One of the main challenges for underwater applications, such as environmental monitoring and disaster management, is achieving efficient data transmission in environments where conditions change rapidly, and resources...
详细信息
One of the main challenges for underwater applications, such as environmental monitoring and disaster management, is achieving efficient data transmission in environments where conditions change rapidly, and resources need for data transport are scarce. The capability of evaluating the Value of information (VoI) enables us to assess these problems by proposing a Value of Information-based Situation-Aware Non-Linear Routing (VoI SANLR/VoI SANL) method. It aims to deal with critical event scenarios using BDI (Belief-Desire-Intention) logic criteria and prioritizing the timely uploading of data-driven information towards the destination. SANLR of VoI is developed to reduce energy consumption, end-to-end latency, jitter, and improve Packet Delivery Ratio (PDR) in underwater communication networks. VoI SANLR introduces principles of priority-based methods and intends to address challenges in terms of underwater environment such as varying channel conditions, lack energy resources, and real-time decision requirements by using SANLR. Energy optimization analysis reveals consistent outperformance, achieving a remarkable 95% reduction in energy consumption compared to other techniques. Low latency is maintained, ranging from 2.5 to 0.5 seconds, showcasing enhanced efficiency and scalability. VoI SANLR demonstrates exceptional performance in both throughput and jitter. It achieves the highest data transfer rates, ranging from 100 kbps to 110 kbps, indicating outstanding efficiency. Additionally, the jitter remains consistently low, between 1.8 ms and 2 ms, ensuring minimal delay variability and improved communication stability. PDR consistently surpasses other techniques, reaching a maximum of 99%. Additionally, network lifetime analysis demonstrates VoI SANLR's superiority, exhibiting the highest network lifetime at each node and a significant 31.25% improvement at Node 100 compared to other methods.
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