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Fog computing middleware for distributed cooperative data analytics

Fog computing middleware for distributed cooperative data analytics

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The Internet of things (IoT) has experienced an exponential growth in the past few years with billions of devices connected to the Internet. At the same time, the storage and the computation capabilities of these devices approximately double every 18 months to support intensive data analytics, while the communication bandwidth does not grow at the same speed and is limited by the spectrum availability. In applications over distributed environments (such as sensor networks), bandwidth limitations make it infeasible to send all data to a central place (e.g., cloud) for post-processing. Furthermore, latency becomes an issue when IoT systems need to transfer data in real time. To overcome the bottlenecks of bandwidth and latency deficiencies and take full advantage of powerful computation of current sensor devices, the fog computing (also called fogging or edge computing) paradigm was introduced. This paradigm brings data processing, networking, storage and analytics closer to the devices and applications.

Chapter Contents:

  • 5.1 Introduction
  • 5.2 Distributed cooperative data analytics
  • 5.2.1 Challenges and solutions
  • 5.3 Fog computing middleware architecture
  • 5.3.1 Database
  • 5.3.2 Library manager and adaptor
  • 5.3.3 Processing unit
  • 5.3.4 Middleware visualizer
  • 5.4 Fog computing middleware formulation and modes of operation
  • 5.4.1 Cooperation mode
  • 5.4.2 Task-sharing mode
  • 5.5 Case studies in subsurface imaging
  • 5.5.1 Case 1: travel-time location
  • 5.5.2 Case 2: ambient noise tomography
  • 5.5.3 Decentralized algorithms
  • 5.6 Middleware evaluation
  • 5.6.1 Equipment
  • 5.6.2 Datasets
  • 5.6.3 Analytics processing
  • 5.6.3.1 Compute travel-time tomography in the proposed middleware
  • 5.6.3.2 Compute ambient noise tomography in the proposed middleware
  • 5.6.4 Results visualization on DCDA middleware
  • 5.6.5 Middleware versus central approaches time evaluation
  • 5.6.6 Scalability, robustness and flexibility
  • 5.6.7 Energy consumption
  • 5.6.8 Communication cost
  • 5.7 Opportunities
  • 5.8 Conclusion
  • References

Inspec keywords: data analysis; Internet of Things; middleware; distributed sensors; cloud computing; synchronisation; cooperative communication

Other keywords: fog computing middleware; IoT systems; distributed cooperative data analytics; data processing; spectrum availability; sensor devices; sensor networks; Internet of Things; latency deficiencies; communication bandwidth

Subjects: Data handling techniques; Other computer networks; Other distributed systems software; Sensing devices and transducers; Computer communications

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