Emerging Approaches to Mobile Cooperative Sensing and Its Applications in Smart Environments

Submission Deadline: CLOSED

IEEE Access invites manuscript submissions in the area of Emerging Approaches to Mobile Cooperative Sensing and Its Applications in Smart Environments.

Mobile cooperative sensing is becoming a popular paradigm to collect information and outsource tasks to mobile users. Detected events are then reported continuously through the integration and cooperation of sensors, actuators, controllers, and artificial intelligence. Mobile cooperative sensing refers to estimating the event information and simulating the situation of smart environments through the analysis of data from sensor networks deployed in the environment. The systems are able to actively obtain information of occupancy, such as identification, activity, and even gestures with mobile cooperative sensing. For instance, intrusion detection and human identification help to detect intruders automatically. In smart environments, light and temperature can be adjusted according to the occupancy, which conserves the energy. For children and the elderly, activities such as falling can be monitored to prevent potential hazards. Mobile cooperative sensing is becoming a vital part of the fvarious emerging applications in smart environments, such as energy consumption estimation, security surveillance, and human behavior analysis. Generally speaking, mobile cooperative sensing provides an active detected input for smart systems, so  it can make judgment or feedback accordingly.

At present, with the increasing mobile cooperative sensing technology in the field of Mobile Internet of Things (M-IoT) and the continuous improvement and upgrading of the current internet infrastructure, application and business model innovations are constantly emerging. Mobile cooperative sensing is further penetrating traditional fields, such as finance, transportation, medical treatment, education, etc… Mobile cooperative sensing technology can already be found in the fields of intelligent transportation, internet finance, and intelligent medical treatment, for example. In the future, some preliminary application results can be expected to be used in other fields, especially in the application areas of smart environments (e.g., smart homes and cities), which will have a far-reaching impact. Mobile cooperative sensing technology is not just about the detection via sensor networks; it is also about enabling a wide range of new capabilities, architectures, and service paradigms. The development of mobile cooperative sensing is set to have major economic, social, and environmental impacts, the intersection of which forms future sustainable growth.

As a result, the development of mobile cooperative sensing in smart environments is fundamentally important; however, many problems still remain.. For example, how to construct and improve the framework and platform for mobile cooperative sensing; how to collect, measure, process, analysis, and optimize mobile cooperative sensing data; and how to improve the effectiveness and decrease the energy in mobile cooperative sensing. These are all vital but unavoidable problems for mobile cooperative sensing in smart environments.

This Special Section is intended to provide a specific opportunity to argue the state-of-the-art technologies around mobile cooperative sensing in smart environments and invite researchers in the relevant fields to share the latest progress, novel methodologies, and potential research topics.

The topics of interest include, but are not limited to:

  • Construction and improvement of framework and platform for mobile cooperative sensing
  • Collection, measurement, processing, analysis, and optimization of mobile cooperative sensing data
  • Improvement of effectiveness and decrease of energy in mobile cooperative sensing
  • Evaluation of performance relevant to mobile cooperative sensing systems
  • Adaptability of mobile cooperative sensing systems in complex and variable environments
  • Emerging technologies in smart environments
  • Artificial intelligence in smart environments
  • Smart environment-based machine learning for mobile cooperative sensing
  • Security and privacy of mobile cooperative sensing in smart environments
  • Quality of Service (QoS) and Mobile Internet of Things (M-IoT) services in mobile cooperative sensing systems

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Associate Editor: Mu Zhou, Chongqing University of Posts and Telecommunications, China

Guest Editors:

    1. Hongying Meng, Brunel University London, UK
    2. Haiying Wang, Ulster University, UK
    3. Lei Chen, Georgia Southern University, USA
    4. Kunjie Xu, Intel Corporation, USA


Relevant IEEE Access Special Sections:

  1. Innovation and Application of Internet of Things and Emerging Technologies in Smart Sensing
  2. Intelligent Information Services
  3. Advances in Machine Learning and Cognitive Computing for Industry Applications


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:

For inquiries regarding this Special Section, please contact: zhoumu@cqupt.edu.cn.