed Systems.

[6]  Mei-Po Kwan,et al.  LiDAR assisted emergency response: Detection of transport network obstructions caused by major disasters , 2010, Comput. Environ. Urban Syst..

[7]  Christian Böhm,et al.  Density-based clustering using graphics processors , 2009, CIKM.

[8]  Hans-Peter Kriegel,et al.  DBDC: Density Based Distributed Clustering , 2004, EDBT.

[9]  M. Tahar Kechadi,et al.  Distributed clustering algorithm for spatial data mining , 2015, 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM).

[10]  Song Guo,et al.  Just-in-Time Code Offloading for Wearable Computing , 2015, IEEE Transactions on Emerging Topics in Computing.

[11]  M. Kulldorff.  A spatial scan statistic , 1997 .

[12]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[13]  Song Guo,et al.  Joint Optimization of Task Scheduling and Image Placement in Fog Computing Supported Software-Defined Embedded System , 2016, IEEE Transactions on Computers.

[14]  Xu Chen,et al.  Decentralized Computation Offloading Game for Mobile Cloud Computing , 2014, IEEE Transactions on Parallel and Distributed Systems.

[15]  Tadao Nagatsuma,et al.  Real-time 100-Gbit/s QPSK transmission using photonics-based 300-GHz-band wireless link , 2016, 2016 IEEE International Topical Meeting on Microwave Photonics (MWP).

[16]  Ivan Stojmenovic,et al.  The Fog computing paradigm: Scenarios and security issues , 2014, 2014 Federated Conference on Computer Science and Information Systems.

[17]  Yong Xiang,et al.  Cost Efficient Resource Management in Fog Computing Supported Medical Cyber-Physical System , 2017, IEEE Transactions on Emerging Topics in Computing.

[18]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[19]  Hans-Peter Kriegel,et al.  A Fast Parallel Clustering Algorithm for Large Spatial Databases , 1999, Data Mining and Knowledge Discovery.

[20]  Yinghui Zhou,et al.  Design of a Best Load Balancing Method for Anti-disaster Mobile Mesh Communication Networks , 2013 .

[21]  M. Tahar Kechadi,et al.  Efficient Distributed Approach for Density-Based Clustering , 2011, 2011 IEEE 20th International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises.

[22]  Satoshi Suyama,et al.  Joint fixed beamforming and eigenmode precoding for super high bit rate massive MIMO systems using higher frequency bands , 2014, 2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC).

[23]  ura,et al.  Parallel Processing for Density-based Spatial Clustering Algorithm using Complex Grid Partitioning and Its Performance Evaluation , 2016 .

[24]  Zixue Cheng,et al.  A Dynamic Spatial Clustering for Emergency Response based on Hierarchical-Partition Model , 2017 .

[25]  Jugal K. Kalita,et al.  Comparing Twitter Summarization Algorithms for Multiple Post Summaries , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[26]  Andrew W. Moore,et al.  Rapid detection of significant spatial clusters , 2004, KDD.

[27]  H. Bremermann.  A method of unconstrained global optimization , 1970 .

[28]  Song Guo,et al.  Big Data Analytics for Emergency Communication Networks: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[29]  Raja Lavanya,et al.  Fog Computing and Its Role in the Internet of Things , 2019, Advances in Computer and Electrical Engineering.

[30]  Barton P. Miller,et al.  Mr. Scan: Extreme scale density-based clustering using a tree-based network of GPGPU nodes , 2013, 2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC).