Embedded systems are deeply integrated into critical applications but, despite their importance, lack an effective means to apply over-the-air software patches without significant downtime. Standard mechanisms for firmware updates require device reboots that wipe important in-memory state. Prior efforts have proposed "live" updates to address this problem, applying patches to an embedded application without a reset, but they tackle a limited set of applications or propose a clean-slate design. In this paper, we present Retcon, a live update toolchain for embedded systems that supports a familiar event-driven programming model and does not require application code changes. Retcon leverages static analysis at compile time to determine when it will be safe to update a device. To find safe update points in the presence of complex asynchronous behavior, we define a novel system state, asynchronous quiescence, in which an update can be applied. We evaluate Retcon on a set of embedded event-driven applications – a dual-chamber pacemaker model, a programmable logic controller runtime, an artificial pancreas system, and a sensing node – and demonstrate Retcon’s ability to make low-overhead updates in less than one millisecond.
LoRa technology has garnered significant attention for its exceptional performance in city-wide applications. LoRa encodes data across multiple samples to enable long-range communication, with the level of redundancy controlled by the Spreading Factor (SF). However, practical limitations restrict how high the SF can be set. To overcome communication challenges at the highest allowable SF settings, we introduce SF-any, a software-based coding paradigm that extends an SFk packet to a quasi-SF(k + m) packet. SF-any encodes a quasi-SF(k + m) symbol with 2 m SFk symbols. Hardware imperfections introduce time-varying frequency drifts and phase offsets, resulting in frequency leakage during quasi-SF(k +m) packet decoding. To mitigate this, we strategically insert pilots into the packet for imperfection estimation and compensation. Additionally, to maintain and exploit the coding structure in LoRa PHY, we employ a grouped repetition code at the transmitter and a joint demodulation and decoding scheme at the receiver. Comprehensive evaluations demonstrate that SF-any’s performance seamlessly scales with increasing SF, achieving up to a 14dB improvement over SF12 packets (the highest SF in LoRa PHY), and up to a 12dB improvement compared with state-of-the-art approaches.
Accurate motion capture of aerial robots in 3D is a key enabler for autonomous operation. Recently, some research considers using MM-Wave radar sensors for drone motion capture. However, due to the high noise and difficulty in capturing the center of an object in MM-Wave radar, the existing traditional methods have achieved unsatisfactory results. We develop a novel adaptive chirps domain window order method for MM-Wave radar data and customize a neural network architecture.
With the advancement of the Internet of Medical Things (IoMT) and cryptographic technologies, remote healthcare services have become more widespread, presenting new challenges for patient privacy and data security. Conventional security mechanisms, such as centralized authentication and key distribution systems, are susceptible to single points of failure and significant management burdens, potentially leading to compromised authentication centers and internal security threats. In response, this study presents a threshold signature algorithm, it uses Distributed Key Generation (DKG) that distributes private keys without the need for a trusted key distributor, requiring the cooperative signature of at least two nodes for authentication. This approach not only circumvents the risk of single points of failure but also enhances the system’s robustness and efficiency. The experimental results validate its prospective utility in safeguarding remote healthcare data.
Battery-free sensing devices have gained growing popularity as they can operate relying solely on harvested energy and environmentally friendly capacitors. However, despite the increasing number of battery-free solutions, their design remains a difficult task. In fact, the limited energy storage capacity and the resulting coupling between energy supply and demand introduce new design trade-offs that cannot be explored using conventional tools that consider a constant power supply. To enable fast design space exploration and facilitate the development of battery-free systems, we introduce Simba, an open-source simulation framework that allows to investigate in detail the complex interplay between various device components. We demonstrate the benefits of Simba in two case studies, evaluated experimentally, targeting real-world, state-of-the-art battery-free devices. First, we illustrate how Simba can explore the dependencies between different component configurations and assess their impact on the overall system performance. Among others, we show that changing the storage capacity or slightly modifying the load behavior can improve data throughput by a factor of up to 5.1x and 9.7x, respectively. Second, we present how Simba allows to automatically select key parameters that optimize the operations of a battery-free system (e.g., its checkpointing mechanism), and showcase how Simba enables performance evaluations based on real-world energy harvesting traces.CCS CONCEPTS• Computer systems organization → Embedded systems.
Continuous detection of human activities and presence is essential for developing a pervasive interactive smart space. Existing literature lacks robust wireless sensing mechanisms capable of continuously monitoring multiple users’ activities without prior knowledge of the environment. Developing such a mechanism requires simultaneous localization and tracking of multiple subjects. In addition, it requires identifying their activities at various scales, some being macro-scale activities like walking, squats, etc., while others are micro-scale activities like typing or sitting, etc. In this paper, we develop a holistic system called MARS using a single Commercial off-the-shelf (COTS) Millimeter Wave (mmWave) radar, which employs an intelligent model to sense both macro and micro activities. In addition, it uses a dynamic spatial time-sharing approach to sense different subjects simultaneously. A thorough evaluation of MARS shows that it can infer activities continuously with an accuracy of > 93% and an average response time of ≈ 2 sec, with 5 subjects and 19 different activities.
We focus on the sensor fusion for vehicle-side and roadside 3D object detection and tracking. Although quite a few sensor fusion algorithms have been proposed, some of which are top-ranked on various leaderboards, a systematic study on how to integrate three crucial sensors (LiDAR, camera and millimeter-wave Radar sensors) to develop effective multi-modal 3D object detection and tracking for vehicle-side perception is still missing. Therefore, we first study the three sensors’ strengths and weaknesses carefully, then compare several different fusion strategies to maximize their utility. Finally, based on the lessons learnt, we propose a simple yet effective multi-modal 3D object detection and tracking framework (namely EZFusion). Without fancy network modules, our proposed EZFusion makes remarkable improvements over the LiDAR-only baseline, and achieves comparable performance. For intelligent transportation, far-range perception with roadside sensors is vital. The main challenge of far-range perception is performing accurate object detection and tracking under far distances (e.g., > 150m) at a low cost. To cope with such challenges, deploying both millimeter wave Radars and high-definition cameras, and fusing their data has become a common practice. Towards this goal, the first question is to conduct the association on the 2D image plane or the BEV plane. We argue that the former is more suitable because the magnitude of location errors in the perspective projection points is smaller at far distances on the 2D plane, leading to more accurate association. Thus, we first project the Radar points to the 2D plane and then associate them with the camera-based 2D object locations. Subsequently, we map the camera-based object locations to the BEV plane through inverse projection mapping (IPM) with the corresponding depth information from the Radar data. Finally, we engage a BEV tracking module to generate target trajectories. Our system is capable of achieving an average location accuracy of 1.3m when we extend the detection range up to 500m.
Urban particulate pollution presents considerable public health hazards, underscoring the need for effective control measures in various cities. A prevalent approach involves employing mobile sprinkling trucks. This paper proposes a Sprinkler-UAV Cooperative Active Scheduling System for enhanced efficiency in reducing particulate pollution. The system employs ground-based sprinkler trucks and airborne air pollution detection drones to actively explore and reduce PM2.5 in environments with dynamic and unknown pollution distributions. Preliminary experiments have demonstrated the effectiveness of using sprinklers for urban particulate matter control.
Recent studies have demonstrated that large language models (LLMs) exhibit exceptional performance across various natural language processing tasks, rivaling or even exceeding human competencies in certain areas [1] – [5] . Typically, LLMs undergo pre-training on extensive text corpora, usually using billions of tokens to develop a foundational model. To better align LLMs with human preferences and directives or to fulfill specific application needs, methods such as supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and direct preference optimization (DPO) have been introduced and demonstrated to be effective. These advancements facilitate more intuitive and efficient human-AI interactions. However, the substantial resource requirements throughout the training process pose challenges for individual users and smaller organizations.
This poster introduces SynMotion, a novel mmWave-based human motion sensing system addressing the scarcity of training datasets. By synthesizing mmWave signals using existing vision-based human motion datasets, this system overcomes the challenge of collecting and labeling mmWave data, facilitating wider adoption of mmWave technology for applications like activity recognition, skeleton tracking and radar placement recommendation.
In this work, we propose a novel re-use of neuromorphic (event) cameras for joint sensing and communications. Event cameras work on the principle of capturing changes in the light intensities, essentially capturing events that lead to such changes. This makes them operable at low power and sample events at fast rates (equivalent to about 40K frames-per-second compared to RGB cameras). We propose a system design to leverage the time-sampling nature of events for optical wireless communication and the ability to sample a collective area of physical space for imaging. In particular, we propose to address the challenges to achieve passive optical wireless (backscatter) communication as well as computer vision functions such as object and path detection using a single neuromorphic camera device. We posit that such an integrated functioning through a single low-power device opens new avenues for visible/invisible light communication and visual scene processing.CCS CONCEPTS•Networks → Mobile networks;•Hardware → Signal processing systems;•Computing methodologies → Computer vision.
The rapid development of WiFi localization poses a serious privacy threat, as eavesdroppers can locate WiFi devices without their consent. In this paper, we present WiCloak, the first system that protects WiFi device location privacy while supporting normal WiFi communication simultaneously. The high-level idea of WiCloak is to inject a fake channel into WiFi CSI at the transmitter, which renders the CIR and time information obtained by eavesdroppers meaningless. We mathematically prove that the injected fake channel is effective in any wireless environment and can strictly protect the location privacy of WiFi devices. To simultaneously support communication for commercial WiFi receivers, we propose a method to cancel out the fake channel impacts in decoding and prove that the method should not impact communication performance. WiCloak can work on commercial WiFi devices without any hardware modification. We evaluate the communication performance of WiCloak on commercial WiFi receivers (e.g., MacBook and Mac Studio) and demonstrate that it achieves the same packet reception rate as normal WiFi. We show that WiCloak increases the localization error by 22× to normal WiFi.
Ultra-wideband technology has the potential to provide precise real-world localization. However, due to Non-Line-of-Sight propagation, the transmitted packet can be incomplete or lost during the ranging, which may lead to communication failure. To minimize this, we propose the scheduled signal reception technique. The proposed approach is tested in a real-world environment with Qorvo DWM3001C modules. The experimental results verify its efficiency, offering a practical solution for UWB-based localization, particularly in dynamic circumstances.
For cyber-physical systems, the predictability of their physical behaviors needs to be ensured by the determinism of cyberspace. Response-time analysis (RTA) can theoretically provide this determinism by analyzing the temporal properties of demands. However, the state-space explosion problem makes it challenging to do exact and sustainable RTA for non-preemptive systems where both release jitter and execution time variation exist, particularly when the system has event-triggered (ET) jobs. To address this issue, we propose an ET-enabled RTA based on the schedule-abstraction graph and preliminarily verify its effectiveness and scalability.
Today’s wireless sensor networks focus on achieving reliable data transfer over a lossy medium at the expense of latency. However, sensor data are often noisy and thus only lossily characterise real-world phenomena, rendering their exact transfer wasteful. Furthermore, many next-generation privacy-sensitive applications, such as smart grid control, real-time distributed object tracking, and inter-vehicle federated learning face latency and traffic bottlenecks due to the sheer amount of data collection required to overcome noise. We tackle this problem by introducing Twofer, a communication approach which reduces latency and traffic in high-noise or high-privacy settings by abandoning the focus on reliable networking. Twofer empowers developers to tune networks for latency-bound rather than reliability-bound performance; the system coordinates ambiguous transmissions, which are used to estimate the network-wide distribution of data, rather than to reliably communicate exact data from individual nodes. Twofer’s full-stack design maintains black-box compatibility with existing application code, but advocates for, and shows the value of, uncommon physical-layer features such as symbol-synchronous communication. The system is therefore implemented and evaluated on a prototype low-latency wireless mesh network called Zero-Wire. Experiments using state-of-the-art local differential privacy protocols show 25–75% latency reductions relative to conventional approaches. The results are also future-proof, with performance advantages increasing with the strength of the privacy guarantees that are offered.
Volunteer activity often undergoes unique transformations with the constant changes in society. The information behind volunteer data was created to enhance public welfare efficiently and boost governmental organization productivity. This research aims to utilize public transit systems for volunteer services, reducing inequality in volunteer service provision across different regions and improving overall service efficiency. We collected and processed large-scale data related to public transit and volunteer services, conducting in-depth analysis using data mining techniques and deep learning methods. Through LDA, we annotated a large amount of volunteer data, and via data analysis, discovered patterns related to population distribution, spatial distribution, and temporal distribution. Combining public transit data and the mined features, we propose a novel spatio-temporal embedding model based on the transformer architecture, which can effectively classify and predict the matching between volunteer service demands and public transit systems. Studying the coupling between volunteer services and transportation systems helps establish a new data-driven mindset, better utilize urban resources, and provide high-quality volunteer services to the public.
MAV (Micro Aerial Vehicle) requires landing on a docking platform for recharging during or after missions due to their limited energy capacity. Inspired by biological tactile sensing, we propose a proprioceptive sensing system that allows MAV to "touch", recognize, and locate the landing platform even when visual or other positioning systems are not functioning properly. We leverage a physical phenomenon: as the MAV approaches a beneath obstacle, it experiences attitude disturbances caused by the airflow generated by the rotor’s reflections from the ground. By employing traditional signal processing and learning-based techniques to analyze signals from the IMU (Inertial Measurement Unit) and motors, the MAV can sense the edges of the platform and further calculate the precise landing coordinates. With a power consumption of less than 40 mW, our system achieves an edge detection error of less than 2 cm and a landing success rate exceeding 90%.CCS CONCEPTS• Applied computing → Aerospace; • Computing methodologies → Machine learning approaches; • Computer systems organization → Sensors and actuators.
Tracking movements in the environment of macroscopic objects enables numerous applications, from monitoring vital signs through body movements to inferring hand gestures. However, current systems overwhelmingly rely on contact-based sensors or energy-consuming radio frequency mechanisms that necessitate complex radio transceivers for receptions. We present ongoing research on a novel low-power sensor that leverages the unique characteristics of tunnel diodes. This sensor can detect minute changes in its vicinity and communicate these changes over radio waves, all while consuming under 150 microwatts of power consumption. Notably, the transmitted radio waves are processed using low-cost, off-the-shelf radio transceivers, resulting in low cost and power consumption. The sensor’s functionality stems from the sensitivity of the resonant frequency of the tunnel diode oscillators to changes in their electromagnetic surroundings. Our early work exhibits its potential for detecting a person’s breathing patterns, and hand gestures.
In this paper, we mainly focus on cooperative perception systems for vehicle-road coordination. Specifically, this paper encompasses two main aspects: 1) discussing the spatio-temporal synchronization issues among roadside multiple LiDARs. In this part, we design a method to synchronize the spatio-temporal data among multiple LiDARs by matching trajectory points between them; 2) designing a cooperative perception system based on uncertainty. In this part, we design a scheme to reduce the communication volume of cooperative perception by lowering the communication frequency.
Developing robust wireless sensing mechanisms for continuously monitoring human activities and presence is crucial for creating pervasive interactive intelligent spaces. The existing literature lacks solutions that continuously monitor multiple users’ activities without prior knowledge of the environment. This requires simultaneous localization and tracking of multiple subjects and identifying their activities at various scales, including macro-scale activities like walking and squats and micro-scale activities like typing or sitting. In this demo, we present MARS , a holistic system using a single off-the-shelf mmWave radar. MARS employs an intelligent model to sense both macro and micro activities and uses a dynamic spatial time-sharing approach to sense different subjects simultaneously. Our thorough evaluation demonstrates that MARS can continuously infer activities with over 93% accuracy and an average response time of approximately 2 seconds, even with five subjects performing 19 different activities.
