Diffusion probabilistic models have set a new standard for generative fidelity but are hindered by a slow iterative sampling process. A powerful training-free strategy to accelerate this process is Schedule Optimization, which aims to find an optimal distribution of timesteps for a fixed and small Number of Function Evaluations (NFE) to maximize sample quality. To this end, a successful schedule optimization method must adhere to four core principles: effectiveness, adaptivity, practical robustness, and computational efficiency. However, existing paradigms struggle to satisfy these principles simultaneously, motivating the need for a more advanced solution. To overcome these limitations, we propose the Hierarchical-Schedule-Optimizer (HSO), a novel and efficient bi-level optimization framework. HSO reframes the search for a globally optimal schedule into a more tractable problem by iteratively alternating between two synergistic levels: an upper-level global search for an optimal initialization strategy and a lower-level local optimization for schedule refinement. This process is guided by two key innovations: the Midpoint Error Proxy (MEP), a solver-agnostic and numerically stable objective for effective local optimization, and the Spacing-Penalized Fitness (SPF) function, which ensures practical robustness by penalizing pathologically close timesteps. Extensive experiments show that HSO sets a new state-of-the-art for training-free sampling in the extremely low-NFE regime. For instance, with an NFE of just 5, HSO achieves a remarkable FID of 11.94 on LAION-Aesthetics with Stable Diffusion v2.1. Crucially, this level of performance is attained not through costly retraining, but with a one-time optimization cost of less than 8 seconds, presenting a highly practical and efficient paradigm for diffusion model acceleration.

The widespread adoption of WiFi has made throughput efficiency a critical concern in wireless networks. While Full-Duplex (FD) technology promises to double network capacity by enabling simultaneous transmission and reception, existing FD-WiFi designs primarily focus on the data transmission phase, leaving the fundamental inefficiencies in channel contention unaddressed. This paper presents CollFree, a novel WiFi protocol that exploits FD capabilities during both contention and data transmission phases. At its core, CollFree introduces a Slotwise Arbitration (SA) mechanism that enables each node to simultaneously transmit contention signals and sense channel status in each contention slot. This dual-mode operation significantly reduces contention time and facilitates collision-free data transmissions through a unique winner-determination process. We then develop theoretical models to analyze CollFree’s contention performance and throughput efficiency under both perfect and imperfect Clear Channel Assessment (CCA) conditions, providing guidelines for parameter optimization in practical deployments. Extensive simulations demonstrate that CollFree enhances throughput efficiency by over 20% compared to state-of-the-art FD-WiFi systems while maintaining distributed control and compatibility with current WiFi standards. These results suggest that CollFree represents a significant step toward realizing the full potential of FD technology in next-generation WiFi networks.

Cross-technology communication (CTC) enables direct communications among devices with heterogeneous wireless technologies, e.g., Bluetooth, WiFi, and ZigBee, thereby reducing the cost and complexity of their interconnections. Yet CTC is unreliable due to the technology heterogeneity, and most existing CTC designs do not provide acknowledgment (ACK) feedback to ensure reliable data transmission. Few ACK designs are only applicable to feedback for ZigBee-WiFi pair and vulnerable to sampling offsets that inherently exist in CTC. In this work, we propose a General and Offset-resistant Physical-layer ACK approach, called GOP-ACK, to support reliable communications. Its core idea lies in encoding ACK messages with offset-resistant signal that has two benefits: 1) it can be adapted to a wide range of CTC scenarios with minimal adjustment, and 2) it can be effortlessly and robustly detected even in the presence of sampling offsets. We offer practical guidelines to tackle key deployment challenges related to signal construction, efficient and robust transmission, and effective firmware module reuse, enabling the application of GOP-ACK to specific CTC scenarios. Based on them, we implement two designs: ZigBee-to-BLE and ZigBee-to-WiFi feedback, and propose a theoretical model to analyze their performance. We then conduct experiments and simulations to verify GOP-ACK’s feasibility and superiority over the state of the art, thereby enhancing the practicality of CTC greatly.

Node-assisted WiFi backscatter communication (NWB) is a promising technology that allows backscatter tags to communicate over long distances and achieve high throughput by using WiFi nodes as relays and enabling concurrent transmissions. However, NWB lacks an accurate theoretical model to evaluate and optimize its network performance, which is challenging to develop due to the location and contention randomness of both WiFi nodes and backscatter tags. Existing backscatter models that only account for one type of randomness are not suitable for NWB. To address this issue, we propose a novel stochastic geometry-based model that captures Location and Contention Randomness as well as the involved dependency and interference (named LoCoR). We use the Matérn hard-core point process and Matérn cluster process to model the repulsive and clustering attributes of the locations of WiFi nodes and backscatter tags, respectively. We also introduce a unified time unit to analyze the randomness and dependency of WiFi and backscatter contentions. Our model factors in various design parameters (e.g., the density and transmission power of tags) and can be used to evaluate their impacts on system throughput. We conduct extensive simulations to validate the accuracy of our model. With our accurate model, one can easily configure the optimal design parameters to maximize system throughput.

Conventional wireless networks are half-duplex and most of them use contention-based protocols. These protocols usually adopt a principle of contention with collision avoidance and infer a collision occurrence very late from the absence of an acknowledgment after data transmission, causing low network performance. Wireless full-duplex (FD) enables simultaneous transmission (TX) and reception (RX) on the same channel. Exploiting this functionality, this article proposes the first design that enables contention with collision detection (CCD) to improve the network performance. We call the proposed design FD-CCD. With FD-CCD, in contention, a node exploits the TX antenna to transmit a signal for channel contention, while exploiting the RX antenna to sense if other nodes are transmitting too. By checking the status of the TX and RX antennas, the node can detect the contention collision before data transmission and, hence, obtain an opportunity to avoid the data collision effectively. FD-CCD also supports priority-based contentions, is of very low contention overhead, and is compatible with conventional 802.11 networks. This article then develops a theoretical model to analyze the system performance and optimize protocol parameter settings. Extensive simulations verify the effectiveness of our design and the accuracy of our model. This study is very helpful in designing efficient FD protocols.

Vehicular Ad-Hoc Networks (VANETs), as the crucial support of Intelligent Transportation Systems (ITS), have received great attention in recent years. With the rapid development of VANETs, various services have generated a great deal of data that can be used for transportation planning and safe driving. Especially, with the advent of Coronavirus Disease 2019 (COVID-19), the transportation system has been impacted, thus novel modes of transportation planning and intelligent applications are necessary. Digital twins can provide powerful support for artificial intelligence applications in Transportation Big Data (TBD). The features of VANETs are varying, which arises the main challenge of digital twins applying in TBD. Network traffic prediction, as part of digital twins, is useful for network management and security in VANETs, such as network planning and anomaly detection. This paper proposes a network traffic prediction algorithm aiming at time-varying traffic flows with a large number of fluctuations. This algorithm combines Deep Q-Learning (DQN) and Generative Adversarial Networks (GAN) for network traffic feature extraction. DQN is leveraged to carry out network traffic prediction, in which GAN is involved to represent Q-network. Meanwhile, the generative network can increase the number of samples to improve the prediction error. We evaluate the performance of our method by implementing it on three real network traffic data sets. Finally, we compare the two state-of-the-art competing methods with our method.

In Internet of Things (IoT), physical-level cross-technology communication (CTC) enables IoT gateways to communicate with heterogeneous nodes economically. However, because of bandwidth asymmetry between heterogeneous technologies, many residual frequency resources are often not fully utilized. Without modification on hardware, in this article, we consider the coexistence of ultralow power (ULP) and WiFi nodes, and propose ERFR-CTC that enables an IoT gateway to fully exploit residual frequency resources without adding additional cost. With ERFR-CTC, the gateway can simultaneously communicate with ULP and WiFi nodes only via a single WiFi network interface card (NIC), which is not only economic but also very efficient. In particular, ERFR-CTC enables ULP nodes to correctly demodulate ULP signals without being interfered by WiFi signals. We then develop theoretical models to quantify available residual frequency resources and analyze the system throughput. Finally, extensive simulations verify that our model is very accurate and show that ERFR-CTC can increase the system throughput by up to 51.4%.

Dynamic frequency scaling (DFS) is a state-of-the-art power-saving technique. Various DFS-based WiFi schemes have been proposed for power saving. These schemes demonstrated the power-saving feasibility of DFS via hardware implementation or simulation. This paper is the first that proposes a general theoretical framework to evaluate the performance of these schemes, where we use Queuing theory to analyze the system throughput and use Semi-Markov theory to quantify the power consumption. In addition, we adopt the energy efficiency (i.e., the throughput per energy cost) to compare the gains of these schemes. This efficiency measure can be used to make a trade-off between system throughput and power consumption and therefore help us choose appropriate parameter settings. Extensive simulations verify that our theoretical model is very accurate and our theoretical results well match with universal software radio peripheral (USRP) experiment results. Our study shows that DFS can greatly improve the energy efficiency of WiFi networks even under low SNR conditions; for example, when SNR = 9.7 dB (i.e., the basic requirement for decoding packets in WiFi), the improvement is around 25 percent for 802.11b at rate 11 Mb/s and 16 percent for 802.11 ac at rate 1300 Mb/s. Our study also shows that DFS can be well integrated with other commonly used power-saving mechanisms to improve energy efficiency further.

The fifth‐generation‐enabled Internet of Things (5G‐enabled IoT) has been considered as a key enabler for the automation of almost all industries. In 5G‐enabled IoT, resource‐limited passive devices are expected to join the IoT using the WiFi backscatter communication (WiFi‐BSC) technology. However, WiFi‐BSC deployment is currently limited due to high equipment cost and short transmission range. To address these two drawbacks, in this paper, we propose a low‐cost and long‐range node‐assisted WiFi backscatter communication scheme. In our scheme, a WiFi node can receive backscatter signals using two cheap regular half‐duplex antennas (instead of using expensive full‐duplex technique or collaborating with multiple other nodes), thereby reducing the equipment cost. Besides, WiFi nodes can help relay backscatter signals to remote 5G infrastructure, greatly extending the backscatter’s transmission range. We then develop a theoretical model to analyze the throughput of WiFi‐BSC. Extensive simulations verify the effectiveness of our scheme and the accuracy of our model.

5G customer premise equipment (5G-CPE) is an IoT gateway technology that integrates 5G and Wi-Fi and therefore can provide Wi-Fi connection for IoT devices and meanwhile benefit from the advantages of 5G. With the increasing number of IoT devices, transmission collisions and hidden/exposed terminal problems on the Wi-Fi connection side become more and more serious. Conventional mechanisms cannot solve these problems well. In this paper, we propose a Wi-Fi sector (Wi-FiS) design, which is compatible with Wi-Fi, to solve them fundamentally. Wi-FiS divides the whole coverage area of Wi-Fi into multiple sectors and utilizes beamforming technology and sector-based scheduling to improve system performance of Wi-Fi dense networks. For a single-cell network, Wi-FiS differentiates uplink and downlink operations and totally excludes collision in downlink. For a multicell network, Wi-FiS can avoid hidden and exposed terminal problems, while enabling parallel transmissions among multiple cells. We then develop a theoretical model to analyze Wi-FiS’s throughput. Extensive simulations verify that our theoretical model is very accurate and Wi-FiS can improve system throughput of Wi-Fi dense networks significantly.

The increasing popularity of drones is bringing many public security and privacy breach issues, such as smuggling, intrusion, and illegal surveillance. Traditional approaches to detecting and localizing drones such as radar and computer vision incur high costs and hence are not desirable for large-scale applications. In this paper, we propose a cost-effective crowdsensing system named CEDAR to achieve such a goal. Specifically, we introduce a novel way of detecting drones by smartphones, exploiting the fact that most drones adopt Wi-Fi for communications with ground control stations. We design an efficient detection algorithm that takes advantage of historical Wi-Fi beacon information and MAC address encoding mechanisms used by drone manufacturers. Using received signal strength, we can also localize the detected drones. Further, to encourage participants' involvement, we design an incentive mechanism based on online auction that guarantees truthfulness and consumer sovereignty. CEDAR can be directly applied to multiple drone scenarios. We implement the system based on Android for the client and Spring, Spring MVC, and Mybatis (SSM) for the centralized platform that supports scalability and hierarchical structure, and enables the coordination between clients and the platform. We perform extensive experiments to validate our analysis. Particularly, the detection rate in the experiments reaches 86.7 percent even without any prior information about drones.

Backscatter communication is a new wireless communication technology, which can enable battery-free devices to communicate with others by backscattering ambient radio-frequency signals, and therefore has great potential to be deployed in the future Internet of Things (IoT) networks. In the existing related studies, the adopted centralized approach might be not suitable for a large-scale IoT network with sporadic backscatter communication, while the adopted distributed approach ignored the demand of the access point (AP). In this paper, we consider a large-scale IoT network consisting of the legacy Wi-Fi communication and the backscatter communication, and aim to propose an efficient distributed backscatter medium access control protocol that takes into account of the demand of the AP. In our protocol, when the AP has a demand to collect the information of backscatter devices, it enables backscatter devices to contend for the channel with Wi-Fi devices in a separate manner, where the backscatter devices are only allowed to participate in the contention in a limited time period. We then characterize this constrained contention process and develop a theoretical model to analyze the performance of the proposed protocol. With this model, we can exactly express the per-node throughput of Wi-Fi and backscatter devices. Finally, extensive simulations verify that our model is much accurate, and our protocol can outperform the related protocol in terms of both per-node throughput and system throughput.

Given the popularity of drones for leisure, commercial, and government (e.g., military) usage, there is increasing focus on drone regulation. For example, how can the city council or some government agency detect and track drones more efficiently and effectively, say, in a city, to ensure that the drones are not engaged in unauthorized activities? Therefore, in this article, we propose a crowdsensing-based cyber-physical system for drone surveillance. The proposed system, CSDrone, utilizes surveillance data captured and sent from citizens’ mobile devices (e.g., Android and iOS devices, as well as other image or video capturing devices) to facilitate jointly drone detection and tracking. Our system uses random finite set (RFS) theory and RFS-based Bayesian filter. We also evaluate CSDrone’s effectiveness in drone detection and tracking. The findings demonstrate that in comparison to existing drone surveillance systems, CSDrone has a lower cost, and is more flexible and scalable.