EcoSense: Energy-Efficient Intelligent Sensing for In-Shore Ship Detection through Edge-Cloud Collaboration

Core Concepts

Proposing an edge-cloud collaborative sensing system for efficient marine object detection.

Abstract

Authors propose a difficulty-aware edge-cloud collaborative sensing system. System splits tasks into object localization and fine-grained classification. Utilizes low-power device-tailored front-end model and transformer-graph convolutional network-based back-end model. Demonstrates superior performance on marine object detection datasets. Reduces data transmission volume by 95.43% and energy consumption by 72.7%. Validates the system across various embedded system platforms and real-world scenarios involving drone deployment.

Stats

Objects are classified either at the edge or within the cloud, based on their estimated difficulty. System demonstrates superior performance (mAP@0.5 +4.3%) on widely used marine object detection datasets. Significantly reduces both data transmission volume (by 95.43%) and energy consumption (by 72.7%) at the system level.

Quotes

"Our system demonstrates superior performance (mAP@0.5 +4.3%) on widely used marine object detection datasets." "Experiments encompassing the entire system demonstrate that our system attains commendable accuracy while significantly reducing both the system’s energy consumption and the volume of transmitted data."

Key Insights Distilled From

EcoSense

by Wenjun Huang... at arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14027.pdf

Deeper Inquiries

How can this proposed edge-cloud collaboration framework be applied to other fields beyond marine object detection

The proposed edge-cloud collaboration framework for marine object detection can be applied to various other fields beyond its current scope. One potential application is in smart agriculture, where the system could assist in monitoring crop health, detecting pests or diseases, and optimizing irrigation practices. By deploying sensors on agricultural machinery or drones equipped with cameras, the system could collect real-time data on crop conditions and transmit it to the cloud for analysis. This information could then be used to make informed decisions regarding fertilization schedules, pest control measures, and water usage. Another field that could benefit from this framework is smart city infrastructure management. By installing sensors throughout a city's infrastructure - such as traffic lights, waste management systems, and public transportation networks - the system could monitor performance metrics in real time. For example, it could detect traffic congestion patterns, optimize waste collection routes based on fill levels of bins, and predict maintenance needs for public transport vehicles. Additionally, this framework could be utilized in healthcare applications like remote patient monitoring. By incorporating wearable devices with sensing capabilities that track vital signs or movement patterns of patients at home, the system can provide early alerts for any concerning changes in health status. These alerts can prompt timely interventions from healthcare providers and improve overall patient care.

What are potential drawbacks or limitations of relying heavily on an edge-cloud collaborative sensing system

While an edge-cloud collaborative sensing system offers numerous benefits such as improved efficiency and reduced data transmission volumes compared to centralized approaches, there are also potential drawbacks and limitations associated with relying heavily on such a system: Latency: Depending on network connectivity issues or delays in data processing at either the edge or cloud level, latency may occur which can impact real-time decision-making processes. Security Concerns: Transmitting sensitive data between edge devices and cloud servers raises security risks related to data breaches or unauthorized access if proper encryption protocols are not implemented robustly. Scalability Challenges: As more edge devices are added to a networked environment over time across different locations or settings (e.g., urban vs rural), managing scalability becomes complex due to varying computational capacities at each node. Cost Considerations: Implementing an extensive network of edge devices along with cloud infrastructure incurs significant costs related to hardware procurement/upkeep as well as ongoing maintenance expenses. 5 .Reliability Issues: The reliability of an edge device might fluctuate depending upon environmental factors like temperature fluctuations affecting sensor accuracy leading towards erroneous results.

How can advancements in deep learning further enhance the efficiency and accuracy of such systems

Advancements in deep learning hold immense potential for further enhancing the efficiency and accuracy of edge-cloud collaborative sensing systems: 1 .Improved Model Architectures: Developing more sophisticated neural network architectures tailored specifically for resource-constrained environments will enable better utilization of available computing power while maintaining high accuracy levels. 2 .Transfer Learning Techniques: Leveraging transfer learning methods allows models trained on large datasets to adapt quickly when deployed at the edge by fine-tuning them using smaller domain-specific datasets relevant to specific applications. 3 .Federated Learning Approaches: Implementing federated learning techniques enables model training directly on distributed edge devices without compromising user privacy by aggregating local updates into a global model hosted centrally within the cloud server 4 .Edge Intelligence Integration: Integrating intelligent algorithms directly onto edge devices enhances their autonomy by enabling localized decision-making processes without constant reliance on cloud resources. By incorporating these advancements into existing frameworks like EcoSense described above , we can expect significant improvements in both operational efficiency