The Rise of Smart Shipping and Maritime IoT
The maritime sector has undergone a significant transformation, driven by the digitization of infrastructure, communication networks, and the modernization of marine services. The concept of Smart Shipping has emerged, encompassing smart services and applications that can be provided on-demand to smart vessels, smart ports, and various stakeholders, such as trading and transportation companies, public authorities, and passengers.
These smart applications are largely powered by Machine Learning (ML) approaches, as they can leverage historical data to optimize critical parameters associated with intelligent maritime transportation systems. These parameters include vessel fuel consumption, vessel speed, ship routing, and predictive maintenance of vessel equipment and port resources.
However, the conventional centralized learning schemes are often unsuitable in the maritime domain due to the considerable data communication overhead, stringent energy constraints, increased transmission failures in the harsh propagation environment, and data privacy concerns. To overcome these challenges, the integration of Federated Learning (FL) and Over-the-Air Computation (AirComp) has emerged as a promising solution.
Federated Learning for Privacy-Preserving Collaboration
Federated Learning enables collaborative, privacy-preserving, and communication-efficient learning by allowing multiple Internet of Maritime Things (IoMT) nodes, such as individual vessels or clusters of IoMT nodes owned by a single stakeholder, to collectively build a global model without sharing their local data. Instead, the local model parameters are transmitted and aggregated at a central server, typically the shore-based base station (SBS), to construct a collaborative global model.
This approach offers several advantages over centralized learning schemes:
- Data Privacy: The data generated by the IoMT nodes remain at their origin, preserving data ownership and addressing privacy concerns.
- Communication Efficiency: The transmission of model parameters is more efficient than the transmission of raw data, reducing the communication overhead.
- Generalizability: The global model exhibits collective intelligence, combining the knowledge extracted from various local data sources, while maintaining high accuracy in ML tasks.
Integrating AirComp for Spectrum-Efficient Model Sharing
While FL addresses the limitations of centralized learning, the resource- and energy-constrained nature of edge devices, such as IoMT nodes, can still pose challenges. Over-the-Air Computation (AirComp) is a spectrum-efficient technique that can be seamlessly integrated with FL to improve computational efficiency and resource utilization.
In an AirComp system, all participating IoMT nodes transmit their pre-processed model parameters simultaneously over a shared multi-access channel. The superposition of these signals is received by the SBS, which then applies a nomographic scaling function to obtain an estimation of the desired aggregated model parameters.
The integration of AirComp and FL addresses several limitations in distributed ML-aided systems:
- Computational Efficiency: By offloading complex ML model computations to more powerful devices, the burden on resource-constrained edge devices is reduced, improving energy efficiency and the lifetime of battery-dependent network nodes.
- Scalability: The AirComp technique enables the scalability of IoT deployments with a massive number of devices, as the communication overhead is significantly reduced compared to traditional methods.
- Spectrum Efficiency: AirComp’s ability to transmit multiple signals over a common multi-access channel improves the overall spectrum utilization of the maritime communication network.
Heterogeneous Maritime Communication Networks
The Maritime Communication Network (MCN) is a key component in the proposed framework, enabling communication and collaboration among diverse IoMT entities, including Underwater IoMT (UWI), Sea-Surface IoMT (SSI), and Aerial-Relay IoMT (ARI) nodes.
The MCN can leverage 6G communication technologies to provide high-capacity links and broadband quality of service to the IoMT devices, allowing the transmission of real-time video and control. Additionally, the inclusion of Unmanned Aerial Vehicles (UAVs), Unmanned Surface Vehicles (USVs), and Unmanned Underwater Vehicles (UUVs) as active nodes in the MCN enables dynamic resource provisioning and extended performance and range of the cellular networks.
In the proposed framework, the MCN Agent, directly linked with the SBS, is responsible for managing the AirComp system. This includes optimizing the transmitting power levels of the IoMT nodes and the scaling factor of the SBS-based Data Fusion, ensuring low computation error and energy-efficient operations while respecting the total power budget.
Experimental Validation and Insights
To validate the proposed FLAirComp framework, a numerical scenario was developed based on a real dataset containing Automatic Identification System (AIS) data for six cargo ships. The goal was to predict the Cargo Ship Propulsion Power (CSPP) using Long Short-Term Memory (LSTM) neural networks.
The results demonstrate the efficiency of the FLAirComp scheme in achieving:
- High Prediction Accuracy: The FL-based LSTM model outperformed a baseline Ensemble Learning (EL) approach by a factor of 3.04 in terms of the Goodness of Fit (GoF) between the predicted and actual CSPP values.
- Communication Efficiency: The AirComp system was evaluated under varying noise conditions and number of IoMT nodes, showcasing its ability to minimize the computation error while respecting the total power budget.
The experiments also highlighted the impact of critical hyper-parameters, such as the learning rate and the LSTM depth, on the optimality and stability of the local LSTM models. Additionally, the study explored the trade-off between the past-values window and the forecast moment parameters, revealing the optimal configuration for accurate CSPP prediction.
The Path Forward: Embracing Distributed Intelligence
The maritime sector is currently experiencing a transformational shift, driven by the digitization of infrastructure, the modernization of marine services, and the increasing presence of IoMT nodes in the marine environment. To address the challenges posed by this evolving landscape, the integration of Federated Learning and Over-the-Air Computation emerges as a promising solution.
By empowering sensor networks with distributed intelligence, the proposed FLAirComp framework offers a communication-efficient, privacy-preserving, and energy-efficient approach to collaborative learning in the maritime domain. This innovative approach not only enhances the accuracy of intelligent maritime transportation systems but also promotes the participation of stakeholders in the data sharing process, overcoming the limitations of centralized learning methods.
As the maritime sector continues to embrace the digital transformation, the adoption of distributed intelligence through the seamless integration of FL and AirComp will play a crucial role in unlocking the full potential of Smart Shipping and enabling the sustainable development of the maritime industry.