Amir Taherkordi
Frank Eliassen
Geir Horn
Skip Abstract Section
Abstract
The convergence of Internet of Things (IoT) and the Cloud has significantly facilitated the provision and management of services in large-scale applications, such as smart cities. With a huge number of IoT services accessible through clouds, it is very important to model and expose cloud-based IoT services in an efficient manner, promising easy and real-time delivery of cloud-based, data-centric IoT services. The existing work in this area has adopted a uniform and flat view to IoT services and their data, making it difficult to achieve the above goal. In this article, we propose a software framework, Context-driven And Real-time IoT (CARIoT) for real-time provisioning of cloud-based IoT services and their data, driven by their contextual properties. The main idea behind the proposed framework is to structure the description of data-centric IoT services and their real-time and historical data in a hierarchical form in accordance with the end-user application’s context model. CARIoT features design choices and software services to realize this service provisioning model and the supporting data structures for hierarchical IoT data access. Using this approach, end-user applications can access IoT services and subscribe to their real-time and historical data in an efficient manner at different contextual levels, e.g., from a municipal district to a street in smart city use cases. We leverage a popular cloud-based data storage platform, called Firebase, to implement the CARIoT framework and evaluate its efficiency. The evaluation results show that CARIoT’s hierarchical structure imposes no additional overhead with less data notification delay as compared to existing flat structures.
- M. M. Rathore, A. Ahmad, A. Paul, and S. Rho. 2016. Urban planning and building smart cities based on the internet of things using big data analytics. Computer Networks 101 (2016), 63--80. Google Scholar
Digital Library
- Gregory D. Abowd and others. 1999. Towards a better understanding of context and context-awareness. In Proc. of the 1st Int. Symposium on Handheld and Ubiquitous Computing (HUC’99). Google ScholarDigital Library
- S. Alam, M. M. R. Chowdhury, and J. Noll. 2010. SenaaS: An event-driven sensor virtualization approach for Internet of Things cloud. In IEEE Conf. on Networked Embedded Systems for Enterprise Applications (NESEA).Google Scholar
- Amazon Redshfit Data Storage Platform. http://aws.amazon.com/redshift.Google Scholar
- Kyoungho An and others. 2012. A publish/subscribe middleware for dependable and real-time resource monitoring in the cloud. In Proc. of the Workshop on Secure and Dependable Middleware for Cloud Monitoring and Management (SDMCMM). Article 3. Google ScholarDigital Library
- P. Barnaghi, Wei Wang, Lijun Dong, and Chonggang Wang. 2013. A linked-data model for semantic sensor streams. In IEEE Int. Conference on Internet of Things (iThings/CPSCom). Google ScholarDigital Library
- J. Boman, J. Taylor, and A. H. Ngu. 2014. Flexible IoT middleware for integration of things and applications. In 2014 International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom).Google Scholar
- Alessio Botta, Walter de Donato, Valerio Persico, and Antonio Pescapé. 2014. On the integration of cloud computing and Internet of Things. In Proc. of the 2014 Int. Conference on Future Internet of Things and Cloud (FICLOUD’14). Washington, DC, 8. Google ScholarDigital Library
- Soumi Chattopadhyay and others. 2014. A Data Distribution Model for Large-Scale Context Aware Systems.Google Scholar
- Guanling Chen and David Kotz. 2002. Context Aggregation and Dissemination in Ubiquitous Computing Systems. Technical Report TR2002-420. Dartmouth College, Computer Science, Hanover, NH.Google Scholar
- B. Cheng, S. Longo, F. Cirillo, M. Bauer, and E. Kovacs. 2015. Building a big data platform for smart cities: Experience and lessons from Santander. In 2015 IEEE International Congress on Big Data. 592--599. Google ScholarDigital Library
- CIaaS specification and reference implementation second release. http://clout-project.eu/deliverables/.Google Scholar
- Denis Conan and others. 2007. Scalable Processing of Context Information with COSMOS. Springer.Google Scholar
- S. De, P. Barnaghi, M. Bauer, and S. Meissner. 2011. Service modelling for the Internet of Things. In 2011 Federated Conference on Computer Science and Information Systems (FedCSIS). 949--955.Google Scholar
- EU ICT ClouT Project. http://clout-project.eu/.Google Scholar
- Firebase Cloud Platform. http://www.firebase.com/.Google Scholar
- G. Fortino, M. Pathan, and G. Di Fatta. 2012. BodyCloud: Integration of cloud computing and body sensor networks. In 2012 IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom) Cloud Computing Technology and Science (CloudCom). 851--856. Google ScholarDigital Library
- Tao Gu, Xiao Hang Wang, Hung Keng Pung, and Da Qing Zhang. 2004. An ontology-based context model in intelligent environments. In Proc. of Communication Networks and Distributed Systems Modeling and Simulation Conference.Google Scholar
- D. Guinard and others. 2010. A resource oriented architecture for the web of things. In Internet of Things (IOT), 2010.Google Scholar
- D. Guinard, V. Trifa, S. Karnouskos, P. Spiess, and D. Savio. 2010. Interacting with the SOA-based Internet of Things: Discovery, query, selection, and on-demand provisioning of web services. IEEE Transactions on Services Computing, 3, 3 (2010), 223--235. Google ScholarDigital Library
- Mohammad Mehedi Hassan, Biao Song, and Eui-Nam Huh. 2009. A framework of sensor-cloud integration opportunities and challenges. In Proc. of the 3rd Intr. Conference on Ubiquitous Information Management and Communication (ICUIMC’09). ACM. Google ScholarDigital Library
- Tom Heath and Christian Bizer. 2011. Linked Data: Evolving the Web into a Global Data Space (1st ed.). Morgan 8 Claypool. Google ScholarDigital Library
- U. Hunkeler, Hong Linh Truong, and A. Stanford-Clark. 2008. MQTT-S 2014; A publish/subscribe protocol for wireless sensor networks. In 3rd Int. Conf. on Communication Systems Software and Middleware and Workshops. COMSWARE.Google Scholar
- IBM Internet of Things Foundation. http://internetofthings.ibmcloud.com.Google Scholar
- Antonio J. Jara, Dominique Genoud, and Yann Bocchi. 2014. Big data for smart cities with KNIME a real experience in the SmartSantander testbed. Software: Practice and Experience 45, 8 (2014), 1145--1160. Google ScholarDigital Library
- Xiongnan Jin, Sejin Chun, Jooik Jung, and Kyong-Ho Lee. 2014. IoT service selection based on physical service model and absolute dominance relationship. In 2014 IEEE 7th International Conference on Service-Oriented Computing and Applications (SOCA). Google ScholarDigital Library
- M. Kovatsch, M. Lanter, and Z. Shelby. 2014. Californium: Scalable cloud services for the Internet of Things with CoAP. In 2014 International Conference on the Internet of Things (IOT). 1--6.Google Scholar
- D. Le-Phuoc, H. Q. Nguyen-Mau, J. X. Parreira, and M. Hauswirth. 2012. A middleware framework for scalable management of linked streams. Web Semantics: Science, Services and Agents on the World Wide Web 16 (2012), 42--51. Google ScholarDigital Library
- Fei Li, S. Sehic, and S. Dustdar. 2010. COPAL: An adaptive approach to context provisioning. In 2010 IEEE 6th International Conference on Wireless and Mobile Computing, Networking and Communications.Google Scholar
- Fei Li, M. Voegler, M. Claessens, and S. Dustdar. 2013. Efficient and scalable IoT service delivery on cloud. In 2013 IEEE 6th International Conference on Cloud Computing (CLOUD). Google ScholarDigital Library
- Jie Liu and Feng Zhao. 2005. Towards semantic services for sensor-rich information systems. In Broadband Networks, 2005. 2nd International Conference on BroadNets 2005.Google Scholar
- Martino Maggio and others. 2014. D4.1-Preliminary Report of City Application Developments and Field Trials. Technical Report. FP7 ClouT project Consortium.Google Scholar
- MongoDB Data Storage Platform. http://www.mongodb.com.Google Scholar
- Orion Context Broker. http://fiware-orion.readthedocs.io.Google Scholar
- C. Perera, A. Zaslavsky, P. Christen, and D. Georgakopoulos. 2014. Context aware computing for the internet of things: A survey. IEEE Communications Surveys Tutorials 16, 1 (2014), 414--454.Google ScholarCross Ref
- Charith Perera, Arkady Zaslavsky, Peter Christen, and Dimitrios Georgakopoulos. 2014. Sensing as a service model for smart cities supported by Internet of Things. Transactions on Emerging Telecommunications Technologies 25, 1 (2014), 81--93. Google ScholarDigital Library
- Danh L. Phuoc and Manfred Hauswirth. 2009. Linked open data in sensor data mashups. In Proc. of the 2nd Int. Workshop on Semantic Sensor Networks (SSN09) in Conjunction with ISWC 2009, Vol. 522. CEUR. Google ScholarDigital Library
- Redis Data Storage Platform. http://redis.io.Google Scholar
- Roland Reichle, Michael Wagner, Mohammad Ullah Khan, Kurt Geihs, Jorge Lorenzo, Massimo Valla, Cristina Fra, Nearchos Paspallis, and George A. Papadopoulos. 2008. A Comprehensive Context Modeling Framework for Pervasive Computing Systems. Springer Berlin.Google Scholar
- Sanjin Sehic and others. 2011. COPAL-ML: A macro language for rapid development of context-aware applications in wireless sensor networks. In Proc. of the 2nd Workshop on Software Engineering for Sensor Network Applications (SESENA). Google ScholarDigital Library
- Zach Shelby, Klaus Hartke, Carsten Bormann, and Brian Frank. 2011. Constrained Application Protocol (CoAP). Technical Report draft-ietf-core-coap-07.txt. IETF Secretariat, Fremont, CA. http://www.rfc-editor.org/internet-drafts/draft-ietf-core-coap-07.txt.Google Scholar
- John Soldatos and others. 2015. OpenIoT: Open source Internet-of-Things in the cloud. In Interoperability and Open-Source Solutions for the Internet of Things. LNCSecture Notes in Computer Science, Vol. 9001. Springer, 13--25.Google Scholar
- P. Spiess and others. 2009. SOA-based integration of the internet of things in enterprise services. In IEEE ICWS. Google ScholarDigital Library
- Amir Taherkordi, Frank Eliassen, and Geir Horn. 2017. From IoT big data to IoT big services. In Proc. of the Symposium on Applied Computing (SAC’17). Google ScholarDigital Library
- Amir Taherkordi, Romain Rouvoy, Quan Le-Trung, and Frank Eliassen. 2008. A self-adaptive context processing framework for wireless sensor networks. In Proc. of the 3rd Int. Workshop on Middleware for Sensor Networks (MidSens’08). Google ScholarDigital Library
- thethings.io IoT Cloud. http://thethings.io/.Google Scholar
- Thingsquare - Connecting the Internet of Things. http://www.thingsquare.com/.Google Scholar
- X. H. Wang, D. Q. Zhang, T. Gu, and H. K. Pung. 2004. Ontology based context modeling and reasoning using OWL. In Pervasive Computing and Communications Workshops, 2004. Proc. of the Second IEEE Conference on. Google ScholarDigital Library
- Shuai Zhao, Yang Zhang, Le Yu, Bo Cheng, Yang Ji, and Junliang Chen. 2015. A multidimensional resource model for dynamic resource matching in Internet of Things. Concurr. Comput. : Pract. Exper. 27, 8 (2015). Google ScholarDigital Library
Index Terms
- Context-Driven and Real-Time Provisioning of Data-Centric IoT Services in the Cloud
Recommendations
Data-Centric IoT Services Provisioning in Fog-Cloud Computing Systems: Poster Abstract
IoTDI '17: Proceedings of the Second International Conference on Internet-of-Things Design and ImplementationFog computing is mainly proposed for IoT applications that are geospatially distributed, large-scale, and latency sensitive. This poses new research challenges in real-time and scalable provisioning of IoT services distributed across Fog-Cloud computing ...
Power-aware provisioning of virtual machines for real-time Cloud services
Reducing power consumption has been an essential requirement for Cloud resource providers not only to decrease operating costs, but also to improve the system reliability. As Cloud computing becomes emergent for the Anything as a Service (XaaS) paradigm,...
Priority, network and energy‐aware placement of IoT‐based application services in fog‐cloud environments
Fog computing is a decentralised model which can help cloud computing for providing high quality‐of‐service (QoS) for the Internet of Things (IoT) application services. Service placement problem (SPP) is the mapping of services among fog and cloud ...
Comments