LoRa has emerged as a crucial technology that provides ubiquitous connectivity for geographically distributed Internet of Things devices. LoRa employs linear Chirp Spread Spectrum (CSS) modulation in its physical layer to enable long-range communication. Recently, nonlinear chirps have been proposed to replace linear chirps in CSS, to enhance the capacity and scalability of LoRa technology. However, the characteristics and advantages of nonlinear chirps are not yet fully understood. To unleash the potential of nonlinear CSS modulation, this paper presents a comprehensive theoretical and experimental analysis of the nonlinear chirp, focusing on key aspects such as noise resilience, error correction, curvature selection, and bandwidth expansion. Our findings reveal that nonlinear chirps are intrinsically more resilient to noise than linear chirps. Moreover, nonlinear chirps typically exhibit a single-place demodulation error under strong noise conditions, which has informed our development of a simple yet effective error correction design. Additionally, users should carefully manage nonlinear chirp curvatures and signal power compensation to avoid distortion while benefiting from the bandwidth expansion offered by nonlinear chirps. Building on these insights, we further refine nonlinear CSS modulation and implement our optimized design on US-RPs. Field experiments demonstrate that our design achieves significant performance improvements, marking a step forward in the practical application of nonlinear CSS modulation in LoRa networks.

Wind speed monitoring is crucial for environmental management and forecasting. However, current solutions often struggle with high power consumption, especially at the end device, which typically has a sensor and wireless radios with limited battery capacity. To this end, we present WindScatter, an ultra-low-power, long-range, and large-scale wind speed monitoring system. WindScatter adopts the Integrated Sensing and Communication (ISAC) paradigm to enable low-power operation. It reuses the sensed data for communication by leveraging a TMR (Tunnel Magneto-Resistance) switch sensor to measure the wind speed information and control the backscatter communication simultaneously, thus avoiding the need for analog-to-digital conversion and a microcontroller for communication control. Our hardware-software co-design enables accurate measurements and stable concurrent transmission. We implement WindScatter and conduct extensive experiments and case studies to evaluate its performance. Results show that WindScatter supports measurements of all wind speed levels on the Extended Beaufort scale, from 1.5 m/s to 60 m/s, with an average error rate of 0.78%. WindScatter can sense and transmit wind speed data at a distance of 800 m with a power consumption of 136.5$\mu$W. Compared with commodity devices, WindScatter achieves comparable measurement range and accuracy while reducing cost by$91.5\times$and power consumption by$8,791\times$.

Multi-tier computation offloading is crucial to address capacity constraints and improve flexibility for mobile devices. However, existing research on multi-layer computing offloading faces challenges like inefficient resource utilization and poor scalability, particularly in handling diverse computational tasks. To address these challenges, this paper proposes a distributed resource allocation and mixed task offloading framework for end-edge-cloud collaborative systems that support partial and full task offloading modes. First, we propose a three-tier network computing architecture and formulate a task-offloading utility maximization problem by jointly optimizing mixed task-offloading and resource allocation. The proposed problem is a mixed integer nonlinear program (MINLP), which we solve by decomposing it into two subproblemsresource allocationandtask offloading. Edge computing resources and bandwidth allocation can be independently optimized at each edge node with a fixed task offloading strategy. Cloud computing resource allocation, while convex, involves a global constraint, which we solve in a decentralized manner using a multi-agent optimization approach. Then, we propose a joint task offloading and resource allocation optimization algorithm, CNO-TORA, to obtain the solution to the formulated problem. The algorithm is supported by strong theoretical guarantees and is almost surely convergent to a globally optimal solution. Experimental results on a real dataset demonstrate that our algorithm is scalable to large-scale networks and outperforms baselines, achieving improvements in average system utility ranging from 4.01%-28.15%.

As data centers continue to expand in size and complexity, obtaining global traffic insights necessitates aggregating statistical data from numerous individual nodes, a process critical for effective network management. However, in data centers, existing approaches often rely on querying individual endpoint hosts to gather cluster-wide statistics, which introduces substantial latency and reduces efficiency, particularly in large-scale deployments. To address this issue, we propose HiMon, a cost-efficient and high-accurate distributed monitoring system for optimizing traffic aggregation. HiMon enables distributed nodes to perform real-time, flow-level statistical processing and report the data to a master node with minimal bandwidth consumption. The master node aggregates the collected data to construct a comprehensive global traffic view. To enable high-speed and high-precision perpacket processing on child nodes, we introduce MaxSketch. MaxSketch’s data structure and update strategy allow it to accurately estimate child node traffic with minimal memory and computational overhead. For high-speed aggregation on the master node, we present PolySketch, which significantly boosts aggregation efficiency by delegating most computational tasks to the child nodes. Together, the hierarchical sketch structures of MaxSketch and PolySketch form the HiMon monitoring system. Experimental evaluations demonstrate that HiMon surpasses baseline algorithms, achieving a 17-210× improvement in traffic processing efficiency, a 25-42× reduction in master node bandwidth consumption, and a 3.69-8.97× increase in accuracy.

In recent years, there has been a growing focus on developing Low Power Wide Area Network (LPWAN) protocols, especially within the LoRa research community. However, the research community for NB-IoT, another crucial LPWAN technology, has not experienced comparable expansion due to the absence of a functional and adaptable software-defined radio (SDR) implementation. To address this gap, we present SoftNB, the first fully functional physical layer SDR implementation for NB-IoT. SoftNB conforms to the latest 3GPP standards and features an efficient and effective signal processing pipeline to mitigate time, frequency, and phase offsets during transmission and reception. Additionally, SoftNB is compatible with various SDR platforms, including USRP, HackRF One, and RTL-SDR Dongle. Extensive evaluations of SoftNB demonstrate its superior performance. Compared to the state-of-the-art baseline, SoftNB achieves an 8× reduction in Block Error Rate (BLER) when the number of repetitions is set to 4 at a distance of 450 meters.

The emergence of sixth generation communication (6G) wireless networks is set to revolutionize vehicular communication by enabling ultra-reliable, low-latency, and high-capacity connectivity in cellular vehicle-to-everything (C-V2X) environments. This paper presents a theoretical framework on novel cooperative vehicular communication and information perception algorithms for large-scale 6G C-V2X networks while leveraging integrated space-air-ground communication system. Specifically, we address key challenges in real-time information exchange and fusion among multiple vehicles. Utilizing inequality theory and functional mapping theory, we derive an upper bound on channel capacity for a fixed number of relays and propose a low-complexity, multi-class relay selection algorithm. Furthermore, we introduce an optimal mobile edge computing (MEC) based correspondence strategy to improve vehicle-to-vehicle communication, alongside an efficient information estimation algorithm to facilitate real-time data sharing. Our simulation results confirm that the proposed algorithms significantly outperform existing cooperative vehicular schemes in terms of channel capacity, while the developed evaluation theory ensures accurate cooperative perception with reduced computational complexity. The proposed framework and theoretical contributions offer a foundational basis for 6G C-V2X networks.

Sensing-assisted beamforming techniques, with the aid of multimodal fusion perception, ensure highly reliable beam selection for V2I communication. However, due to the frequent communication path updates in high-mobility scenarios and the limited computing resources of base stations, the high-burden multimodal fusion computation make communication delays unavoidable. In this paper, we propose BeMamba, a novel multimodal fusion framework based on state space model for beamforming to balance the reliability and low latency of communication. Benefiting from the hidden state’s efficient sequence modeling ability with linear computational complexity, we designTime Sequence MambaandModal Sequence Mambato achieve intra-modal temporal fusion and cross-modal feature fusion. In addition, we develop dedicated data pre-processing methods as well as modality-specific feature extractors for the accessible modalities: image, LiDAR, radar, and GPS. On the DeepSense6G benchmark, our method achieves a 5.16% improvement in beam prediction accuracy, a 77.88% reduction in computational load, and a 4.56 times increase in inference speed.

The emerging multi-modal applications exemplified by multi-DNN inference have renewed interests for mobile intelligence. The goal is to utilize heterogeneous processors on-board to maximize throughput and resource utilization. Among a variety of options, building model-parallel pipelines across different processors is a promising way. However, the existing efforts either focus on optimizing homogeneous DNN executions or simply ignore co-execution slowdown on the shared memory bus. Based on extensive empirical studies and insights with various degrees of resource contention, in this work, we introduce Hetero2Pipe, a two-step pipeline planner based on dynamic programming, and contention-mitigated pipeline bubble minimization to make the problem tractable within manageable search space. The extensive evaluation across three commercial SoCs demonstrates 2-8× speedup compared to the state-of-the-art schemes.

Modeling and solving the flexible job shop scheduling problem (FJSP) is critical for modern manufacturing. However, existing works primarily focus on the time-related makespan target, often neglecting other practical factors, such as transportation. To address this, we formulate a more comprehensive multitarget FJSP that integrates makespan with varied transportation times and the total energy consumption of processing and transportation. The combination of these multiple real-world production targets renders the scheduling problem highly complex and challenging to solve. To overcome this challenge, this article proposes an end-to-end multiagent proximal policy optimization (PPO) approach. First, we represent the scheduling problem as a disjunctive graph (DG) with designed features of subtasks and constructed machine nodes, additionally integrating information of arcs denoted as transportation and standby time, respectively. Next, we use a graph neural network (GNN) to encode features into node embeddings, representing the states at each decision step. Finally, based on the vectorized value function and local critic networks, the PPO algorithm and DG simulation environment iteratively interact to train the policy network. Our extensive experimental results validate the performance of the proposed approach, demonstrating its superiority over the state-of-the-art in terms of high-quality solutions, online computation time, stability, and generalization.

Whilesmalldrone video streaming systems create unprecedented video content, they also place a power burden exceeding 20% on the drone's battery, limiting flight endurance. We present${\sf Mighty}$, a hardware-software solution to minimize the power consumption of a drone's video streaming system by offloading power overheads associated with both video compression and transmission to a ground controller.${\sf Mighty}$innovates a high performance co-design among:(1)a ring oscillator-based, ultra-low power backscatter radio;(2)a spectrally-efficient, non-linear, low-power physical layer modulation and multi-chain radio architecture; and(3)a lightweight video compression codec-bypassing software design. Our co-design exploits synergies among these components, resulting in joint throughput and range performance that pushes the known envelope. We prototype${\sf Mighty}$on PCB board and conduct extensive field studies both indoors and outdoors. The power efficiency of${\sf Mighty}$is about 16.6 nJ/bit. A head-to-head comparison with aDJI Mini2drone's default video streaming system shows that${\sf Mighty}$achieves similar throughput at a drone-to-controller distance of up to 150 meters, with 34–55× improvement of power efficiency than WiFi-based video streaming solutions.

Frequency switching is a fundamental capability for wireless communication systems. However, this capability is significantly constrained in backscatter systems. The difficulty is to generate tunable high-frequency modulation signals on a backscatter tag at an acceptable power budget. In this paper, we present Twaltz, a new design paradigm for backscatter communication that enables frequency switching across large frequency bands. By exploiting a low-power semiconductor device, i.e., tunnel diode, and carefully addressing its physical features, Twaltz generates oscillation signals up to 1.2 GHz while maintaining micro-watt level power consumption. Twaltz further facilitates on-tag oscillation signal stabilization and programmable oscillation frequency tuning. We prototype Twaltz on a PCB board, demonstrating its efficiency in cross-band communication for LoRa backscatter, and verifying its performance in concurrent transmission, channel hopping, and data transmission.

Face authentication (FA) schemes are widely adopted in smart homes nowadays. However, existing FA systems for smart appliances are commonly camera-based and hence experience performance degradation in poor illumination conditions. Mainstream FA systems based on radio frequency require dedicated hardware that is inaccessible to many appliances. In this paper, we propose an acoustic signals-based FA scheme that extracts acoustic signal features associated with facial 3D geometries to achieve FA named SoundFace . This scheme can be widely deployed on most appliances in home environments. We propose a novel two-stage locating approach based on acoustic sensing to capture the signal variation of the user’s face and separate the face region echoes from multipath interferences in the distance dimension. To obtain distinguishable facial features, we design a Convolutional Neural Network (CNN)-based feature extractor. In addition, the acoustic signal is highly susceptible to different changes in practical authentication. To overcome it, we utilize a transfer learning technique with little training overhead to enable SoundFace resilient to various authentication changes. Extensive evaluations demonstrate that SoundFace achieves an average true authentication rate of over 96.2% and an equal error rate of 4.2%, and it is robust to various real-world settings.

LoRa has emerged as a crucial technology that provides ubiquitous connectivity for geographically distributed Internet of Things devices. LoRa employs linear Chirp Spread Spectrum (CSS) modulation in its physical layer to enable long-range communication. Recently, nonlinear chirp has been proposed to replace linear chirp in CSS, to enhance the capacity and scalability of LoRa technology. However, the characteristics and advantages of nonlinear chirps are not yet fully understood. To unleash the potential of nonlinear CSS modulation, this paper presents a comprehensive theoretical and experimental analysis of the nonlinear chirp, focusing on key aspects such as noise resilience, error correction, and bandwidth expansion. Our findings reveal that nonlinear chirps are intrinsically more resilient to noise than linear chirps. Moreover, nonlinear chirps typically exhibit a single-place demodulation error under strong noise conditions, which has informed our development of a simple yet effective error correction design. Additionally, users should carefully manage signal power compensation to avoid distortion while benefiting from the bandwidth expansion offered by nonlinear chirps. Building on these insights, we further refine nonlinear CSS modulation and implement our optimized design on USRPs. Field experiments demonstrate that our design achieves significant performance improvements, marking a step forward in the practical application of nonlinear CSS modulation in LoRa networks.

The millimeter-wave (mmWave) radar has been exploited for gesture recognition. However, existing mmWave-based gesture recognition methods cannot identify different users, which is important for ubiquitous gesture interaction in many applications. In this paper, we propose GesturePrint, which is the first to achieve gesture recognition and gesture-based user identification using a commodity mmWave radar sensor. GesturePrint features an effective pipeline that enables the gesture recognition system to identify users at a minor additional cost. By introducing an efficient signal preprocessing stage and a network architecture GesIDNet, which employs an attention-based multi-level feature fusion mechanism, GesturePrint effectively extracts unique gesture features for gesture recognition and personalized motion pattern features for user identification. We implement GesturePrint and collect data from 17 participants performing 15 gestures in a meeting room and an office, respectively. GesturePrint achieves a gesture recognition accuracy (GRA) of 98.87% with a user identification accuracy (UIA) of 99.78% in the meeting room, and 98.22% GRA with 99.26% UIA in the office. Extensive experiments on three public datasets and a new gesture dataset show GesturePrint's superior performance in enabling effective user identification for gesture recognition systems.

The design of Low Power Wide Area Network (LPWAN) protocols has attracted increasing attention in recent years, particularly within the LoRa research community. However, NB-IoT, another critical LPWAN technology, has not seen similar growth in its research community due to the lack of a functional and flexible software-defined radio (SDR) implementation. To address this gap, we present SoftNB, the first fully functional physical layer SDR implementation for NB-IoT. SoftNB conforms to the latest 3GPP standards and features an efficient and effective signal processing pipeline to mitigate time, frequency, and phase offsets during transmission and reception. Additionally, SoftNB is compatible with various SDR platforms, including USRP, HackRF One, and RTL-SDR Dongle. Extensive evaluations of SoftNB demonstrate its superior performance. Compared to the state-of-the-art baseline, SoftNB achieves an$8\times$reduction in Block Error Rate (BLER) when the number of repetitions is set to 4 at a distance of 450 meters.

Time-Sensitive Networking (TSN) serves as a one-size-fits-all solution for mixed-criticality communication, in which flow scheduling is vital to guarantee real-time transmissions. Traditional approaches statically assign priorities to flows based on their associated applications, resulting in significant queuing delays. In this paper, we observe that assigning different priorities to a flow leads to varying delays due to different shaping mechanisms applied to different flow types. Leveraging this insight, we introduce a new scheduling method in mixed-criticality TSN that incorporates a priority adjustment scheme among diverse flow types to mitigate queuing delays and enhance schedulability. Specifically, we propose dependency-aware priority adjustment algorithms tailored to different link-overlapping conditions. Experiments in various settings validate the effectiveness of the proposed method, which enhances the schedulability by 20.57% compared with the SOTA method.

This paper presents the design, implementation, and evaluation of MagWear, a novel biomagnetism-based system that can accurately and inclusively monitor the heart rate and respiration rate of mobile users with diverse skin tones. MagWear's contributions are twofold. Firstly, we build a mathematical model that characterizes the magnetic coupling effect of blood flow under the influence of an external magnetic field. This model uncovers the variations in accuracy when monitoring vital signs among individuals. Secondly, leveraging insights derived from this mathematical model, we present a softwarehardware co-design that effectively handles the impact of human diversity on the performance of vital sign monitoring, pushing this generic solution one big step closer to real adoptions. We have implemented a prototype of MagWear on a two-layer PCB board and followed IRB protocols to conduct system evaluations. Our extensive experiments involving 30 volunteers demonstrate that MagWear achieves high monitoring accuracy with a mean percentage error (MPE) of 1.55% for heart rate and 1.79% for respiration rate. The head-to-head comparison with Apple Watch 8 further demonstrates MagWear's consistently high performance in different user conditions.

Facial recognition is an important user authentication function in many systems. Traditional vision-based approaches are vulnerable to spoofing attacks and are affected by lighting conditions. In this work, we propose a human facial recognition system using a single millimeter-wave (mmWave) radio. Our system actively transmits the mmWave signal toward the user's face and receives the echoes that contain the geometric face information. We design a novel signal processing pipeline to defend against spoofing attacks and extract facial features. The extracted features are fed into a CNN-based classifier to recognize user identities. We implement and evaluate our system on a tiny and low-cost mmWave radar (25$). The experimental results show that the system can detect the spoofing samples with an average accuracy of 99.12% and achieves an average recognition accuracy of 97.5% over 21 volunteers in different environments.

Time-Sensitive Networking (TSN) offers deterministic communication for time-sensitive applications, using Cyclic Queuing and Forwarding (CQF) to manage time-triggered flows (TT flows). Scheduling TT flows is essential for optimizing network resource utilization and scheduling success rates. Existing works prefer heuristic algorithms due to their favorable trade off between computational overhead and performance. However, they overlook two critical factors during design: search space approximation efficiency and the impact of routing policy. In this study, we present H-GATS (Hybrid Genetic Algorithm and Tabu Search) for CQF-based TSN flow routing and scheduling. H-GATS combines the global search of Genetic Algorithms with the local search of Tabu Search, achieving fine-grained search efficiency and reduced execution time in complex networks. Moreover, H-GATS considers the offset and routing of flows, further improving scheduling performance. Compared to GA, Tabu, and JRS-LB, H-GATS is 5.8×, 3.05×, and 1.6× faster, respectively, in achieving the same success rates. Additionally, H-GATS improves the success rate by 5.5%, 9.6%, and 3.9% and enhances the resource utilization rate by 18%, 13.3%, and 3.3% over these baselines.

Time-sensitive software-defined networking (TSSDN) is an emerging technology that combines the real-time network configuration capabilities of software-defined networking (SDN) with the deterministic flow delivery capabilities of time-sensitive networking (TSN), making it ideal for use in the Industrial Internet of Things (IIoT). However, as data flows generated by industrial applications grow exponentially, it is challenging to achieve low-latency and reliable data flow transmission at the same time in TSSDN due to the limited network resources. To address this issue, we propose the adoption of the frame replication and elimination for reliability (FRER) mechanism in TSSDN-based IIoT systems. However, it is important to acknowledge that the FRER mechanism introduces stress on the already restricted network resources by generating redundant paths. In light of this concern, we construct an end-to-end delay bound model and a reliability model to analyze this issue. To mitigate the stress imposed on the network, we formulate an optimization problem for maximizing the overall system utility while adhering to the transmission requirements of business flows and the limitations of hardware resources. Consequently, we devise an algorithm for reliability-enhanced flow routing and scheduling, which effectively solves the aforementioned optimization problem. To validate the effectiveness and performance of our proposed algorithm, we conduct numerical simulations on four data sets. The results demonstrate the superior performance of our approach compared to existing methods.

This paper presents the design, implementation, and evaluation of MagWear, a novel biomagnetism-based system that can accurately and inclusively monitor the heart rate, respiration rate, and blood pressure of users. MagWear's contributions are twofold. First, we build a mathematical model that characterizes the magnetic coupling effect of blood flow under the influence of an external magnetic field. This model uncovers the variations in accuracy when monitoring vital signs among individuals. Second, leveraging insights derived from this mathematical model, we present a software-hardware co-design that effectively handles the impact of human diversity on the performance of vital sign monitoring, pushing this generic solution one big step closer to real adoptions. Following IRB protocols, our extensive experiments involving 30 volunteers demonstrate that MagWear achieves high monitoring accuracy with a mean percentage error (MPE) of 1.55% for heart rate (HR), 1.79% for respiration rate (RR), 3.35% for systolic blood pressure (SBP), and 3.89% for diastolic blood pressure (DBP). MagWear can also be extended to detect anemia and blood oxygen saturation, which is also our ongoing work.

In the past few years, dockless bike-sharing has become a popular means of public transportation and brought significant convenience to millions of citizens. As one of the key components of a shared bike, the smart locking/unlocking module has proposed a new challenge of how to provide robust power supplement for them. Current charging solutions for shared bikes are mainly based on mechanical power and solar power, and rarely take user experience and charging delay into consideration. In this article, we design a robust RF-based wireless charging system for dockless bike-sharing. Our system utilizes radio frequency (RF) power to provide stable charging service while preserving the quality of service. In our system, an RF wireless charging sensing node is integrated on the bike's basket, so that the mutual interference during charging process and space occupation can be reduced. In order to reduce charging delay, we first design an efficient charging direction scheduling algorithm for a single charger. Then, we extend the solution to multiple-charger scenarios via dynamic programming. Our system has been successfully implemented on a dockless bike-sharing system. The experimental results verify that our design can satisfy the charging demands of shared-bikes and achieve 85% of the optimal solution.

Covert channel, which can break the logical protections of the computer system and leak confidential or sensitive information, has long been considered a security issue in the network research community. However, recent research has shown that cooperative agents can use the “covert” channel to augment the communication of legitimate applications, rather than by adversaries seeking to compromise computer security. This further broadens the potential applications of covert channels. Despite this, the design and implementation of covert channels in the context of Low Power Wide Area Networks (LPWANs) have not been widely discussed. Current state-of-the-art uses On-off keying (OOK) on LoRa PHY to create a covert channel, but this channel has limited transmission distance and capacity. In this paper, we proposeLoPhy, a resilient and fast covert channel over LoRa physical layer (PHY).LoPhyuses the Chirp Spreading Spectrum (CSS) modulation scheme to increase its resilience and explore the trade-off between the covert channel’s capacity and the legitimate channel’s resilience. We implement the proposed covert channel on off-the-shelf devices and software-defined radios and show thatLoPhyachieves a 0.57% bit error rate at a distance of$700\,\text {m}$with slight impact on legitimate channel’s performance. Moreover, we present two applications enabled byLoPhyto demonstrate the potential ofLoPhy. Compared with the state-of-the-art,LoPhybrings up to$18\times $reduction of bit errors and$63\times $gain on noise resilience.

Flow scheduling plays a pivotal role in enabling Time-Sensitive Networking (TSN) applications. Current flow scheduling mainly adopts a centralized scheme, posing challenges in adapting to dynamic network conditions and scaling up for larger networks. To address these challenges, we first thoroughly analyze the flow scheduling problem and find the inherent locality nature of time scheduling tasks. Leveraging this insight, we introduce the first distributed framework for IEEE 802.1Qbv TSN flow scheduling. In this framework, we further propose a multi-agent flow scheduling method by designing Deep Reinforcement Learning (DRL)-based route and time agents for route and time planning tasks. The time agents are deployed on field devices to schedule flows in a distributed way. Evaluations in dynamic scenarios validate the effectiveness and scalability of our proposed method. It enhances the scheduling success rate by 20.31% compared to state-of-the-art methods and achieves substantial cost savings, reducing transmission costs by 410× in large-scale networks. Additionally, we validate our approach on edge devices and a TSN testbed, highlighting its lightweight nature and ease of deployment.

Gait recognition is widely used in diversified practical applications. Currently, the most prevalent approach is to recognize human gait from RGB images, owing to the progress of computer vision technologies. Nevertheless, the perception capability of RGB cameras deteriorates in rough circumstances, and visual surveillance may cause privacy invasion. Due to the robustness and non-invasive feature of millimeter wave (mmWave) radar, radar-based gait recognition has attracted increasing attention in recent years. In this research, we propose a Hierarchical Dynamic Network (HDNet) for gait recognition using mmWave radar. In order to explore more dynamic information, we propose point flow as a novel point clouds descriptor. We also devise a dynamic frame sampling module to promote the efficiency of computation without deteriorating performance noticeably. To prove the superiority of our methods, we perform extensive experiments on two public mmWave radar-based gait recognition datasets, and the results demonstrate that our model is superior to existing state-of-the-art methods.

Covert channel, which can break the logical protections of the computer system and leak confidential or sensitive information, has long been considered a security issue in the network research community. However, recent research has shown that cooperative agents can use the "covert" channel to augment the communication of legitimate applications, rather than by adversaries seeking to compromise computer security. This further broadens the potential applications of covert channels. Despite this, the design and implementation of covert channels in the context of Low Power Wide Area Networks (LPWANs) have not been widely discussed. Current state-of-the-art uses On-off keying (OOK) on LoRa PHY to create a covert channel, but this channel has limited transmission distance and capacity. In this paper, we propose LoPhy, a resilient and fast covert channel over LoRa physical layer (PHY). LoPhy uses the Chirp Spreading Spectrum (CSS) modulation scheme to increase its resilience and explore the trade-off between the covert channel’s capacity and the legitimate channel’s resilience. We implement the proposed covert channel on off-the-shelf devices and software-defined radios and show that LoPhy achieves a 0.57% bit error rate at a distance of 700 m without affecting the legitimate channel’s performance. Moreover, we present two applications enabled by LoPhy to demonstrate the potential of LoPhy. Compared with the state-of-the-art, LoPhy brings up to 18 × reduction of bit errors and 63 × gain on noise resilience.

The widespread deployment of storage disaggregation in the cloud has facilitated flexible scaling and storage overprovisioning, allowing for high utilization of storage capacity and IOPS. Instead of utilizing remote storage protocols to access remote disks, a middle-tier is introduced between compute servers and storage servers in order to serve I/O requests from compute servers and provide computations such as compression and decompression. However, due to the need for a cloud to concurrently serve millions of VMs that require access to disaggregated storage, the middle-tier requires a massive number of servers to process network traffic between computing and storage nodes. For example, a major cloud company may deploy hundreds of thousands of high-end servers to provide such a service for its cloud storage, because the existing CPU-based middle-tier suffers from a severe issue of compute-intensive compression/decompression on high-throughput storage traffic. To address this issue, we introduce SmartDS, a middle-tier-centric SmartNIC that serves storage I/O requests with low latency and high throughput, while maintaining high flexibility and programmability. The key idea behind SmartDS is the application-aware message split (AAMS) mechanism, which allows for the processing of the message's header on the host CPU to achieve high flexibility, and the message's payload on the SmartDS. Experimental results demonstrate that SmartDS provides up to 4.3× more throughput than a CPU-based middle-tier and enables the linear scale-up of multiple network ports and multiple SmartNICs, thus significantly reducing cloud infrastructure costs for disaggregated block storage.

Human activity recognition (HAR) will be an essential function of various emerging applications. However, HAR typically encounters challenges related to modality limitations and label scarcity, leading to an application gap between current solutions and real-world requirements. In this work, we propose MESEN, a multimodal-empowered unimodal sensing framework, to utilize unlabeled multimodal data available during the HAR model design phase for unimodal HAR enhancement during the deployment phase. From a study on the impact of supervised multimodal fusion on unimodal feature extraction, MESEN is designed to feature a multi-task mechanism during the multimodal-aided pre-training stage. With the proposed mechanism integrating cross-modal feature contrastive learning and multimodal pseudo-classification aligning, MESEN exploits unlabeled multimodal data to extract effective unimodal features for each modality. Subsequently, MESEN can adapt to downstream unimodal HAR with only a few labeled samples. Extensive experiments on eight public multimodal datasets demonstrate that MESEN achieves significant performance improvements over state-of-the-art baselines in enhancing unimodal HAR by exploiting multimodal data.

Artificial Intelligence-Generated Content (AIGC) refers to the paradigm of automated content generation utilizing AI models. Mobile AIGC services in the Internet of Vehicles (IoV) network have numerous advantages over traditional cloud-based AIGC services, including enhanced network efficiency, better reconfigurability, and stronger data security and privacy. Nonetheless, AIGC service provisioning frequently demands significant resources. Consequently, resource-constrained roadside units (RSUs) face challenges in maintaining a heterogeneous pool of AIGC services and addressing all user service requests without degrading overall performance. Therefore, in this paper, we propose a decentralized incentive mechanism for mobile AIGC service allocation, employing multi-agent deep reinforcement learning to find the balance between the supply of AIGC services on RSUs and user demand for services within the IoV context, optimizing user experience and minimizing transmission latency. Experimental results demonstrate that our approach achieves superior performance compared to other baseline models.

Time-sensitive networking (TSN), an emerging network technology, requires high-performance scheduling mechanisms to deliver deterministic service in Industry 5.0. Cyclic queuing and forwarding (CQF) is launched to simplify the configuration complexity of the early stage mechanism time-aware shaper in TSN flow scheduling. Previous CQF studies adopt an inflexible incremental flow scheduling scheme, which consists of flow sorting, offset search, and resource judgment. However, we observe that flow sorting and offset search are mutually interdependent. The offset of a flow helps determine the resource status on the flow path, which can guide flow sorting. By utilizing the interaction between flow and offset, we design a novel scheduling approach that achieves high scheduling performance and time efficiency. Specifically, the proposed approach combines flow sorting and offset search together to select flow and its offset (i.e., (flow, offset)) simultaneously. To effectively determine the selecting priority and select the potential optimal flow-offset combination, we define a unified metric,$mapping\,score$, to quantify the schedulability of different flow and offset combinations. The extensive experiments demonstrate that the scheduling success rate of our proposed approach is on average 31.69% higher than the baseline and 4.57% higher than the state-of-the-art flow judgement approach (FLJ) method. Moreover, it outperforms the state-of-art FLJ method by 7.62% in large-scale linear topologies, indicating its great scalability in different network scales and complex topologies.

Current LoRa networks including those following the LoRaWAN specification use the primitive ALOHA mechanism for media access control due to LoRa’s lack of carrier sense capability. From our extensive measurements, the channel activity detection feature that was recently introduced to LoRa for energy-efficiently detecting preamble chirps can also detect payload chirps reliably. This sheds light on an efficient carrier-sense multiple access protocol that we refer to as LMAC for LoRa networks. This article presents the designs of three advancing versions of LMAC that respectively implement carrier-sense multiple access, and balance the communication loads among the channels defined by frequencies and spreading factors based on the end nodes’ local information and then additionally the gateway’s global information. Experiments on a 50-node lab testbed and a 16-node university deployment show that, compared with ALOHA, LMAC brings up to 2.2× goodput improvement and 2.4× reduction of radio energy per successfully delivered frame. Thus, should LoRaWAN’s ALOHA be replaced with LMAC, network performance boosts can be realized.

With the emergence of Industry 5.0, it is significant to enable efficient cooperation between humans and machines in the Industrial Internet of Things (IIoT). However, achieving real-time and reliable transmission of data flows deriving from time-sensitive applications in IIoT remains an open challenge. In this paper, we propose a three-layer software-defined IIoT (SDIIoT) architecture to enable multiple industrial services and flexible network configuration. In particular, when network services change frequently in SDIIoT, the delay of the control plane has a great influence on the end-to-end delay of data flows. To address this issue, we portray two different service curves of OpenFlow switches to adapt to dynamic network status based on Network Calculus (NC). To elevate resource efficiency and comply with friendly environments, we minimize the total worst-case network cost under strict resource constraints and transmission requirements by exploiting the joint flow routing and scheduling algorithm (JFRSA). Our numerical simulation results demonstrate the effectiveness and efficiency of our solution.

Intelligent transformation for traditional factories is a widely discussed topic. The key to this transformation is ensuring the integration between information technology and operational technology. However, it is a challenging task in industry owing to the communication heterogeneity of the underlying production equipment (horizontal communication), and inefficient interactions between the equipment and information decision center (vertical communication). In this paper, we explore asset administration shell (AAS), an asset virtualization technology, shielding heterogeneous physical communication protocol of production equipment. Besides, to promote inefficient communication between the equipment and information decision center, we adapt OPC UA protocol as the communication protocol of AAS for vertical communication. In addition, time-sensitive networking (TSN) is applied to ensure communication between the AAS and the corresponding physical device. Above operations ensure devices interconnection and interoperability. On this basis, we propose an AAS-based production management platform (AASPMP), which aims at the coverage from the demand side to the production side. Such an intelligent system characterizes three layers to decompose complicated system functionalities, and a visible client is provided for the convenience of remote operation and maintenance. We deploy our system on the actual production system and demonstrate the effectiveness of our design.

As an emerging and prospective paradigm, the industrial Internet of Things (IIoT) enable intelligent manufacturing through the interconnection and interaction of industrial production elements. The traditional approach that transmits data in a single physical network is undesirable because such a scheme cannot meet the network requirements of different industrial applications. To address this problem, in this article, we propose a network slicing orchestration system for remote adaptation and configuration in smart factories. We exploit software-defined networking and network functions virtualization to slice the physical network into multiple virtual networks. Different applications can use a dedicated network that meets its requirements with limited network resources with this scheme. To optimize network resource allocation and adapt to the dynamic network environments, we propose two heuristic algorithms with the assistance of artificial intelligence and the theoretical analysis of the network slicing system. We conduct numerical simulations to learn the performance of the proposed algorithms. Our experimental results show the effectiveness and efficiency of our proposed algorithms when multiple network services are concurrently running in the IIoT. Finally, we use a case study to verify the feasibility of the proposed network slicing orchestration system on a real smart manufacturing testbed.

Low-power wide-area network technologies such as long-range wide-area network (LoRaWAN) are promising for collecting low-rate monitoring data from geographically distributed sensors, in which timestamping the sensor data is a critical system function. This article considers a synchronization-free approach to timestamping LoRaWAN uplink data based on signal arrival time at the gateway, which well matches LoRaWAN’s one-hop star topology and releases bandwidth from transmitting timestamps and synchronizing end devices’ clocks at all times. However, we show that this approach is susceptible to a frame delay attack consisting of malicious frame collision and delayed replay. Real experiments show that the attack can affect the end devices in large areas up to about 50,000, m 2 . In a broader sense, the attack threatens any system functions requiring timely deliveries of LoRaWAN frames. To address this threat, we propose a LoRaTS gateway design that integrates a commodity LoRaWAN gateway and a low-power software-defined radio receiver to track the inherent frequency biases of the end devices. Based on an analytic model of LoRa’s chirp spread spectrum modulation, we develop signal processing algorithms to estimate the frequency biases with high accuracy beyond that achieved by LoRa’s default demodulation. The accurate frequency bias tracking capability enables the detection of the attack that introduces additional frequency biases. We also investigate and implement a more crafty attack that uses advanced radio apparatuses to eliminate the frequency biases. To address this crafty attack, we propose a pseudorandom interval hopping scheme to enhance our frequency bias tracking approach. Extensive experiments show the effectiveness of our approach in deployments with real affecting factors such as temperature variations.

Outsourcing the design of deep neural networks may incur cybersecurity threats from the hostile designers. This paper studies a new covert channel attack that leaks the inference results over the air through a hostile design of the neural network architecture and the computing device's electromagnetic radiation when executing the neural network. Specifically, the hostile neural network consists of a series of binary models that correspond to all classes and are executed sequentially. The execution terminates once any binary model given the input is positive about its responsible class. We describe an approach to generate such binary models by pruning a benign neural network that is trained using the standard method to deal with all the classes. Compared with the benign neural network, the hostile one has similar memory usage and negligible classification accuracy drop, but distinct inference times for the samples of different classes. As a result, the hostile neural network's classification result can be eavesdropped by measuring the duration of the electromagnetic radiation emanated from the computing device. As neural networks are stored and transmitted as data files, this covert channel attack is more stealthy to the anti-malware than other code-based attacks. We implement the described attack on two edge computing devices that execute the hostile neural network on CPU or GPU. Evaluation shows 100% empirical accuracy in eavesdropping the inference results.

Smartphone indoor location awareness is increasingly demanded by a variety of mobile applications. The existing solutions for accurate smartphone indoor localization rely on additional devices or pre-installed infrastructure (e.g., dense WiFi access points, Bluetooth beacons). In this demo, we present EchoLoc, an infrastructure-free smartphone indoor localization system using room acoustic response to a chirp emitted by the phone. EchoLoc consists of a mobile client for echo data collection and a cloud server hosting a deep neural network for location inference. EchoLoc achieves 95% accuracy in recognizing 101 locations in a large public indoor space and a median localization error of 0.5 m in a typical lab area. Demo video is available at https://youtu.be/5si0Cq6LzT4.

Low-power wide-area network technologies such as LoRaWAN are promising for collecting low-rate monitoring data from geographically distributed sensors, in which timestamping the sensor data is a critical system function. This paper considers a synchronization-free approach to timestamping LoRaWAN uplink data based on signal arrival time at the gateway, which well matches LoRaWAN’s one-hop star topology and releases bandwidth from transmitting timestamps and synchronizing end devices’ clocks at all times. However, we show that this approach is susceptible to a frame delay attack consisting of malicious frame collision and delayed replay. Real experiments show that the attack can affect the end devices in large areas up to about 50, 000 m2. In a broader sense, the attack threatens any system functions requiring timely deliveries of LoRaWAN frames. To address this threat, we propose a LoRaTS gateway design that integrates a commodity LoRaWAN gateway and a low-power software-defined radio receiver to track the inherent frequency biases of the end devices. Based on an analytic model of LoRa’s chirp spread spectrum modulation, we develop signal processing algorithms to estimate the frequency biases with high accuracy beyond that achieved by LoRa’s default demodulation. The accurate frequency bias tracking capability enables the detection of the attack that introduces additional frequency biases. Extensive experiments show the effectiveness of our approach.

Current LoRa networks including those following the LoRaWAN specification use the primitive ALOHA mechanism for media access control due to LoRa's lack of carrier sense capability. From our extensive measurements, the Channel Activity Detection (CAD) feature that is recently introduced to LoRa for energy-efficiently detecting preamble chirps, can also detect payload chirps reliably. This sheds light on an efficient carrier-sense multiple access (CSMA) protocol that we call LMAC for LoRa networks. This paper presents the designs of three advancing versions of LMAC that respectively implements CSMA, balances the communication loads among the channels defined by frequencies and spreading factors based on the end nodes' local information and then additionally the gateway's global information. Experiments on a 50-node lab testbed and a 16-node university deployment show that, compared with ALOHA, LMAC brings up to 2.2× goodput improvement and 2.4× reduction of radio energy per successfully delivered frame. Thus, should the LoRaWAN's ALOHA be replaced with LMAC, network performance boosts can be realized.

Executing deep neural networks for inference on the server-class or cloud backend based on the data generated at the edge of the Internet of Things is desirable due primarily to the limited compute power of the edge devices and the need to protect the confidentiality of the inference neural networks. However, such a remote inference scheme incurs concerns regarding the privacy of the inference data transmitted by the edge devices to the curious backend. This article presents a lightweight and unobtrusive approach to obfuscate the inference data at the edge devices. It is lightweight in that the edge device only needs to execute a small-scale neural network; it is unobtrusive in that the edge device does not need to indicate whether obfuscation is applied. Extensive evaluation by three case studies of free-spoken digit recognition, handwritten digit recognition, and American sign language recognition shows that our approach effectively protects the confidentiality of the raw forms of the inference data while effectively preserving backend's inference accuracy.

LoRa is an emerging low-power wide-area network technology. Existing studies have focused on LoRa's communication performance. Differently, we study two physical properties of LoRa, i.e., its performance in timing the signal propagation and the transmitters' frequency traits. Signal timing is a basis for implementing clock synchronization, ranging, and advanced physical (PHY) layer techniques such as concurrent decoding. However, LoRa end devices do not provide PHY-layer timestamping that is needed for accurate timing. We propose a SoftLoRa design that integrates a low-power software-defined radio receiver with a LoRa transceiver to provide PHY-layer access. Experiments show that SoftLoRa achieves microseconds timing accuracy over one kilometer and in a multistory building with strong signal attenuation.

Separation of Control and Data Planes (SCDP) is a desirable paradigm for low-power multi-hop wireless sensor networks requiring high network performance and manageability. Existing SCDP networks generally adopt an in-band control plane scheme in that the control-plane messages are delivered by their data-plane networks. The physical coupling of the two planes may lead to undesirable consequences. Recently, multi-radio platforms (e.g., TI CC1350 and OpenMote B) are increasingly available, which make the physical separation of the control and data planes possible. To advance the network architecture design, we propose to leverage on the long-range communication capability of the Low-Power Wide-Area Network (LPWAN) radios to form one-hop out-of-band control planes. LoRaWAN, an open, inexpensive, and ISM band based LPWAN radio, is chosen to prototype our out-of-band control plane called LoRaCP. Several characteristics of LoRaWAN such as downlink-uplink asymmetry and primitive ALOHA media access control need to be dealt with to achieve high reliability and efficiency. To address these challenges, a TDMA-based multi-channel transmission control is designed, which features an urgent channel and negative acknowledgment. On a testbed of 16 nodes, LoRaCP is applied to physically separate the control-plane network of the Collection Tree Protocol (CTP) from its Zigbee-based data-plane network. Extensive experiments show that LoRaCP increases CTP’s packet delivery ratio from 65% to 80% in the presence of external interference, while consuming a per-node average radio power of 2.97mW only.

Separation of control and data planes (SCDP) is a desirable paradigm for low-power multi-hop wireless networks requiring high network performance and manageability. Existing SCDP networks generally adopt an in-band control plane scheme in that the control-plane messages are delivered by their data-plane networks. The physical coupling of the two planes may lead to undesirable consequences. To advance the network architecture design, we propose to leverage on the long-range communication capability of the increasingly available low-power wide-area network (LPWAN) radios to form one-hop out-of-band control planes. We choose LoRaWAN, an open, inexpensive, and ISM band based LPWAN radio to prototype our out-of-band control plane called LoRaCP. Several characteristics of LoRaWAN such as downlink-uplink asymmetry and primitive ALOHA media access control (MAC) present challenges to achieving reliability and efficiency. To address these challenges, we design a TDMA-based multi-channel MAC featuring an urgent channel and negative acknowledgment. On a testbed of 16 nodes, we demonstrate applying LoRaCP to physically separate the control-plane network of the Collection Tree Protocol (CTP) from its ZigBee-based data-plane network. Extensive experiments show that LoRaCP increases CTP's packet delivery ratio from 65 % to 80 % in the presence of external interference, while consuming a per-node average radio power of 2.97mW only.