4.8 Article

A Path Planning Scheme for AUV Flock-Based Internet-of-Underwater-Things Systems to Enable Transparent and Smart Ocean

Journal

IEEE INTERNET OF THINGS JOURNAL
Volume 7, Issue 10, Pages 9760-9772

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2020.2988285

Keywords

Path planning; Software; Task analysis; Internet of Things; Proposals; Laboratories; Network architecture; Autonomous underwater vehicle (AUV); AUV flock; Internet of Underwater Things (IoUT); path planning; software-defined networking (SDN); underwater wireless networks (UWNs)

Funding

  1. National Natural Science Foundation of China [61971206]
  2. Fundamental Research Funds for the Central Universities [DUT20RC(5)016, DUT19ZD206]
  3. Fund of Acoustics Science and Technology Laboratory [6142108191212]
  4. Science and Technology on Underwater Vehicle Technology [6142215180101]
  5. China Postdoctoral Science Foundation [2019M661096]

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As an emergent Internet-of-Underwater-Things (IoUT) system, the underwater wireless networks (UWNs), especially the autonomous underwater vehicle (AUV)-based UWNs are considered to be future of deep-sea exploration. Instead of underwater exploring or data collection based on an independent AUV, the multi-AUVs cooperative system or the AUV flock-based UWNs perform more efficiently and accurately in some particular underwater exploring tasks. In this article, we focus on improving the scalability or controllability of the AUV flock-based UWNs and utilize the paradigm of software-defined networking (SDN) to improve the flexibility and controllability of the AUV flock-based UWNs. With the proposed SDN-enabled architecture for the AUV flock-based UWNs, the UWNs are divided into three layers, and the data transmission, synchronization, and collection among the AUVs are implemented by the proposed software-defined beacon and control frameworks. By the centralized management feature of SDN, we define the concept of AUV flock and the united control model based on the artificial potential field theory. Then, we propose an exact path planning scheme for the AUV flock, especially when potential underwater obstacles or no-go areas are taken into account. We will show how an SDN controller can be a director/leader for the AUV flock-based UWNs to perform an exact underwater path planning mission. The simulation results show that our proposal is efficient in managing the operation of the AUV flock, especially the proposal SDN controller-guided path planning scheme performs more efficiently than the normal-distributed path planning scheme.

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