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Robust communication is vital for multi-agent robotic systems involving heterogeneous agents like Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) operating in dynamic and contested environments. These agents often communicate to collaboratively execute critical tasks for perception awareness and are faced with different communication challenges: (a) The disparity in velocity between these agents results in rapidly changing distances, in turn affecting the physical channel parameters such as Received Signal Strength Indicator (RSSI), data rate (applicable for certain networks) and most importantly "reliable data transfer", (b) As these devices work in outdoor and network-deprived environments, they tend to use proprietary network technologies with low frequencies to communicate long range, which tremendously reduces the available bandwidth. This poses a challenge when sending large amounts of data for time-critical applications. To mitigate the above challenges, we propose DACC-Comm, an adaptive flow control and compression sensing framework to dynamically adjust the receiver window size and selectively sample the image pixels based on various network parameters such as latency, data rate, RSSI, and physiological factors such as the variation in movement speed between devices. DACC-Comm employs state-of-the-art DNN (TABNET) to optimize the payload and reduce the retransmissions in the network, in turn maintaining low latency. The multi-head transformer-based prediction model takes the network parameters and physiological factors as input and outputs (a) an optimal receiver window size for TCP, determining how many bytes can be sent without the sender waiting for an acknowledgment (ACK) from the receiver, (b) a compression ratio to sample a subset of pixels from an image. We propose a novel sampling strategy to select the image pixels, which are then encoded using a feature extractor. To optimize the amount of data sent across the network, the extracted feature is further quantized to INT8 with the help of post-training quantization. We evaluate DACC-Comm on an experimental testbed comprising Jackal and ROSMaster2 UGV devices that communicate image features using a proprietary radio (Doodle) in 915-MHz frequency. We demonstrate that DACC-Comm improves the retransmission rate by ≈17% and reduces the overall latency by ≈12%. The novel compression sensing strategy reduces the overall payload by ≈56%. 

Many IoT applications have increasingly adopted machine learning (ML) techniques, such as classification and detection, to enhance automation and decision-making processes. With advances in hardware accelerators such as Nvidia’s Jetson embedded GPUs, the computational capabilities of end devices, particularly for ML inference workloads, have significantly improved in recent years. These advances have opened opportunities for distributing computation across the edge network, enabling optimal resource utilization and reducing request latency. Previous research has demonstrated promising results in collaborative inference, where processing units in the edge network, such as end devices and edge servers, collaboratively execute an inference request to minimize latency.This paper explores approaches for implementing collaborative inference on a single model in resource-constrained edge networks, including on-device, device-edge, and edge-edge collaboration. We present preliminary results from proof-of-concept experiments to support each case. We discuss dynamic factors that can impact the performance of these inference execution strategies, such as network variability, thermal constraints, and workload fluctuations. Finally, we outline potential directions for future research.