Learning-Based Task Offloading for Marine Fog-Cloud Computing Networks of USV Cluster
Round 1
Reviewer 1 Report
The authors develop a learning-based task offloading framework based on the multi-armed bandit (MAB) theory, which enables USV cluster nodes to learn the potential task offloading performance of its neighboring team nodes with excessive computing resources, and minimizes the average offloading delay.
They propose an optimized algorithm named adaptive upper confidence bound (AUCB) algorithm and design corresponding simulations to verify the performance of the proposed algorithm for load-awareness and occurrence-awareness.
The authors say that in recent years, unmanned surface vehicles (USV) have made significant advances in civil, maritime and military applications. However, they do not establish that previous work exists on this research issue.
Nor do they adequately justify because the continuous improvement of autonomy, the increasing complexity of tasks and the appearance of several types of advanced sensors, impose higher requirements on the computing performance of USV clusters.
Authors should significantly improve the review of the literature and present a greater number of related works, as well as better justify the research problem.
Author Response
Response to Reviewer 1 Comments
Point 1: The authors say that in recent years, unmanned surface vehicles (USV) have made significant advances in civil, maritime and military applications. However, they do not establish that previous work exists on this research issue. Nor do they adequately justify because the continuous improvement of autonomy, the increasing complexity of tasks and the appearance of several types of advanced sensors, impose higher requirements on the computing performance of USV clusters.
Response 1:
In complex mission operations, USV team nodes must be assigned different roles, depending on limited platform size, energy, and payloads. For example, some nodes need to be installed with more payloads to complete the detection and sensing functions for the cluster, and no more computing resources can be configured. At the same time, some nodes can be configured with stronger computing resources, but fewer other payloads. By assigning different roles to cluster nodes, it is possible to make unmanned node functions more specific and fully utilized.
At the level 1 of unmanned system’s autonomy, cluster nodes do not need to have strong computing resources. All sensor data will be transmitted to the remote control center, and the operator judges the situation and sets the corresponding commands. As the task complexity increases, the cluster node autonomy must be improved, and remote control mode is far from meeting the requirements of complex tasks, and cannot take the advantages of unmanned systems.
At the same time, cluster nodes need to be equipped with more advanced sensors to achieve higher requirements of detection and sensing through more sensor data to meet the needs of complex tasks. This large amount of data cannot be handled by the remote control center because it will generate a large delay and cannot meet the requirements of cluster nodes to respond to situational changes in a short time.
In the past few years, we focused on research of collaborative autonomy and control of USVs, and we had achieved level 2 of unmanned system’s autonomy. In recent years, we have begun to research new communication technologies because we found that the existing maritime communication technologies cannot be able to support autonomy of USVs to cope with more complex tasks, especially those latency-sensitive tasks.
Point 2: Authors should significantly improve the review of the literature and present a greater number of related works, as well as better justify the research problem.
Response 2: Please see Annotation A2, A4, A5 for details of the revised article in the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The authors propose and evaluate a learning-based task offloading scheme for marine fog-cloud computing networks of USV clusters.
The paper comes timely and fills a gap. Solutions provided are technically solid.
Presentation leaves more to work on. References in the running text, like "Literature [10]" MUST BE AVOIDED. Language overall needs a refresh and proofread.
Also, related work and the list of references need improvements. Only 25 references are provided, some of them being not very new (older than 5 years).
Author Response
Response to Reviewer 2 Comments
Point 1: Presentation leaves more to work on. References in the running text, like "Literature [10]" MUST BE AVOIDED. Language overall needs a refresh and proofread.
Response 1: Please see Annotation A3 and A6 for details of the revised article in the attachment.
Point 2: Also, related work and the list of references need improvements. Only 25 references are provided, some of them being not very new (older than 5 years).
Response 2: Please see Annotation A2, A3, A4, A5 and A6 for details of the revised article in the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
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