A DISTRIBUTED APPROACH TO CONTEXT DATA PROCESSING CHEN PENGHE B.Eng.(Hons.), NUS A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF ELECTRICAL & COMPUTER ENGINEERING NUS GRADUATE SCHOOL FOR INTEGRATIVE SCIENCES AND ENGINEERING NATIONAL UNIVERSITY OF SINGAPORE 2015 Declaration I hereby declare that the thesis is my original work and has been written by me in its entirety. I have duly acknowledged all the sources of information which have been used in the thesis. This thesis has also not been submitted for any degree in any university previously. ------------------------------------------------------------ Chen Penghe 27 Feb 2015 i ii Acknowledgement First and foremost, I would like to thank my main supervisor Prof Lawrence Wong Wai Choong and co-supervisor A/Prof Pung Hung Keng for guiding me through the perilous journey of obtaining a PhD and providing constant encouragement during my moments of self-doubt and having faith in me. The valuable suggestions imparted by them concerning all aspects of research ranging from writing papers, giving presentations, conducting experiments as well as their ideas regarding the direction of my PhD project have been extremely helpful to me and have enabled me to become a better researcher. I would also like to thank my thesis advisory committee members, A/Prof Tham Chen Khong and A/Prof Adrian David Cheok for their valuable suggestions and critical yet beneficial comments for the improvement of the thesis work. I would also like to thank Dr. Zhu Jian, Dr. Shubhabrata Sen and Dr. Xue Mingqiang for their guidance and support during the pursuance to my PhD. Their help during my study was instrumental in the formulation of my PhD project. I would like to acknowledge the financial, academic and technical support of National University of Singapore, and NUS Graduate School for Integrative Sciences, which provided me the opportunity to pursue my PhD study as well as financial support for my research work. I would like to thank all the members of the IDMI Ambient Intelligence Laboratory for all their support during the course of my PhD study. In particular, I would like to thank Mr. Song Xianlin and Ms Guo Jie for providing assistance in carrying out my research. I would also like to thank the Network System and Services Lab technicians Ms Lim Chew Eng and Mr. Chan Chee Heng for providing all the necessary assistance to establish the experimental setup for testing my work. I would also like to thank students Wong Yong Jie, Tan Yong Jie, Pang Kang Wei Joshua, Ng Jin Sheng, Fok Kar Wai, Ng Yung Yi and Nicholas Teo for their sample applications on Coalition. Last but not least, I would like to thank my parents, my wife, my brother and sister-in-law, my sister and brother-in-law, and all my friends for their continuous encouragement and emotional support during the entire duration of my PhD without which the completion of this journey would have been impossible. iii iv Table of Contents DECLARATION I ACKNOWLEDGEMENT . III ABSTRACT IX LIST OF FIGURES XI LIST OF TABLE .XIII LIST OF ABBREVIATIONS XV CHAPTER INTRODUCTION . 1.1 UBIQUITOUS COMPUTING AND CONTEXT AWARENESS 1.2 ARCHITECTURE OF UBIQUITOUS COMPUTING SYSTEMS . 1.3 DESIGN CHALLENGES IN CONTEXT-AWARE UBIQUITOUS COMPUTING SYSTEMS . 1.4 MOTIVATION AND PROBLEMS 15 1.5 SUMMARY OF ACHIEVEMENTS AND CONTRIBUTIONS 21 CHAPTER BACKGROUND AND RELATED WORK 25 2.1 INTRODUCTION TO CONTEXT DATA MANAGEMENT 26 2.2 SURVEY OF CONTEXT-AWARE SYSTEMS . 29 2.3 RELATED WORK ABOUT CONTEXT MANAGEMENT OF MOBILE ENTITIES . 36 2.3.1 Context Data Management of Mobile Entities . 36 2.3.2 Mobility Management of Mobile Entities 41 2.4 CONTEXT QUERY LANGUAGE 43 2.4.1 SQL-like CQL . 44 2.4.2 Non-SQL like CQL . 47 2.5 CONTEXT PROCESSING . 49 2.6 SUMMARY . 53 CHAPTER COALITION-I: OVERVIEW .55 3.1 ARCHITECTURAL APPROACH AND DESIGN CONSIDERATIONS 56 3.2 ARCHITECTURE OF COALITION . 58 3.3 CONTEXT DATA MANAGEMENT . 60 3.4 QUERY PROCESSING . 65 3.5 PROPOSED MECHANISMS 69 3.6 SUMMARY . 72 CHAPTER CONTEXT DATA MANAGEMENT OF MOBILE ENTITIES .73 4.1 INTRODUCTION . 74 4.2 MOBILE SPACE 76 4.3 MOBILE PHYSICAL SPACE GATEWAY (MPSG) 80 4.3.1 Service Management 81 4.3.2 Context Data Management . 83 4.3.3 Network Communication Management 87 v 4.4 AVAILABILITY MANAGEMENT OF MOBILE SPACE . 88 4.4.1 Availability Updating Service . 89 4.4.2 Application Callback Service 94 4.5 FRAMEWORK VALIDATION . 99 4.5.1 Experimental Setup . 99 4.5.2 MPSG Validation . 99 4.5.3 Callback Framework Validation 104 4.6 RELATED WORK DISCUSSION . 108 4.6.1 Context Management of Mobile Entities 109 4.6.2 Mobility Management of Mobile Entities . 111 4.7 SUMMARY . 112 CHAPTER SQL-LIKE CONTEXT QUERY LANGUAGE . 113 5.1 INTRODUCTION . 114 5.2 REQUIREMENTS 116 5.3 PROPOSED CONTEXT QUERY LANGUAGE . 119 5.3.1 Description . 120 5.3.2 Context Processing Functions . 125 5.3.3 Constraint Representation (WHERE clause) . 127 5.4 EVALUATION . 130 5.4.1 Representing Capability Analysis 130 5.4.2 Comparison with MUSIC CQL 132 5.5 RELATED WORK DISCUSSION . 142 5.5.1 SQL-like CQL 142 5.5.2 RDF based CQL . 143 5.6 SUMMARY . 144 CHAPTER DISTRIBUTED CONTEXT PROCESSING MECHANISM . 145 6.1 INTRODUCTION . 146 6.2 PROCESSING OF SIMULTANEOUS SIMPLE CONTEXT QUERIES 150 6.2.1 Query Processing Parts 151 6.2.2 Context Query Object . 152 6.2.3 Architecture Design . 153 6.2.4 Workflow of Distributed Context Processing Mechanism 155 6.2.5 Performance Evaluations 156 6.3 PROCESSING OF SINGLE COMPLEX QUERY . 161 6.3.1 Context Constraint Plan . 163 6.3.2 Modified Context Query object 167 6.3.3 Context Query Plan 168 6.3.4 Context Processor . 171 6.3.5 Context Processing Plan 172 6.3.6 Framework Architecture . 174 6.3.7 Query Processing Flow . 176 6.3.8 Mechanism Validation 177 6.4 DISTRIBUTED CONTEXT PROCESSING FOR SIMULTANEOUS COMPLEX QUERIES . 184 6.4.1 Distributed Query Processing Mechanism 186 6.4.2 Computing Power Management 188 vi 6.4.3 Framework Architecture . 190 6.4.4 Workflow 192 6.4.5 Performance Evaluation 194 6.5 RELATED WORK DISCUSSION . 199 6.6 SUMMARY . 201 CHAPTER CONCLUSION AND FUTURE WORK 203 7.1 CONCLUSION 204 7.2 FUTURE RESEARCH DIRECTION . 209 REFERENCES 213 LIST OF PUBLICATIONS . 223 vii viii analyzed and processed in different M/PSGs. Additionally, in order to better utilize the computing capability of each device, we further extend each device with the context processor capability. Along with the proposed and developed computing power management mechanism that can monitor the real time computing capability of each computing device, we can distribute the context processor of each context processing plan into different devices. As a result, the entire context processing process can be handled in a distributed manner so that system performance in term of handling simultaneous context queries can be improved. 7.2 Future Research Direction With the advances of ubiquitous computing technologies, UbiComp will definitely have a big influence to people’s life and context-aware computing will be sure to play an essential role of it. Nevertheless, there is much that remains to be done and learned in this field. Despite of the work proposed in this thesis, the following issues should be possible future research directions: Context Processor Reusability The context processor applies different operations on the incoming context data to generate higher level context data. The main idea of the proposed distributed context processing mechanism is to utilize the various context processors to generate the required context data step-by-step. As a result, an individual context processor plan is built for each individual complex query. However, this approach may produce a large number of repeated context processors generated by the common tasks between different context queries, which is not efficient and causes a waste of resource. In order to address this issue, we should think about reusing the created common context processor rather than recreating them. A possible solution is to 209 create a context processor pool to manage all the created context processors, so a check can be done before any new context processor should be created. Of course, we need to solve the issues in representing context processors and matching context processors with requirements and operations. In addition, there is also an issue in managing the lifetime of each context processors. Popular context processors should have longer lifetime and be avoided with frequent creating and deleting operations, while seldom used context processor should be killed early to avoid resource wasting. Context Reasoning / Context Operations Integration with Context Processing Context-aware computing targets to identify user situations so that applications can automatically adapt to the situations without users intervened, so how to convert primary data into information or knowledge becomes an essential issue. The knowledge or situation detection is usually realized by context reasoning. Reasoning operations are usually computation intensive and not easy to in real time. Also, reasoning operations are usually predefined for specific situations and separated from query processing part. This makes reasoning operations inflexible enough to support context query answering process. In addition, the context query processing mechanism also plays a significant part in data management as it takes care of how a user obtains the required context data. Also, a SQL-like CQL is more preferred for context query representation as it is more flexible and powerful. As a result, how to integrate the reasoning operations with the SQL-like CQL becomes an essential issue. On one hand, we want to have the derived information through reasoning operations. On the other hand, we also want to utilize the SQL-like CQL to query context data. One possible solution is to execute all the reasoning operations and generate all possible context data in advance, which is a very large project and practically 210 challenging and it becomes a waste if the derived information is not needed. Another possible solution is to convert the reasoning operations to SQL-like CQL scripts so that a context query requiring derived context data can be converted into a new context query with corresponding reasoning operations inside. How to define, identify and execute those operations will be significant for this integration. Also, it needs a place to manage those reasoning operations with SQL-like CQL operation scripts. Context Event Management Context-aware computing aims to detect situation changes and make the applications adapt to these changes in an automatic manner. These changes are usually defined as context events. Applications require context event information so that they can respond to them automatically without user intervention. However, unlike normal context data, these context events are sporadic and unpredictable, so we may not be able to acquire these context events as the approach used for obtaining normal context data like location information of person of interest. As a result, how to manage these context events and make them available to corresponding parties are quite important for context data management. One commonly used method to obtain these context events is the publish/subscribe based method in which interested parties pre-subscribe specific context events with the system first. Later, when context events happen, the system is triggered to publish the context event information to these subscribers. One possible issue is how to manage the notifying process. 211 212 References [1] SmartSantander. Available: http://www.smartsantander.eu [2] (2015). 2014:Build a Smart Nation. Available: http://www.ida.gov.sg/blog/insg/featured/2014-building-a-smart-nation/ [3] IBM Smarter Planet. 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Wong, “A context management framework for context-aware applications in mobile spaces,” International Journal of Pervasive Computing and Communications, vol. 8, no. 2, 2012, pp. 185-210. 2. J. Zhu, P. Chen, H. K. Pung, M. Oliya, S. Sen, and W. C. Wong, “Coalition: A Platform for Context-Aware Mobile Application Development.” Ubiquitous Computing and Communication Journal, Vol 6, No. 1, 2011 3. P. Chen, S. Sen, H. K. Pung, and W. C. Wong, “Distributed context processing mechanism for context data management,” Context-aware Systems and Applications (Under Review) Conference papers 1. P. Chen, S. Sen, H. K. Pung, and W. C. Wong, “Context data management for mobile spaces,” Mobile and Ubiquitous Systems: Computing, Networking, and Services, Springer, 2012, pp. 340-341. 2. P. Chen, S. Sen, H. K. Pung, and W. C. Wong, “Context Processing: A Distributed Approach,” Proc. INTELLI 2013, The Second International Conference on Intelligent Systems and Applications, 2013, pp. 58-64. 3. P. Chen, S. Sen, H. K. Pung, and W. C. Wong, “MPSG: a generic context management framework in mobile spaces,” Proc. Proceedings of the 8th International Conference on Body Area Networks, ICST (Institute for 223 Computer Sciences, Social-Informatics and Telecommunications Engineering), 2013, pp. 112-115. 4. P. Chen, S. Sen, H. K. Pung, and W. C. Wong, “A SQL-based Context Query Language for Context-Aware System,” Proc. IMMM 2014 The Fourth International Conference on Advances in Information Mining and Management, 2014. 224 [...]... physical space gateway (PSG) with other PSGs or entities external to that space A cyber-physical space is a physical space using the Internet as its network infrastructure Most people now take context-aware systems as systems that can manage context data and enable context-aware applications that automatically adapt their behaviors with situational information of users, environment or the applications...Abstract Context-aware computing, as a key enabling technology of ubiquitous computing, aims to provide automatic application adaptation with respect to context changes Context is defined as any information that can be used to characterize the situation of an entity An entity is a person, place, or object that is considered relevant to the interaction between a user and an application In order to. .. unreliable and asynchronous in nature while decision-making may require reliable data and some form of data synchronization All these requirements make the development of the reasoning algorithm for context data different from conventional data It is also worth noting that any reasoning processing is somewhat related to the nature of applications, which gives raise to further issues in reasoning: a What... representation One of the main objectives of the context data management system is to decouple the application logic from details of the context data management so that application developers can be liberated from the laborious task of programming the process of data retrieval as well as data handling which is data source dependent In order to realize this objective, a context accessing interface should be properly... in Chapter 3 A context space is the name of a contextual class of physical spaces having a large set of contextual attributes in common Examples of context space are ‘home’, ‘shop’ or ‘clinic’ A physical space refers to a contextual networked proximity (space) consisting of hardware, software and entities wherein context data are communicating through a single authoritative access point known as the... (PSG) for static physical ambience to the Mobile Physical Space Gateway (MPSG); each MPSG contains a context model for the mobile space that it represents, as well as functions for managing context data and services of the mobile space Secondly, to enable ix systematic data access and lower level context data manipulations, we extend and rewrite our existing SQL-like Context Query Language (CQL) By... cyber-physical sub-space directly or indirectly through an edge network such as a sensor network, a wireless LAN or a Bluetooth network Context data may be extracted as raw data directly from sources or from an intermediate storage of the components where fresh data or their inferred data have been stored How context 7 processing (see page 4 for its definition) should be done by the data management component... smaller services This layer provides smaller re-usable service units to the upper layer context 8 applications Based on the context data provided by the underlying context data management layer, different context-aware services can be developed and deployed The context-aware application layer manages the context-aware applications that interact with the eventual users and provide different functionalities... processing operations of different queries into different devices to achieve a parallel processing so that system performance in handling simultaneous queries can be improved A prototype has been developed to validate and evaluate proposed mechanisms through experiments Through experimental validation, we can observe that proposed mobile space and MPSG does be able to model and manage the context data of mobile... Plan CP Context Processor CPP Context Processing Plan CQL Context Query Language CSG Context Space Gateway CSM Context Space Manager IoT Internet of Thing MCM Middleware Callback Manager MCSG Mobile Context Space Gateway MPAU MPSG Availability Updater MPCM MPSG Callback Manager MPSG Mobile Physical Space Gateway M/PSG Mobile PSG and Normal PSG MSID Middleware Session ID MSM Middleware Session Manager . computing, as a key enabling technology of ubiquitous computing, aims to provide automatic application adaptation with respect to context changes. Context is defined as any information that can be. functions for managing context data and services of the mobile space. Secondly, to enable x systematic data access and lower level context data manipulations, we extend and rewrite our. into different devices to achieve a parallel processing so that system performance in handling simultaneous queries can be improved. A prototype has been developed to validate and evaluate