Autonomous Delivery Vehicles (ADVs) are increasingly used for transporting goods in 5G network-enabled smart factories, with the compute-intensive localization module presenting a significant opportunity for optimization. We propose ACCESS-AV , an energy-efficient Vehicle-to-Infrastructure (V2I) localization framework that leverages existing 5G infrastructure in smart factory environments. By opportunistically accessing the periodically broadcast 5G Synchronization Signal Blocks (SSBs) for localization, ACCESS-AV obviates the need for dedicated Roadside Units (RSUs) or additional onboard sensors to achieve energy efficiency as well as cost reduction. We implement an Angle-of-Arrival (AoA)-based estimation method using the Multiple Signal Classification (MUSIC) algorithm, optimized for resource-constrained ADV platforms through an adaptive communication-computation strategy that dynamically balances energy consumption with localization accuracy based on environmental conditions such as Signal-to-Noise Ratio (SNR) and vehicle velocity. Experimental results demonstrate that ACCESS-AV achieves an average energy reduction of 43.09% compared to non-adaptive systems employing AoA algorithms such as vanilla MUSIC, ESPRIT, and Root-MUSIC. It maintains sub-30 cm localization accuracy while also delivering substantial reductions in infrastructure and operational costs, establishing its viability for sustainable smart factory environments.

Deep learning algorithms are becoming an essential component of many artificial intelligence (AI) driven applications, many of which run on resource-constrained and energy-constrained systems. For efficient deployment of these algorithms, although different techniques for the compression of neural networks models are proposed, neural pruning is one of the fast and effective methods which can provide a high compression gain with minimal cost. To harness enhanced performance gain with respect to model complexity, we propose a novel neural network pruning approach utilizing Hessian-vector products that approximates crucial curvature information in the loss function which significantly reduces the computation demands. By employing a power iteration method, our algorithm effectively identifies and preserves the essential information, ensuring a balanced trade-off between model accuracy and computational efficiency. Herein, we introduce CAMP-HiVe, a cyclic pair merging based pruning with Hessian Vector approximation by iteratively consolidating weight pairs, combining significant and less significant weights, thus effectively streamlining the model while preserving its performance. This dynamic, adaptive framework allows for real-time adjustment of weight significance, ensuring that only the most critical parameters are retained. Our experimental results demonstrate that our proposed method achieves significant reductions in computational requirements while maintaining high performance across different neural network architectures, e.g., ResNet18, ResNet56, and MobileNetv2 on standard benchmark datasets, e.g., CIFAR10, CIFAR-100, and ImageNet, and it outperforms the existing state-of-the-art neural pruning methods.

The existence of variable factors within the environment can cause a decline in camera localization accuracy, as it violates the fundamental assumption of a static environment in Simultaneous Localization and Mapping (SLAM) algorithms. Recent semantic SLAM systems towards dynamic environments either rely solely on 2D semantic information, or solely on geometric information, or combine their results in a loosely integrated manner. In this research paper, we introduce 3DS-SLAM, 3D Semantic SLAM, tailored for dynamic scenes with visual 3D object detection. The 3DS-SLAM is a tightly-coupled algorithm resolving both semantic and geometric constraints sequentially. We designed a 3D part-aware hybrid transformer for point cloud-based object detection to identify dynamic objects. Subsequently, we propose a dynamic feature filter based on HDBSCAN clustering & Weighted Cluster selection to extract objects with significant absolute depth differences. When compared against ORB-SLAM2, CFP-SLAM, and DYNA-SLAM, 3DS-SLAM exhibits an average improvement of 98.01%, 28.54%, and 50.92%, respectively, across the dynamic sequences of the TUM RGB-D dataset. Furthermore, it surpasses the performance of the other four leading SLAM systems designed for dynamic environments. The code and pre-trained models are available at https://github.com/sai-krishnaghanta/3DS-SLAM

Deep learning is a proven method in many applications. However, it requires high computation resources and usually has a constant architecture. Mobile systems are good candidates to benefit from deep learning applications since they are closely integrated in people’s life. However, mobile systems experience varying conditions for the same reason. Constant deep learning architectures against varying resources cannot satisfy the requirements of the applications, so dynamic deep learning architectures are needed. In this work, we propose SLEXNet, a slimmable early exit neural network architecture. SLEXNet combines dynamic depth and width architectures to adapt to varying time and power conditions. Moreover, we propose a runtime scheduling algorithm that can estimate inference time and power consumption of SLEXNet variations on runtime. We train SLEXNet on real aerial drone images and implement the runtime on NVIDIA Jetson Orin. We show that our approach achieves significantly better responses to time and power requirements in varying conditions than baseline dynamic depth and width techniques in a wide range of experiments.

With the advancement of industrial manufacturing and an increase in introduction of robots in the workspace, the need of safe operation, communication and information sharing is paramount. The work presented here focuses on cyber-physical system integration through Digital Twin (DT) technology. Our novel DT architecture is based on a model-free Neuro-Adaptive controller (NAC), and an edge-computing scheme for scene monitoring. The NAC can account for varying robot dynamics in both real and virtual environments, and allows for the DT system to expand the realm of cyber-physical integration without expensive model tuning. The edge-computing device introduced in our architecture, observes the robot's workspace from a distance with a wider field of view. This wide viewpoint, enhances the detection and mitigation of any obstacles entering the robot's workspace during operation. We experimentally evaluated the performance of our proposed architecture by introducing dynamic obstacles during a pick-and-place task that both the physical robot and its digital twin had to avoid. Results show that the proposed DT architecture successfully integrates the novel controller and edge-computing elements and successfully performs the given navigation task. The results also show that NAC outperforms a PD controller with more than 70% improvement in joint tracking error between the physical and virtual robots. It was observed that the latency experienced while using NAC is about 48 % lower than when Proportional-Derivative (PD) controller was operational.

Modern cyber-physical systems use deep-learning based algorithms for many applications for intelligent decision-making. Many of these systems are resource-constrained due to small form factor or finite energy budget. However, these systems often use multiple deep-learning algorithms simultaneously for a given mission or task. Due to the diverse nature of the algorithms and their performance needs, we need to allocate optimal software and hardware resources for their coexistence. To this aim, in this paper, we study and evaluate the performance tradeoff which will enable the users to choose the size and complexity of the deep learning models, the capacity of the device and also the software framework. With real-world experiments with a wide range of hardware and software, we demonstrate and evaluate the performance of the coexisting deep neural networks (DNN) based applications.

Digital Twin technology is being envisioned to be an integral part of the industrial evolution in modern generation. With the rapid advancement in the Internet-of-Things (IoT) technology and increasing trend of automation, integration between the virtual and the physical world is now realizable to produce practical digital twins. However, the existing definitions of digital twin is incomplete and sometimes ambiguous. Herein, we conduct historical review and analyze the modern generic view of digital twin to create its new extended definition. We also review and discuss the existing work in digital twin in safety-critical robotics applications. Especially, the usage of digital twin in industrial applications necessitates autonomous and remote operations due to environmental challenges. However, the uncertainties in the environment may need close monitoring and quick adaptation of the robots which need to be safety-proof and cost effective. We demonstrate a case study on developing a framework for safety-critical robotic arm applications and present the system performance to show its advantages, and discuss the challenges and scopes ahead.

The current Cellular Vehicle-to-Everything (C-V2X) Sidelink communication protocol provides a low latency interface for sharing short safety messages among Road-Side Units and vehicles. However, while its packets are broadcasted in the channel, the throughput is vulnerable to channel conditions and cannot meet the needs of the emerging connected and autonomous vehicles applications (e.g. vehicular fusion tasks), which require multimodal and multi-source sensor data sharing. In this work, we establish a C-V2X testbed on the campus of the University of California, San Diego, to study the feasibility of using C-V2X Sidelink communications for transmitting sensor data in real-time. We implement an end-to-end RGB sensor data (i.e. camera image frames) transmission mechanism on the C-V2X Sidelink testbed and explore the corresponding Quality of Service (QoS) characteristics under two configurable link-level parameters, the Modulation and Coding Scheme (MCS) and packet size. We then propose a cross-layer predictive and adaptive framework which adjusts, in real-time, the MCS and packet size settings based on side-channel information to optimize the QoS for image frame transmission. The real-world trace-driven emulation shows that the proposed policy improves the average frame goodput performance by 28% compared to fixed configuration policies that are used in current Sidelink communications.

Real-time machine vision applications running on resource-constrained embedded systems face challenges for maintaining performance. An especially challenging scenario arises when multiple applications execute at the same time, creating contention for the computational resources of the system. This contention results in increase in inference delay of the machine vision applications, which can be unacceptable for time-critical tasks. To address this challenge, we propose an adaptive model selection framework that mitigates the impact of system contention and prevents unexpected increases in inference delay by trading off the application accuracy minimally. The framework has two parts, which are performed pre-deployment and at runtime. The pre-deployment part profiles the system for contention in a black-box manner and produces a model set that is specifically optimized for the contention levels observed in the system. The runtime part predicts the inference delays of each model considering the system contention and selects the best model according to the predictions for each frame. Compared to a fixed individual model with similar accuracy, our framework improves the performance by significantly reducing the inference delay violations against a specified threshold. We implement our framework on the Nvidia Jetson TX2 platform and show that our approach achieves greater than 20% reductions in delay violations over the individual baseline models.

During the past few years, serverless computing has changed the paradigm of application development and deployment in the cloud and edge due to its unique advantages, including easy administration, automatic scaling, built-in fault tolerance, etc. Nevertheless, serverless computing is also facing challenges such as long latency due to the cold start. In this paper, we present an in-depth performance analysis of cold start in the serverless framework and propose HotC, a container-based runtime management framework that leverages the lightweight containers to mitigate the cold start and improve the network performance of serverless applications. HotC maintains a live container runtime pool, analyzes the user input or configuration file, and provides available runtime for immediate reuse. To precisely predict the request and efficiently manage the hot containers, we design an adaptive live container control algorithm combining the exponential smoothing model and Markov chain method. Our evaluation results show that HotC introduces negligible overhead and can efficiently improve the performance of various applications with different network traffic patterns in both cloud servers and edge devices.

Emerging connected and autonomous vehicles involve complex applications requiring not only optimal computing resource allocations but also efficient computing architectures. In this paper, we unfold the critical performance metrics required for emerging vehicular computing applications and show with preliminary experimental results, how optimal choices can be made to satisfy the static and dynamic computing requirements in terms of the performance metrics. We also discuss the feasibility of edge computing architectures for vehicular computing and show tradeoffs for different offloading strategies. The paper shows directions for light weight, high performance and low power computing paradigms, architectures and design-space exploration tools to satisfy evolving applications and requirements for connected and autonomous vehicles.

Infrastructure assistance has been proposed as a viable solution to improve the capabilities of commercial Unmanned Aerial Vehicles (UAV), especially toward fully autonomous operations. The airborne nature of these devices imposes constrains limiting the onboard available energy supply and computing power. The assistance of the surrounding communication and computing infrastructure can mitigate such limitations by extending the communication range and taking over the execution of compute-intense tasks. However, autonomous operations impose specific, and rather extreme in some cases, demands to the infrastructure. Focusing on flight assistance and task offloading to edge servers, this paper presents an in-depth evaluation of the ability of the communication infrastructure to support the necessary flow of information from the UAV to the infrastructure. The study is based on our recently proposed FlyNetSim, an open-source UAV-network simulator accurately modeling both UAV and network operations.

The analysis of information rich signals is at the core of autonomy. In airborne devices such as Unmanned Aerial Vehicles (UAV), the hardware limitations imposed by the weight constraints make the continuous execution of these algorithms challenging. Edge computing can mitigate such limitations and boost the system and mission performance of the UAVs. However, due to the UAVs motion characteristics and complex dynamics of urban environments, remote processing-control loops can quickly degrade. This paper presents Hydra, a framework for the dynamic selection of communication/computation resources in this challenging environment. A full - open-source - implementation of Hydra is discussed and tested via real-world experiments.

Unmanned Aerial Vehicle (UAV) systems are being increasingly used in a broad range of applications requiring extensive communications, either to interconnect the UAVs with each other or with ground resources. Focusing either on the modeling of UAV operations or communication and network dynamics, available simulation tools fail to capture the complex interdependencies between these two aspects of the problem. The main contribution of this paper is a flexible and scalable open source simulator -- FlyNetSim -- bridging the two domains. The overall objective is to enable simulation and evaluation of UAV swarms operating within articulated multi-layered technological ecosystems, such as the Urban Internet of Things (IoT). To this aim, FlyNetSim interfaces two open source tools, ArduPilot and ns-3, creating individual data paths between the devices operating in the system using a publish and subscribe-based middleware. The capabilities of FlyNetSim are illustrated through several case-study scenarios including UAVs interconnecting with a multi-technology communication infrastructure and intra-swarm ad-hoc communications.

In the Urban Internet of Things (IoT), devices and systems are interconnected at the city scale to provide innovative services to the citizens. However, the traffic generated by the sensing and processing systems may overload local access networks. A coexistence problem arises where concurrent applications mutually interfere and compete for available resources. This effect is further aggravated by the multiple scales involved and heterogeneity of the networks supporting the urban IoT. One of the main contributions of this paper is the introduction of the notion of content- oriented cognitive interference control in heterogeneous local access networks supporting computing and data processing in the urban IoT. A network scenario where multiple communication technologies, such as Device-to- Device and Long Term Evolution (LTE), is considered. The focus of the present paper is on city monitoring applications, where a video data stream generated by a camera system is remotely processed to detect objects. The cognitive network paradigm is extended to dynamically shape the interference pattern generated by concurrent data streams and induce a packet loss trajectory compatible with video processing algorithms. Numerical results show that the proposed cognitive transmission strategy enables a significant throughput increase of interfering applications for a target accuracy of the monitoring application.