Wireless Communications and Mobile Computing

Big IoT Data Analytics in Fog Computing


Status
Published

Lead Editor

1University of Auckland, Auckland, New Zealand

2University of Huddersfield, West Yorkshire, UK

3University of Technology Sydney, Ultimo, Australia

4University of Canterbury, Christchurch, New Zealand


Big IoT Data Analytics in Fog Computing

Description

The number of devices within Internet of Things (IoT) that are connected and available via Internet will be between 50 and 100 billion by 2020. The IoT devices are typically the sensors embedded in environments, buildings, vehicles, manufacturing processes, and products or attached to the people. The amount of the data generated by IoT devices grows exponentially as these devices operate nonstop, 24/7, creating an avalanche of data that is out of the control of existing and foreseeable data processing and analytics techniques. On the other hand, we can create numerous opportunities to extract unprecedented insightful information. Unlocking the value of big data through analytics and mining has been regarded as the key enabler of many innovation and marketing strategies which, in turn, has pushed more efforts and supports to the IoT and big data related R&D. While data processing is typically envisaged to be conducted in clouds, it alone is suffering from growing limitations in meeting demands of numerous applications where the local computation nearby data sources is required for low-latency response, contextual information integration, or networking load reduction. Meanwhile, moving all the data generated from IoT devices into cloud server farms for further processing or storage poses overwhelming challenges on the Internet infrastructure and is often prohibitively expensive, technically impractical, and mostly unnecessary.

Fog computing is an emerging paradigm based on creation of micro clouds (called fog nodes) near the sources of data. It is a promising approach to processing data before they even attempt to reach cloud, shortening the communication times and cost, as well as reducing the need for huge data storage. It seamlessly bridges IoT devices and the remote cloud data centers by pushing cloud computing, storage, and networking services down closer to end IoT devices. Fog computing has seen a rapidly increasing number of applications in many industries such as manufacturing, e-health, oil and gas, smart cities, smart homes, and smart grids. However, it is still in its early stages and presents a set of new challenges with the increasing adoption of this computing paradigm, such as fog architecture, frameworks and standards, computing, storage and networking resource provisioning and scheduling, programming abstracts and models, and security and privacy issues. In particular, big IoT data analytics with fog computing infrastructure is in its nascent stage but of paramount importance and requires extensive research in order to conduct more efficient knowledge discovery and smart decision support.

Many relevant theoretical and technical issues have not been answered well yet, for example, how to abstract programming interfaces of fog infrastructure and platforms for data analytics, how to design scalable data mining algorithms with the use of fog infrastructure, how to achieve secure and privacy-preserving data analytics in fog computing. As such, it is high time that the related issues in big IoT data analytics with fog infrastructure are investigate by examining fog architecture, platforms, and applications in detail, hence the call for this special issue.

Potential topics include but are not limited to the following:

  • Fog architectures, frameworks, standards, and platforms for IoT data analytics
  • Fog programming abstracts, models, and toolkits for data analytics
  • Wireless communication supports for fog computing
  • Mobile computing with the support of fog computing
  • Load balancing and resource scheduling and management in fog computing
  • Middleware for distributed data management in fog computing
  • Data mining and machine learning algorithm design in fog computing
  • Theory and modelling of distributed intelligence in fog computing for IoT data analytics
  • Multisource and heterogeneous IoT data analytics with fog
  • Time-critical and low-latency data analytics with fog
  • Spatial and temporal data processing and analytics in fog computing
  • Fog data analytics applications, for example, smart cities, e-health, and smart homes
  • Information retrieval design and knowledge assistance for fog computing data analytics
  • Context-aware IoT applications in the fog
  • Disaster and emergency management in IoT with fog
  • Recovery schemes in case of fog down
  • Pricing models for IoT data analytics in fog computing
  • Privacy and security issues related to fog data analytics

Articles

  • Special Issue
  • - Volume 2018
  • - Article ID 9596141
  • - Research Article

Energy-Efficient User Association with Congestion Avoidance and Migration Constraint in Green WLANs

Wenjia Wu | Junzhou Luo | ... | Zhen Ling
  • Special Issue
  • - Volume 2018
  • - Article ID 8196906
  • - Research Article

Multiactivation Pooling Method in Convolutional Neural Networks for Image Recognition

Qi Zhao | Shuchang Lyu | ... | Wenquan Feng
  • Special Issue
  • - Volume 2018
  • - Article ID 3794175
  • - Research Article

Processing Optimization of Typed Resources with Synchronized Storage and Computation Adaptation in Fog Computing

Zhengyang Song | Yucong Duan | ... | Donghai Zhu
  • Special Issue
  • - Volume 2018
  • - Article ID 8475604
  • - Research Article

A Service-Based Method for Multiple Sensor Streams Aggregation in Fog Computing

Zhongmei Zhang | Chen Liu | ... | Yanbo Han
  • Special Issue
  • - Volume 2018
  • - Article ID 2940952
  • - Research Article

RePage: A Novel Over-Air Reprogramming Approach Based on Paging Mechanism Applied in Fog Computing

Jiefan Qiu | Sai Li | Bin Cao
  • Special Issue
  • - Volume 2018
  • - Article ID 3681270
  • - Research Article

A Novel UDT-Based Transfer Speed-Up Protocol for Fog Computing

Zhijie Han | Weibei Fan | ... | Miaoxin Xu
  • Special Issue
  • - Volume 2018
  • - Article ID 5176569
  • - Research Article

Boundary Region Detection for Continuous Objects in Wireless Sensor Networks

Yaqiang Zhang | Zhenhua Wang | ... | Zhangbing Zhou
  • Special Issue
  • - Volume 2018
  • - Article ID 7157192
  • - Review Article

Fog Computing: An Overview of Big IoT Data Analytics

Muhammad Rizwan Anawar | Shangguang Wang | ... | Salman Raza
  • Special Issue
  • - Volume 2018
  • - Article ID 5018053
  • - Research Article

Predicting Short-Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment

Chuanbin Li | Xiaosen Zheng | ... | Li Kuang
  • Special Issue
  • - Volume 2018
  • - Article ID 7823979
  • - Research Article

NTRU Implementation of Efficient Privacy-Preserving Location-Based Querying in VANET

Bo Mi | Darong Huang | Shaohua Wan
Wireless Communications and Mobile Computing
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Acceptance rate33%
Submission to final decision81 days
Acceptance to publication37 days
CiteScore2.900
Impact Factor1.819
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