This paper proposes a UAV-assisted cooperative perception framework for vehicular networks, ensuring the timely transmission of image feature extraction results from vehicles to the RSU, thereby enabling cooperative perception such as HD map construction and object-level tracking in smart cities. We analyze the success probability that vehicles can complete image feature extraction and transmit the result before leaving the RSU’s communication range with the assistance of UAVs. The simulation results demonstrate that the proposed algorithm outperforms benchmark schemes in terms of success probability.
State-of-the-art deep learning models rely on large GPU clusters and various parallelism strategies, which in turn depend on collective communication (CC) operators to synchronize data. While vendor libraries (e.g., NCCL, RCCL) provide standard CC algorithms, they often suffer from bandwidth bottlenecks in imbalanced topologies. Recent synthesis-based methods improve performance but face three key limitations: poor scalability due to the combinatorial explosion of scheduling space, lack of support for multistage execution, and suboptimal communication throughput. We propose Canvas, a scalable and near-optimal CC scheduling framework that addresses these challenges. Canvas introduces: (1) Hierarchical synthesis to decompose the global scheduling problem into tractable subproblems for scalability. (2) Collective decomposition to enable structured, multi-stage algorithm generation. (3) Cross-micro-batch pipeline scheduling to parallelize communication across micro-batches and maximize link utilization. Evaluations show that Canvas achieves up to 1.98× bandwidth speedup over TACCL and 3.56× over TE-CCL, and synthesizes algorithms for 512-GPU topologies within 1.77 hours, whereas TACCL fails to produce results within 24 hours.
In recent years, significant progress has been made towards scalable network control-plane verification. Yet, operators are still hesitant to deploy such systems. We argue that this reluctance is in part due to a semantic gap between operators reasoning about routing states and verifiers exploring the space of environments. Indeed, operators express the specification in terms of behavior of routing states, while verifiers usually rely on solvers to find specific environments that violate the specification. This semantic gap prevents users from guiding these solvers to directly explore routing states that violate the specification, or to search for states that are most relevant or likely.In this paper, we present a new approach for flexible control-plane verification. Instead of relying on rigid off-the-shelf solvers, we design a novel backtracking algorithm to directly explore the space of routing states. This enables users to guide the exploration according to the specification and domain-specific knowledge from operators. This algorithm paves the way for novel use cases, ranging from finding relevant (e.g., likely) counterexamples to performing verification of probabilistic specifications.
Mobile-cloud collaborative Convolutional Neural Network (CNN) inference enables the efficient execution of CNN models by offloading partial inference workloads from mobile devices to the cloud. Although model partitioning for collaborative inference has been extensively studied, most existing approaches assume reliable mobile-cloud transmission, which often breaks down in real-world wireless environments with packet loss. In such scenarios, incomplete feature transmission can result in a significant drop in inference accuracy. In this paper, a joint scheduling approach is proposed to address this challenge, leveraging a search-based algorithm to determine both the model partition layer and redundancy level, to balance inference accuracy and latency under packet loss conditions. The proposed method is evaluated in a real-world mobile-cloud environment. Results show that it reduces the inference latency by up to 30.3% compared to the non-redundant baseline with the same accuracy threshold.
