The growth of the Internet of Things has enabled a new generation of applications, pushing computation and intelligence toward the network edge. This trend, however, exposes challenges, as the heterogeneity of devices and the complex requirements of applications are often misaligned with the assumptions of traditional routing protocols, which lack the flexibility to accommodate application-layer metrics and policies. This work addresses this gap by proposing a software framework that enhances routing flexibility by dynamically incorporating application-aware decisions. The core of the work establishes a multi-hop Wi-Fi network of heterogeneous devices, specifically ESP8266, ESP32, and Raspberry Pi 3B. The routing layer follows a proactive approach, while the network is fault-tolerant, maintaining operation despite both node loss and message loss. On top of this, a middleware layer introduces three strategies for influencing routing behavior: two adapt the path a message traverses until arriving at the destination, while the third allows applications to shape the network topology. This layer offers a flexible interface for diverse applications. The framework was validated on a physical testbed through edge intelligence use cases, including distributing neural network inference computations across multiple devices and offloading the entire workload to the most capable node. Distributed inference is useful in scenarios requiring low latency, energy efficiency, privacy, and autonomy. Experimental results indicated that device heterogeneity significantly impacts network performance. Throughput and inference duration analysis showed the influence of the strategies on application behaviour, revealed that topology critically affects decentralized performance, and demonstrated the suitability of the framework for complex tasks.

翻译：物联网的发展催生了新一代应用，推动计算与智能向网络边缘迁移。然而，这一趋势也暴露了诸多挑战：设备的异构性与应用的复杂需求往往与传统路由协议的预设条件不匹配，后者缺乏灵活性以纳入应用层度量指标与策略。本研究通过提出一种软件框架来弥补这一差距，该框架通过动态整合应用感知决策来增强路由灵活性。研究核心构建了一个基于异构设备（具体包括ESP8266、ESP32和树莓派3B）的多跳Wi-Fi网络。路由层采用主动式路由方法，网络具备容错能力，即使在节点丢失或消息丢失的情况下仍能维持运行。在此基础上，中间件层引入了三种影响路由行为的策略：其中两种策略调整消息到达目的地前所经过的路径，第三种策略允许应用程序塑造网络拓扑结构。该层为多样化应用提供了灵活的接口。框架通过边缘智能用例在物理测试平台上进行了验证，包括在多个设备间分布式执行神经网络推理计算，以及将全部工作负载卸载至性能最强的节点。分布式推理在需要低延迟、高能效、隐私保护和自主运行的场景中具有重要价值。实验结果表明，设备异构性对网络性能有显著影响。吞吐量与推理时长分析揭示了策略对应用行为的影响，证实拓扑结构对去中心化性能具有关键影响，并证明了该框架适用于处理复杂任务。