American Sign Language (ASL) is the primary language of many Deaf and Hard of Hearing (DHH) individuals. However, existing learning resources often lack timely, individualized feedback, leaving learners uncertain about signing accuracy. We introduce a novel egocentric ASL learning system that integrates stereo vision, error detection across four manual ASL parameters (handshape, orientation, location, movement), and large language model (LLM)–driven natural language feedback. To our knowledge, this is the first system to deliver error-aware, pedagogically grounded feedback for ASL learners. A formative study with 15 ASL teachers and 30 learners (both Deaf and hearing backgrounds) supports the motivation and design goals, while a system evaluation with 13 Deaf ASL participants (novice to advanced) practicing 230 signs provides initial evidence of system feasibility and short-term, pedagogically promising behavior within the primary user community. Across two complementary studies, we identify key design principles: prioritizing reliability over sensitivity, stratifying feedback by error severity, and leveraging egocentric alignment for natural practice. Collectively, these contributions establish a foundation for scalable ASL education and provide generalizable insights for designing AI-mediated feedback in Human-Computer Interaction (HCI).

This paper introduces mmWave-Whisper, a system that demonstrates the feasibility of full-corpus automated speech recognition (ASR) on phone calls eavesdropped remotely using off-the-shelf frequency modulated continuous wave (FMCW) millimeter-wave radars. Operating in the 77-81 GHz range, mmWave-Whisper captures earpiece vibrations from smart-phones, converts them into audio, and processes the audio to produce speech transcriptions automatically. Unlike previous work that focused on loudspeakers or a limited vocabulary, this is the first to perform this kind of speech recognition by handling a large vocabulary and full sentences on earpiece vibrations from smartphones. This approach expands the potential for radar-audio eavesdropping. mmWave-Whisper addresses challenges such as the lack of large-scale training datasets, low SNR, and limited frequency information in radar data through a systematic data pipeline designed to leverage synthetic training data, domain adaptation, and inference by incorporating OpenAI’s Whisper automatic speech recognition model. The system achieves a word accuracy rate of 44.74% and a character accuracy rate of 62.52% over a range of 25 cm to 125 cm. The paper highlights emerging misuse modalities of AI as the technology evolves rapidly.

This paper presents WirelessTap, a system that demonstrates the potential for automated speech recognition (ASR) on phone call audio eavesdropped remotely using commercially available frequency modulated continuous wave millimeter-wave (mmWave) radars operating in the 77-81 GHz range. WirelessTap detects minute vibrations from smartphone earpieces, converts them into audio, and processes this audio for speech transcription. This work presents the first full-sentence ASR using mmWave radars on earpiece vibrations using a 10,000-word vocabulary, achieving a 300 cm attack range across multiple smartphone models. It surpasses prior radar-based eavesdropping studies limited to loudspeakers, small vocabularies, or constrained evaluations. To address challenges like the absence of large mmWave radar-based audio datasets, low signal-to-noise ratio, and limited voice frequency ranges extractable from radar data, WirelessTap incorporates synthetic data generation, domain adaptation, and inference using OpenAI's Whisper ASR model. Our experiments systematically show how word accuracy rate gradually decreases with distance, from as high as 59.25% at 50 cm to 2% at 300 cm; additionally, we deploy this attack to a real-world setting with a user study targeting a victim holding a smartphone to their ear. This paper highlights the evolving risks of artificial intelligence and sensor systems being misused as technology advances.

Deep neural networks (DNNs) have achieved tremendous success in various applications including video action recognition, yet remain vulnerable to backdoor attacks (Trojans). The backdoor-compromised model will mis-classify to the target class chosen by the attacker when a test instance (from a non-target class) is embedded with a specific trigger, while maintaining high accuracy on attack-free instances. Although there are extensive studies on backdoor attacks against image data, the susceptibility of video-based systems under backdoor attacks remains largely unexplored. Current studies are direct extensions of approaches proposed for image data, e.g., the triggers are independently embedded within the frames, which tend to be detectable by existing defenses. In this paper, we introduce a simple yet effective backdoor attack against video data. Our proposed attack, adding perturbations in a transformed domain, plants an imperceptible, temporally distributed trigger across the video frames, and is shown to be resilient to existing defensive strategies. The effectiveness of the proposed attack is demonstrated by extensive experiments with various well-known models on two video recognition benchmarks, UCF101 and HMDB51, and a sign language recognition benchmark, Greek Sign Language (GSL) dataset. We delve into the impact of several influential factors on our proposed attack and identify an intriguing effect termed "collateral damage" through extensive studies.

Cloud gaming (also referred to as Game Streaming) is a rapidly emerging application that is changing the way people enjoy video games. However, if the user demands a high-resolution (e.g., 2 K or 4 K) stream, the game frames require high bandwidth and the stream often suffers from a significant number of frame drops due to network congestion degrading the Quality of Experience (QoE). Recently, the DNN-based Super Resolution (SR) technique has gained prominence as a practical alternative for streaming low-resolution frames and upscaling them at the client for enhanced video quality. However, performing such DNN-based tasks on resource-constrained and battery-operated mobile platforms is very expensive and also fails to meet the real-time requirement (60 frames per second (FPS)). Unlike traditional video streaming, where the frames can be downloaded and buffered, and then upscaled by their playback turn, Game Streaming is real-time and interactive, where the frames are generated on the fly and cannot tolerate high latency/lags for frame upscaling. Thus, state-of-the-art (SOTA) DNN-based SR cannot satisfy the mobile Game Streaming requirements. Towards this, we propose GameStreamSR, a framework for enabling real-time Super Resolution for Game Streaming applications on mobile platforms. We take visual perception nature into consideration and propose to only apply DNN-based SR to the regions with high visual importance and upscale the remaining regions using traditional solutions such as bilinear interpolation. Especially, we leverage the depth data from the game rendering pipeline to intelligently localize the important regions, called regions of importance (RoI), in the rendered game frames. Our evaluation of ten popular games on commodity mobile platforms shows that our proposal can enable realtime (60 FPS) neurally-augmented SR. Our design achieves a $13 \times$ frame rate speedup (and $\approx 4 \times$ Motion-to-Photon latency improvement) for the reference frames and a $1.6 \times$ frame rate speedup for the non-reference frames, which translates to, on average $2 \times$ FPS performance improvement and 26-33% energy savings over the SOTA DNN-based SR execution, while achieving about 2dB PSNR gain and better perceptual quality than the current SOTA.

While localization has gained a tremendous amount of attention from both academia and industry, much less attention has been paid to equally important orientation estimation. Traditional orientation estimation systems relying on gyroscopes suffer from cumulative errors. In this paper, we propose UWBOrient, the first fine-grained orientation estimation system utilizing ultra-wideband (UWB) modules embedded in smartphones. The proposed system presents an alternative solution that is more accurate than gyroscope estimates and free of error accumulation. We propose to fuse UWB estimates with gyroscope estimates to address the challenge associated with UWB estimation alone and further improve the estimation accuracy. UWBOrient decreases the estimation error from the state-of-the-art 7.6° to 2.7° while maintaining a low latency (20 ms) and low energy consumption (40 mWh). Comprehensive experiments with both iPhone and Android smartphones demonstrate the effectiveness of the proposed system under various conditions including natural motion, dynamic multipath and NLoS. Two real-world applications, i.e., head orientation tracking and 3D reconstruction are employed to showcase the practicality of UWBOrient.

This article presents EARFace , a system that shows the feasibility of tracking facial landmarks for 3D facial reconstruction using in-ear acoustic sensors embedded within smart earphones. This enables a number of applications in the areas of facial expression tracking, user interfaces, AR/VR applications, affective computing, and accessibility, among others. Although conventional vision-based solutions break down under poor lighting and occlusions, and also suffer from privacy concerns, earphone platforms are robust to ambient conditions while being privacy-preserving. In contrast to prior work on earable platforms that perform outer-ear sensing for facial motion tracking, EARFace shows the feasibility of completely in-ear sensing with a natural earphone form factor, thus enhancing the comfort levels of wearing. The core intuition exploited by EARFace is that the shape of the ear canal changes due to the movement of facial muscles during facial motion. EARFace tracks the changes in shape of the ear canal by measuring ultrasonic channel frequency response of the inner ear, ultimately resulting in tracking of the facial motion. A transformer-based machine learning model is designed to exploit spectral and temporal relationships in the ultrasonic channel frequency response data to predict the facial landmarks of the user with an accuracy of 1.83 mm. Using these predicted landmarks, a 3D graphical model of the face that replicates the precise facial motion of the user is then reconstructed. Domain adaptation is further performed by adapting the weights of layers using a group-wise and differential learning rate. This decreases the training overhead in EARFace . The transformer-based machine learning model runs on smart phone devices with a processing latency of 13 ms and an overall low power consumption profile. Finally, usability studies indicate higher levels of comforts of wearing EARFace ’s earphone platform in comparison with alternative form factors.

Search Engines such as Google, Baidu, and Bing have revolutionized the way we interact with the cyber world with a number of applications in recommendations, learning, advertisements, healthcare, entertainment, etc. In this paper, we design search engines for sign languages such as American Sign Language (ASL). Sign languages use hand and body motion for communication with rich grammar, complexity, and vocabulary that is comparable to spoken languages. This is the primary language for the Deaf community with a global population of ≈ 500 million. However, search engines that support sign language queries in native form do not exist currently. While translating a sign language to a spoken language and using existing search engines might be one possibility, this can miss critical information because existing translation systems are either limited in vocabulary or constrained to a specific domain. In contrast, this paper presents a holistic approach where ASL queries in native form as well as ASL videos and textual information available online are converted into a common representation space. Such a joint representation space provides a common framework for precisely representing different sources of information and accurately matching a query with relevant information that is available online. Our system uses low-intrusive wearable sensors for capturing the sign query. To minimize the training overhead, we obtain synthetic training data from a large corpus of online ASL videos across diverse topics. Evaluated over a set of Deaf users with native ASL fluency, the accuracy is comparable with state-of-the-art recommendation systems for Amazon, Netflix, Yelp, etc., suggesting the usability of the system in the real world. For example, the re-call@10 of our system is 64.3%, i.e., among the top ten search results, six of them are relevant to the search query. Moreover, the system is robust to variations in signing patterns, dialects, sensor positions, etc.

Ubiquitous finger motion tracking enables a number of exciting applications in augmented reality, sports analytics, rehabilitation-healthcare, haptics, etc. This article presents NeuroPose, a system that shows the feasibility of 3-D finger motion tracking using a platform of wearable electromyography (EMG) sensors. EMG sensors can sense electrical potential from muscles due to finger activation, thus offering rich information for fine-grained finger motion sensing. However, converting the sensor information to 3-D finger poses is non trivial since signals from multiple fingers superimpose at the sensor in complex patterns. Toward solving this problem, NeuroPose fuses information from anatomical constraints of finger motion with machine learning architectures on recurrent neural networks (RNNs), encoder–decoder networks, and ResNets to extract 3-D finger motion from noisy EMG data. The generated motion pattern is temporally smooth as well as anatomically consistent. Furthermore, a transfer learning algorithm is leveraged to adapt a pretrained model on one user to a new user with minimal training overhead. A systematic study with 12 users demonstrates a median error of 6.24° and a 90%-ile error of 18.33° in tracking 3-D finger joint angles. The accuracy is robust to natural variation in sensor mounting positions as well as changes in wrist positions of the user. In addition, this article validates the feasibility of mirrored bilateral training approach with applications in prosthetic devices. Finally, NeuroPose is comprehensively evaluated on both low-end and recent smartphones with a processing latency of 0.019 s and low energy overhead.

This paper presents a system mmSpy that shows the feasibility of eavesdropping phone calls remotely. Towards this end, mmSpy performs sensing of earpiece vibrations using an off-the-shelf radar device that operates in the mmWave spectrum (77GHz, and 60GHz). Given that mmWave radars are becoming popular in a number of autonomous driving, remote sensing, and other IoT applications, we believe this is a critical privacy concern. In contrast to prior works that show the feasibility of detecting loudspeaker vibrations with larger amplitudes, mmSpy exploits smaller wavelengths of mmWave radar signals to detect subtle vibrations in the earpiece devices used in phonecalls. Towards designing this attack, mmSpy solves a number of challenges related to non-availability of large scale radar datasets, systematic correction of various sources of noises, as well as domain adaptation problems in harvesting training data. Extensive measurement-based validation achieves an endto-end accuracy of 83-44% in classifying digits and keywords over a range of 1-6ft, thereby compromising the privacy in applications such as exchange of credit card information. In addition, mmSpy shows the feasibility of reconstruction of the audio signals from the radar data, using which more sensitive information can be potentially leaked.

Ubiquitous finger motion tracking enables a number of exciting applications in augmented reality, sports analytics, rehabilitation-healthcare, haptics etc. This paper presents NeuroPose, a system that shows the feasibility of 3D finger motion tracking using a platform of wearable ElectroMyoGraphy (EMG) sensors. EMG sensors can sense electrical potential from muscles due to finger activation, thus offering rich information for fine-grained finger motion sensing. However converting the sensor information to 3D finger poses is non trivial since signals from multiple fingers superimpose at the sensor in complex patterns. Towards solving this problem, NeuroPose fuses information from anatomical constraints of finger motion with machine learning architectures on Recurrent Neural Networks (RNN), Encoder-Decoder Networks, and ResNets to extract 3D finger motion from noisy EMG data. The generated motion pattern is temporally smooth as well as anatomically consistent. Furthermore, a transfer learning algorithm is leveraged to adapt a pretrained model on one user to a new user with minimal training overhead. A systematic study with 12 users demonstrates a median error of 6.24° and a 90%-ile error of 18.33° in tracking 3D finger joint angles. The accuracy is robust to natural variation in sensor mounting positions as well as changes in wrist positions of the user. NeuroPose is implemented on a smartphone with a processing latency of 0.101s, and a low energy overhead.

Finger motion tracking has a number of applications in user-interfaces, sports analytics, medical rehabilitation and sign language translation. This paper presents a system called FinGTrAC that shows the feasibility of fine grained finger gesture tracking using low intrusive wearable sensor platform (smart-ring worn on the index finger and a smart-watch worn on the wrist). Such sparse sensors are convenient to wear but cannot track all fingers and hence provide under-constrained information. However application specific context can fill the gap in sparse sensing and improve the accuracy of gesture classification. This paper shows the feasibility of exploiting such context in an application of American Sign Language (ASL) translation. Non-trivial challenges arise due to noisy sensor data, variations in gesture performance across users and the inability to capture data from all fingers. FinGTrAC exploits a number of opportunities in data preprocessing, filtering, pattern matching, context of an ASL sentence to systematically fuse the available sensory information into a Bayesian filtering framework. Culminating into the design of a Hidden Markov Model, a Viterbi decoding scheme is designed to detect finger gestures and the corresponding ASL sentences in real time. Extensive evaluation on 10 users shows a detection accuracy of 94.2% for 100 most frequently used ASL finger gestures over different sentences.

A rich body of work has focused on motion tracking techniques using inertial sensors, namely accelerometers, gyroscopes, and magnetometers. Applications of these techniques are in indoor localization, gesture recognition, inventory tracking, vehicular motion, and many others. This paper identifies room for improvement over today's motion tracking techniques. The core observation is that conventional systems have trusted gravity more than the magnetic North to infer the 3D orientation of the object. We find that the reverse is more effective, especially when the object is in continuous fast motion. We leverage this opportunity to design MUSE, a magnetometer-centric sensor fusion algorithm for orientation tracking. Moreover, when the object's motion is somewhat restricted (e.g., human-arm motion restricted by elbow and shoulder joints), we find new methods of sensor fusion to fully leverage the restrictions. Real experiments across a wide range of uncontrolled scenarios show consistent improvement in orientation and location accuracy, without requiring any training or machine learning. We believe this is an important progress in the otherwise mature field of IMU-based motion tracking.

This paper shows the feasibility of identifying liquids by shining ultra-wideband (UWB) wireless signals through them. The core opportunity arises from the fact that wireless signals experience distinct slow-down and attenuation when passing through a liquid, manifesting in the phase, strength, and propagation delay of the outgoing signal. While this intuition is simple, building a robust system entails numerous challenges, including (1) pico-second scale time of flight estimation, (2) coping with integer ambiguity due to phase wraps, (3) pollution from hardware noise and multipath, and (4) compensating for the liquid-container's impact on the measurements. We address these challenges through multiple stages of signal processing without relying on any feature extraction or machine learning. Instead, we model the behavior of radio signals inside liquids (using principles of physics), and estimate the liquid's permittivity, which in turn identifies the liquid. Experiments across 33 different liquids (spread over the whole permittivity spectrum) show median permittivity error of 9%. This implies that coke can be discriminated from diet coke or pepsi, whole milk from 2% milk, and distilled water from saline water. Our end system, LiquID, is cheap, non-invasive, and amenable to real-world applications.

This paper explores the possibility of injecting mobility into wireless network infrastructure. We envision WiFi APs on wheels that move to optimize user performance. Movements need not be all around the floor, neither do they have to operate on batteries. As a first step, WiFi APs at home could remain tethered to power and Ethernet outlets while moving in small areas (perhaps under the couch). If such systems prove successful, perhaps future buildings could offer explicit support for network infrastructure mobility. This paper begins with a higher level discussion of robotic wireless networks-the opportunities and the hurdles-and then pivots by developing a smaller slice of the vision through a system called iMob. With iMob, a WiFi AP is mounted on a Roomba robot and made to periodically move within a 2x2 sqft region. The core questions pertain to finding the best location to move to, such that the SNRs from its clients are strong, and the interferences from other APs are weak. Our measurements show that the richness of wireless multipath offers significant opportunities-even within a 2x2 sqft region, locations exist that are 1:7x better than the average location in terms of throughput. When multiple APs in a neighborhood coordinate, the gains can be even higher. In sum, although infrastructure mobility has been discussed in the context of Google Balloons, ad hoc networks, and delay tolerant networks, we believe that the possibility of moving our personal devices in homes and offices is relatively unexplored, and could open doors to new kinds of innovation.

Randomized backoff is a well-established approach for avoiding collisions in CSMA networks. Today's backoff operation, such as in WiFi, attempts to create a total ordering among all the nodes contending for the channel. Total ordering requires assigning a unique backoff to each node, which is achieved by having nodes choose their back-offs from a large range, ultimately leading to channel wastage. This paper observes that total ordering can be achieved more efficiently. We propose “hierarchical backoff” in which nodes pick random numbers from a smaller range, resulting in groups of nodes picking the same number (i.e., partial order). Now, the group of nodes that picks the smallest number is advanced to a second round, where they again perform the same operation. This results in more efficient backoff because the time for partially ordering all nodes plus totally ordering each small groups is actually less than the time needed to totally order all nodes. Realizing the above intuition requires addressing new protocol challenges in group signaling, the feasibility of which is demonstrated on a USRP/GNUradio prototype. Large scale simulations also show consistent throughput gains by incorporating the proposed backoff approach into two CSMA protocols - WiFi and oCSMA. We also show that the proposed approach can be complementary to and even outperform existing backoff optimization schemes.

Internet connectivity on mobile devices is an essential commodity in today's world. While outdoors, most people connect through cellphone towers on 3G or 4G. However, cellphone tower coverage is not uniform and is affected by electromagnetic shadows cast by large structures, multipath, and absorption by various surfaces. Users with high data needs suffer in such locations due to insufficient network bandwidth. A similar insufficiency can also be felt by flash crowds in locations with otherwise moderate signal strength due to division of the available bandwidth.

Inertial sensors continuously track the 3D orientation of a flying drone, serving as the bedrock for maneuvers and stabilization. However, even the best inertial measurement units (IMU) are prone to various types of correlated failures. We consider using multiple GPS receivers on the drone as a fail-safe mechanism for IMU failures. The core challenge is in accurately computing the relative locations between each receiver pair, and translating these measurements into the drone's 3D orientation. Achieving IMU-like orientation requires the relative GPS distances to be accurate to a few centimeters -- a difficult task given that GPS today is only accurate to around 1-4 meters. Moreover, GPS-based orientation needs to be precise even under sharp drone maneuvers, GPS signal blockage, and sudden bouts of missing data. This paper designs SafetyNet, an off-the-shelf GPS-only system that addresses these challenges through a series of techniques, culminating in a novel particle filter framework running over multi-GNSS systems (GPS, GLONASS, and SBAS). Results from 11 sessions of 5-7 minute flights report median orientation accuracies of 2° even under overcast weather conditions. Of course, these improvements arise from an increase in cost due to the multiple GPS receivers, however, when safety is of interest, we believe that tradeoff is worthwhile.

This paper explores the possibility of injecting mobility into wireless network infrastructure. We envision WiFi access points on wheels that move to optimize user performance. Movements need not be all around the floor, neither do they have to operate on batteries. As a first step, WiFi APs at home could remain tethered to power and Ethernet outlets while moving in small areas (perhaps under the couch). If such systems prove successful, perhaps future buildings and cities could offer explicit support for network infrastructure mobility. This paper begins with a higher level discussion of robotic wireless networks -- the opportunities and the hurdles -- and then pivots by developing a smaller slice of the vision through a system called iMob. With iMob, a WiFi AP is mounted on a Roomba robot and made to periodically move within a 2x2 sqft region. The core research questions pertain to finding the best location to move to, such that the SNRs from its clients are strong, and the interferences from other APs are weak. Our measurements show that the richness of wireless multipath offers significant opportunities -- even within a 2x2 sqft region, locations exist that are 1.7x better than the average location in terms of throughput. When multiple APs in a neighborhood coordinate, the gains can be even higher. In sum, although infrastructure mobility has been discussed in the context of Google Balloons, ad hoc networks, and delay tolerant networks, we believe that the possibility of moving our personal devices in homes and offices is relatively unexplored, and could open doors to new kinds of innovation.

Software-defined radio (SDR) brings the flexibility of software to wireless protocol design, promising an ideal platform for innovation and rapid protocol deployment. However, implementing modern wireless protocols on existing SDR platforms often requires careful hand-tuning of low-level code, which can undermine the advantages of software. Ziria is a new domain-specific language (DSL) that offers programming abstractions suitable for wireless physical (PHY) layer tasks while emphasizing the pipeline reconfiguration aspects of PHY programming. The Ziria compiler implements a rich set of specialized optimizations, such as lookup table generation and pipeline fusion. We also offer a novel -- due to pipeline reconfiguration -- algorithm to optimize the data widths of computations in Ziria pipelines. We demonstrate the programming flexibility of Ziria and the performance of the generated code through a detailed evaluation of a line-rate Ziria WiFi 802.11a/g implementation that is on par and in many cases outperforms a hand-tuned state-of-the-art C++ implementation on commodity CPUs.

Software-defined radios (SDR) have the potential to bring major innovation in wireless networking design. However, their impact so far has been limited due to complex programming tools. Most of the existing tools are either too slow to achieve the full line speeds of contemporary wireless PHYs or are too complex to master. In this demo we present our novel SDR programming environment called Ziria. Ziria consists of a novel programming language and an optimizing compiler. The compiler is able to synthesize very efficient SDR code from high-level PHY descriptions written in Ziria language. To illustrate its potential, we present the design of an LTE-like PHY layer in Ziria. We run it on the Sora SDR platform and demonstrate on a test-bed that it is able to operate in real-time.

Mobile computing has traditionally implied mobile clients connected to a static infrastructure. This paper breaks away from this point of view and envisions the possibility of injecting mobility into infrastructure. We envision a WiFi access point on wheels, that moves to optimize desired performance metrics. Movements need not necessarily be all around the floor of a home or office, neither do they have to operate on batteries, or connect wirelessly to the Internet. At homes, they could remain tethered to power and Ethernet outlets while moving in small areas (perhaps under the study table). In offices of the future, perhaps APs could move on tracks installed on top of false ceilings.

Software-defined radio (SDR) brings the flexibility of software to the domain of wireless protocol design, promising an ideal platform both for research and innovation and rapid deployment of new protocols on existing hardware. However, existing SDR programming platforms require either careful hand-tuning of low-level code, negating many of the advantages of software, or are too slow to be useful in the real world.

Software-defined radios (SDR) have the potential to bring major innovation in wireless networking design. However, their impact so far has been limited due to complex programming tools. Most of the existing tools are either too slow to achieve the full line speeds of contemporary wireless PHYs or are too complex to master. In this demo we present our novel SDR programming environment called Ziria. Ziria consists of a novel programming language and an optimizing compiler. The compiler is able to synthesize very efficient SDR code from high-level PHY descriptions written in Ziria language. To illustrate its potential, we present the design of an LTE-like PHY layer in Ziria. We run it on the Sora SDR platform and demonstrate on a test-bed that it is able to operate in real-time.

Cooperative packet recovery has been widely investigated in wireless networks, where corrupt copies of a packet are combined to recover the original packet. While previous work such as MRD (Multi Radio Diversity) and Soft apply combining to bits and bit-confidences, combining at the symbol level has been avoided. The reason is rooted in the prohibitive overhead of sharing raw symbol information between different APs of an enterprise WLAN. We present Epicenter that overcomes this constraint, and combines multiple copies of incorrectly received “symbols” to infer the actual transmitted symbol. Our core finding is that symbols need not be represented in full fidelity - coarse representation of symbols can preserve most of their diversity, while substantially lowering the overhead. We then develop a rate estimation algorithm that actually exploits symbol level combining. Our USRP/GNURadio testbed confirms the viability of our ideas, yielding 40% throughput gain over Soft, and 25-90% over 802.11. While the gains are modest, we believe that they are realistic, and available with minimal modifications to today's EWLAN systems.

The Wi-Fi interface is one of the major energy consuming components in smart phones. Wi-Fi scanning process contributes significantly to this. We present a solution based on partial scan, which produces full scan results, by performing only incomplete scan process. Using a decision tree algorithm, we propose such a prediction scheme, with the help of cached scan results.

Macroprogramming is an application development technique for wireless sensor networks (WSNs) where the developer specifies the behavior of the system, as opposed to that of the constituent nodes. In this proposed demonstration, we would like to present Srijan, a toolkit that enables application development for WSNs in a graphical manner using data-driven macroprogramming. It can be used in various stages of application development, viz. i) specification of application as a task graph, ii) customization of the autogenerated source files with domain-specific imperative code, iii) specification of the target system structure, iv) compilation of the macroprogram into individual customized runtimes for each constituent node of the target system, and finally v) deployment of the auto generated node-level code in an over-the-air manner to the nodes in the target system. The current implementation of Srijan targets both the Sun SPOT sensor nodes and larger nodes with J2SE. Our demonstrattion will encourage users to perform end-to-end WSN application development on the SPOTs using Srijan.