AQUASENSE-AMC：一种用于水下物联网网络中高效通信的自适应调制控制模型

《Sustainable Computing: Informatics and Systems》：AQUASENSE-AMC: AN ADAPTIVE MODULATION-CONTROL MODEL FOR ENERGY-EFFICIENT COMMUNICATION IN UNDERWATER IoT NETWORKS

水下物联网（IoUT）面临能源消耗高、信道动态性强、通信模式单一等问题。本文提出AquaSense-AMC模型，通过Channel-Aware Modulation Switching（CAMS）动态调整调制参数，Energy-Constrained Control Mechanism（ECCM）优化能效管理，Hybrid Acoustic-Optical Relay（HAOR）结合声光通信，实现28%能效提升、35%包投递率提高和22%网络寿命延长。

　　
underwater communication technology faces three critical challenges: energy inefficiency, limited adaptability to dynamic environments, and unbalanced performance between long-range acoustic links and high-speed optical links. Current solutions often rely on static parameters that fail to address these interconnected issues. The AquaSense-AMC framework tackles these challenges through three integrated components that work synergistically.

Channel-Aware Modulation Switching (CAMS) dynamically adjusts communication parameters based on real-time environmental conditions. This adaptive approach ensures that modulation depth and symbol rate are optimized according to the current state of the underwater channel, which is subject to significant variations caused by water density changes, turbulence, and obstacles. By continuously monitoring channel characteristics, CAMS maintains an equilibrium between data transmission efficiency and power consumption, addressing the fundamental problem of static modulation schemes in existing systems.

The Energy-Constrained Control Mechanism (ECCM) introduces a proactive energy management strategy that predicts network demands and adjusts transmission power accordingly. This system not only prevents unnecessary energy waste from fixed power allocations but also proactively manages energy reserves to extend node lifetimes. The predictive capability enables nodes to anticipate communication needs and optimize power usage before actual energy depletion occurs, a critical feature for battery-operated underwater devices.

Hybrid Acoustic-Optical Relay (HAOR) creates a dual-mode communication infrastructure that combines the strengths of acoustic and optical links. While acoustic transmission provides reliable long-range communication (up to several kilometers), it suffers from low data rates and high latency. Optical links, though limited in range, deliver much higher data rates and lower latency. HAOR dynamically selects between these modes based on distance to the destination and current network conditions, ensuring optimal performance across varying underwater environments.

The framework's innovation lies in its unified approach to addressing energy efficiency, communication reliability, and environmental adaptability simultaneously. Previous studies focused on isolated aspects: some improved energy efficiency at the expense of communication reliability, while others enhanced data rates without considering energy constraints. AquaSense-AMC uniquely integrates these considerations through its three components working in concert.

Key technical advancements include:
1. Real-time channel monitoring that informs modulation and power adjustments
2. Energy management that predicts rather than reacts to power demands
3. Dynamic relay selection balancing coverage and data rate requirements

Experimental validation under diverse conditions demonstrated significant improvements:
- 28% energy savings compared to baseline methods through optimized power allocation and predictive energy management
- 35% higher packet delivery ratio achieved by dynamic modulation switching and hybrid relay selection
- 22% extended network lifetime through reduced energy waste and intelligent node management

The framework's success stems from its holistic design philosophy. By continuously adapting to changing underwater conditions - which can fluctuate between shallow coastal areas with high turbidity and deep-sea environments with extreme pressure variations - AquaSense-AMC maintains consistent performance across different operational scenarios. This adaptability is particularly crucial for applications like:
- Real-time pollution monitoring requiring frequent data transmission
- Offshore disaster response systems needing reliable long-range communication
- Smart aquaculture projects requiring high data rate short-range transfers

Current limitations of underwater communication systems include:
1. Energy waste from static power allocation in varying channel conditions
2. Inefficient modulation schemes that can't adapt to dynamic fading
3. Single-mode communication systems that either sacrifice range or data rate

The proposed solution addresses these through three complementary mechanisms:
CAMS ensures communication parameters are always aligned with the actual channel state, preventing unnecessary retransmissions and energy waste.
ECCM maintains energy reserves through predictive management, preventing premature node failures.
HAOR enables seamless switching between acoustic and optical modes, providing optimal coverage and data rates across different network topologies.

Practical implementation benefits include:
- Reduced maintenance costs from extended node lifetimes
- Enhanced operational reliability through adaptive communication
- Improved data collection accuracy from minimized packet loss
- Scalability for both small-scale experimental deployments and large-scale commercial applications

The framework's validation involved comprehensive simulations covering various underwater environments:
1. Shallow coastal regions with high turbidity and frequent obstacles
2. Mid-deep areas with stable conditions but limited optical transmission
3. Deep-sea environments with extreme pressure and limited acoustic clarity
4. Dynamic scenarios with rapidly changing water conditions

Comparative analysis against 12 baseline methods revealed consistent performance advantages. Notably, the system maintained energy efficiency even in scenarios with intermittent optical transmission windows, demonstrating robustness against environmental variability. The predictive energy management proved particularly effective in scenarios with unpredictable communication patterns, such as sudden marine currents disrupting channel conditions.

Future directions include integrating machine learning algorithms for more sophisticated channel prediction and energy optimization. Additionally, exploring energy harvesting techniques could further extend node lifetimes, though this would require separate optimization strategies. The framework's modular design allows for incremental improvements, such as adding new communication protocols or expanding predictive models.

This research contributes to the field of underwater IoT by providing a comprehensive solution that simultaneously addresses energy efficiency, communication reliability, and environmental adaptability. The systematic approach of combining real-time channel awareness, proactive energy management, and hybrid communication modes sets a new benchmark for underwater IoT system design. The 28% energy savings and 35% improvement in packet delivery ratio demonstrate substantial practical benefits, while the 22% network lifetime extension highlights the framework's long-term viability for marine applications.

The proposed model has important implications for various underwater IoT applications:
- Environmental monitoring: Consistent data transmission even in fluctuating conditions
- Offshore operations: Reliable long-range communication for disaster response
- Aquaculture systems: High data rate short-range transfers for real-time tracking
- Research applications: Extended network lifetime for long-term studies

The technical validation involved rigorous testing under simulated and real-world conditions. The system demonstrated superior performance in scenarios where existing methods failed:
- Dynamic channel shifts causing communication breakdowns
- Energy-depleting retransmissions in high-latency environments
- Incompatibility between long-range acoustic and short-range optical needs
- Network expansion challenges with limited energy resources

The framework's adaptability is best illustrated through its handling of conflicting requirements. For instance, in scenarios requiring both long-range communication and high data rates, HAOR automatically selects acoustic links for coverage and optical links when within range, while ECCM adjusts power allocation to prevent energy depletion during extended acoustic transmissions. CAMS ensures that even when switching between high and low data rate modes, the modulation schemes remain optimized for current channel conditions.

This research advances the state-of-the-art in underwater IoT systems by demonstrating a unified solution that previously required trade-offs between different performance metrics. The framework's success in real-world tests with 50 nodes under varying conditions validates its scalability potential. Future implementations could benefit from additional features like:
- Adaptive node positioning for optimal coverage
- Energy-efficient data compression tailored to underwater conditions
- Redundant communication pathways for enhanced reliability
- Learning-based predictive models for dynamic environments

The AquaSense-AMC model represents a significant step forward in underwater communication systems, addressing the three core challenges through integrated adaptive mechanisms. Its successful validation under diverse conditions suggests strong potential for real-world deployment across various maritime applications, from environmental monitoring to industrial automation in underwater settings. The framework's modular architecture allows for incremental enhancements and integration with emerging technologies, ensuring its relevance in the evolving field of underwater IoT.