Split Federated Learning is a system-efficient federated learning paradigm that leverages the rich computing resources at a central server to train model partitions. Data heterogeneity across silos, however, presents a major challenge undermining the convergence speed and accuracy of the global model. This paper introduces Step-wise Momentum Fusion (SMoFi), an effective and lightweight framework that counteracts gradient divergence arising from data heterogeneity by synchronizing the momentum buffers across server-side optimizers. To control gradient divergence over the training process, we design a staleness-aware alignment mechanism that imposes constraints on gradient updates of the server-side submodel at each optimization step. Extensive validations on multiple real-world datasets show that SMoFi consistently improves global model accuracy (up to 7.1%) and convergence speed (up to 10.25x). Furthermore, SMoFi has a greater impact with more clients involved and deeper learning models, making it particularly suitable for model training in resource-constrained contexts.

Machine learning models can now run on microcontrollers. Thanks to the advances in neural architectural search, we can automatically identify tiny machine learning (tinyML) models that satisfy stringent memory and energy requirements. However, existing methods often overlook the energy used during event detection and data gathering. This is critical for devices powered by renewable energy sources like solar power, where energy efficiency is paramount. To address it, we introduce SolarML, a solution designed specifically for solar-powered tinyML platforms, which optimizes the end-to-end system's inference accuracy and energy consumption, from data gathering and processing to model inference. Considering two applications of gesture recognition and keywords spotting, SolarML has the following contributions: 1) a hardware platform with an optimal event detection mechanism that reduces event detection costs by up to 10 x compared to state-of-the-art alternatives; 2) a joint optimization framework eNAS that reduces the energy consumption of the sensor and inference model by up to 2 x, compared to methods that only optimize the inference model. Jointly, they enable SolarML to run end-to-end gesture and audio inference on a battery-free tinyML platform by only harvesting solar energy for 30 and 57 seconds, respectively, in an office environment (500 lux). Source code is available at [1].

Hidden spy cameras are a growing global threat to personal privacy. With the emergence of translucent screen technology, a new security risk has arisen: cameras can now hide behind devices' screens like TVs and monitors that are common in private places, e.g., hotel rooms. The screen's covering over the hidden camera not only makes the cameras behind it unnoticeable to human eyes but also makes existing camera detection methods less effective. Inspired by recent advances in representing real-world scenes accurately using neural networks, we propose Neural Infrared Reflectance Field (NIRF) to learn the intricate optical properties of the screen and the cameras hidden behind it. Through NIRF, we design a new camera detection system by leveraging the unique reflective properties of behind-screen cameras and screens. We evaluate NIRF with thorough experiments on five smartphones. Our NIRF archives over 90% detection rate and is robust to different conditions, including varied backgrounds, ambient light levels, screen protectors, and screen contents. Besides, we conduct a field study by deploying 18 common spy cameras behind a 65-inch translucent TV and recruiting 27 people to compare NIRF with commercial hidden camera detectors. NIRF achieves an 89.5% detection rate, significantly outperforming the best commercial hidden camera detector that only has a 14.4% detection rate of behind-screen cameras.

Achieving accurate and low-latency spectrum sensing on resource-constrained devices is essential but very difficult. Traditional In-phase and Quadrature (I/Q)-based and the ShortTime Fourier Transform (STFT)-based methods fail to balance the computational overhead and classification accuracy. In this paper, we propose a novel framework –Spectrum Painting (SP)– which enables on-device signal classification with low latency and high accuracy. We design new signal processing methods to compress spectrograms while keeping global signal features and augmenting the salient features of small objects. SP achieves high-accuracy signal classification, assisted further by our proposed Dual-channel Convolutional Neural Network (DualCNN). We collect diverse datasets to evaluate the proposed SP, including synthesized data, and testbed data (from up to 18 commodity devices) obtained from real-world environments in the wild and office settings. Experimental results of SP running on Raspberry Pi 4B show a great reduction in latency up to $20 \times$ while maintaining a 95% accuracy. Furthermore, SP demonstrates superior performance within both the centralized learning architecture and the Federated Learning (FL) architecture. For example, the challenging cross-environment evaluation of the SP in the iid-FL scenario yields a substantial accuracy improvement, on average from 24.6% to 83.8%.

Over the past decade, visible light positioning has become increasingly important for precise localization systems, yet its widespread adoption is limited due to the necessity of modifying existing lighting systems. This article presents HueLoc, a novel method that bypasses this issue by using inherent features of light, such as the dominant colors in white light-emitting diode (LED) lights, and employs affordable, energy-efficient hue sensors for location services. We propose that by extracting the power at dominant wavelengths of LEDs, these can be uniquely identified using a specifically designed signature. The unique signatures can be used by mobile objects for spatial awareness and further localization using the proposed regression-based learning approach. Our experiments demonstrate that HueLoc attains a location-mapping accuracy of 100% and achieves decimeter-level localization precision with a moving object in uncontrolled lighting conditions. Moreover, these unique signatures can be combined with other RF-based technologies to enhance their localization accuracy. As an example, this article details the integration of Bluetooth features with light signatures using a three-stage incremental learning approach. The experimental results show that this fusion method significantly improves Bluetooth localization by over 75%, overcoming challenges associated with severe indoor multipath and achieving highly precise location accuracy within decimeters.

Utilizing the mmWave band can potentially achieve the high data rate needed for realistic and seamless interaction within a virtual reality (VR) application. To this end, beamforming in both the access point (AP) and head-mounted display (HMD) sides is necessary. The main challenge in this use case is the specific and highly dynamic user movement, which causes beam misalignment, degrading the received signal level and potentially leading to outages. This study examines mmWave-based coordinated multi-point networks for VR applications, where two or multiple APs cooperatively transmit the signals to an HMD for connectivity diversity. Instead of using omni-reception, we propose dual-beam reception based on the analog beamforming at the HMD, enhancing the receive beamforming gain towards serving APs while achieving diversity. Evaluation using actual HMD movement data demonstrates the effectiveness of our approach, showcasing a reduction in outage rates of up to 13% compared to quasi-omnidirectional reception with two serving APs, and a 17% decrease compared to steerable single-beam reception with a serving AP. Widening the separation angle between two APs can further reduce outage rates due to head rotation as rotations can still be tracked using the steerable multi-beam, albeit at the expense of received signal levels reduction during the non-outage period.

Federated Learning (FL) is an important privacy-preserving learning paradigm that plays an important role in the Intelligent Internet of Things. Training a global model in FL, however, is vulnerable to the noise in the heterogeneous data across the clients. In this paper, we introduce **FedTrans**, a novel client-transparent client utility estimation method designed to guide client selection for noisy scenarios, mitigating performance degradation problems. To estimate the client utility, we propose a Bayesian framework that models client utility and its relationships with the weight parameters and the performance of local models. We then introduce a variational inference algorithm to effectively infer client utility, given only a small amount of auxiliary data. Our evaluation demonstrates that leveraging FedTrans as a guide for client selection can lead to a better accuracy performance (up to 7.8\%), ensuring robustness in noisy scenarios.

While out-vehicle sensing has achieved great success with the development of vehicle radar and Lidar systems, invehicle sensing attracts a lot of attention recently. However, the popular camera-based solutions raise privacy concerns and pose requirement on lighting conditions. Researchers recently utilize wireless signals for sensing. However, besides requiring dedicated hardware, the rich multipath in a small cabin space causes severe interference, degrading the sensing reliability. In this paper, we propose a new sensing modality for in-vehicle sensing, leveraging the leaked EM signals from electric vehicles. The key observation is that the human body can capture the leaked signals, and body motions affect the signal variation patterns. Our solution involves designing conductive cloth tags on the seat to effectively collect body-captured signals and adopting a reference tag to deal with interference. Through extensive experiments conducted over 100 hours, covering a driving distance of 4000 kilometers on various real roads, our system, EVLeSen, can achieve over 90% accuracy in recognizing body motions utilizing just the leaked ambient signals.

This paper presents a novel transparent amplifying intelligent surface (TAIS) architecture for uplink enhancement in indoor-to-outdoor mmWave communications. The TAIS is an amplifier-based transmissive intelligent surface that can refract and amplify the incident signal, instead of only refracting it with adjustable phase shift by most passive reconfigurable intelligent surfaces (RIS). With advanced indium tin oxide film and printing technology, TAIS can be fabricated on the windows without any visual effects. This paper primarily focuses on exploiting the TAIS-based architecture to boost the uplink spectral efficiency (SE) in indoor-to-outdoor mmWave communications. By jointly optimizing the TAIS’s phase shift matrix and transmit power of the user equipment, the uplink SE can be maximized by exploiting the nonlinearity in the TAIS’s amplification process. The key enabler is that we drive the optimal phase shift matrix that maximizes the SE and deduces its closed-form representation. The SE maximization is then proved to be transferred to the transmit power optimization problem. Another important enabler is that we design a low-complexity algorithm to solve the optimization problem using the difference of convex programming. Moreover, the asymptotic spectral efficiency under nonlinear amplification and power scaling law with infinitely large elements under both the sparse and rich scattering channel models are analyzed. Simulation results show that our proposed TAIS can increase the SE by up to 24.7% as compared to its alternative methods.

With the development of communication networks and Artificial Intelligence (AI) technologies, Digital Twin (DT) now emerges to support various applications such as engineering, monitoring, controlling, healthcare and the optimization of cyber-physical systems. There is an increasing demand to create DTs that can represent physical entities for improving operational efficiency. A conventional DT consists of monitoring, imitation, and feedback control. However, conventional DTs cannot ensure efficient real-time imitation due to the high dynamics of physical systems such as UAV-based target tracking scenario. To address this issue, we propose a federated DT framework to support the imitation of mobile systems. It can guarantee real-time and accurate imitations under the prerequisite of comprehensive information acquired by a cooperative collection algorithm with the aid of UAVs. The framework can rapidly aggregate local DT models using an attention-based mechanism to improve mobile imitation accuracy. Additionally, we propose a multimodal-based DT inspection algorithm that can correct the postures of UAVs affected by winds for reliable imitations. We implement the framework in Gazebo. Our system simulations demonstrate the efficiency of the proposed federated DT framework. Our solution can reduce the imitation latency by an average of 68.4%, meanwhile, can improve the imitation accuracy by 16.4% on average when compared to traditional centralized and distributed imitation schemes.

Cardiac patterns are being used to provide hard-to-forge biometric signatures in identification applications. However, this performance is obtained undercontrolled scenarioswhere cardiac signals maintain a relatively uniform pattern, facilitating the identification process. In this work, we analyze cardiac signals collected in morerealistic (uncontrolled) scenariosand show that their high signal variability makes them harder to obtain stable and distinct features. When faced with these irregular signals, the state-of-the-art (SOTA) reduces its performance significantly. To solve these problems, we propose the CardioID framework 1 with two novel properties. First, we design an adaptive method that achieves stable and distinct features by tailoring the filtering process according to each user’s heart rate. Second, we show that users can have multiple cardiac morphologies, offering us a bigger pool of cardiac signals compared to the SOTA. Considering threeuncontrolleddatasets, our evaluation shows two main insights. First, while using a PPG sensor with healthy individuals, the SOTA’s balanced accuracy (BAC) reduces from 90–95% to 75–80%, while our method maintains a BAC above 90%. Second, under more challenging conditions (using smartphone cameras or monitoring unhealthy individuals), the SOTA’s BAC reduces to values between 65–75%, and our method increases the BAC to values between 75–85%.

The development of the intelligent Internet of Things has facilitated the adoption of high-efficiency Multiple Targets Tracking (MTT) in many civil security applications. However, existing MTT technologies cannot offer full capability in accurate and real-time MTT for civil security. Many attractive applications in the next-generation wireless network, like Unmanned Aerial Vehicle (UAV) swarm, are envisioned to be exploited for enhanced MTT with the advantage of flexibility. Nonetheless, highly dynamic moving targets impose some new challenges. UAVs cannot always perform expected cooperative tracking in conventional architectures as well. To address these problems, we design a tiered Digital Twin-assisted tracking framework in this paper, which leverages multi-grained imitation for real-time and accurate MTT. We imitate a coarse-grained MTT to ensure a high successful tracking ratio. We then design a fine-grained imitation with a reaction-diffusion mechanism to explore the feasible cooperators based on trajectory prediction. Hardware-in-the-loop simulations demonstrate that our tiered framework can reduce 66.7% of the system latency overhead compared to the conventional DDPG benchmark while improving the successful tracking ratio by 30.6%.

This paper presents a novel transparent amplifying intelligent surface (TAIS) architecture for uplink enhancement in indoor-to-outdoor mmWave communications. The TAIS is an amplifier-based transmissive intelligent surface that can refract and amplify the incident signal, instead of only refracting it with adjustable phase shift by most passive reconfigurable intelligent surfaces (RIS). With advanced indium tin oxide film and printing technology, TAIS can be fabricated on the windows without any visual effects. This paper primarily focuses on exploiting the TAIS-based architecture to boost the uplink spectral efficiency (SE) in indoor-to-outdoor mmWave communications. By jointly optimizing the TAIS's phase shift matrix and transmit power of the user equipment, the uplink SE can be maximized by exploiting the nonlinearity in the TAIS's amplification process. The key point is that we drive the optimal phase shift matrix that maximizes the SE and deduces its closed-form representation. The SE maximization is then proved to be transferred to the transmit power optimization problem. Another important aspect is that we design a low-complexity algorithm to solve the problem using the difference of convex programming. Simulations show that our proposed TAIS can increase the SE by up to 32.6% as compared to its alternative methods.

In recent years, wireless sensing has attracted lots of research attention with a large range of applications enabled. However, several critical issues still hinder wireless sensing from being adopted in daily use: (a) requiring dedicated devices and (or) dedicated signals; (b) limited sensing coverage; and (c) affecting the original function of the wireless technology (e.g., communication). In this work, we propose a new sensing modality, i.e., leveraging the pervasive powerline leakage for sensing. The key observation is that human body can capture such leaked signals, and the received signals vary with body gestures. We design a cheap ring antenna to collect the powerline leaked signals at human body and establish a body-empowered model to sense body motions. We prototype the proposed system with designs spanning both hardware and software. Comprehensive experiments show that the proposed sensing modality can realize a large range of applications in a different way from existing sensing methods. We showcase the powerful capability of this sensing modality using three typical sensing applications: body gesture recognition, sleep posture sensing, and fall detection.

Designing a reliable and highly accurate indoor localization system is challenging due to the non-uniformity of indoor spaces, multipath fading, and satellite signal blockage. To address these issues, we propose a Deep Neural Network-based localization system that combines passive Visible Light Positioning (p-VLP) and Bluetooth Low Energy (BLE) technologies to achieve stable, energy-efficient, and accurate indoor localization. Our solution leverages incremental learning to fuse data from visible light and BLE, overcoming their individual limitations and achieving centimeter-level localization accuracy. We build a prototype using low-cost S9706 hue sensors for p-VLP and low-power nrf52830 BLE boards to collect data simultaneously from both technologies in a 25m2 testbed. Our approach demonstrates a significant localization accuracy improvement of approximately 47% and 64% compared to individual p-VLP and BLE technologies, respectively, achieving a mean localization error of 20 cm.

Smartphones are moving towards the fullscreen design for better user experience. This trend forces front cameras to be placed under screen, leading to Under-Screen Cameras (USC). Accordingly, a small area of the screen is made translucent to allow light to reach the USC. In this paper, we utilize the translucent screen's features to inconspicuously modify its pixels, imperceptible to human eyes but inducing perturbations on USC images. These screen perturbations affect deep learning models in image classification and face recognition. They can be employed to protect user privacy, or disrupt the front camera's functionality in the malicious case. We design two methods, one-pixel perturbation and multiple-pixel perturbation, that can add screen perturbations to images captured by USC and successfully fool various deep learning models. Our evaluations, with three commercial full-screen smartphones on testbed datasets and synthesized datasets, show that screen perturbations significantly decrease the average image classification accuracy, dropping from 85% to only 14% for one-pixel perturbation and 5.5% for multiple-pixel perturbation. For face recognition, the average accuracy drops from 91% to merely 1.8% and 0.25%, respectively.

Radio-Frequency (RF) backscatter has emerged as a low-power communication technique. Backscatter systems either rely on active signal generators (spectrum efficient, but dedicated infrastructure) or existing ambient wireless transmissions (existing infrastructure, but spectrum inefficient). In this paper, we aim to make RF backscatter spectrum efficient and at the same time work with existing infrastructure. We propose to leverage the deployment of LiFi networks built upon LED bulbs for pervasive RF backscatter. We experimentally demonstrate that LiFi, which passively leaks RF signals, can be exploited as a radio carrier generator for low-power RF backscatter. We further design LeakageScatter, the first backscatter system operating in the ISM band and exploiting LiFi-leaked RF signals, without the need to actively generate the carrier wave. We customize the design of the loop at the LiFi transmitter, as well as the coil antennas at the tag and RF backscatter receiver, to optimize the system performance. We propose to opportunistically enable the oscillator of the backscatter tag in the software that could reduce the energy consumption on backscattering by up to 75%. Experimental results show that LeakageScatter achieves a backscattering distance up to 10 m and 18 m in indoor and outdoor scenarios, respectively, without using a dedicated RF carrier generator.

While radio communication still dominates in 5G, light and radios are expected to complement each other in the coming 6G networks. Visible Light Communication (VLC) is therefore attracting a tremendous amount of attention from both academia and industry. Recent studies showed that the front camera of pervasive smartphones is an ideal candidate to serve as the VLC receiver. While promising, we observe a recent trend with smartphones that can greatly hinder the adoption of smartphones for VLC, i.e., smartphones are moving towards full-screen for the best user experience. This trend forces front cameras to be placed under the devices' screen---leading to the so-called Under-Screen Camera (USC)---but we observe a severe performance degradation in VLC with USC: the transmission range is reduced from a few meters to merely 0.04 m, and the throughput is decreased by more than 90%. To address this issue, we leverage the unique spatiotemporal characteristics of the rolling shutter effect on USC to design a pixel-sweeping algorithm to identify the sampling points with minimal interference from the translucent screen. We further propose a novel slope-boosting demodulation method to deal with color shift brought by the leakage interference. We build a proof-of-concept prototype using two commercial smart-phones. Experiment results show that our proposed design reduces the BER by two orders of magnitude on average and improves the data rate by 59×: from 914 b/s to 54.43 kb/s. The transmission range is extended by roughly 100×: from 0.04 m to 4.2 m.

Federated Learning (FL) is an important privacy-preserving learning paradigm that is expected to play an essential role in the future Intelligent Internet of Things (IoT). However, model training in FL is vulnerable to noise and the statistical heterogeneity of local data across IoT clients. In this paper, we propose FedNaWi, a “Go Narrow, Then Wide” client selection method that speeds up the FL training, achieves higher model performance, while requiring no additional data or sensitive information transfer from clients. Our method first selects reliable clients (i.e., going narrow) which allows the global model to quickly improve its performance and then includes less reliable clients (i.e., going wide) to exploit more IoT data of clients to further improve the global model. To profile client utility, we introduce a unified Bayesian framework to model the client utility at the FL server, assisted by a small amount of auxiliary data. We conduct extensive evaluations with 5 state-of-the-art FL methods, on 3 IoT tasks and under 7 different types of label and feature noise. We build an FL testbed with 38 IoT nodes (20 nodes run on Raspberry Pi 4B and 18 nodes run on Jetson Nano) for the evaluation. Our results show that FedNaWi improves the FL accuracy substantially and significantly reduces energy consumption. In particular, FedNaWi improves the accuracy from 35% to 75% in the non-IID Dirichlet setting, and reduces the average energy consumption by 55%.

Various interconnected Internet of Things (IoT) devices have emerged, led by the intelligence of the IoT, to realize exceptional interaction with the physical world. In this context, UAV swarm-enabled Multiple Targets Tracking (UAV-MTT), which can sense and track mobile targets for many applications such as hit-and-run, is an appealing topic. Unfortunately, UAVs cannot implement real-time MTT based on the traditional centralized pattern due to the complicated road network environment. It is also challenging to realize low-overhead UAV swarm cooperation in a distributed architecture for the real-time MTT. To address the problem, we propose a cyber-twin-based distributed tracking algorithm to update and optimize a trained digital model for real-time MTT. We then design a distributed cooperative tracking framework to promote MTT performance. In the design, both short-distance and long-distance distributed tracking cooperation manners are firstly realized with low energy consumption in communication by integrating resources of sensing and communication. Resource integration promotes target sensing efficiency with a highly successful tracking ratio as well. Theoretical derivation proves our algorithmic convergence. Hardware-in-the-loop simulation results demonstrate that our proposed algorithm can remarkably save 65.7% energy consumption in communication compared to other benchmarks while efficiently promoting 20.0% sensing performance.

Visible Light Communication (VLC) is widely considered a promising technology for the coming 6G networks. Recent studies show that a VLC transmitter not only emits visible light signals but also leaks RF signals during the transmission. In this work, we devote effort to harvesting the free leaked RF energy from VLC transmissions. We observe that the surrounding objects could help a coil antenna harvest significantly more RF energy. Based on this observation, we propose our system Bracelet+, which involves the human body in the harvesting system to increase the harvested power. After careful analysis of the influence of the human body on the harvested power, we prototype the coil antenna as a bracelet that achieves both high harvested power and convenience for wearing. The average power of the RF energy harvested by our design is 10× larger than that of the conventional coil antenna, without causing any interference to the communication of VLC systems. The harvested power can reach up to micro-watts in our tested scenarios. Such a micro-watt level of harvested energy has the potential to power up ultra-low-power sensors such as temperature sensors and glucose sensors.

With the advantages of easy deployment and flexible usage, unmanned aerial vehicle (UAV) has advanced the multitarget tracking (MTT) applications. The UAV-MTT system has great potentials to execute dull, dangerous, and critical missions for frontier defense and security. A key challenge in UAV-MTT is how to coordinate multiple UAVs to track diverse invading targets accurately and consecutively. In this article, we propose a UAV swarm-based cooperative tracking architecture to systematically improve the UAV tracking performance. We design an intelligent UAV swarm-based cooperative algorithm for consecutive target tracking and physical collision avoidance. Moreover, we design an efficient cooperative algorithm to predict the trajectory of invading targets accurately. Our simulation results demonstrate that the swarm behaviors stay stable in realistic scenarios with perturbing obstacles. Compared with state-of-the-art solutions, such as the matched deep$Q$-network, our algorithms can increase tracking accuracy by 60%, reduce tracking delay by 23%, and achieve physical collision-avoidance during the tracking process.

We exploit a dense cell-free mmWave network where User Equipments (UEs) are served by multiple highly directional beams provided by multiple Base Stations (BSs) simultaneously. Such multi-beam scenarios can either offer high spectral efficiency when different information is transmitted through each beam or a diversity gain when each beam transmits the same information. However, this increased spectral efficiency or diversity gain costs a more complex network association phase. A UE requires finding multiple nearby serving BSs and determining the optimal beam pair for each one. Thus, an efficient association process is urgently needed. In this work, we propose a UE-initiated association method for dense cell-free mmWave networks. We design an efficient beam training mechanism with multiple BSs using hybrid beamforming. We evaluate the proposed association method under different network configurations. The simulation results show that compared to traditional solutions, our proposed association method can lead to maximally 100% faster beam training and reduce energy consumption by up to 77%. The proposed UE-initiated association method is also scalable to the number of RF chains and antennas at BSs and UEs, making it very suitable for dense cell-free networks.

In visible light communication (VLC), the quality of communication is primarily dominated by line-of-sight links. To ensure an appropriate link quality anywhere, beamsteering has been proposed where transmitters (TXs) dynamically steer their beams to create beamspots on the users. However, these highly dynamic TXs face the beam tracking problem and result in highly variable illumination. In this work, we propose BlendVLC, a cell-free network architecture to improve the mobility robustness of users by blending the beamspots from both steerable and fixed TXs. We solve the beam tracking by designing a centimeter-level visible light positioning algorithm empowered by a neural network. Relying on this location information, we formulate and solve an optimization problem on the beamspot blending, and design a fast and scalable heuristic for large networks. We build a proof-of-concept testbed as well as a simulator to evaluate BlendVLC. We show that it achieves superior performance compared to denser networks with fully fixed TXs. For example, in a large-scale VLC network of 8 m x 4 m, BlendVLC improves the average system throughput by 30%, while only requiring half the number of TXs.

Visible Light Communication (VLC) is considered a new paradigm for next-generation wireless communication. Recently, studies show that during the process of VLC transmission, besides the visible light signals, the transmitter also leaks out RF signals through a side channel. What is interesting is that the data transmitted in the VLC channel can be inferred from the leaked RF signals. Fundamentally, it means the leaked RF signals carry a copy of the same data in the VLC channel. In this work, we show for the first time that besides inferring the original VLC data, the leaked side channel can be smartly leveraged to carry new data, significantly increasing the data rate of current VLC systems. To realize this objective, we propose a system named RadioInLight, with designs spanning across hardware and software. Without any dedicated active RF transmission front-end which consumes power and hardware resources, RadioInLight is able to double the data rate of the VLC system by purely manipulating the free passively leaked RF signals without affecting the data rate of the original VLC transmissions.

Motivated by the trend of realizing full screens on devices such as smartphones, in this work we propose through-screen sensing with visible light for the application of fingertip air-writing. The system can recognize handwritten digits with under-screen photodiodes as the receiver. The key idea is to recognize the weak light reflected by the finger when the finger writes the digits on top of a screen. The proposed air-writing system has immunity to scene changes because it has a fixed screen light source. However, the screen is a double-edged sword as both a signal source and a noise source. We propose a data preprocessing method to reduce the interference of the screen as a noise source. We design an embedded deep learning model, a customized model ConvRNN, to model the spatial and temporal patterns in the dynamic and weak reflected signal for air-writing digits recognition. The evaluation results show that our through-screen fingertip air-writing system with visible light can achieve accuracy up to 91%. Results further show that the size of the customized ConvRNN model can be reduced by 94% with less than a 10% drop in performance.

We are now witnessing a trend of realizing full-screen on electronic devices such as smartphones to maximize their screen-to-body ratio for a better user experience. Thus the bezel/narrow-bezel on today's devices to host various line-of-sight sensors would disappear. This trend not only is forcing sensors like the front cameras to be placed under the screen of devices, but also will challenge the deployment of the emerging Visible Light Communication (VLC) technology, a paradigm for the next-generation wireless communication.

We propose a concept of coverage-bounding and `visual' wireless communication-HODOR1-to secure the Internet of Things (IoT). Coverage-bounding means the communication coverage is controlled accurately in 3-dimensions. `Visual' implies that the communication coverage and process are visible to user, representing an important and user-friendly side-channel for se-curing IoT. HODOR can provide secure wireless communication both psychologically (visible to users) and technically (nodes only communicate with each other within their delimited coverage). It can benefit IoT applications for secure wireless communications, especially those that demand secure interactions in proximity.

Visible light communication (VLC) is gaining a significant amount of interest as a new paradigm to meet rapidly increasing demands on wireless capacity required by a digitalized world. VLC is considered as a secure wireless communication scheme because VLC signals can be easily constrained within physical boundaries. In this paper, for the first time, we show that VLC is not as secure as people thought: VLC can be sniffed through walls! The key principle behind this is that in VLC transmissions, a VLC transmitter not only emits visible light signals but also leaks out 'side channel RF signals'. The leaked RF signals can be sniffed by a receiver to decode the VLC transmissions even the receiver is blocked (e.g., by walls) from the VLC transmitter. In this work, we establish a theoretical model to quantify the amplitude of the leaked RF signal and verify the model with comprehensive experiments. We design and implement a VLC sniffing system including receiver coil design, signal processing and frame decoding, spanning across hardware and software. Field studies show that with a cheap receiver design, our system can simultaneously sniff transmissions from multiple VLC transmitters 6.4 meters away with a 14 cm concrete wall in between, where the distance exceeds the communication range of most state-of-the-art VLC systems. By simply twining a wired earphone on the arm, we can sniff the VLC transmission 1.9 meters away.

Visible Light Communication (VLC) is a promising technology for future wireless communications. By modulating the visible light---that has about 10,000x larger frequency band than that of radios---to transmit data, VLC has the potential to provide ultra-high-speed wireless connectivities. However, it also has limitations such as i) surrounding objects can easily block VLC links, and ii) intense ambient light can saturate the photodiodes of VLC receivers.

Localization is crucial for nowadays' communication systems, especially for beamforming techniques in massive MIMO systems. Large-scale MIMO systems have exhibited their advantages in communications. In the meantime, they also have the potential to provide accurate localization with their high angular resolution. In this paper, we study indoor localization performance of a Massive MIMO system with a 64-antenna Uniform Linear Array (ULA). Based on the sparse reconstruction method, we propose a Mixed field Sparse Bayesian Learning (MSBL) algorithm to localize devices for both near-field and far-field scenarios. Using the measurement results from our massive MIMO testbed, we show that our proposed MSBL algorithm can improve the localization accuracy by 49% with only a few snapshots. The performance of our algorithm is also robust to low Signal-to-Noise Ratio (SNR) conditions.

Positioning based on visible light is gaining significant attention. But most existing studies rely on a key requirement: The object of interest needs to carry an optical receiver (camera or photodiode). We remove this requirement and investigate the possibility of achieving accurate positioning in a passive manner—that is, without requiring objects to carry any optical receiver. To achieve this goal, we propose PassiveVLP, in which we exploit the reflective surfaces of objects and the unique propagation properties of LED luminaires. We present geometric models, a testbed implementation, and empirical evaluations to showcase the opportunities and challenges posed by this new type of passive positioning. Overall, we show that our PassiveVLP can track with high accuracy (a few centimeters) a subset of an object’s trajectory, and it can also identify passively the object’s ID.

Visible Light Communication (VLC) based on LEDs has been a hot topic investigated for over a decade. However, most of the research efforts assume the intensity of LED light is constant. This hypothesis is not true when Smart Lighting is introduced to VLC, which requires LEDs to adapt their brightness based on the intensity of natural ambient light. Smart lighting saves power consumption and improves user comfort. However, intensity adaptation severely affects the throughput performance of data communication. In this paper, we propose SmartVLC, a system that can maximize the throughput (benefit communication) while still maintaining the LEDs' illumination function (benefit smart lighting). A novel Adaptive Multiple Pulse Position Modulation (AMPPM) scheme is proposed to support fine-grained dimming levels to avoid flickering while maximizing the throughput under each dimming level. SmartVLC is implemented on off-the-shelf commodity hardware. Several real-life challenges in both hardware and software are addressed to make it a robust real-time system. Comprehensive experiments are carried out to evaluate the system performance under multifaceted scenarios. Experimental results demonstrate that SmartVLC supports a communication distance up to 3.6m, and improves the throughput achieved with two state-of-the-art approaches by 40 and 12 percent on average, respectively, without bringing any flickering to users.

LED luminaries are now deployed densely in indoor areas to provide uniform illumination. Visible Light Communication (VLC) can also benefit from this dense LED infrastructure. In this paper, we propose DenseVLC, a cell-free massive MIMO networking system enabled by densely distributed LEDs, that forms different beamspots to simultaneously serve multiple receivers. This is a cell-free system, as there is no notion of autonomous cells and transmitters cooperate to jointly serve the users. Given a power budget for communication, DenseVLC assigns the power budget among the distributed LEDs to optimize the system throughput and user fairness. We formulate an optimization problem to derive the optimal policy for the power allocation. Our insights from the optimal policies allow us to simplify DenseVLC's system design and propose a heuristic algorithm that can reduce the complexity by 99.96%. Besides, we propose a novel synchronization method using non-line-of-sight VLC to synchronize all the transmitters that will form a beamspot to serve the same receiver. We implement DenseVLC with off-the-shelf devices, solve practical challenges in the system design, and evaluate it with extensive and realistic experiments in a system of 36 transmitters and 4 receivers in an area of 3 m × 3 m. Our results show that DenseVLC can improve the average system throughput by 45%, or improve the average power efficiency by 2.3 times, while maintaining the requirement for uniform illumination. Finally, we demonstrate that DenseVLC is robust against blockage.

A massive MIMO network can serve ten's of users simultaneously. However, in dense scenarios the users are potentially closely-spaced, potentially resulting in substantial inter-user interference. Scheduling can overcome this by selecting the users that lead to the highest combined spectral efficiency. As scheduling comes with a significant pilot overhead, an alternative strategy could minimize user correlation by distributing the antenna elements in space. In this paper, we propose a comprehensive system study including antenna topology and distribution, user scheduling and pilot overhead reduction. Our user scheduling and pilot reduction algorithms are evaluated using system level simulations relying on indoor line-of-sight channel measurements from a 64 antenna base station at 2.61GHz. To have a thorough evaluation of the proposed algorithm, we consider four different antenna topologies, including co-located and distributed placement of the base station arrays. Our evaluation shows that in a conference room with 64 densely deployed users, our proposed low complexity algorithm can improve the spectral efficiency by at least 14% compared to random user selection with the best antenna distribution strategy. Finally, our results show that by relying on channel hardening, we reduce the pilot overhead by 3.2$\times$ .

The number of deployed Internet of Things (IoT) devices is steadily increasing to manage and interact with community assets of smart cities, such as transportation systems and power plants. This may lead to degraded network performance due to the growing amount of network traffic and connections generated by various IoT devices. To tackle these issues, one promising direction is to leverage the physical proximity of communicating devices and inter-device communication to achieve low latency, bandwidth efficiency, and resilient services. In this work, we aim at enhancing the performance of indoor IoT communication (e.g., smart homes, SOHO) by taking advantage of emerging technologies such as visible light and ultrasound. This approach increases the network capacity, robustness of network connections across IoT devices, and provides efficient means to enable distance-bounding services. We have developed communication modules using off-the-shelf components for visible light and ultrasound and evaluate their network performance and energy consumption. In addition, we show the efficacy of our communication modules by applying them in a practical indoor IoT scenario to realize secure IoT group communication.

Internet-of-Vehicles (IoV) has the potentials of enhancing automatic driving in various transportation environment. However, there is very little investigation on quantifying the potential influence of automatic driving applications with the road efficiency in IoV. This paper studies the connection of safety distance to the road congestion under different IoV resource conditions. We propose an elastic wave equation model to reveal the relation between safety distance and road congestion. It can be found that the propagation speed of road congestion is largely affected by the safety distance. To recoup the efficient road safety and alleviate road congestion, an optimization problem is formulated with cooperative communication and computing via platoons that aims to minimize the total safety distance. Since the optimization is a complicated 0-1 programming problem, we propose a practical resource allocation algorithm and solve the problem through Lagrangian relaxation. Simulation experiments show that the proposed algorithm leads to near-optimal results with low complexity but no overhead of vehicular information exchange.

We analyze and optimize the performance of a new type of channel that exploits sunlight for wireless communication. Recent advances on visible light backscatter have shown that if mobile objects attach distinctive reflective patterns to their surfaces, simple photosensors deployed in our environments can decode the reflected light signals. Although the vision is promising, only initial feasibility studies have been performed so far. There is no analysis on how much information this channel can transmit or how reliable the links are. Achieving this vision is a complex endeavour because we have no control over (i) the sun or clouds, which determine the amount and direction of light intensity, and (ii) the mobile object, which determines the modulated reflection of sunlight. We investigate the impact of the surrounding light intensity and physical properties of the object (reflective materials, size and speed) to design a communication system that optimizes the encoding and decoding of information with sunlight. Our experimental evaluation, performed with a car moving on a regular street, shows that our analysis leads to significant improvements across many dimensions. Compared to the state of the art, we can encode seven times more information, and decode this information reliably from an object moving three times faster (53km/h) at a range that is four times longer (4m) and with three times lower light intensity (cloudy day).

LED luminaries are now deployed densely in indoor areas to provide uniform illumination. Visible Light Communication (VLC) can also benefit from this dense LED infrastructure. In this paper, we propose DenseVLC, a cell-free massive MIMO system enabled by densely distributed LEDs, that forms different beamspots to serve multiple receivers simultaneously. Given a power budget for communication, DenseVLC can optimize the system throughput by properly assigning the power budget among the distributed LEDs. We formulate an optimization problem to derive the optimal policy for the power allocation. Our insights from the optimal policies allow us to simplify DenseVLC's system design and propose a heuristic algorithm that can reduce the complexity by 99.96%. Besides, we propose a novel synchronization method using non-line-of-sight VLC to synchronize all the transmitters that will form a beamspot to serve the same receiver. We implement DenseVLC with off-the-shelf devices, solve practical challenges in the system design, and evaluate it with extensive and realistic experiments in a system of 36 transmitters and 4 receivers in an area of 3 m x 3 m. Our results show that DenseVLC can improve the average system throughput by 45%, or improve the average power efficiency by 2.3 times, while maintaining the requirement for uniform illumination.

LED luminaries are densely deployed indoors to provide uniform illumination for better user comfort. To achieve energy efficient uniform illumination, the 'cells' of neighboring LED luminaries are strongly overlapping to increase the cell edge light intensity. Visible Light Communication (VLC) can also benefit from this dense LED infrastructure by exploiting Distributed Multiple-Input-Single-Output (D- MISO). However, overlapping LED cells cause strong interference when multiple transmitters are active. Therefore, in this work, we propose an Adaptive and Distributed MISO (AD-MISO) method to improve the system throughput of a dense-LED-transmitter VLC network that communicates with multiple users. We formulate an optimization problem for deriving the D-MISO transmission strategy adapting the inter- cell interference dynamically, with the goal to increase the system throughput of multiple users. Given the measured link qualities between the distributed transmitters and users, AD-MISO exploits both spatial and time-division multiplexing by dynamically allocating the transmitters to multiple users such that the system throughput is maximized. We also design a heuristic algorithm to reduce the complexity of AD-MISO, and propose a technique to support heterogeneous service requirements. We evaluate the performance of AD-MISO through simulations under various scenarios. Our results demonstrate that AD-MISO outperforms the pure time-division based D-MISO greatly by increasing the average system throughput up to 35.6%, and our heuristic can achieve similar gains while reducing the complexity by 94%.

Localization based on visible light is gaining significant attention. But most existing studies rely on a key requirement: the object of interest needs to carry an optical receiver (camera or photodiode). We remove this requirement and investigate the possibility of achieving accurate localization in a passive manner, that is, without requiring objects to carry any optical receiver. To achieve this goal, we exploit the reflective surfaces of objects and the unique propagation properties of LED luminaires. We present geometric models, a testbed implementation, and empirical evaluations to showcase the opportunities and challenges posed by this new type of localization. Overall, we show that our method can track with high accuracy (few centimeters) a subset of an object's trajectory and it can also identify passively the object's ID.

In this poster, we envision the promising cooperation between autonomous drones and driverless cars. We discuss potential applications and opportunities enabled by this cooperation.

We introduce PassiveVLN, a flexible, modular and software-defined platform for visible light backscatter networks. PassiveVLN incorporates a modular hardware design and a full-stack software implementation, enabling convenient and scalable deployment as well as rapid prototyping for testing new protocols and applications.

Providing low power and long range (LoRa) connectivity is the goal of most Internet of Things networks, e.g., LoRa, but keeping communication reliable is challenging. LoRa networks are vulnerable to the capture effect. Cell-edge nodes have a high chance of losing packets due to collisions, especially when high spreading factors (SFs) are used that increase time on air. Moreover, LoRa networks face the problem of scalability when they connect thousands of nodes that access the shared channels randomly. In this paper, we propose a new MAC layer-RS-LoRa-to improve reliability and scalability of LoRa wide-area networks (LoRaWANs). The key innovation is a two-step lightweight scheduling: 1) a gateway schedules nodes in a coarse-grained manner through dynamically specifying the allowed transmission powers and SFs on each channel and 2) based on the coarse-grained scheduling information, a node determines its own transmission power, SF, and when and on which channel to transmit. Through the proposed lightweight scheduling, nodes are divided into different groups, and within each group, nodes use similar transmission power to alleviate the capture effect. The nodes are also guided to select different SFs to increase the network reliability and scalability. We have implemented RS-LoRa in NS-3 and evaluated its performance through extensive simulations. Our results demonstrate the benefit of RS-LoRa over the legacy LoRaWAN, in terms of packet error ratio, throughput, and fairness. For instance, in a single-cell scenario with 1000 nodes, RS-LoRa can reduce the packet error ratio of the legacy LoRaWAN by nearly 20%.

LEDs in our buildings, vehicles, and consumer products are rapidly gaining visible light communication capabilities. LED links however are notorious for being unreliable: shadowing, blockage, mobility, external light, all of these issues can disrupt the connectivity easily. Therefore, unless a reliable and cost-efficient data link layer is designed, VLC will be confined to niche applications. In this paper, we reveal a reason for unreliable VLC: a single type of photodetector at the receiver cannot establish a reliable link. We show that the photodetectors with complementary properties, in terms of optical spectral response and field-of-view, are necessary to handle the wide dynamic range of optical noise (such as the sun and other unwanted light sources) and mobility of users. Motivated by our experimental observations, we design a reliable and adaptive receiver for VLC (REAL-VLC) for low-end communication systems, an inexpensive receiver that senses light with complementary photodetectors and configures itself (physical and data link layers) dynamically to maintain the communication link. We implement the hardware and the software of REAL-VLC in low-end platforms, and experimentally validate it in representative test scenarios and a proof-of-concept application that consists of mobile nodes maintaining a VLC link under various lighting and path conditions.

Visible light is gaining significant attention as a medium to achieve accurate relative localization. Most of the studies in the area focus on indoor positioning and rely on two important assumptions: (i) lights are static, and (ii) the receiver has line-of-sight with multiple lights. These requirements limit the application of localization methods in scenarios where nodes have a single light and are mobile, such as motorbikes or swarms of robots. In general, this particular type of scenarios (single lights moving on a plane) leads to under-determined localization systems where no unique solution can be found. We follow a holistic approach that includes theory, simulations, and experiments to overcome some of the limitations present in such type of scenarios. Our theoretical and simulation results show that if nodes are enhanced with sensors providing relative directions (such as compasses), we can derive dependencies in the system to obtain unique solutions. Our proof-of-concept implementation validates our model by showing that single lights can provide relative localization with high accuracy: an average error below 5 cm.

Visible Light Communication (VLC) based on LEDs has been a hot topic investigated for over a decade. However, most of the research efforts in this area assume the intensity of the light emitted from LEDs is constant. This is not true any more when Smart Lighting is introduced to VLC in recent years, which requires the LEDs to adapt their brightness according to the intensity of the natural ambient light. Smart lighting saves power consumption and improves user comfort. However, intensity adaptation severely affects the throughput performance of the data communication. In this paper, we propose SmartVLC, a system that can maximize the throughput (benefit communication) while still maintaining the LEDs' illumination function (benefit smart lighting). A new adaptive multiple pulse position modulation scheme is proposed to support fine-grained dimming levels to avoid flickering and at the same time, maximize the throughput under each dimming level. SmartVLC is implemented on low-cost commodity hardware and several real-life challenges in both hardware and software are addressed to make SmartVLC a robust realtime system. Comprehensive experiments are carried out to evaluate the performance of SmartVLC under multifaceted scenarios. The results demonstrate that SmartVLC supports a communication distance up to 3.6m, and improves the throughput achieved with two state-of-the-art approaches by 40% and 12% on average, respectively, without bringing any flickering to users.

In this work, we propose a new communication system for illuminated areas, indoors and outdoors. Light sources in our environments --such as light bulbs or even the sun-- are our signal emitters, but we do not modulate data at the light source. We instead propose that the environment itself modulates the {ambient} light signals: if mobile elements `wear' patterns consisting of distinctive reflecting surfaces, single photodiode could decode the disturbed light signals to read passive information. Achieving this vision requires a deep understanding of a new type of communication channel. Many parameters can affect the performance of passive communication based on visible light: the size of reflective surfaces, the surrounding light intensity, the speed of mobile objects, the field-of-view of the receiver, to name a few. In this paper, we present our vision for a passive communication channel with visible light, the design challenges and the evaluation of an outdoor application where our receiver decodes information from a car moving at 18 km/h.

OpenVLC (www.openvlc.org) is an open-source project for research in Visible Light Communication (VLC) systems. It has the potential to help create a new type of infrastructure, an Internet of Lights (IoL), where LED- based devices (e.g., car lights, city lights, billboards, toy, etc) and photodetectors become inter- connected. OpenVLC is built upon an embedded platform and adopts off-the-shelf optical devices and essential electronic components. It has been proved to be a starter kit for VLC research, thanks to its deployments by tens of top universities/research centers in the world (www.openvlc.org/list-of-users.html). This demo introduces its latest version: OpenVLC1.1. The new features include: (1) higher resilience to ambient light noise, e.g., indoor interfering lighting; (2) interface for sensor application. A new board (OpenVLC1.1 cape) is designed and the printed circuit is developed. The cape is plugged directly into the main embedded board and external sensors can be easily connected to the cape. This demo demonstrates OpenVLC1.1's networking performance through standard networking diagnostic tools and shows an application wherein temperature and humidity sensed data are transmitted through VLC links.

The optical antenna's directionality of nodes forming a visible light communication (VLC) network, i.e., their field-of-view (FOV), varies greatly from device to device. This encompasses wide FOVs of ambient light infrastructure and directional FOVs of light from low-end embedded devices. This variety of light propagation can severely affect the transmission reliability, despite pointing the devices to each other may seem enough for a reliable communication. The presence of interference among nodes with different FOVs makes traditional access protocols in VLC unreliable, and it also exacerbates the hidden-node problem. In this paper, we propose a carrier sensing multiple access/collision detection and hidden avoidance (CSMA/CD-HA) medium access control protocol for a network, where each node solely uses one light-emitting diode to transmit and receive data. The CSMA/CD-HA can enable in-band intra-frame bidirectional transmission with just one optical antenna. The key idea is to exploit the intra-frame data symbols without the emission of light to introduce an embedded communication channel. This approach enables the transmission of additional data while receiving in the same optical frequency band, and it makes the communication robust to different types of FOVs. We implement the CSMA/CD-HA protocol in a software-defined embedded platform running Linux, and evaluate its performance through analysis and experiments. Results show that collisions caused by hidden nodes can largely be reduced, and our protocol can increase the saturation throughput by nearly up to 50% and 100% under the two- and four-node scenarios, respectively.

Built around a cost-effective embedded Linux platform, OpenVLC is an open source project (www.openvlc.org) for research in Visible Light Communication (VLC) Networks. In this work, we introduce and demonstrate the OpenVLC1.0 platform, a flexible, software-defined, and low-cost research platform. OpenVLC1.0 consists of a simple electronic design, and a new driver of the Linux operating system that implements the MAC, part of the PHY layers and it offers an interface to Internet protocols. The electronics of OpenVLC implement a flexible optical front-end consisting of commodity low- and high-power Light Emitting Diodes (LEDs), photodiodes (PDs), and ancillary electronic circuitry. In order to quickly start playing with VLC Networks, we have designed and developed a printed circuit board (OpenVLC1.0 cape). The cape can be plugged into the main embedded Beaglebone board. Researchers can then swiftly build PHY and MAC protocols using the software implementation (OpenVLC1.0 driver), and prototype innovative solutions in realistic network setups. In this demo, we show that OpenVLC1.0 can switch between different MAC protocols, it can choose different optical channel for data transmission and reception, and it can be employed jointly with standard TCP/IP diagnostic tools.

Unlike traditional radio frequency communication of consumer devices, the "optical antenna" direction of Visible Light Communication (VLC), i.e., the Field-Of-View (FOV), varies greatly from device to device. This encompasses wide FOVs of ambient infrastructure and directional FOVs of light emitted by low-end embedded devices. This variety of light wave propagation can severely affect the transmission reliability, despite ``pointing'' devices to each other may seem enough for a reliable link. In particular, the fact that FOVs are unknown makes traditional access protocols in VLC unreliable in presence of interference among nodes of different FOVs and exacerbates the hidden-node problem. In this paper, we propose a Carrier Sensing Multiple Access/Collision Detection&Hidden Avoidance (CSMA/CD-HA) Medium Access Control protocol for a network where each node solely uses one Light Emitting Diode (LED) to transmit and receive data. The CSMA/CD-HA can enable in-band intra-frame bidirectional transmission with just one optical antenna. The key idea is to exploit the intra-frame data symbols without emission of light to introduce an embedded communication channel. This approach enables the transmission of additional data while receiving in the same optical channel and it makes the communication robust to different types of FOVs. We build a software-defined embedded platform running on Linux operating system, implement the CSMA/CD-HA protocol, and evaluate its performance through experiments. Results show that collisions caused by hidden nodes can be reduced and our protocol can increase the saturation throughput by nearly up to 50% and 100% under the two-node and four-node scenarios, respectively.

Dense deployment of small cells is an important, emerging trend to enable future cellular networks to cope with growing traffic demand. However, this reduces the number of users per cell and thus opportunistic scheduling gain. We propose a base station-driven energy-aware approach to exploit user-user communication to increase the opportunistic gain. We use tools from stochastic Lyapunov optimization to determine the optimal scheduling policy subject to a constraint on energy consumption for user-user communication. Our simulation results show that with a large energy budget, packet transfer delay is reduced by up to 70%. The bulk of the performance improvement can be achieved with only a small increase in energy consumption, where 60% of the improvement is achieved at only 20% of the additional energy consumption. Further, we evaluate our algorithm using realistic video traffic traces and show that frame loss ratio is reduced by 90% and PSNR is improved by 4dB.

To meet the increasing demand for wireless capacity, future networks are likely to consist of dense layouts of small cells. The number of users in each cell is thus reduced, which results in diminished gains from opportunistic scheduling, particularly under dynamic traffic loads. We propose a user-initiated base station (BS)-transparent traffic spreading approach that leverages user-user communication to increase BS scheduling flexibility. The proposed scheme can increase opportunistic gain and improve user performance. For a specified tradeoff between performance and power expenditure, we characterize the optimal policy by modeling the system as a Markov decision process and also present a heuristic algorithm that yields significant performance gains. Our simulations show that, in the performance-centric case, average file transfer delays are lowered by up to 20% even in homogeneous scenarios and up to 50% with heterogeneous users. Further, we show that the bulk of the performance improvement can be achieved with a small increase in power expenditure, e.g., in an energy-sensitive case, up to 78% of the performance improvement can be typically achieved at only 20% of the power expenditure of the performance-centric case.

To meet the increasing demand for wireless capacity, future networks are likely to consist of dense layouts of small cells. Thus, the number of concurrent users served by each base station (BS) is likely to be small which results in diminished gains from opportunistic scheduling, particularly under dynamic traffic loads. We propose user-initiated BS-transparent traffic spreading that leverages user-to-user communication to increase BS scheduling flexibility. The proposed scheme is able to increase opportunistic gains and improve user performance. For a specified tradeoff between performance and power expenditure, we characterize the optimal policy by modeling the system as a Markov decision process and also present a heuristic algorithm that yields significant performance gains. Our simulations show that, in the performance-centric case, average file transfer delays are lowered by up to 20% even in homogeneous scenarios, and up to 50% with heterogeneous users. Further, we show that the bulk of the performance improvement can be achieved with a small increase in power expenditure, e.g., in an energy-sensitive case, up to 78% of the performance improvement can be typically achieved at only 20% of the power expenditure of the performance-centric case.

In recent years, governments, standardization bodies, automobile manufacturers, and academia are working together to develop vehicular ad hoc network (VANET)-based communication technologies. VANETs apply multiple channels, i.e., control channel (CCH) and service channels (SCHs), to provide open public road safety services and the improve comfort and efficiency of driving. Based on the latest standard draft IEEE 802.11p and IEEE 1609.4, this paper proposes a variable CCH interval (VCI) multichannel medium access control (MAC) scheme, which can dynamically adjust the length ratio between CCH and SCHs. The scheme also introduces a multichannel coordination mechanism to provide contention-free access of SCHs. Markov modeling is conducted to optimize the intervals based on the traffic condition. Theoretical analysis and simulation results show that the proposed scheme is able to help IEEE 1609.4 MAC significantly enhance the saturated throughput of SCHs and reduce the transmission delay of service packets while maintaining the prioritized transmission of critical safety information on CCH.

The emerging wireless vehicular communication technology is intended to improve safety and comfort of transportation systems. Different types of traffic information could be delivered through vehicle-to-vehicle and vehicle-to-infrastructure communications. This paper proposes a Quality-of-Service (QoS) supported multi-channel MAC scheme for Vehicular Ad Hoc Networks (VANETs), which can adaptively tune the contention window for different services at each node, and dynamically adjust the intervals of the Control Channel (CCH) and the Service Channels (SCHs) working in multi-rate. Theoretical model is proposed to obtain the contention window and optimize the intervals based on traffic conditions. Analysis and simulation results show that the proposed MAC is able to help IEEE 1690.4 MAC support QoS services, while ensuring the high saturation throughput and the prioritized transmission of critical safety information.

Abstract The distinguishing properties of VehicularAd hocwireless Networks (VANETs) strongly challenge the design of Medium Access Control (MAC) protocols, which are responsible for the medium access coordination among active vehicles, as well as the accommodation of both driving safety applications and non‐safety applications. In this paper, we focus on a comprehensive survey of VANET MAC schemes by integrating various related issues and challenges. Our analysis not only deepens the understanding of MAC techniques in VANETs but also presents the key ideas and potential directions for future research in this area. In order to significantly improve the communication performance of VANETs, more research efforts on MAC techniques must be made for optimizing multichannel coordination and allocation approaches, enhancing the Quality of Service (QoS) capability, and combating the hidden terminal problem, broadcast storm problem and even ACK (acknowledgment) explosion problem. Copyright © 2009 John Wiley & Sons, Ltd.