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AGRA: AI-augmented geographic routing approach for IoT-based incident-supporting applications

机译：AGRA：针对基于IoT的事件支持应用程序的AI增强型地理路由方法

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摘要

Applications that cater to the needs of disaster incident response generate large amount of data and demand large computational resource access. Such datasets are usually collected in real-time at the incident scenes using different Internet of Things (IoT) devices. Hierarchical clouds,i.e., core and edge clouds, can help these applications’ real-time data orchestration challenges as well as with their IoT operations scalability, reliability and stability by overcoming infrastructure limitations at the ad-hoc wireless network edge. Routing is a crucial infrastructure management orchestration mechanism for such systems. Current geographic routing or greedy forwarding approaches designed for early wireless ad-hoc networks lack efficient solutions for disaster incident-supporting applications, given the high-speed and low-latency data delivery that edge cloud gateways impose. In this paper, we present a novel Artificial Intelligent (AI)-augmented geographic routing approach, that relies on an area knowledge obtained from the satellite imagery (available at the edge cloud) by applying deep learning. In particular, we propose a stateless greedy forwarding that uses such an environment learning to proactively avoid the local minimum problem by diverting traffic with an algorithm that emulates electrostatic repulsive forces. In our theoretical analysis, we show that our Greedy Forwarding achieves in the worst case a3.291path stretch approximation bound with respect to the shortest path, without assuming presence of symmetrical links or unit disk graphs. We evaluate our approach with both numerical and event-driven simulations, and we establish the practicality of our approach in a real incident-supporting hierarchical cloud deployment to demonstrate improvement of application level throughput due to a reduced path stretch under severe node failures and high mobility challenges of disaster response scenarios.
机译：满足灾难事件响应需求的应用程序会生成大量数据，并需要大量的计算资源访问。通常使用不同的物联网（IoT）设备在事件现场实时收集此类数据集。分层云（即核心云和边缘云）可以克服临时无线网络边缘的基础架构限制，从而帮助这些应用程序应对实时数据编排挑战，以及物联网运营的可扩展性，可靠性和稳定性。路由是此类系统的关键基础结构管理流程机制。考虑到边缘云网关所提供的高速和低延迟数据传输，为早期无线自组织网络设计的当前地理路由或贪婪转发方法缺乏支持灾难事件的有效解决方案。在本文中，我们提出了一种新颖的人工智能（AI）增强地理路由方法，该方法依赖于通过应用深度学习从卫星图像（可在边缘云获得）获得的区域知识。特别地，我们提出了一种无状态贪婪转发，该贪婪转发使用这种环境学习来通过使用模拟静电排斥力的算法转移流量来主动避免局部最小问题。在理论分析中，我们表明，在最坏情况下，相对于最短路径，我们的贪婪转发达到了3.291路径拉伸近似界限，而没有假设存在对称链接或单位圆盘图。我们通过数值模拟和事件驱动模拟来评估我们的方法，并在实际的事件支持分层云部署中确立了我们方法的实用性，以证明由于严重节点故障和高移动性下的路径扩展减少而提高了应用程序级吞吐量灾难应对方案的挑战。

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