This paper presents G-WHARP, for Green Wake-up and HARvesting-based energy-Predictive forwarding, a wake-up radio-based forwarding strategy for wireless networks equipped with energy harvesting capabilities (green wireless networks). Following a learning-based approach, G-WHARP blends energy harvesting and wake-up radio technology to maximize energy efficiency and obtain superior network performance. Nodes autonomously decide on their forwarding availability based on a Markov Decision Process (MDP) that takes into account a variety of energy-related aspects, including the currently available energy and that harvestable in the foreseeable future. Solution of the MDP is provided by a computationally light heuristic based on a simple threshold policy, thus obtaining further computational energy savings. The performance of G-WHARP is evaluated via GreenCastalia simulations, where we accurately model wake-up radios, harvestable energy, and the computational power needed to solve the MDP. Key network and system parameters are varied, including the source of harvestable energy, the network density, wake-up radio data rate and data traffic. We also compare the performance of G-WHARP to that of two state-of-the-art data forwarding strategies, namely GreenRoutes and CTP-WUR. Results show that G-WHARP limits energy expenditures while achieving low end-to-end latency and high packet delivery ratio. Particularly, it consumes up to 34% and 59% less energy than CTP-WUR and GreenRoutes, respectively.

本文介绍了G-WHARP，用于基于绿色唤醒和HARvesting的能量预测转发，这是一种针对具有能量收集功能的无线网络（绿色无线网络）的基于无线电的唤醒转发策略。遵循基于学习的方法，G-WHARP 融合了能量收集和唤醒无线电技术，以最大程度地提高能源效率并获得出色的网络性能。节点根据马尔可夫决策过程（MDP）自主决定其转发可用性，该过程考虑了各种与能源有关的方面，包括当前可用的能源以及在可预见的将来可收获的能源。MDP的解决方案是基于简单阈值策略的轻计算启发式方法提供的，从而进一步节省了计算能耗。G-WHARP的性能 通过GreenCastalia模拟进行评估，我们可以在其中精确建模唤醒无线电，可收集的能量以及解决MDP所需的计算能力。关键网络和系统参数各不相同，包括可收获能源，网络密度，唤醒无线电数据速率和数据流量。我们还将G-WHARP的性能与 两种最先进的数据转发策略GreenRoutes 和CTP-WUR的性能进行了比较。结果表明，G-WHARP 限制了能量消耗，同时实现了较低的端到端延迟和较高的数据包传输率。特别是，与CTP-WUR 和GreenRoutes相比，其能耗分别低34％和59％。