Wireless Communications and Mobile Computing

Privacy Protection and Incentive for AI-Driven IoT


Publishing date
01 Nov 2021
Status
Closed
Submission deadline
25 Jun 2021

Lead Editor

1James Madison University, Harrisonburg, USA

2Yantai University, Yantai, China

3Shaaxi Normal University, Xi'an, China

4Kennesaw State University, Kennesaw, USA

This issue is now closed for submissions.
More articles will be published in the near future.

Privacy Protection and Incentive for AI-Driven IoT

This issue is now closed for submissions.
More articles will be published in the near future.

Description

With the development of the Internet of Things (IoT) and the development of wearable sensors, a participatory sensor network is formulated through daily mobile devices, in which a large number of sensors act as participants to perform sensing tasks, such as environmental monitoring, behaviour monitoring, traffic monitoring, and other tasks. In IoT, while vast amounts of data are perceived, collected, analyzed, and then uploaded by participants, the privacy concerns on collecting data have received wide attention and become a research hotspot.

The rapid development of the fifth-generation (5G) cellular technology has enabled a new way to collect data and raises new privacy issues to IoT. Most participatory sensing privacy protection methods only protect participants' private information locally (i.e., on a daily mobile device) by some means, but do not take into account the protection of communication (i.e., the process of network transmission). Attackers may also monitor and steal private information from participants on unsecured channels, increasing the risk of revealing participants' private data. Therefore, how to combine traditional cryptography methods with participatory sensing to realize participants' privacy protection in a diversified service environment has become a critical problem in IoT. Another common key challenge for participatory sensor networks is how to inspire mobile devices to collect data. Unfortunately, it is still an open problem to consider privacy protection and incentive problem for data in IoT simultaneously. More importantly, Artificial intelligence (AI), machine learning, big data analytics, etc., have paved the path for a new era of competition where data collected from IoT is considered as a living and evolving asset that can unlock enormous new opportunities.

This Special Issue aims to provide a platform for researchers to present novel and effective privacy protection and incentive for AI-driven IoT. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

  • Privacy protection for cloud computing in IoT
  • Privacy protection for edge computing in IoT
  • Secure communications on data collection for IoT
  • Privacy protection for IoT under resource constraints
  • Task allocation with privacy protection for IoT
  • Quality control with privacy protection for IoT
  • Privacy protection and machine learning algorithms for IoT
  • Privacy and security of sensed data for IoT
  • Privacy-aware incentive mechanism
  • Trust-oriented design for IoT
  • Blockchain and privacy protection for distributed network
  • Blockchain and trust computing for IoT
  • Privacy solutions of IoT with edge and blockchain
  • Security design and enhancement in edge computing and IoT
  • Optimization of the utility-privacy tradeoffs for IoT
  • AI for Quality-of-Service (QoS) Management in IoT

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 9945530
  • - Research Article

Temporal Index Scheme of Hyperledger Fabric System in IoT

Yongqiang Lu | Zhaobin Liu | ... | Xuhui Chen
  • Special Issue
  • - Volume 2021
  • - Article ID 6948293
  • - Research Article

A Face Occlusion Removal and Privacy Protection Method for IoT Devices Based on Generative Adversarial Networks

Wenqiu Zhu | Xiaoyi Wang | ... | Guang Zou
  • Special Issue
  • - Volume 2021
  • - Article ID 6685762
  • - Research Article

A Blockchain-Based Medical Data Sharing Mechanism with Attribute-Based Access Control and Privacy Protection

Yingwen Chen | Linghang Meng | ... | Guangtao Xue
  • Special Issue
  • - Volume 2021
  • - Article ID 5557543
  • - Research Article

AI-Driven Multiobjective Scheduling Algorithm of Flood Control Materials Based on Pareto Artificial Bee Colony

Banteng Liu | Junjie Lu | ... | Zegao Yin
  • Special Issue
  • - Volume 2021
  • - Article ID 5590895
  • - Research Article

Multiscale Anchor-Free Region Proposal Network for Pedestrian Detection

Zhiwei Cao | Huihua Yang | ... | Xipeng Pan
  • Special Issue
  • - Volume 2021
  • - Article ID 6682580
  • - Research Article

Traceable Multiauthority Attribute-Based Encryption with Outsourced Decryption and Hidden Policy for CIoT

Suhui Liu | Jiguo Yu | ... | Mengmeng Li
  • Special Issue
  • - Volume 2021
  • - Article ID 6621659
  • - Research Article

Participant Recruitment Method Aiming at Service Quality in Mobile Crowd Sensing

Weijin Jiang | Junpeng Chen | ... | Sijian Lv
  • Special Issue
  • - Volume 2021
  • - Article ID 6619715
  • - Research Article

Anti-Attack Scheme for Edge Devices Based on Deep Reinforcement Learning

Rui Zhang | Hui Xia | ... | Xiang-guo Cheng
  • Special Issue
  • - Volume 2021
  • - Article ID 6679453
  • - Research Article

D-(DP)2SGD: Decentralized Parallel SGD with Differential Privacy in Dynamic Networks

Yuan Yuan | Zongrui Zou | ... | Dongxiao Yu
  • Special Issue
  • - Volume 2021
  • - Article ID 6660709
  • - Research Article

A High-Quality Authenticatable Visual Secret Sharing Scheme Using SGX

Denghui Zhang | Zhaoquan Gu
Wireless Communications and Mobile Computing
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