Internet of Things and Data Science: Osmotic Computing Approach

If you want to understand more about how Osmotic Computing approach can be applied to solving resource and data management problems in IoT and Data Science, then please watch the following video presentation [Osmotic Computing]. This talk was presented at Indian Institute of Technology, Mumbai on October 29, 2019.  Please download and use [VLC player] for correctly playing the video.

In the last decade, we have been transitioning from a data-poor to a data-rich world with the promise of unparalleled intelligence. Such transition will definitely require significant investments in every aspect in our societies including social, political, economic and cultural. Much of the (unprecedented) increase in data generation can be attributed to the abundance of mobile devices and wearables, the increase of instrumentation in every industry vertical, the mass adoption of social networks and the digitization of every aspect of our lives. Generically, the bulk of such data collection falls under the Internet of Things (IoT). IoT data comes from a variety of sources that can be classified into (a) machine-based (e.g., environmental, weather, air quality, water quality, flows, traffic speeds, people flows and GPS location) or (b) people-based (e.g., social media, crowdsourced data collection, and simple text messaging) providing data and situational observations associated with events.

The increase in data collection, along with advances in infrastructure development and intelligence, has led to an opportunity for developing several new usage scenarios, ranging from smart cities, smart transportation, smart health care, to Industry 4.0 as depicted in Figure 1. However, the potential of these different paradigms/technologies requires coordination across several layers, leading to important research challenges to be addressed.

The emergence of computing paradigms such as Edge, Fog, and Osmotic Computing for supporting the analysis of data near the data sources are especially applicable for IoT use cases where insights need to be action on in the least amount of time possible. Figure 2 depicts a typical IoT application infrastructure consisting of the Things, the Edge, and the Cloud layers. The layers are connected to each other in a plethora of ways. But the most interesting one is connecting the Things to the Edge of directly to the cloud. Examples of networking protocols include (but not limited to) WiFi, Cellular (e.g., 4G & 5G),  Bluetooth, Bluetooth Low Energy, LoRa-WAN [Lora], and Narrowband IoT (NB-IoT). On the other hand, the Edge layer consists of network gateways/middleboxes, Content Delivery Networks (CDNs), or micro datacenters, which provide limited computing and storage resources. The edge resources usually communicate with Cloud layer via wide Area Networks (WANs). The last layer is the Cloud, which is provided by different cloud providers such as Amazon, Microsoft, Tencent, Google, and Alibaba. Cloud datacenters offer unlimited computational resources and their cloud services are usually offered in a pay-as-you-go fashion.

Currently, existing IoT applications processing data run on remote Cloud infrastructure. To support new application scenarios, novel software/application abstractions are needed that can utilize distributed and dynamic infrastructure supported at Edge and Things layers (as shown in Figure 2). Moreover, IoT data is typified by the heterogeneity of data formats and types, which usually results in bespoke platforms and code that make subsequent integration and processing problematic and time-consuming. The provenance of data is another key aspect that IoT needs to address, not just to ensure the physical integrity of bytes produced, but to be able to trace decision making from model outputs to individual sensors or sensor platforms. This is significant to enable “trust” to be established in the analysis that is carried out on such data. IoT systems currently deployed are largely passive observers of the environment that transmit data to a remote location (with a varying and limited degree of on-board processing). Retasking this one-way behavior in a reliable fashion (e.g. changing sampling rates triggered by external stimuli) is a prerequisite for developing and deploying future IoT applications.

Below articles discuss research challenges related to devising a new IoT programming paradigm (such as Osmotic Computing and Osmotic-Flow) for orchestrating IoT applications’ composition and data processing across heterogeneous computing infrastructure (Cloud, Edge, and Things).

**Copyright on my articles is held by respective (IE) publishers. They are posted here for educational purpose only. If you want to use them for commercial purpose, please consult copyright owners!

  1. R. Ranjan, O. Rana, S. Nepal, M. Yousif, P. James, Z. Wen, S.  Barr, P. Watson, P. P. Jayaraman, D. Georgakopoulos, M.Villari, M. Fazio, S. Garg, R. Buyya, L. Wang, A. Y. Zomaya, and S. Dustdar, “The Next Grand Challenges: Integrating the Internet of Things and Data Science,” Volume 5,  Issue 3, Pages 12-26, May./Jun. 2018, doi: 10.1109/MCC.2018.032591612,  (Reviewed by Editorial Board) [ISI impact factor: 2.92]
  2. R.K. Naha, S. Garg, D. Georgekopolous, P. P. Jayaraman, L. Gao, Y. Xiang, and R. Ranjan, “Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions”, IEEE Access. (Accepted Aug 2018, to appear) [ISI Impact Factor 3.5]
  3. T. Rausch, S. Dustdar, and R. Ranjan,”Osmotic Message-Oriented Middleware for the Internet of Things,” IEEE Cloud Computing, IEEE Computer Society. (To Appear May 2018, Reviewed by Editorial Board) [ISI impact factor: 2.92]
  4. A. Morshed, P. P. Jayaraman, T. Sellis, D. Georgakopoulos, M. Villari, and R. Ranjan, Deep OSMOSIS: Holistic Distributed Deep Learning in Osmotic Computing,” IEEE Cloud Computing, IEEE Computer Society. (To Appear December 2017, Reviewed by Editorial Board) [ISI impact factor: 2.92]
  5. M. VillariM. Fazio, S. Dustdar, O. Rana, L. Chenand R. Ranjan, Software- Defined Membrane: Policy-Driven Edge and IoT Security,” IEEE Cloud Computing, IEEE Computer Society. (To Appear July 2017, Reviewed by Editorial Board) [ISI impact factor: 2.92]
  6. Matteo Nardelli, Stefan Nastic, Schahram Dustdar, Massimo Villari, R. Ranjan, “Osmotic Flow: Osmotic Computing + IoT Workflow,” IEEE Cloud Computing, IEEE Computer Society. (To Appear April 2017, Reviewed by Editorial Board) [ISI impact factor: 2.92]
  7. G. Kecskemeti, G. Casale, D. N. Jha, J. Lyon, R. Ranjan, “Modelling and Simulation Challenges in the Internet of Things,” IEEE Cloud Computing, IEEE Computer Society. (To Appear January 2017, Reviewed by Editorial Board) [ISI impact factor: 2.92]
  8. M. Villari, M. Fazia, S. Dustdar, O. Rana, and R. Ranjan, “Osmotic Computing: A New Paradigm for Edge/Cloud Integration,” IEEE Cloud Computing, IEEE Computer Society. (To Appear December 2016, Reviewed by Editorial Board) [ISI impact factor: 2.92]
  9. D. Georgakopoulos, P. P. Jayaraman, M. Fazia, M. Villari, and R. Ranjan, “Internet of Things and Edge Cloud Computing Roadmap for Manufacturing,” IEEE Cloud Computing, Volume 3, Issue 5, 2016, IEEE Computer Society. [ISI impact factor: 2.92]
  10. D. Puthal, S. Nepal, R. Ranjan, and J. Chen, “Threats to Networking Cloud and Edge Data Center in the IoT,”  IEEE Cloud Computing, Volume 3, Issue 4, 2016, IEEE Computer Society. [ISI impact factor: 2.92]
  11. M. Vogler, J. M. Schleicher, C. Inzinger, S. Dustdar, and R. Ranjan, “Migrating Smart City Applications to the Cloud,” IEEE Cloud Computing, Volume 3, Issue 2, 2016, IEEE Computer Society. [ISI impact factor: 2.92]
  12. G. Singh, N.Kumar, A. Zomaya, and R. Ranjan, “Optimal Decision Making for Big Data Processing at Edge-Cloud Environment: An SDN Perspective,” IEEE Transactions on Industrial Informatics, IEEE Industrial Electronics Society. (Accepted July 2017)  [ISI Impact Factor 6.7]
  13. T. Shah, A. Yavari, K. Mitra, S. Saguna, P. P. Jayaraman, F. Rabhi, and R. Ranjan,  “Remote Healthcare Cyber-Physical-System: Quality of Service Challenges and Opportunities,” IET Cyber-Physical Systems: Theory & Applications, IET Press. (accepted November 2016, in press)
  14. P. Jayaraman, C. Perera, D. Georgakopoulos, S. Dustdar, D. Thakker, and R. Ranjan, “Analytics-as-a-Service in a Multi-Cloud Environment through Semantically enabled Hierarchical Data Processing,” Journal of Software Practice and Experience (SPE), Wiley. [ERA A, ISI Impact Factor: 0.89] (Accepted July 2016)
  15. A. Souza, N. Cacho, A. Noor, P. P. Jayaraman,  A.  Romanovsky,  and R. Ranjan, “Osmotic Monitoring of Microservices between the Edge and Cloud,” The 20th IEEE International Conference on High Performance Computing and Communications (HPCC 2018), Exeter, United Kingdom. [ERA B Ranked]
  16. A. Khoshkbarforoushha, R. Ranjan, Q. Wang, and C. Friedrich, “Flower: A Data Analytics Flow Elasticity Manager,” 43rd International Conference on Very Large Data Bases, Springer. [CORE A+/ERA A* Ranked]