In recent years, Deep Neural Networks (DNNs) have been increasingly adopted in a wide range of time-critical applications running on edge platforms equipped with heterogeneous multiprocessors. Given the limited resources available on these platforms, efficiently utilizing both CPU and GPU resources for time-sensitive DNN inference is crucial. However, this cross-processor inference paradigm poses significant challenges due to inherent performance imbalances between different processors. In this paper, we introduce BlastNet, a system that leverages duo-blocks—a novel model inference abstraction designed to enable highly efficient cross-processor, time-sensitive DNN inference. Each duo-block features a dual model structure, facilitating fine-grained, alternate inference across different processors. Duo-blocks are optimized during design and dynamically scheduled at runtime to maximize the resource utilization of CPU and GPU. To address memory constraints on edge devices, we also propose a duo-block selection algorithm that selectively constructs duo-blocks based on performance gains. BlastNet is implemented on an indoor autonomous driving platform and three popular edge platforms. Extensive evaluations demonstrate that BlastNet reduces the deadline missing rate by$35.07\,\%$with only a mere$1.63 \%$loss in model accuracy.

As the adoption of LiDAR expands across various fields such as autonomous driving, robotics, and smart cities, the demand for adaptive scanning capabilities to better capture dynamic and complex scenes becomes paramount. Current LiDAR technologies, limited by fixed uniform scan patterns, struggle to prioritize critical areas, resulting in reduced perception accuracy and performance inefficiencies. This paper introduces SmartLiDAR, an advanced LiDAR system that enhances scanning efficiency and performance by adaptively optimizing scan focus through an intelligent, software-defined micro-mirror controller. Unlike traditional systems, SmartLiDAR dynamically adjusts its scan pattern based on environmental characteristics and application-specific requirements, concentrating sample points on key objects without increasing power consumption or scan time. SmartLiDAR achieves this by integrating a novel quadratic micro-mirror controller, an adaptive algorithm for generating fine-grained attention map with prioritized scan focus, and a carefully designed optimization algorithm that maps attention maps to practical scanning patterns. We prototype SmartLiDAR by building a software-defined LiDAR using commercially available optical components and FPGA. Our experimental results demonstrate that SmartLiDAR significantly enhances resolution in regions of interest by 3x and increases average object detection precision by up to 16.11%. Additionally, SmartLiDAR maintains negligible extra energy consumption and processing latency, making it suitable for real-time applications, such as autonomous vehicles.

Recently, smart roadside infrastructure (SRI) has demonstrated the potential of achieving fully autonomous driving systems. To explore the potential of infrastructure-assisted autonomous driving, this paper presents the design and deployment of Soar, the first end-to-end SRI system specifically designed to support autonomous driving systems. Soar consists of both software and hardware components carefully designed to overcome various system and physical challenges. Soar can leverage the existing operational infrastructure like street lampposts for a lower barrier of adoption. Soar adopts a new communication architecture that comprises a bi-directional multi-hop I2I network and a downlink I2V broadcast service, which are designed based on off-the-shelf 802.11ac interfaces in an integrated manner. Soar also features a hierarchical DL task management framework to achieve desirable load balancing among nodes and enable them to collaborate efficiently to run multiple data-intensive autonomous driving applications. We deployed a total of 18 Soar nodes on existing lampposts on campus, which have been operational for over two years. Our real-world evaluation shows that Soar can support a diverse set of autonomous driving applications and achieve desirable real-time performance and high communication reliability. Our findings and experiences in this work offer key insights into the development and deployment of next-generation smart roadside infrastructure and autonomous driving systems.

Recent years have witnessed the emergence of a new class of cooperative edge systems in which a large number of edge nodes can collaborate through local peer-to-peer connectivity. In this paper, we propose CoEdge, a novel cooperative edge system that can support concurrent data/compute-intensive deep learning (DL) models for distributed real-time applications such as city-scale traffic monitoring and autonomous driving. First, CoEdge includes a hierarchical DL task scheduling framework that dispatches DL tasks to edge nodes based on their computational profiles, communication overhead, and real-time requirements. Second, CoEdge can dramatically increase the execution efficiency of DL models by batching sensor data and aggregating the inferences of the same model. Finally, we propose a new edge containerization approach that enables an edge node to execute concurrent DL tasks by partitioning the CPU and GPU workloads into different containers. We extensively evaluate CoEdge on a self-deployed smart lamppost testbed on a university campus. Our results show that CoEdge can achieve up to reduction on deadline missing rate compared to baselines.

Wi-Fi sensing systems leverage wireless signals from widely deployed Wi-Fi devices to realize sensing for a broad range of applications. However, current Wi-Fi sensing systems heavily rely on the channel state information (CSI) to learn the signal propagation characteristics, while the availability of CSI is highly dependent on specific Wi-Fi chipsets. Through a city-scale measurement, we discover that the availability of CSI is extremely limited in operational Wi-Fi devices. In this work, we propose a new wireless sensing system called BeamSense that exploits the compressed beamforming reports (CBR). Due to the extensive support of transmit beamforming in operational Wi-Fi devices, CBR is commonly accessible and hence enables a ubiquitous sensing capability. BeamSense adopts a novel multi-path estimation algorithm that can efficiently and accurately map bidirectional CBR to a multi-path channel based on intrinsic fingerprints. We implement BeamSense on several prevalent models of Wi-Fi devices and evaluated its performance with microbenchmarks and three representative Wi-Fi sensing applications. The results show that BeamSense is capable of enabling existing CSI-based sensing algorithms to work with CBR with high sensing accuracy and improved generalizability.

In recent years, Deep Neural Network (DNN) has been increasingly adopted by a wide range of time-critical applications running on edge platforms with heterogeneous multiprocessors. To meet the stringent timing requirements of these applications, heterogeneous CPU and GPU resources must be efficiently utilized for the inference of multiple DNN models. Such a cross-processor real-time DNN inference paradigm poses major challenges due to the inherent performance imbalance among different processors and the lack of real-time support for cross-processor inference from existing deep learning frameworks. In this work, we propose a new system named BlastNet that exploits duo-block - a new model inference abstraction to support highly efficient cross-processor real-time DNN inference. Each duo-block has a dual model structure, enabling efficient fine-grained inference alternatively across different processors. BlastNet employs a novel block-level Neural Architecture Search (NAS) technique to generate duo-blocks, which accounts for computing characteristics and communication overhead. The duo-blocks are optimized at design time and then dynamically scheduled to achieve high resource utilization of heterogeneous CPU and GPU at runtime. BlastNet is implemented on an indoor autonomous driving platform and three popular edge platforms. Extensive results show that BlastNet achieves 35.07 % less deadline missing rate with a mere 1.63% of model accuracy loss.

Recent years have seen a widespread deployment of NB-IoT networks for massive machine-to-machine communication in the emerging 5G era. Unfortunately, the key aspects of NB-IoT networks, such as radio access performance and power consumption have not been well-understood due to lack of effective tools and closed nature of operational cellular infrastructure. In this paper, we develop NB-Scope - the first hardware NB-IoT diagnostic tool that supports fine-grained fusion of power and protocol traces. We then conduct a large-scale field measurement study consisting of 30 nodes deployed at over 1,200 locations in 4 regions during a period of three months. Our in-depth analysis of the collected 49 GB traces showed that NB-IoT nodes yield significantly imbalanced energy consumption in the wild, up to a ratio of 75:1, which may lead to short battery lifetime and frequent network partition. Such a high performance variance can be attributed to several key factors including diverse network coverage levels, long tail power profile, and excessive control message repetitions. We then explore the optimization of NB-IoT base station settings on a software-defined eNodeB testbed, and suggest several important design aspects that can be considered by future NB-IoT specifications and chipsets.

Recent years have seen a widespread deployment of NB-IoT networks for massive machine-to-machine communication in the emerging 5G era. Unfortunately, the key aspects of NB-IoT networks, such as radio access performance and power consumption have not been well-understood due to lack of effective tools and closed nature of operational cellular infrastructure. In this paper, we develop NB-Scope - the first hardware NB-IoT diagnostic tool that supports fine-grained fusion of power and protocol traces. We then conduct a large-scale field measurement study consisting of 30 nodes deployed at over 1,200 locations in 3 regions during a period of three months. Our in-depth analysis of the collected 49 GB traces showed that NB-IoT nodes yield significantly imbalanced energy consumption in the wild, up to a ratio of 75:1, which may lead to short battery lifetime and frequent network partition. Such a high performance variance can be attributed to several key factors including diverse network coverage levels, long tail power profile, and excessive control message repetitions. We then explore the optimization of NB-IoT base station settings on a software-defined eNodeB testbed, and suggest several important design aspects that can be considered by future NB-IoT specifications and chipsets.