Real-time semantic recognition is crucial for virtual reality (VR) applications, but the efficient fusion of multi-modal data poses significant challenges under resource-constrained VR scenarios. While integrating millimeter-wave (mmWave) radar point clouds with vision data offers a promising solution, existing methods often suffer from excessive data overhead and degraded accuracy due to redundant and noisy information. To address this limitation, this paper presents VR-PCT, a multi-modal transformer for edge-client collaborative VR semantic recognition that fuses mmWave radar point cloud and vision data for VR applications. VR-PCT introduces a novel collaborative design where VR clients perform lightweight semantic region detection while VR edge processes multi-modal VR semantic recognition. Through efficient edge-client collaboration, VR-PCT optimizes the transmission of mmWave point cloud and vision data by transmitting only the VR semantic region of vision data instead of the entire video. Additionally, it incorporates adaptive cross-modal data selection and fusion strategies to achieve real-time semantic recognition while significantly reducing data redundancy. Across 22 participants engaged in four experimental scenes utilizing VR devices from three different manufacturers, our evaluation demonstrates that VR-PCT achieves 97.6% recognition accuracy while reducing transmission overhead by 81.5% compared to existing approaches. These results highlight the effectiveness of VR-PCT in enabling efficient and accurate multi-modal VR semantic recognition for VR applications. The code and data of VR-PCT are released onhttps://github.com/luoyumei1-a/VR-PCT.

In data center networks, various types of traffic with strict performance requirements operate simultaneously, necessitating effective isolation and scheduling through priority queues. However, most switches support only around ten priority queues. Virtual priority can address this limitation by emulating multi-priority queues on a single physical queue, but existing solutions often require complex switch-level scheduling and hardware changes. Our key insight is that virtual priority can be achieved by carefully managing bandwidth contention in a physical queue, which is traditionally handled by congestion control (CC) algorithms. Hence, the virtual priority mechanism needs to be tightly coupled with CC. In this paper, we propose PrioPlus, a CC enhancement algorithm that can be integrated with existing congestion control schemes to enable virtual priority transmission. PrioPlus assigns specific delay ranges to different priority levels, ensuring that flows transmit only when the delay is within the assigned range, effectively meeting virtual priority requirements. Compared to Swift CC with physical priority queues, PrioPlus provides strict priority for high-priority flows without impacting performance sensibly. Meanwhile, it benefits low-priority flows from 25% to 41% as its priority-aware design enhances CC's ability to fully utilize available bandwidth once higher-priority traffic completes. As a result, in coflow and model training scenarios, PrioPlus improves job completion times by 21% and 33%, respectively, compared to Swift with physical priority queues.

Pulse modulation based communication techniques have enabled various Internet of Things (IoT) applications, such as smart meters and automotive systems. However, the existing pulse modulators rely on platform-specific hardware components, leading to limited extensibility and hardware dependency when supporting diverse variants such as Pulse Position Modulation (PPM) and Pulse Amplitude Modulation (PAM). This paper introduces NN-Pulse, an innovative neural networkdefined pulse modulator designed to enhance extensibility and flexibility, ensuring compatibility with multiple pulse modulation schemes. Specifically, NN-Pulse realizes the pulse modulation process using fundamental neural network modules with carefully tailored weights, via the proposed spike neural network (SNN)-based position selection module and transposed convolutional layers for phase modulation. Evaluations show that NNPulse generates PPM and PAM signals with bit error ratios of 0.6% and 0.2%, respectively. Moreover, the time consumption of modulating a pulse symbol via NN-Pulse is only$1.4 \mu \mathrm{s}$, outperforming traditional methods by 47 times.

Millimeter-wave (mmWave) has emerged as a promising solution for contact-free sensing due to its high resolution. Although promising, it faces several critical issues, including occlusion from the surrounding environment, unstable orientation-dependent sensing performance, and significant interference when multiple targets are in close proximity. These fundamental issues hinder the widespread adoption of mmWave sensing in the real world. In this paper, we propose SeRadar, the first systematic framework that leverages all useful secondary reflections to significantly enhance reliability and bring mmWave sensing one step closer to real-world adoption. Unlike primary reflections commonly used in wireless sensing, secondary reflections—typically much weaker due to being reflected multiple times—are generally ignored in existing literature. However, we observe that secondary reflections are common in various scenarios and carry valuable information about target movements, which could also contribute to sensing. To effectively utilize secondary reflections for sensing, SeRadar addresses several challenges associated with secondary reflections. Specifically, it boosts weak secondary reflections to improve their sensing capability, identifies useful ones from a large number of secondary reflections captured in the environment, and mitigates primary-secondary interference in multi-target scenarios. We evaluate the performance of SeRadar in various environments, including offices, apartments, and vehicle cabins. Extensive experiments demonstrate SeRadar can enhance accuracy and reliability in diverse sensing scenarios.

LoRa technology holds great promise for wide-area wireless sensing, owing to its long-range connectivity and strong penetration capability. However, existing LoRa sensing systems face a fundamental limitation, i.e., they assume that the gateway only receives signals from the sensing node, without any interference from other communication nodes. This is because transmissions from communication nodes inevitably distort the sensing pattern extracted from the received sensing signal, leading to sensing failure. To address this challenging issue, we propose InterSen, a novel solution specifically designed to address communication interference on LoRa sensing. We conduct an in-depth analysis of how communication interference disrupts LoRa sensing and design innovative signal processing methods to recover the sensing pattern corrupted by interference. We evaluate the performance of InterSen across three real-world sensing applications, i.e., respiration monitoring, walking sensing, and gesture recognition. Comprehensive experiments demonstrate that InterSen achieves accurate sensing even in the presence of multiple communication nodes. This brings LoRa sensing one step closer to practical deployment in real-world scenarios.

Survey research is a vital tool for understanding human opinions, behaviors, and preferences, but traditional methods face challenges such as high operational costs, limited sample diversity, and privacy concerns. Large Language Model (LLM)-based agents offer a novel approach to simulate human responses, generating high-quality synthetic data that preserves privacy while enabling researchers to explore patterns in public opinions. Our study introduces a flexible LLM-based survey simulation platform, which personalizes responses using real-world social media data to enhance realism and relevance. We conducted a validation case study using data from Steemit users, comparing the personalized simulated responses generated by LLM agents with actual survey responses collected from these users. This comparative analysis revealed key differences and potential biases, providing insights for refining survey methodologies. Personalized LLM simulations were highly effective for factual questions but faced limitations with subjective topics, often demonstrating lower variance compared to human responses. The quality of social media data and careful model selection also significantly influenced the accuracy of simulations. Our platform enables social science researchers to explore new methodologies for survey design, pre-test the impact of framing, and interact dynamically with LLM agents at reduced cost. This work provides valuable insights into the use of LLM-based agents for enhancing survey research, supporting their application in social science.

Wi-Fi is a very common means for providing wireless access to the Internet, e.g., using the 2.4GHz Industrial, Scientific, and Medical (ISM) band and more recently also the 6 GHz band via Wi-Fi 6E. Thanks to a chip recently launched by Semtech, in the same 2.4GHz band now can also operate Long Range (LoRa), which is widely used in Internet of Things (IoT) applications due to its low power consumption and wide coverage range. To allow for data interchange among these technologies, multi-radio gateways are needed, which introduce additional costs, complexities, and potential points of failure. To address this challenge, we propose the concept of Wireless to LoRa (WiLo) to make directional communication from Wi-Fi to LoRa. WiLo uses physical-layer (PHY) communication and dedicated input chips in the 2.4 GHz band to transmit information. To overcome the modulation technique differences between Wi-Fi and LoRa, WiLo leverages narrow-band communication, a technique that generates ultra-narrowband signals using single-tone sinusoidal signals by manipulating the payload of Wi-Fi devices. These signals can be detected by LoRa Wide Area Network base stations due to their high receiver sensitivity for long-range communication. Our experiments, which make use of both Universal Software Radio Peripheral (USRP) and commodity devices, demonstrate that WiLo can achieve concurrent wireless communication over a distance of 500 m, from commercial Wi-Fi chips to a LoRaWAN, with more than 96% frame reception rate. These findings show the effectiveness of WiLo in enabling reliable and efficient wireless communication over long distances, making it particularly relevant for applications such as remote monitoring systems, sensor networks, and smart cities.

The delivery of goods to numerous indoor stores poses significant safety risks, with heavy, high-stacked packages on delivery trolleys posing a potential hazard to passersby. This paper reports our experiences of developing and operating DeMo, a practical system for real-time monitoring of indoor delivery. DeMo employs sensors attached to trolleys, utilizing Inertial Measurement Unit (IMU) and Bluetooth Low Energy (BLE) readings to detect delivery violations, such as speeding and the use of non-designated delivery paths, and ensure accurate matching of each delivery to its intended destination store. Unlike typical indoor localization applications, DeMo addresses unique challenges, including sensor placement and the complex electromagnetic characteristics encountered in underground settings. Specifically, DeMo adapts the classical logarithmic radio signal model to facilitate fingerprint-free localization, significantly reducing deployment and maintenance costs. DeMo has been operating since May 2020, covering more than 200 shops with 74,537 deliveries (6193.2 km) across 12 subway stations in Hong Kong. DeMo's 4-year operation witnessed a significant violation rate drop, from 19% (May 2020) to 0.9% (Mar 2024).

Modern intelligent vehicles (IVs) are equipped with a variety of sensors and communication modules, empowering Advanced Driver Assistance Systems (ADAS) and enabling inter-vehicle connectivity. This paper focuses on multi-vehicle cooperative perception, with a primary objective of achieving low latency. The task involves nearby cooperative vehicles sending their camera data to an edge server, which then merges the local views to create a global traffic view. While multi-camera perception has been actively researched, existing solutions often rely on deep learning models, resulting in excessive processing latency. In contrast, we propose leveraging thestate estimationtechnique from the robotics field for this task. We explicitly model and solve for the system state, addressing additional challenges brought by object mobility and vision obstruction. Furthermore, we introduce aprogressive state estimationpipeline to further accelerate system state notifications, supported by a motion prediction method that optimizes position accuracy and perception smoothness. Experimental results demonstrate the superiority of our approach over the deep learning method, with 12.0 × to 27.4 × reductions in server processing delay, while maintaining mean absolute errors below 1 m.

The explosive growth of the Internet of Things (IoT) has dramatically increased the demand for secure mechanisms to protect against unauthorized access and attacks. Traditionally, expensive Software-Defined Radios (SDRs) have been utilized to gather IoT physical features, which are critical for reliable authentication. However, the high cost of SDRs makes them impractical for widespread deployment across the vast and diverse IoT ecosystem. In contrast, this paper presents AUTHFi, a novel cross-technology device authentication framework that transforms the SDR approach for collecting and authenticating IoT device signals (e.g., ZigBee and Bluetooth) by utilizing commercial WiFi devices. Specifically, AUTHFi leverages the recent advances in Cross-Technology Communication (CTC) to reconstruct the partial waveform of IoT transmission, thus eliminating the requirement for expensive SDRs. AUTHFi requires us to address several unique challenges. First, AUTHFi compensates for signal losses of the partial waveform to get more signal information. Then, it introduces an enhanced Carrier Frequency Offset (CFO) estimation and a fusion neural network that combines CFO and the reconstructed waveform for accurate device authentication. We implement AUTHFi based on RTL8812au (commodity WiFi) and CC2652P (commodity ZigBee/Bluetooth). Our thorough evaluation confirms that AUTHFi offers reliable authentication under various settings, achieving a maximum accuracy of 94.2%.

Deep learning (DL) based Wi-Fi sensing has witnessed great development in recent years. Although decent results have been achieved in certain scenarios, Wi-Fi based activity recognition is still difficult to deploy in real smart homes due to the limited cross-environment adaptability, i.e. a well-trained Wi-Fi sensing neural network in one environment is hard to adapt to other environments. To address this challenge, we proposeAdaWiFi, a DL-based Wi-Fi sensing framework that allows multiple Internet-of-Things (IoT) devices to collaborate and adapt to various environments effectively. The key innovation ofAdaWiFiincludes a collective sensing model architecture that utilizes complementary information between distinct devices and avoids the biased perception of individual sensors and an accompanying model adaptation technique that can transfer the sensing model to new environments with limited data. We evaluate our system on a public dataset and a custom dataset collected from three complex sensing environments. The results demonstrate thatAdaWiFiis able to achieve significantly better sensing adaptation effectiveness (e.g. 30% higher accuracy with one-shot adaptation) as compared with state-of-the-art baselines.

LoRa technology has become critical in numerous IoT applications, offering long-range connections with low energy consumption. However, their low-power nature makes them vulnerable to cross-technology interference (CTI) from other wireless technologies sharing the same unlicensed frequency bands. Existing solutions address this issue through signal analysis and coding techniques. Despite these efforts, the research on CTI mitigation needs to go one step further - to design a systematic framework for enhanced interference resilience. This article proposes SLoRa+, a systematic framework that integrates symbol recovery with soft decoding to achieve synergic interference resilience for LoRa. SLoRa+’s symbol recovery employs a two-stage analysis to recover corrupted LoRa symbols at a low cost. It also estimates the confidence of the recovered symbols by considering the characteristics of both LoRa and CTI. This confidence information is then utilized in soft decoding to improve error correction capabilities. Supported by theoretical analysis and real testbed evaluations, including commercial LoRa nodes and USRP B210, our experiments under different settings, i.e., various CTI sources and frequency bands, demonstrate SLoRa+’s effectiveness. Compared with state-of-the-art techniques, SLoRa+ enhances CTI protection capability by 1.6× with only 1% of the computation cost.

People with Dementia (PwD) suffer from a high risk of getting lost due to their cognitive deterioration, leading to potential safety hazards and significant search efforts. In this paper, we propose DEmentia Caring System (DECS), an effective crowdsourcing platform to search for missing PwD. Specifically, PwD carry our customized Bluetooth Low Energy (BLE) tags that broadcast BLE packets, which are detected and then uploaded by mobile volunteers via their smartphones. To further enhance search efficiency, DECS deploys BLE gateways as its infrastructure and analyzes PwD's daily spatial-temporal mobility patterns. DECS has been deployed in Hong Kong since 2019, supporting 3,100+ PwD's families with over 45,000 app downloads by volunteers. More importantly, it has successfully served the search for 254 missing cases. This paper reports the unique lessons and experiences learned through our 4-year citywide deployment of DECS.

This paper presents Mission, the first-of-this-kind cross-modal reidentification (ReID) design for mmWave Radar and RGB cameras. Given a person of interest detected by Radar in camera-restricted scenarios, Mission can identify the image of the person from cameras that are ubiquitously deployed in camera-allowed areas. We envision that cross Vison-RF ReID can significantly enrich mmWave human sensing with a wide spectrum of applications in security surveillance, tracking, and personalized services. Technically, we introduce a novel method for cross-modal similarity estimation that exploits inherent synergies between fine-grained 2D images and coarse-grained 3D Radar point clouds to effectively overcome their modal discrepancy. Through extensive experiments, we demonstrated that our proposed system can achieve 85% top-1 accuracy and 90% top-5 accuracy among 58 volunteers.

As an emerging Low-Power Wide Area Networks (LPWAN) technology, LoRa is dedicated to providing long-range connections for pervasive Internet-of-Things devices. As LoRa operates in the unlicensed spectrum with an ALOHA-based MAC-layer protocol stack, transmissions from multiple LoRa end-devices inevitably collide with each other, leading to packet losses and increased transmission delay. Targeting at collisions caused by interferences under thesamespreading factor (SF) settings, researchers introduce multiple lines of techniques. Despite their efforts, these techniques commonly neglect the potential collisions caused by interferences underdifferentSF settings, resulting in imperfect orthogonality. Given the disparate transmission power configurations and diverse deployed locations, the collisions under different SFs commonly exist in practical networks and significantly limit the LoRa reliability. This paper presents X-MAC, the first scheduler aware of imperfect orthogonality. Technically, X-MAC detects the collisions under different SFs via tracking historical transmissions, and performs dynamic channel scheduling to avoid collisions caused by interferences under the same and different SFs. Extensive evaluations on testbed devices show that, compared with the state-of-the-art methods, X-MAC boosts the network scalability (number of concurrent end-devices) by 1.26× to 2.41× with packet reception rate requirement of > 95%.

Low-Power Wide-Area Networks (LPWANs), extensively utilized for connecting billions of IoT devices, encounter wireless interference challenges in unlicensed frequency bands. Cutting-edge research suggests employing Received Signal Strength Indication (RSSI) sequences for error detection to mitigate interference-related issues. Nevertheless, the effectiveness of this method significantly declines under low signal-to-noise ratios (SNRs). Additionally, long-range communication often results in low SNR received signals, sometimes even below the noise floor. Targeting this fundamental issue, this article proposes the LPWAN packet technique, broadly applicable across diverse scenarios through edge–cloud collaboration. On the edge side, we propose an innovative architecture that fully exploits spatial distribution and interference independence in the field. Rather than utilizing resource-intensive RSSI-based error detection, we leverage a lightweight coding scheme for error detection at the Long Range (LoRa) edge, forwarding correct frames to the cloud. On the cloud side, packet recovery is achieved utilizing group-weighted voting. We design and implement ECRLoRa with commercially available devices (SemTech’s SX1278 and SX1302 LoRa chipsets) and assess its performance in low SNR environments. Our thorough evaluation demonstrates that our approach attains a Packet Recovery Ratio of 96% with low SNR (i.e., below −10 dB), resulting in 1.8× throughput, 7.5× faster recovery time, and 4.92× average accuracy compared to state-of-the-art cloud-optimized application layer solutions.

The successful operation of autonomous vehicles hinges on their ability to accurately identify objects in their vicinity, particularly living targets such as bikers and pedestrians. However, visual interference inherent in real-world environments, such as omnipresent billboards, poses substantial challenges to extant vision-based detection technologies. These visual interference exhibit similar visual attributes to living targets, leading to erroneous identification. We address this problem by harnessing the capabilities of mmWave radar, a vital sensor in autonomous vehicles, in combination with vision technology, thereby contributing a unique solution for liveness target detection. We propose a methodology that extracts features from the mmWave radar signal to achieve end-to-end liveness target detection by integrating the mmWave radar and vision technology. This proposed methodology is implemented and evaluated on the commodity mmWave radar IWR6843ISK-ODS and vision sensor Logitech camera. Our extensive evaluation reveals that the proposed method accomplishes liveness target detection with a mean average precision of 98.1%, surpassing the performance of existing studies.

By offering long-range wireless connections with low energy consumption, Low-Power Wide Area Networks (LP- WANs) have been widely deployed in many critical Internet-of- Things (IoT) applications. However, LPWAN's low-power nature leads to inevitable packet corruption under cross-technology interference (CTI) emitted from other wireless technologies that coexist in the same unlicensed band. To address this fundamental issue, researchers propose multiple lines of solutions, such as signal analysis and coding. Despite these efforts, the research on CTI mitigation needs to go one step further - to design a systematic framework for enhanced interference resilience. This paper introduces SLoRa, a systematic framework inte-grating symbol recovery with soft decoding for synergic inter-ference resilience. Without loss of generality, we focus on LoRa, the dominating LPWAN technology. SLoRa's symbol recovery examines the corrupted signal through a two-stage analysis to recover incorrect LoRa symbols accurately at a low cost. To estimate the confidence of recovered symbols, SLoRa examines the statuses of LoRa and CTI, and it further leverages the estimated confidence via soft decoding to improve the error correction capability. We analyze SLoRa theoretically and evaluate it with real testbeds, including SX1280 LoRa chips and USRP B210. Experiments across multiple settings, e.g., different CTI sources and frequency bands, demonstrate that SLoRa effectively enhances interference resilience at a low cost. Compared with the state-of-the-art technique, SLoRa improves the CTI protection capability by 1.4x while only requiring 1 % computation cost.

We organized an online fingerprint-based indoor localization competition in 2021. It attracted 1,170 teams worldwide. The teams were provided with a 60 GB dataset including WiFi, BLE, IMU, and geomagnetic field strength data collected from 204 buildings to build their localization algorithms, which were then evaluated against a separate test dataset. The competition received 28,009 submissions. The top team achieved an average accuracy of 1.50m. This paper reports the lessons we learned from analyzing the submissions, as well as our experiences in organizing the competition, through both qualitatively studying the teams' algorithms and quantitatively characterizing the competition results.

Federated learning (FL) is a machine learning technique that allows for on-site data collection and processing without sacrificing data privacy and transmission. Heterogeneity is a key challenge in federated settings. Recently, cross-technology communication (CTC) has emerged as a solution for Internet of Things (IoT) heterogeneity, enabling direct communication between different wireless devices without the need for hardware modifications or gateway intervention. For example, a sophisticated WiFi device can serve as a central coordinator for other heterogeneous devices, such as LoRa, ZigBee, Bluetooth, and LTE, leading to more efficient and ubiquitous cross-network information exchange. However, heterogeneous wireless technologies present different data transmission rates and computing resources, making it difficult to achieve high accuracy in predictions due to large amounts of multidimensional data, communication delays, transmission latency, limited processing capacity, and data privacy concerns. In this work, we propose an FL framework based on CTC for heterogeneous IoT applications, called FLCTC. To demonstrate the usability of FLCTC, we implemented FLCTC and a specific solution for forest fire prediction. FLCTC was concretely implemented as a federal deep learning based on long and short-term memory and used for forest fire prediction, addressing the challenge of data characterization in heterogeneous IoT networks. FLCTC promises to improve communication efficiency and prediction accuracy. Our platform-based evaluation results show that FLCTC is feasible, with a recall of 96% and an accuracy of 88%, offering valuable insights into the use of FL with CTC for heterogeneous IoT applications.

This paper presents CurveLight, an accurate and practical light positioning system. In CurveLight, the signal transmitter includes an infrared LED, covered by a hemispherical and rotatable shade, and the receiver detects the light signals with a photosensitive diode. When the shade is rotating, the transmitter generates a unique sequence of light signals for each point in the covered space. The main novelty of the system design is a set of curves that define different regions, either transparent or translucent, on the shade. The regions allow the light signals to create patterns from which the receiver can calculate its angles with respect to the transmitter. We design the curves in such a way that the angular information is most robust to errors caused by signal noise and motor jitters. Moreover, the shade is divided into multiple sectors, each providing independent positioning function, so as to maximize the position update rate. Experiments in various environments show that the system achieves 2-3 cm accuracy on average, with a 36 Hz update rate with a single transmitter. We present a product quality implementation of the system, and report the deployment experience in real-world environments, including autonomous driving and robotics navigation. CurveLight consistently offers centimeter-level accuracy and low latency, serving as a key component of the hybrid navigation solution for real systems in challenging scenarios.

Recent researches have mitigated interference by utilizing cloud assistance or cloud-edge collaboration for Low-Power Wide-Area Networks. However, the issue of long interference recovery time prevents these methods from being well utilized in practical scenarios. In this paper, we propose a novel method, called FDR, for Edge-Cloud collaborative interference mitigation with Fuzzy Detection Recovery, which recovers errors in real-time. Our design (i) utilizes gateways and cloud servers and (ii) reduces data transmissions with fuzzy detection codes for real-time error recovery. In our design, each gateway detects and reports the fuzzy positions of errors to the cloud. Then the cloud restores packets with fuzzy detection results. FDR takes the advantage of both the computational ability of the cloud and the error detection benefit of each gateway. We design and implement FDR with commodity devices including LoRa SX1280 and the USRP-B210 platform. Experimental results show that FDR reduces recovery time by 78.53% compared with the state-of-art, and recovers interfered data packets accurately when the packet damage rate reaches 45.72%.

As an emerging Low-Power Wide Area Networks (LPWAN) technology, LoRa is dedicated to providing long-range connections for pervasive Internet-of-Things devices. As LoRa operates in the unlicensed spectrum, transmissions from multiple LoRa end-devices inevitably collide into each other, leading to packet losses and increased transmission delay. Targeting at collisions caused by interferences under the same spreading factor (SF) settings, researchers introduce multiple lines of techniques. Despite their efforts, these techniques commonly neglect the potential collisions caused by interferences under different SF settings, which are resulted by the imperfect orthogonality. Given the disparate transmission power configurations and diverse deployed locations, the collisions under different SFs commonly exist in practical networks, and significantly limit the LoRa reliability. In this paper, we present X-MAC, the first scheduler that is aware of imperfect orthogonality. Technically, X-MAC detects the collisions under different SFs via tracking historical transmissions, and further performs dynamic channel scheduling to avoid collisions caused by interferences both under the same and different SFs. Extensive evaluations on testbed devices show that, compared with the state-of-the-art methods, X-MAC boosts the network scalability (number of concurrent end-devices) by 2.41× with packet reception rate (PRR) requirement of > 95%.

We report our experiences of developing, deploying, and evaluating MLoc, a smartphone-based indoor localization system for malls. MLoc uses Bluetooth Low Energy RSSI and geomagnetic field strength as fingerprints. We develop efficient approaches for large-scale, outsourced training data collection. We also design robust online algorithms for localizing and tracking users' positions in complex malls. Since 2018, MLoc has been deployed in 7 cities in China, and used by more than 1 million customers. We conduct extensive evaluations at 35 malls in 7 cities, covering 152K m2 mall areas with a total walking distance of 215 km (1,100 km training data). MLoc yields a median location tracking error of 2.4m. We further characterize the behaviors of MLoc's customers (472K users visiting 12 malls), and demonstrate that MLoc is a promising marketing platform through a promotion event. The e-coupons delivered through MLoc yield an overall conversion rate of 22%. To facilitate future research on mobile sensing and indoor localization, we have released a large dataset (43 GB at the time when this paper was published) that contains IMU, BLE, GMF readings, and the localization ground truth collected by trained testers from 37 shopping malls.

Low-Power Wide Area Networks (LPWANs) are widely used to connect large-scale Internet of Things (IoT) applications. Long Range (LoRa) is a promising LPWAN technology sensitive to energy consumption, since LoRa nodes are generally battery-powered, and the battery life will influence the lifetime of the LoRa network. In practice, the battery life of LoRa nodes is short in many scenarios, due to the long transmission distance form the gateway leading to high energy consumption. Existing techniques for energy-efficient data transmission mainly focus on static gateways, and will consume huge energy of remote nodes. In this paper, we propose to integrate LoRa with mobility to minimize the energy consumption of nodes by effectively shortening the transmission distances, and design the first mobile LoRa data transmission system called LoRaDrone by leveraging the unmanned aerial vehicle (UAV) gateway flying close to nodes. Specifically, we present a low-power communication mechanism and a dynamic channel allocation policy to minimize the energy consumed in sensing and communicating with the UAV gateway, while considering the distinctive LoRa parallel reception and complex transmission collisions. Then, an optimal speed scheduling strategy is designed to ensure the reliability of data transmission, and minimize the energy consumption of the UAV. Evaluations on various scales verify the effectiveness of LoRaDrone under different nodes' distributions and UAV paths. Compared with the baselines, the energy consumption of nodes using LoRaDrone is at most reduced by$\mathbf{70.37}\times$at 5000 nodes.

Due to the emergence of heterogeneous Internet of Medical Things (IoMT) (e.g., wearable health devices, smartwatch monitoring, and automated insulin delivery systems), large volumes of patient data are dispatched to central cloud servers for disease analysis and diagnosis. Although this direct mode brings a lot of convenience for both patients and medical professionals (MPs), the open communication channel between them also incurs several security and privacy issues, such as man-in-the-middle attacks, eavesdropping attacks, and tracking attacks. Based on the unsolved challenges in wireless medical sensor networks (WMSNs), several researchers have proposed various authentication and key agreement (AKA) protocols for this type of healthcare system recently. However, most of these protocols do not perceive physical-layer security and over-centralized server problem in WMSN. In this article, to address these two open problems, we propose a lightweight and reliable authentication protocol for WMSN, which is composed of cutting-edge blockchain technology and physically unclonable functions (PUFs). In addition, a fuzzy extractor scheme is introduced to deal with biometric information. Subsequently, two security evaluation methods are used to prove the high reliability of our proposed scheme. Finally, performance evaluation experiments illustrate that the proposed mutual authentication protocol requires the least computation and communication cost among the compared schemes.

Given the explosive growth of portable devices, mobile crowdsensing (MCS) is becoming an essential approach that fully utilizes pervasive idle resources to accomplish sensing tasks. The traditional MCS relies on the centralized server for task handle is susceptible to a single point of failure. Targeting this security issue, researchers have proposed a series of blockchain-based MCS. However, nodes in the blockchain suffer from high computation cost for data processing. Simultaneously, most blockchain-based MCS systems lack an efficient incentive mechanism for service requesters and workers. In this work, we integrate the smart contract and mobile devices to establish a secure, interactive, and fair blockchain-based MCS system called BSIF. To prevent illegitimate participants, BSIF requests all users to verify their identities using private keys from the registration phase. In the case of worker location privacy leakage, the location-based symmetric key generator is adopted to coordinate a session key for target range worker selection. Besides, we transfer the data evaluation process to the requester side (e.g., a personal computer), reducing computation cost in the blockchain nodes. Due to the homomorphic feature of the Paillier Cryptosystem and common interest, the requester cannot violate the directives from the blockchain. Subsequently, the Stackelberg game is adopted to investigate the participation level of the workers and the fair reward mechanism for the requesters to achieve a dynamic balance. Finally, the security analysis and performance evaluation demonstrate that our BSIF can defend against possible adversaries while significantly cutting overhead and giving participants the utmost incentive.

Since the outbreak of COVID-19 pandemic in 2020, a dramatic loss of human life has occurred and this trend presents an unprecedented challenge to public health, economic systems and social operations. Hence, it is urgent for us to take some countermeasures to restrain and dispel epidemic diffusion to the uttermost. Data freshness plays an inevitable role in timely infestor determination during this process. However, existing works pay little attention to optimizing this indicator in health monitoring. To make up this research gap, in this paper, we propose a mixed game-based Age of Information (AoI) optimization scheme, where the edge-based wireless technologies and AI-empowered diagnostic bots are adopted. Firstly, we establish the system model for Epidemic Prevention and Control Center (EPCC)-based health state monitoring network, where ultimate biosensing data is transmitted from AI bots via edge servers. Then, upon deriving AoI expression with a closed form, the minimization goal between edge servers and bots is specified. Simultaneously, we reformulate the AoI optimization problem from the mixed game viewpoint (i.e., coalition formation game and ordinary potential game), and then propose two algorithms for cooperative order-based bot deployment and stochastic learning-based channel selection. Finally, compared with the typical baselines, the experiment result shows our scheme can reach the lower AoI value for biosensing data transmission under different parameter settings.

Industrial Scientific Medical (ISM) band has become more and more crowded due to the ever-growing size of many mainstream technologies, e.g., Wi-Fi, ZigBee and Bluetooth. Though the coexistence issue has led to severe Cross Technology Interference (CTI), it provides great opportunities to better utilize the scarce bandwidth resources. A fundamental question is how to ensure harmonious and effective operations for different networks To exploit it, a novel global cooperation framework is proposed. In particular, our work enables reliable concurrent Cross Technology Communication (CTC) from Wi-Fi to Wi-Fi, ZigBee and Bluetooth commodity devices. Compared to existing CTC approaches, our scheme improves the communication efficiency significantly, and hence is the foundation for effective global cooperation. Based on the proposed CTC scheme, a unified Media Access Control (MAC) approach is introduced to cooperate CTC message transmission and reception for heterogeneous networks with different MACs. Three proof-of-concepts applications, e.g. global synchronization, global CTI coordination and global Location Based Service (LBS) broadcasting are discussed to fully leverage the benefits of global cooperation. Extensive evaluations show that compared with existing schemes, the proposed framework achieves 8.1 times lower synchronization error, 9 times lower packet delivery delay, and 2.7 times lower energy consumption for acquiring LBS message.

The vast demand for diverse applications raises new networking challenges, which have encouraged the development of a new paradigm of Internet of Things (IoT), e.g., LoRa. LoRa is a proprietary spread spectrum modulation technique that provides a solution for long-range and ultra-low power-consumption transmission. Due to promising prospects of LoRa, significant effort has been made on this compelling technology since its emergence. In this article, we provide a comprehensive survey of LoRa from a systematic perspective: LoRa analysis, communication, security, and its enabled applications. First, we summarize works focusing on analyzing the performance of LoRa networks. Then, we review studies enhancing the performance of LoRa networks in communication. Afterward, we analyze the security vulnerabilities and countermeasures. Finally, we survey the various LoRa-enabled applications. We also present comparisons of existing methods, together with insightful observations and inspiring future research directions.

Recent years have witnessed the proliferation of Low-power Wide Area Networks (LPWANs) in the unlicensed band for various Internet-of-Things (IoT) applications. Due to the ultra-low transmission power and long transmission duration, LPWAN devices inevitably suffer from high power Cross Technology Interference (CTI), such as interference from Wi-Fi, coexisting in the same spectrum. To alleviate this issue, this paper introduces the Partial Symbol Recovery (PSR) scheme for improving the CTI resilience of LPWAN. We verify our idea on LoRa, a widely adopted LPWAN technique, as a proof of concept.At the PHY layer, although CTI has much higher power, its duration is relatively shorter compared with LoRa symbols, leaving part of a LoRa symbol uncorrupted. Moreover, due to its high redundancy, LoRa chips within a symbol are highly correlated. This opens the possibility of detecting a LoRa symbol with only part of the chips. By examining the unique frequency patterns in LoRa symbols with time-frequency analysis, our design effectively detects the clean LoRa chips that are free of CTI. This enables PSR to only rely on clean LoRa chips for successfully recovering from communication failures. We evaluate our PSR design with real-world testbeds, including SX1280 LoRa chips and USRP B210, under Wi-Fi interference in various scenarios. Extensive experiments demonstrate that our design offers reliable packet recovery performance, successfully boosting the LoRa packet reception ratio from 45.2% to 82.2% with a performance gain of 1.8×.

Despite the popularity of Bluetooth low energy (BLE) location-based services (LBS) in Internet of things applications, large-scale BLE LBS are extremely challenging due to the expenses of deploying and maintaining BLE beacons. To alleviate this issue, this work presents WiBeacon, which repurposes ubiquitously deployed WiFi access points (AP) into virtual BLE beacons via only moderate software upgrades. Specifically, a WiBeacon-enabled AP can broadcast elaborately designed WiFi packets that could be recognized as iBeacon-compatible location identifiers by unmodified mobile BLE devices. This offers fast deployment of BLE LBS with zero additional hardware costs and low maintenance burdens. WiBeacon is carefully integrated with native WiFi services, retaining transparency to WiFi clients. We implement WiBeacon on commodity WiFi APs (with various chipsets such as Qualcomm, Broadcom, and MediaTek) and extensively evaluate it across various scenarios, including a real commercial application for courier check-ins. During the two-week pilot study, WiBeacon provides reliable services, i.e., as robust as conventional BLE beacons, for 697 users with 150 types of smartphones.

The large-scale Bluetooth low energy (BLE) location-based services (LBS) are challenging due to the requirement of additional Bluetooth beacons, which inevitably incur tremendous hardware and maintenance cost. To alleviate this issue, this work presents WiBeacon which repurposes ubiquitously deployed WiFi access points into virtual beacons via cross-technology communication (CTC). WiBeacon only requires moderate software updates in APs, thus enabling fast deployment with zero additional hardware and also low maintenance cost via the remote Internet access.

This paper presents CurveLight, an accurate and practical light positioning system. In CurveLight, the signal transmitter includes an infrared LED, covered by a hemispherical and rotatable shade, and the receiver detects the light signals with a photosensitive diode. When the shade is rotating, the transmitter generates a unique sequence of light signals for each point in the covered space. The main novelty of the system design is a set of curves that define different regions, either transparent or translucent, on the shade. The regions allow the light signals to create patterns from which the receiver can calculate its angles with respect to the transmitter. We design the curves in such a way that the angular information is most robust to errors caused by signal noise and motor jitters. Moreover, the shade is divided into multiple sectors, each providing independent positioning function, so as to maximize the position update rate. Experiments in various environments show that the system achieves 2-3 cm accuracy on average, with a 36 Hz update rate with a single transmitter. We present a product quality implementation of the system, and report the deployment experience in real-world environments, including autonomous driving and robotics navigation. CurveLight consistently offers centimeter-level accuracy and low latency, serving as a key component of the hybrid navigation solution for real systems in challenging scenarios.

Recent research on physical layer cross technology communication (PHY-CTC) brings a timely answer for escalated wireless coexistence and open spectrum movement. PHY-CTC achieves direct communication among heterogeneous wireless technologies (e.g.,WiFi, Bluetooth, and ZigBee) in physical layer and thus brings communication support for coexistence service such as spectrum management and IoT device control. To put PHY-CTC into service, however, there still exists a gap due to its transmission failure and asymmetric link (i.e., one-way PHY-CTC) issues. In this paper, we propose NetCTC – the first networking support design for PHY-CTC to establish feedbacks (e.g., ACKs) and thus meet the upper layer networking requirements in heterogeneous unicast, multicast and broadcast. The core design of NetCTC is a real-time interaction mechanism which achieves reliable, transmission efficient and concurrent interactive communication among heterogeneous devices. We implement and evaluate NetCTC on commodity devices and the USRP-N210 platform. Our extensive evaluation demonstrates that NetCTC achieves reliable bidirectional cross technology communication under a full range of wireless configurations including stationary, mobile and duty-cycled settings.

Overcrowded wireless devices in unlicensed bands compete for spectrum access, generating excessive cross-technology interference (CTI), which has become a major source of performance degradation especially for low-power IoT (e.g., ZigBee) networks. This paper presents a new forward error correction (FEC) mechanism to alleviate CTI, named SafetyNet. Designed for ZigBee, SafetyNet is inspired by the observation that ZigBee is overly robust for environment noises, but insufficiently protected from high-power CTI. By effectively embedding correction code bits into the PHY layer SafetyNet significantly enhances CTI robustness without compromising noise resilience. SafetyNet additionally offers a set of unique features including (i) transparency, making it compatible with millions of readily-deployed ZigBee devices and (ii) zero additional cost on energy and spectrum, as it does not increase the frame length. Such features not only differentiate SafetyNet from known FEC techniques (e.g., Hamming and Reed-Solomon), but also uniquely position it to be critically beneficial for today's crowded wireless environment. Our extensive evaluation on physical testbeds shows that SafetyNet significantly improves ZigBee's CTI robustness under a wide range of networking settings, where it corrects 55% of the corrupted packets.

LoRa as a representative of Low-Power Wide Area Networks (LPWAN) technologies has emerged as an attractive communication platform for the Internet of Things. Since its dense deployment, signal collisions at base stations caused by concurrent transmissions degrade network performance. Existing approaches utilize the signal feature, e.g., frequency, to separate packets from collisions. They do not work well in burst traffic networks because the feature is not stable or fine-grained enough and the information for directed signal separation is not sufficient. In this paper, we leverage multidimensional information and propose a novel PHY layer approach called SCLoRa to decode collided LoRa transmissions. SCLoRa utilizes cumulative spectral coefficient, which integrates both frequency and power information, to separate symbols in the overlapped signal. The practical factors of channel fading, similar symbol boundary, and spectrum leakage are taken into account. The SCLoRa design requires neither hardware nor firmware changes in commodity devices – a feature allowing fast deployment on LoRa base stations. We implement and evaluate SCLoRa on USRP B210 base stations and commodity LoRa devices (i.e., SX1278). The experiment results in different scenarios with different radio parameters show that the throughput of SCLoRa is 3× than the state-of-the-art.

Wireless technologies are increasingly diversified to serve various Internet-of-things applications. Yet, our mobile devices (e.g., smartphones) are manufactured with limited types of wireless radio, making it challenging to access the data in the heterogeneous IoT devices. To address this fundamental problem, this work proposes XFi, which enables mobile devices to use commodity WiFi radio to directly and simultaneously collect data from diverse heterogeneous IoT devices. Our critical insight is that when an IoT frame collides with an ongoing WiFi transmission, its IoT data is captured by WiFi receiver and retained even after the demodulation procedures in WiFi hardware. Motivated by this observation, XFi proposes a general approach to obtain IoT data by analyzing the decoded WiFi payload. The method is fully compatible with existing commodity WiFi hardware and generally applicable to various IoT protocols. We implement XFi on commodity devices (e.g., RTL8812au, CC2650, and SX1280). Our comprehensive evaluation demonstrates that XFi can collect data from 8 IoT devices in parallel with over 97% accuracy, offering reliable cross-technology data collection.

Industrial Scientific Medical (ISM) band has become more and more crowded due to the ever-growing size of many mainstream technologies, e.g., Wi-Fi, ZigBee and Bluetooth. Though they compete for limited spectrum resources leading to severe Cross Technology Interference (CTI), it also provides great opportunities to better utilize the scarce bandwidth resources. A fundamental question is how to ensure harmonious and effective operations for these networks? To exploit this issue, a novel global cooperation framework is proposed. In particular, our work enables direct and simultaneous Cross Technology Communication (CTC) from a single Wi-Fi to ZigBee, Bluetooth and Wi-Fi commodity devices sharing the same band. Compared to existing CTC approaches, our scheme improves the communication efficiency significantly, and hence is the foundation for effective global cooperation. Based on the proposed CTC scheme, a unified Media Access Control (MAC) approach is introduced to cooperate CTC message transmission and reception for heterogeneous devices with different MACs. Two proof-of-concepts applications, e.g. global synchronization and global CTI coordination are discussed to fully leverage the benefits of global cooperation. Extensive evaluations show that compared with existing schemes, the proposed framework achieves 13 times lower synchronization error and 9 times lower average packet delay in CTI intensive environments.

Low Power Wide Area Networks (LPWAN) are an emerging well-adopted platform to connect the Internet-of-Things. With the growing demands for LPWAN in IoT, the number of supported end-devices cannot meet the IoT deployment requirements. The core problem is the transmission collisions when large-scale end-devices transmit concurrently. The previous research mainly includes transmission scheduling strategies, collision detection and avoidance mechanism. The use of these existing approaches to address the above limitations in LPWAN may introduce excessive communication overhead, end-devices cost, power consumption, or hardware complexity. In this paper, we present S-MAC, an adaptive MAC-layer scheduler for LPWAN. The key innovation of S-MAC is to take advantage of the periodic transmission characteristics of LPWAN applications and also the collision behaviour features of LoRa PHY-layer to enhance the scalability. Technically, S-MAC is capable of adaptively perceiving clock drift of end-devices, adaptively identifying the join and exit of end-devices, and adaptively performing the scheduling strategy dynamically. Meanwhile, it is compatible with native LoRaWAN, and adaptable to existing Class A, B and C devices. Extensive implementations and evaluations on commodity devices show that S-MAC increases the number of connected end-devices by 4.06× and improves network throughput by 4.01× with PRR requirement of > 95%.

Conventional gateway solutions are limited in satisfying the demand for ubiquitous connections among heterogeneous wireless devices, e.g., wide-area and personal-area network devices, due to the deployment complexity, high cost, and the incurred extra traffic. Recent advances propose the physical layer cross-technology communication to address these issues. However, existing CTC techniques commonly emulate the target waveform in the frequency domain (FDE). Despite their success, these FDE based techniques inherently suffer from high quantization errors and are insufficient for IoT applications that require high communication reliability.

The cross-technology communication (CTC) is a promising technique proposed recently to bridge heterogeneous wireless technologies in the ISM bands. Existing solutions use only the coarse-grained packet-level information for CTC modulation, suffering from a low throughput (e.g., 10 b/s). Our approach, called BlueBee, explores the dense PHY-layer information for CTC by emulating legitimate ZigBee frames with the Bluetooth radio. Uniquely, BlueBee achieves dual-standard compliance and transparency for its only modifying the payload of Bluetooth frames, requiring neither hardware nor firmware changes at either the Bluetooth sender or the ZigBee receiver. Our implementation on both USRP and commodity devices shows that BlueBee can achieve standard ZigBee bit rate of 250 kb/s at more than 99% accuracy, which is over 10000 x faster than the state-of-the-art packet-level CTC technologies.

Recent research on physical layer cross technology communication (PHY-CTC) brings a timely answer for escalated wireless coexistence and open spectrum movement. PHY-CTC achieves direct communication among heterogeneous wireless technologies (e.g.,WiFi, Bluetooth, and ZigBee) in physical layer and thus brings communication support for coexistence service such as spectrum management and IoT device control. To put PHY-CTC into service, however, there still exists a gap due to its transmission failure and asymmetric link (i.e., one-way PHYCTC) issues. In this paper, we propose NetCTC - the first networking support design for PHY-CTC to establish feedbacks (e.g., ACKs) and thus meet the upper layer networking requirements in heterogeneous unicast, multicast and broadcast. The core design of NetCTC is a real-time interaction mechanism which achieves reliable, transmission efficient and parallel interactive communication among heterogeneous devices. We implement and evaluate NetCTC on commodity devices (Laptops with Atheros AR2425 WiFi NIC, smart phones with Broadcom BCM4330 WiFi chip and MicaZ CC2420) and the USRP-N210 platform. Our extensive evaluation demonstrates that NetCTC achieves reliable bidirectional cross technology communication under a full range of wireless configurations including stationary, mobile and dutycycled settings.

Under significant coexistence in the ISM band, the impact of cross-technology interference (CTI) has become a major threat to low-power IoT. This paper presents ECC that uniquely enables explicit channel coordination among heterogeneities via cross-technology communication (CTC) introduced in the latest studies, while maintaining full compatibility to commodity devices. Unlike any implicit coordination designs adopting statistical models to probabilistically predict white spaces, ECC generates the white space using WiFi CTS, which is then explicitly notified to ZigBee through CTC for immediate use. Technical highlight of ECC lies in ensuring ZigBee communication under CTI, without disrupting WiFi operation. This is effectively achieved by the dynamic adjustment of CTS duration with respect to traffic amount and spectrum availability, which essentially enables ECC to be generally applied to various scenarios without prior knowledge. Lastly, ECC significantly reduces delay and energy in low duty cycled ZigBee, by waking them up upon channel availability (via CTC). We evaluate ECC on commercial platforms: Atheros AR2425 WiFi card and TelosB motes. Experiment results show that ECC achieves 1.8x ZigBee packet reception ratio, and cuts down delay and energy by 98.6% and 51% under the low duty cycle.

In wireless communication, heterogeneous technologies such as WiFi, ZigBee and BlueTooth operate in the same ISM band.With the exponential growth in the number of wireless devices, the ISM band becomes more and more crowded. These heterogeneous devices have to compete with each other to access spectrum resources, generating cross-technology interference (CTI). Since CTI may destroy wireless communication, this field is facing an urgent and challenging need to investigate spectrum efficiency under CTI. In this paper, we introduce a novel framework to address this problem from two aspects. On the one hand, from the perspective of each communication technology itself, we propose novel channel/link models to capture the channel/link status under CTI. On the other hand, we investigate spectrum efficiency from the perspective by taking all heterogeneous technologies as a whole and building crosstechnology communication among them. The capability of direct communication among heterogeneous devices brings great opportunities to harmoniously sharing the spectrum with collaboration rather than competition.

Cross-technology communication (CTC) techniques are introduced in recent literatures to explore the opportunities of collaboration between heterogeneous wireless technologies, such as WiFi and ZigBee. Their applications include context-aware services and global channel coordination. However, state-of-the-art CTC schemes either suffer from channel inefficiency, low throughput, or disruption to existing networks. This paper presents the CTC via data packets (DCTC), which takes advantage of abundant existing data packets to construct recognizable energy patterns. DCTC features (i) a significant enhancement in CTC throughput while (ii) keeping transparent to upper layer protocols and applications. Our design also features advanced functions including multiplexing to support concurrent transmissions of multiple DCTC senders and adaptive rate control according to the traffic volume. Testbed implementations across WiFi and ZigBee platforms demonstrate reliable bidirectional communication of over 95% in accuracy while achieving throughput 2.3x of the state of the art. Meanwhile, experiment results show that DCTC has little and bounded impact on the delay and throughput of original data traffic.

Recent research on CTC (cross-technology communication) demonstrates the viability of direct coordination among heterogeneous devices (e.g., WiFi and ZigBee) with incompatible physical layers. Although encouraging, current solutions suffer from either severe inefficiency in channel utilization or low throughput using limited beacons. To address these limitations, this paper presents C-Morse, which leverages all traffic (such as through data packets, beacons and other control frames) to achieve a high cross-technology communication throughput. The key idea of C-Morse is to slightly perturb the transmission timing of existing WiFi packets to construct recognizable radio energy patterns without introducing noticeable delays to upper layers. At the receiver side, ZigBee captures such patterns by sensing the RSSI value, and then decodes the transmitted symbols. C-Morse also introduces a novel timing-based multiplexing technique to allow the coexistence of multiple C-Morse access points and reject other interference, showing a reliable symbol delivery ratio. As a result, C-Morse achieves a free side-channel, whose CTC throughput is as much as 9 χ of the present state of the art, while maintaining the through traffic within a negligible delay that goes unnoticed by applications and end-users.

The applicability of existing Cross-Technology Communication (CTC) methods, which rely on packet-level modulation, is severely limited due to their very low throughput, e.g., tens of bps. Our work, named as WEBee, opens a promising direction for high throughput CTC via physical-level emulation. Specifically, WEBee synthesizes the time-domain signals by choosing appropriate frequency-domain components fed into the subcarriers of WiFi OFDM. WE-Bee can emulate the desired physical-layer ZigBee signals by manipulating only the data bits in WiFi packet payload, requiring neither hardware nor firmware changes in commodity technologies. Moreover, WEBee enables the parallel CTC, where one WiFi frame emulates two ZigBee frames simultaneously. To evaluate the performance, we implemented WEBee on commodity devices (the Atheros AR2425 WiFi card, BCM 4330 WiFi card and CC2420, CC2530 ZigBee devices). Our comprehensive evaluation reveals that WEBee can achieve the CTC between WiFi and ZigBee with a reliable throughput of 126Kbps in noisy environment, 16,000x faster than current state-of-the-art CTC methods.

Cross-Technology Communication is an emerging research direction providing a promising solution to the wireless coexistence problem in the ISM bands. However, the state-of-the-art CTC designs have intrinsic limitations in the throughput due to their use of coarse-grained packet-level information. In contrast, we propose to exploit the fine-grained signal modulation information via a technique called PHY-layer emulation to boost CTC throughput. We can embed a legitimate packet of a target technology, e.g., ZigBee, within the payload of a source technology, e.g., WiFi or Bluetooth Low Energy (BLE). At the mean time, we require no modification at the hardware or firmware at either sender or receiver. We can achieve 8,000x throughput from WiFi to ZigBee and 10,000x throughput from BLE to ZigBee compared to the state of the art. We also have a demo showcasing how our designs can be implemented on off-the-shelf smartphones for smart light bulbs control.

Cross-Technology Communication is a promising solution proposed recently to the coexistence problem of heterogeneous wireless technologies in the ISM bands. The existing works use only the coarse-grained packet-level information for cross-technology modulation, suffering from a low throughput (e.g., 10bps). Our approach, called BlueBee, proposes a new direction by emulating legitimate ZigBee frames using a Bluetooth radio. Uniquely, BlueBee achieves dual-standard compliance and transparency by selecting only the payload of Bluetooth frames, requiring neither hardware nor firmware changes at the Bluetooth senders and ZigBee receivers. Our implementation on both USRP and commodity devices shows that BlueBee can achieve a more than 99% accuracy and a throughput 10,000x faster than the state-of-the-art CTC reported so far.

Recent research has shown that network coding has great potential to improve network performance in wireless communication. The performance of network coding in real-world scenarios, however, varies dramatically. It is reported that network coding brings negligible improvements but extra coding overhead in some scenarios. In this article, for the first time, we analyze the impact of link correlation on network coding and quantify the coding benefits. We propose correlated coding, which encodes packets only when performance improvement is achieved. Correlated coding uses only one-hop information, which makes it work in a fully distributed manner and introduces minimal communication overhead. The highlight of the design is its broad applicability and effectiveness. We implement the design with four broadcast protocols and three unicast protocols, and we evaluate them extensively on one 802.11 testbed and three 802.15.4 testbeds. The experimental results show that (i) more coding operations do not lead to fewer transmissions, and (ii) compared to existing network coding protocols, the number of transmissions is reduced with lower coding overhead.

Recent studies on CTC (cross-technology communication) have demonstrated the possibility of building direct communication between heterogeneous wireless communication technologies, such as WiFi and ZigBee. However, current solutions suffer from significant spectrum usage or limited throughput. To address the issues, this paper presents Transparent Cross-technology Communication (TCTC), a novel CTC technique that establishes a side channel by exploiting today's abundant wireless traffic. The key idea of TCTC is to embed messages in the timings of on-going legacy traffic (e.g., HTTP packets in WiFi), with small and bounded impact on upper-layer applications. Feasibility and effectiveness of our design has been validated via test-bed implementations and experiments on bidirectional communication between WiFi and ZigBee platforms.

Scalable routing for large-scale wireless networks needs to find near shortest paths with low state on each node, preferably sublinear with the network size. Two approaches are considered promising toward this goal: compact routing and geometric routing (geo-routing). To date, the two lines of research have been largely independent, perhaps because of the distinct principles they follow. In particular, it remains unclear how they compare to each other in the worst case, despite extensive experimental results showing the superiority of one or another in particular cases. We develop a novel Trap Array topology model that provides a unified framework to uncover the limiting behavior of 10 representative geo-routing algorithms. We present a series of new theoretical results, in comparison to the performance of compact routing as a baseline. In light of their pros and cons, we further design a Compact Geometric Routing (CGR) algorithm that attempts to leverage the benefits of both approaches. Theoretical analysis and simulations show the advantages of the topology model and the algorithm.

Geometric routing or geo-routing has been shown as a promising approach to scalable routing in sensor networks. Despite its success in 2-D networks, very few designs are available for 3-D networks that can ensure short routes using only small per-node state, without incurring high load imbalance on the nodes. In this paper, we propose a novel addressing and routing scheme, i.e., OnionMap, for 3-D sensor networks that achieve the above goals, using solely connectivity information and at a linear message cost. The key idea is to decompose a 3-D network into a set of connected layers, which are then mapped to a set of concentric sphere structures (similar to an onion). On each sphere, a discrete Ricci flow method is used to assign each node a set of coordinates that permits purely greedy routing within that sphere; across the different spheres, a layer alignment algorithm helps rotate and scale the spheres, to form a coherent global coordinate system that guides global routing. Theoretical analysis and simulation show OnionMap's advantages over state-of-the-art solutions in path stretch, per-node storage, and load balance.

Diversity-based protocols such as network coding and opportunistic routing have been proposed in recent years to exploit spatial diversity in wireless communication. By utilizing concurrent links, these protocols achieve significantly better performance than traditional approaches. However, they explicitly or implicitly assume that wireless links are independent, which overestimates the true spatial diversity in reality. For the first time, this paper analyzes the impact of link correlation on network coding and introduces Correlated Coding, a link correlation-aware design that seeks to optimize the transmission efficiency by maximizing necessary coding opportunities. Correlated coding uses only one-hop information, which makes it work in a fully distributed manner and introduces minimal communication overhead. The highlight of our design is its broad applicability and effectiveness. We implement our design with four broadcast protocols and three unicast protocols, and evaluate them extensively with one 802.11 test bed and three 802.15.4 test beds running TelosB, MICAz, and Green Orbs nodes. The experiment results show that (i) more coding opportunities do not lead to more transmission benefits, and (ii) compared to coding aware protocols, the number of coding operations is reduced while the transmission efficiency is improved.

Scalable routing for large-scale wireless networks needs to find near shortest paths with low state on each node, preferably sub-linear with the network size. Two approaches are considered promising toward this goal: compact routing and geometric routing (geo-routing). To date the two lines of research have been largely independent, perhaps because of the distinct principles they follow. In particular, it remains unclear how they compare with each other in the worst case, despite extensive experimental results showing the superiority of one or another in particular cases. We develop a novel Trap Array topology model that provides a unified framework to uncover the limiting behavior of ten representative geo-routing algorithms. We present a series of new theoretical results, in comparison with the performance of compact routing as a baseline. In light of their pros and cons, we further design a Compact Geometric Routing (CGR) algorithm that attempts to leverage the benefits of both approaches. Theoretic analysis and simulations show the advantages of the topology model and the algorithm.

A connectivity-based and anchor-free three-dimensional localization (CATL) scheme is presented for large-scale sensor networks with concave regions. It distinguishes itself from previous work with a combination of three features: (1) it works for networks in both 2D and 3D spaces, possibly containing holes or concave regions; (2) it is anchor-free and uses only connectivity information to faithfully recover the original network topology, up to scaling and rotation; (3) it does not depend on the knowledge of network boundaries, which suits it well to situations where boundaries are difficult to identify. The key idea of CATL is to discover the notch nodes , where shortest paths bend and hop-count-based distance starts to significantly deviate from the true Euclidean distance. An iterative protocol is developed that uses a notch-avoiding multilateration mechanism to localize the network. Simulations show that CATL achieves accurate localization results with a moderate per-node message cost.