Wireless Communications and Mobile Computing

Crowdsourcing for Mobile Networks and IoT


Status
Published

1Chinese Academy of Sciences, Shenzhen, China

2Dalian University of Technology, Dalian, China

3University of Waterloo, Waterloo, Canada

4Uppsala University, Uppsala, Sweden

5Tencent Company, Shenzhen, China

6Cornell University, New York, USA


Crowdsourcing for Mobile Networks and IoT

Description

As the deep integration of ubiquitous sensors, intelligent devices, and social networks, mobile networks and IoT are formed by the opportunity of virtual mobile communication networks and social communities between mobile carriers. People involved in a mobile network can easily interact and share information with each other anytime and anywhere though the popular use of intelligent devices. As a result, there is a remarkable trend to enable crowdsourcing for mobile networks and IoT to address various problems that involve real-time collection, processing, and collaborations among participants in highly mobile environments. Thus, crowdsourcing could be an efficient strategy to improve quality and user experiences of applications in mobile networks and IoT, which not only potentially brings enormous benefits for economics but also leads to revolution for our daily life. Particularly, mobile crowdsensing takes advantage of the mobile terminal’s mobility and provides context-aware services in large scale areas.

The embedded sensors including accelerometer, compass, gyroscope, GPS, microphone, and camera in mobile phones are leveraged to gather the required information to support location-based services, for example, environmental measurements, personal activity sharing, and online recommendation. Currently, a number of crowdsourcing based mobile applications have been applied in mobile networks and IoT, targeting at real time services and recommendation, for example, Uber, Elance, Amazon, and Airbnb. However, mobile crowdsourcing may face some limitations caused by the mobile devices, such as computation, memory, and energy constraints. Besides, most of them focus on the application functionalities, ignoring the users’ willingness and operationality. Therefore, feasible and efficient crowdsourcing schemes are desirable for the applications of mobile networks and IoT, such as suitable incentive scheme, appropriate task assignment, and user-friendly mobile applications. The objective of this special issue is to collect articles on the state of the art and practices of crowdsourcing for mobile networks and IoT. In particular, we are soliciting theoretical and applied research in crowdsourcing solutions for mobile networks and IoT including algorithms, modeling, technologies, and applications.

Potential topics include but are not limited to the following:

  • Architecture, strategies, and/or algorithms for IoT based crowdsensing
  • Protocols, scheduling, and/or designs for crowdsensing mobile networking
  • Privacy and security for crowdsourcing schemes in mobile networks and IoT
  • Data source reliability estimation and assurance for crowdsourcing in IoT
  • Incentive schemes for motivating users to participate in crowdsourcing applications
  • Crowdsourced image/video processing and retrieval in mobile networks and IoT
  • Crowdsourcing in secure IoT
  • Plausible and user-friendly software design and implementation for crowdsourcing application
  • Standards, policy, and regulation for V2X communication systems using crowdsourcing schemes
  • Trust establishment and measurement for crowdsensing based IoT
  • Task assignment and resource management in crowdsensing based IoT
  • Novel crowdsensing applications of Internet robots and intelligent devices in IoT

Articles

  • Special Issue
  • - Volume 2018
  • - Article ID 6231236
  • - Editorial

Crowdsourcing for Mobile Networks and IoT

Xiping Hu | Zhaolong Ning | ... | Fei Wang
  • Special Issue
  • - Volume 2018
  • - Article ID 8235127
  • - Research Article

Intelligent Smoke Alarm System with Wireless Sensor Network Using ZigBee

Qin Wu | Jiashuo Cao | ... | Guanghui Pan
  • Special Issue
  • - Volume 2018
  • - Article ID 9729014
  • - Research Article

A Research on Fast Face Feature Points Detection on Smart Mobile Devices

Xiaohe Li | Xingming Zhang | Haoxiang Wang
  • Special Issue
  • - Volume 2018
  • - Article ID 9821946
  • - Research Article

Congestion Control and Traffic Scheduling for Collaborative Crowdsourcing in SDN Enabled Mobile Wireless Networks

Dawei Shen | Wei Yan | ... | Qingxu Deng
  • Special Issue
  • - Volume 2018
  • - Article ID 6931765
  • - Research Article

Channel Assignment Mechanism for Multiple APs Cochannel Deployment in High Density WLANs

Jianjun Lei | Jianhua Jiang | Fengjun Shang
  • Special Issue
  • - Volume 2018
  • - Article ID 3027269
  • - Research Article

IoT-B&B: Edge-Based NFV for IoT Devices with CPE Crowdsourcing

He Zhu | Changcheng Huang
  • Special Issue
  • - Volume 2018
  • - Article ID 3193974
  • - Research Article

An SAT-Based Method to Multithreaded Program Verification for Mobile Crowdsourcing Networks

Long Zhang | Wanxia Qu | ... | Sikun Li
  • Special Issue
  • - Volume 2018
  • - Article ID 6163475
  • - Research Article

HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data

Linlin Guo | Lei Wang | ... | Bingxian Lu
  • Special Issue
  • - Volume 2018
  • - Article ID 8052620
  • - Research Article

A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks

Fulong Ma | Xiao Liu | ... | Tian Wang
  • Special Issue
  • - Volume 2018
  • - Article ID 1260860
  • - Research Article

Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks

Laisen Nie | Xiaojie Wang | ... | Dingde Jiang
Wireless Communications and Mobile Computing
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 Journal metrics
Acceptance rate33%
Submission to final decision81 days
Acceptance to publication37 days
CiteScore2.900
Impact Factor1.819
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