Large Language Models (LLMs) have shown remarkable capabilities in text and multimodal processing, yet they fundamentally lack physical awareness--understanding of real-world physical phenomena. In this work, we present ACORN, a framework that teaches LLMs physical awareness through sound, focusing on fundamental physical phenomena like the Doppler effect, multipath effect, and spatial relationships. To overcome data scarcity, ACORN introduce a physics-based simulator combining real-world sound sources with controlled physical channels to generate diverse training data. Using this simulator, we build AQA-PHY, a comprehensive Audio Question-Answer dataset, and propose an audio encoder that processes both magnitude and phase information. By connecting our audio encoder to state-of-the-art LLMs, we demonstrate reasonable results in both simulated and real-world tasks, such as line-of-sight detection, Doppler effect estimation, and Direction-of-Arrival estimation, paving the way for enabling LLMs to understand physical world.

We present a novel approach to monitor the Body in White (BiW), the fundamental metallic structure of a vehicle. Existing monitoring methods, including both wired and wireless sensor systems, face significant challenges due to integration complexity, weight considerations, material costs, and signal blockage within the metallic environment. To overcome these limitations, we introduce Arach-Net, an acoustic backscatter network that leverages the conductive properties of the BiW to propagate vibration signals for energy transfer and data communication. This system comprises battery-free tags that harvest energy from BiW vibrations and utilize a backscatter technique for efficient communication, thereby eliminating the need for external power sources and reducing the power consumption. We address key challenges such as power sufficiency for tag activation and sustained operation, and collision reduction in network communication, by designing an ultra-low power backscatter tag and a distributed slot allocation protocol. We implement ArachNet, and deploy 12 tags onto the BiW of an electric SUV car. The evaluation results show that the power consumption of the tag is 51.0 μW for uplink packet transmission, and 24.8 μW for downlink packet reception. With our network protocol, the slot utilization can be up to 81.2%.

Campus networks are widely deployed for organizations like universities and large companies. Applications and network-based services require campus networks to guarantee short queue and provide low latency and large bandwidth. However, the widely adopted packet-loss-based congestion control mechanism in client hosts builds up long queues in the switch buffer, which is prone to packet loss in burst scenarios, resulting in great network delay. Therefore, a scheme for efficiently controlling queue length of shallow buffer switches in campus networks is urgently needed. Explicit Congestion Notification(ECN) as an explicit feedback mechanism is widely adopted in data center networks to build lossless networks. In this paper, we propose Clean, an efficient queue length control scheme based on transparent ECN-proxy for campus networks. Clean is able to exert fine-grained control over arbitrary client TCP stacks by enforcing per-flow congestion control in the access point(AP). It allows the campus network switches to maintain a low queue length, resulting in high throughput, low latency and zero packet loss. Evaluation results demonstrate that Clean reduces the maximum queue length of the switch by 86% and reduces the 99th percentile latency by 85%. Clean also achieves zero packet loss in burst scenarios.

High speed routers in Internet are becoming increasingly more powerful, as well as more energy hungry. However, they always show power-inefficient property due to we unilaterally in pursuit of high speed before. In response to this problem, we present a power-efficient router architecture named GreenRouter in this paper. GreenRouter separates a line card into two parts physically: the network interface card (named as DB) and the packet processing card (named as MB), which are interconnected by a two-stage unidirectional switch fabric. Traffic from all the DBs shares all the MBs in GreenRouter, thus the traffic can be aggregated to a few active MBs when traffic is light and the inactive MBs can be shut down to save power. We give the detailed architectural design of GreenRouter. Real-trace driven experiments show that GreenRouter can save about 50% power compared to the conventional router when the average traffic load is 30%, while providing quality of service guarantee at the same time.