Human avatars, defined as animatable 3D digital people, have diverse applications, including teleconferencing, motion guidance, remote care, and gaming. With the widespread adoption of smart mobile devices, the demand for real-time viewing of dynamic digital humans on mobile platforms has grown significantly. However, existing avatar reconstruction methods, such as MagicStream and ExAvatar, struggle to produce high-fidelity avatars from monocular videos captured by mobile devices. Furthermore, most related research focuses on individual algorithmic components and does not provide an end-to-end pipeline that jointly addresses avatar reconstruction, 4D data transmission, and mobile rendering. In this work, we introduce HoloMobile, the first end-to-end mobile avatar system with two key capabilities: (1) High-fidelity avatar reconstruction from monocular videos captured on mobile phones, using a hybrid explicit-implicit model. (2) Efficient polynomial compression, transmission, and web-based rendering of dynamic 3D avatars on mobile devices. With avatar reconstruction performed on a PC or cloud with a single consumer-grade GPU, HoloMobile provides an end-to-end solution for mobile users: from data capture to streaming and real-time viewing. Evaluation on 20 self-captured avatars reveals that HoloMobile achieves an average PSNR of 30.36 dB, a compression rate of 99.57%, and over 200 FPS for mobile rendering, demonstrating superiority over existing baselines. The demo video of HoloMobile is available at https://youtu.be/kfOFV7AiP5A.

Multimodal human action recognition (HAR) utilizes complementary data for activity classification. Built on traditional HAR tasks, recent advances in Large Language Models (LLMs) enable detailed descriptions and causal reasoning of human actions, advancing new tasks of human action understanding (HAU) and human action reasoning (HARn). However, most LLMs, especially multimodal Large Vision-Language Models (LVLMs), struggle with modalities other than RGB images, like depth, IMU, ormmWave, due to a lack of large-scale datasets in these task domains. Existing HAR datasets provide only coarse-grained annotations, in-sufficient for depicting the detailed action dynamics required in HAU and HARn tasks. Simply combining annotations and generating captions with LLMs often lacks necessary logical and spatiotemporal consistency. In this paper, we introduce CUHK-X, a large-scale multi-modal dataset and benchmarks for HAR, HAU, and HARn. It includes 64,267 samples of 40 actions performed by 30 participants across two indoor environments, covering diverse daily scenarios. To address the challenge of spatiotemporal inconsistencies in captions, we propose a prompt-based scene creation method that leverages LLMs to generate logically connected activity sequences. CUHK-X also includes three benchmarks with six tasks to evaluate state-of-the-art models. Experimental results show average accuracies of 76.52% for HAR, 40.76% for HAU, and 70.25% for HARn. This large-scale multimodal dataset aims to empower the research community to apply, develop, and adapt data-intensive learning techniques for a wide range of human activity-related tasks.

Reconfigurable Intelligent Surfaces (RIS) can significantly enhance Wi-Fi performance by reshaping RF propagation, but controlling them effectively remains a major challenge. Existing approaches rely on either RSSI or CSI feedback, yet neither can simultaneously deliver high performance and broad deployability. We introduce WiMirror, the first 802.11-compliant and highly effective algorithm for real-time RIS control in Wi-Fi networks. WiMirror leverages the Compressed Beamforming Report (CBR) in modern 802.11 standards, which retains essential channel information absent in RSSI, while eliminating the deployment barrier of CSI-based, chip-specific solutions. We design an efficient modeling and estimation framework capable of accurately inferring the RIS reflection angles toward the client using the compressed channel information embedded in CBRs. Extensive experimental results show that WiMirror achieves near-optimal performance with minimal measurement overhead, outperforming state-of-the-art RSSI- and CSI-based methods in both accuracy and efficiency.

Modern smart manufacturing pipelines have pervasively collaborated human workers, mobile robots, and industrial Internet of Things (IIoT) in shared workspaces for versatile production tasks. Despite the promising capacity of individual entities, the performance of these IIoT systems largely relies on pipeline coordination, i.e., task dispatching between humans and robots, which is particularly challenging under heterogeneous physical constraints and complex environmental uncertainties. Nonetheless, existing works either rely on traditional operation frameworks that lack scalability for large-scale complex production, or propose customized solutions for fixed agent models, overlooking the evolving nature of IIoT environments. To address these limitations, this paper proposes COACH, a human-robot collaborative manufacturing system that enables robust constraint-aware coordination across humans, robots, and IIoT. Specifically, COACH designs a scalable contextual encoder to represent the evolving relationships among human and robot agents in dynamic heterogeneous graphs. With that, a novel experience-driven task dispatcher is developed, enabling both high-performance and computation-efficient policy generation concerning the status of IIoT. To accommodate changing human fatigue and pipeline scales, COACH further develops a curriculum-enhanced reinforcement learning module for efficient dispatcher adaptation. Extensive evaluations using both synthetic testbeds and real-world manufacturing datasets demonstrate that COACH improves the feasible ratio of manufacturing pipelines by up to 27.4% and achieves up to 13.9% improvement in time efficiency compared to competing baselines across diverse job scales and environmental settings.

Autonomous farm vehicles (AFVs) encounter significant challenges in large-scale networking and massive data transmission. The rapid development of global low Earth orbit (LEO) satellite networks provides reliable support for AFVs. However, the time-varying characteristics of the satellite-terrestrial channel and large-scale collaborative scheduling among AFVs pose challenges for joint computation offloading between satellites and AFVs. This paper proposes a satellite and autonomous farm vehicle integrated network (SAFVIN) architecture. We formulate the joint satellite and AFVs computation offloading problem as a Markov decision process (MDP). We propose a deep rein forcement computation offloading (DRCO) method that adapts to satellite networks. Unlike traditional computation offloading methods, the proposed DRCO takes into account the time varying satellite network channel states. The DRCO can rapidly converge to high-quality decisions in satellite network with strong randomness, thereby adapting to dynamic environments more quickly and achieving superior performance. We compare the proposed DRCO with the heuristic coordinate descent (CD), and with deep Q-network (DQN) and deep deterministic policy gradient (DDPG) algorithms. The DRCO achieves a 2% lower latency loss while only incurring 21% of the time overhead required by the CD. Furthermore, unlike DQN and DDPG algorithms, which rely on continuous time frame input and output for network updates, the proposed DRCO can directly leverage past experience to adapt to dynamic satellite network. Compared with other deep reinforcement learning algorithms including DQN and DDPG, the DRCO achieves an average energy consumption reduction of approximately 10%.

In recent years, Deep Neural Networks (DNNs) have been increasingly adopted in a wide range of time-critical applications running on edge platforms equipped with heterogeneous multiprocessors. Given the limited resources available on these platforms, efficiently utilizing both CPU and GPU resources for time-sensitive DNN inference is crucial. However, this cross-processor inference paradigm poses significant challenges due to inherent performance imbalances between different processors. In this paper, we introduce BlastNet, a system that leverages duo-blocks—a novel model inference abstraction designed to enable highly efficient cross-processor, time-sensitive DNN inference. Each duo-block features a dual model structure, facilitating fine-grained, alternate inference across different processors. Duo-blocks are optimized during design and dynamically scheduled at runtime to maximize the resource utilization of CPU and GPU. To address memory constraints on edge devices, we also propose a duo-block selection algorithm that selectively constructs duo-blocks based on performance gains. BlastNet is implemented on an indoor autonomous driving platform and three popular edge platforms. Extensive evaluations demonstrate that BlastNet reduces the deadline missing rate by$35.07\,\%$with only a mere$1.63 \%$loss in model accuracy.

Collaborative agents, consisting of at least one human and one mobile robot agent working toward a common objective, are increasingly prevalent and effective in both social and industrial spheres, such as manufacturing. The inherent heterogeneity of these agents requires efficient and scalable Task Scheduling and Allocation (TSA) schemes that match individuals to tasks based on their abilities and meet specific temporal constraints, maximizing performance in less time. Existing works face challenges as exact methods rely on assumptions and deterministic models, which struggle to scale and infer time-varying, stochastic human task performance. While offline reinforcement learning shows promise, it is time-consuming and heavily dependent on training data that is often scarce in practical factory settings. To address these challenges, we formulate the TSA problem in mobile multi-agent teams as a temporal-constrained contextual decision-making process and propose the Bayesian Optimization-augmented Team coordination among Heterogeneous agents (BOTH), a novel scalable and training-free scheduling approach. The core idea is to use Gaussian Processes (GP) to iteratively infer agent dynamics in real-time, enabling the automatic derivation of a robust TSA solution that requires no prior data and adapts to varying problem sizes. We start by employing a heterogeneous graph-based encoder to extract representative context from the individual differences among team agents and tasks, considering strict temporal constraints. Following this, we propose a GP-driven Bayesian optimizer to intelligently explore and exploit optimal task assignments for each context, without making assumptions about the system. Experiments on synthetic and real datasets demonstrate that BOTH boosts accuracy and time efficiency compared to competing baselines, even within a few iterations.

Artificial intelligence (AI) models have been widely deployed on edge devices, enabling various IoT applications. However, lightweight on-device AI models on resource-limited edge devices hinder their adaptability to dynamic environments and tasks. Despite the superior generalization capabilities of recently developed Foundation Models (FMs), utilizing their extensive knowledge on the resource-constrained edge platforms remains unexplored. In this work, we introduce DeepEdgeFM, an edge-cloud collaborative system with FMs that enables open-set learning, simultaneously achieving generalizability and efficiency for IoT applications. DeepEdgeFM employs a spatiotemporalaware semantic customization approach that leverages spatial, temporal, and domain-specific knowledge from FMs to continuously customize edge models using unlabeled sensor data in emerging IoT environments. Meanwhile, DeepEdgeFM utilizes a dynamic model switching strategy to selectively query the knowledge of FMs based on sensor-data uncertainty and real-time network fluctuations. We implement DeepEdgeFM on five FMs and multi-modal large language models (MLLMs), covering four types of sensor data modalities. We evaluate DeepEdgeFM on two edge platforms, five public datasets, and two self-collected datasets covering both indoor and outdoor real-world environments. The results show that DeepEdgeFM outperforms state-ofthe- art baselines, achieving up to an 18.6% accuracy gain and a 38.6

Large language models (LLMs) have unlocked a plethora of powerful applications at the network edge, such as intelligent personal assistants. Data privacy and security concerns have prompted a shift towards edge-based fine-tuning of personal LLMs, away from cloud reliance. However, this raises issues of computational intensity and resource scarcity, hindering training efficiency and feasibility. While current studies investigate parameter-efficient fine-tuning (PEFT) techniques to mitigate resource constraints, our analysis indicates that these techniques are not sufficiently resource-efficient for edge devices. Other studies focus on exploiting the potential of edge devices through resource management optimization, yet are ultimately bottlenecked by the resource wall of individual devices. To tackle these challenges, we proposePAC+, a resource efficient collaborative edge AI framework for in-situ personal LLMs fine-tuning.PAC+breaks the resource wall of personal LLMs fine-tuning with a sophisticated algorithm-system co-design. (1) Algorithmically,PAC+implements a personal LLMs fine-tuning technique that is efficient in terms of parameters, time, and memory. It utilizes Parallel Adapters to circumvent the need for a full backward pass through the LLM backbone. Additionally, an activation cache mechanism further streamlining the process by negating the necessity for repeated forward passes across multiple epochs. (2) Systematically,PAC+leverages edge devices in close proximity, pooling them as a collective resource for in-situ personal LLMs fine-tuning, utilizing a hybrid data and pipeline parallelism to orchestrate distributed training. The use of the activation cache eliminates the need for forward pass through the LLM backbone, enabling exclusive fine-tuning of the Parallel Adapters using data parallelism. Extensive evaluation of the prototype implementation demonstrates thatPAC+significantly outperforms existing collaborative edge training systems, achieving up to a$9.7\times$end-to-end speedup. Furthermore, compared to mainstream LLM fine-tuning algorithms,PAC+reduces memory footprint by up to$88.16\%$.

As the adoption of LiDAR expands across various fields such as autonomous driving, robotics, and smart cities, the demand for adaptive scanning capabilities to better capture dynamic and complex scenes becomes paramount. Current LiDAR technologies, limited by fixed uniform scan patterns, struggle to prioritize critical areas, resulting in reduced perception accuracy and performance inefficiencies. This paper introduces SmartLiDAR, an advanced LiDAR system that enhances scanning efficiency and performance by adaptively optimizing scan focus through an intelligent, software-defined micro-mirror controller. Unlike traditional systems, SmartLiDAR dynamically adjusts its scan pattern based on environmental characteristics and application-specific requirements, concentrating sample points on key objects without increasing power consumption or scan time. SmartLiDAR achieves this by integrating a novel quadratic micro-mirror controller, an adaptive algorithm for generating fine-grained attention map with prioritized scan focus, and a carefully designed optimization algorithm that maps attention maps to practical scanning patterns. We prototype SmartLiDAR by building a software-defined LiDAR using commercially available optical components and FPGA. Our experimental results demonstrate that SmartLiDAR significantly enhances resolution in regions of interest by 3x and increases average object detection precision by up to 16.11%. Additionally, SmartLiDAR maintains negligible extra energy consumption and processing latency, making it suitable for real-time applications, such as autonomous vehicles.

In-home resistance training (RT) is a convenient and effective way to maintain health and well-being. However, incorrect exercise execution can result in unintended muscle engagement and an increased risk of injury. Without access to professional coaching, an accurate muscle-aware motion feedback system becomes essential for safe and effective training. However, existing visual language models (VLMs) struggle to provide accurate and effective muscle-aware movement guidance due to their limited understanding of RT motion and the absence of related expert knowledge. In this work, we introduce Myo-Trainer, the first vision-based muscle-aware motion feedback system that uses explicit muscle-aware motion analysis and domain-specific expert knowledge to provide corrective guidance on muscle engagement and movement execution. Also, we propose a novel DAGCN-Former network that integrates both spatial and temporal modeling capabilities to capture the complex dynamics of human RT motion. Experiments involving 26 subjects and 1000+ minutes of RT demonstrate that Myo-Trainer improves the accuracy of motion analysis by 17.22%, achieves a 2.5x reduced inference latency and a BertScore of 85.88% of generated feedback compared to those provided by experienced certified trainers, outperforming existing solutions. Additionally, Myo-Trainer received higher satisfaction ratings from participants compared to other AI trainers and video tutorials, highlighting its potential for real-world applications.

Human activity monitoring in the water is essential for pool management and drowning prevention. Existing camera-based solutions pose significant concerns about privacy and extra installation costs. Although sonars have been widely used for underwater sensing in open aquatic environments such as oceans and lakes, monitoring human activities with sonars in a pool setup is challenging. In this work, we propose AquaScan, the first scanning sonar-based underwater sensing system for human activity monitoring. To overcome the low frame rate, we propose a novel scanning strategy and apply an image reconstruction method to accelerate the scanning speed without compromising the performance of motion detection. We develop a novel signal processing pipeline based on a physical model to remove noises and localize human subjects. We extract features like motion, time, and spatial information from sonar images and develop a state-transfer-based activity recognition system to recognize five common water activities. We deployed AquaScan on three public swimming pools for a total period of 94 hours. The evaluation results show that AquaScan can successfully recognize the five activities in the water at about 91.5%.

Few-shot learning (FSL) aims to classify unseen examples (query set) into labeled data (support set) through low-dimensional embeddings. However, the diversity and unpredictability of environments and capture devices make FSL more challenging in real-world applications. In this paper, we propose Dual Support Query Shift (DSQS), a novel challenge in FSL that integrates two key issues: inter-set shifts (between support and query sets) and intra-set shifts (within each set), which significantly hinder model performance. To tackle these challenges, we introduce a Dual Alignment framework (DUAL), whose core insight is that clean features can improve optimal transportation (OT) alignment. Firstly, DUAL leverages a robust embedding function enhanced by a repairer network trained with perturbed and adversarially generated “hard” examples to obtain clean features. Additionally, it incorporates a two-stage OT approach with a negative entropy regularizer, which aligns support set instances, minimizes intra-class distances, and uses query data as anchor nodes to achieve effective distribution alignment. We provide a theoretical bound of DUAL and experimental results on three image datasets, compared against 10 state-of-the-art baselines, showing that DUAL achieves a remarkable average performance improvement of 25.66%. Our code is available at https://github.com/siyang-jiang/DUAL.

Recent advances in Large Language Models (LLMs) have propelled intelligent agents from reactive responses to proactive support. While promising, existing proactive agents either rely exclusively on observations from enclosed environments (e.g., desktop UIs) with direct LLM inference or employ rule-based proactive notifications, leading to suboptimal user intent understanding and limited functionality for proactive service. In this paper, we introduce ContextAgent, the first context-aware proactive agent that incorporates extensive sensory contexts surrounding humans to enhance the proactivity of LLM agents. ContextAgent first extracts multi-dimensional contexts from massive sensory perceptions on wearables (e.g., video and audio) to understand user intentions. ContextAgent then leverages the sensory contexts and personas from historical data to predict the necessity for proactive services. When proactive assistance is needed, ContextAgent further automatically calls the necessary tools to assist users unobtrusively. To evaluate this new task, we curate ContextAgentBench, the first benchmark for evaluating context-aware proactive LLM agents, covering 1,000 samples across nine daily scenarios and twenty tools. Experiments on ContextAgentBench show that ContextAgent outperforms baselines by achieving up to 8.5% and 6.0% higher accuracy in proactive predictions and tool calling, respectively. We hope our research can inspire the development of more advanced, human-centric, proactive AI assistants. The code and dataset are publicly available at https://github.com/openaiotlab/ContextAgent.

Emerging cyber-physical systems have embraced a large number of IoT devices spanning geo-distributed, which generate and consume massive volumes of data continuously. Accurate and timely spatiotemporal predictions (STP) over these streaming sensor data are critical and, in growing demand, ubiquitous across various edge scenarios such as traffic flow forecasting. Towards that, recent advanced systems have developed sophisticated optimizations among STP pipelines, aiming at optimal prediction performance. However, based on our empirical studies in real-world settings, we identify a previously overlooked bottleneck of end-to-end STP performance: data staleness. To mitigate this issue, in this work, we investigate a new task, namely stream interception, which deliberately terminates the acceptance of incoming sensor data and anticipates model execution with imputed missing features. We propose a novel dynamic interception strategy to determine the time slot to exit waiting and present Grape, an STP system that implements it with practical system designs. Extensive evaluations on real-world traces show that Grape can strike a superior tradeoff between prediction accuracy and serving latency, achieving 1.69-1.90× speedup against traditional all-waiting baselines across various STP services with high prediction accuracy on par with offline optimal cases.

In this paper, we propose Argus, a wearable add-on system based on stripped-down (i.e., compact, lightweight, low-power, limited-capability) mmWave radars. It is the first to achieve egocentric human mesh reconstruction in a multi-view manner. Compared with conventional frontal-view mmWave sensing solutions, it addresses several pain points, such as restricted sensing range, occlusion, and the multipath effect caused by surroundings. To overcome the limited capabilities of the stripped-down mmWave radars (with only one transmit antenna and three receive antennas), we tackle three main challenges and propose a holistic solution, including tailored hardware design, sophisticated signal processing, and a deep neural network optimized for high-dimensional complex point clouds. Extensive evaluation shows that Argus achieves performance comparable to traditional solutions based on high-capability mmWave radars, with an average vertex error of 6.5 cm, solely using stripped-down radars deployed in a multi-view configuration. It presents robustness and practicality across conditions, such as with unseen users and different host devices.

Alzheimer's Disease (AD) has become an increasingly critical global health concern, which necessitates effective monitoring solutions in smart health applications. However, the development of such solutions is significantly hindered by the scarcity of AD-specific activity datasets. To address this challenge, we propose SHADE-AD, a Large Language Model (LLM) framework for Synthesizing Human Activity Datasets Embedded with AD features. Leveraging both public datasets and our own collected data from 99 AD patients, SHADE-AD synthesizes human activity videos that specifically represent AD-related behaviors. By employing a three-stage training mechanism, it broadens the range of activities beyond those collected from limited deployment settings. We conducted comprehensive evaluations of the generated dataset, demonstrating significant improvements in downstream tasks such as Human Activity Recognition (HAR) detection, with enhancements of up to 79.69%. Detailed motion metrics between real and synthetic data show strong alignment, validating the realism and utility of the synthesized dataset. These results underscore SHADE-AD's potential to advance smart health applications by providing a cost-effective, privacy-preserving solution for AD monitoring.

An increasing number of environments, such as smart homes and factories, are being equipped with multiple sensor systems to enable diverse intelligent applications. However, most existing sensor coordination systems require manually predefined rules, limiting their ability to handle flexible and complex tasks. While recent approaches leverage large language models (LLMs) to interact with external APIs, they struggle to fully understand the capabilities and data dependencies of practical sensor systems. This paper introduces TaskSense, a novel system that coordinates multiple sensor systems in response to users' complex queries. TaskSense introduces a sensor language that automatically translates the capabilities and data dependencies of sensor systems into vocabularies and grammar rules that can be understood by LLMs. It then interprets user intentions into executable task plans for sensor systems using this sensor language in combination with LLMs. Meanwhile, TaskSense checks the solvability of user queries and verifies the correctness of task plan dependencies. To further enhance robustness, TaskSense incorporates a dynamic plan execution mechanism that adjusts plans based on real-time feedback from sensor data availability, data quality and execution results. TaskSense is deployed on real-world smart home systems, utilizing six popular LLMs. The system is evaluated across 4 scenarios involving 9 types of sensor systems, over 60 APIs, 170 tasks and 5 types of data modalities. Results show that TaskSense achieves up to 2× higher planning accuracy and a 75% increase in answer accuracy using the similar amount of tokens compared with baseline approaches.

Transformer models have revolutionized fields like natural language processing and computer vision but face privacy concerns in sensitive applications such as medical diagnostics. Existing confidential serving methods, including cryptography-based, memory isolation-based, and access control-based, offer trade-offs between privacy and efficiency but often struggle with high latency or hardware dependencies. This work proposes stateless KV-cache encoding (SCX), a novel framework that encodes the intermediate key-value cache during Transformer inference using user-controlled keys. SCX ensures that the cloud can neither recover the input nor independently complete the next token prediction, effectively preserving privacy. By introducing efficient encoding and decoding schemes, SCX addresses communication complexity and attack vulnerabilities while ensuring zero loss of inference quality. Experiments on large Transformer models demonstrate that SCX achieves lower latency (e.g., 36ms for LLaMA-7B), outperforming state-of-the-art cryptography and memory isolation methods by orders of magnitude. Moreover, SCX can complementarily work with advanced KV-cache management techniques to further enhance KV-cache communication efficiency by 85%, marking a significant step toward practical, privacy-preserving large Transformer serving.

Serving machine learning models on edge, mobile, and embedded devices places stringent requirements on inference latency. From operating a real enterprise service, we observed that even a fully optimized model could lead to severe violations of latency objectives when the load surges. A straightforward and mature approach is to auto-scale multiple models to balance the load. However, unlike cloud clusters, edge or mobile devices usually cannot afford to deploy multiple model replicas. Therefore, in this paper, we explore a new idea: in addition to the original model, we deploy one (or more) heterogeneous model(s) with much smaller resource overhead on the device, and perform load balancing among all models. We overcame the technical challenges posed by performance dynamics and developed InferRouter based on queuing theory. We implement and evaluate InferRouter on three real on-device inference systems, covering mobile sensing, video analytics, and natural language processing applications. Experimental results show that compared with strong baselines, InferRouter can decrease 85.2% P99 latency (5.8x faster) and improve 5.9% accuracy on the mobile workload. For a traffic video analytics task, InferRouter achieves 55.1% higher accuracy with zero deadline misses. InferRouter also shows its advantages in saving resources compared with auto-scaling and offloading approaches.

Large language models (LLMs) have shown tremendous success in following user instructions and generating helpful responses. Nevertheless, their robustness is still far from optimal, as they may generate significantly inconsistent responses due to minor changes in the verbalized instructions. Recent literature has explored this inconsistency issue, highlighting the importance of continued improvement in the robustness of response generation. However, systematic analysis and solutions are still lacking. In this paper, we quantitatively define the inconsistency problem and propose a two-stage training framework consisting of instruction-augmented supervised fine-tuning and consistency alignment training. The first stage helps a model generalize on following instructions via similar instruction augmentations. In the second stage, we improve the diversity and help the model understand which responses are more aligned with human expectations by differentiating subtle differences in similar responses. The training process is accomplished by self-rewards inferred from the trained model at the first stage without referring to external human preference resources. We conduct extensive experiments on recent publicly available LLMs on instruction-following tasks and demonstrate the effectiveness of our training framework.

With the increasing demand for smart health applications that emphasize privacy and efficiency, we introduce AD-CLIP, a synthetic data generation framework using CLIP-based models for Alzheimer’s patients. Leveraging the public dataset and data we collected from Alzheimer’s patients, AD-CLIP synthesizes human action videos featuring Alzheimer’s disease. To address privacy concerns, labeling cost, and imbalanced data distribution, AD-CLIP generates a comprehensive labeled human action skeleton dataset from depth cameras with balanced data distribution. Our preliminary experiments confirm the effectiveness of the synthesized dataset by improving the accuracy of human activity recognition up to 76.56%, which demonstrates AD-CLIP’s potential to enhance smart health applications.

Federated Learning (FL) has emerged as a prominent paradigm for distributed machine learning, crucial for mission-critical applications such as autonomous driving and smart health. However, existing FL systems have not adequately addressed the dynamic real-time requirements of these applications due to stringent inference deadlines and resource limitations on edge devices. In this paper, we propose ArtFL, a novel federated learning system designed to support dynamic runtime inference through multi-scale training. The key idea of ArtFL is to utilize the data resolution, i.e., frame resolution of videos, as a knob to accommodate dynamic inference latency requirements. Specifically, we initially propose data-utility-based multi-scale training, allowing the trained model to process data of varying resolutions during inference. Subsequently, we introduce an innovative strategy for frame resolution selection in inference, based on the similarity of adjacent frames. Finally, leveraging latency-based dynamic data dropping, we propose a systematic scheme to reduce the overall training time by shortening the waiting time in FL. For evaluation, we build two real-world FL testbeds for smart vehicles and healthcare applications, utilizing a heterogeneous edge platform. Extensive experiments across our testbeds and three public datasets show that ArtFL outperforms state-of-the-art baselines in overall accuracy and system performance up to 36.36% and 47.81%, respectively. A demo video of ArtFL on our smart vehicle testbed is available at https://youtu.be/eeK6yRVEG3U, and our code is available at https://github.com/siyang-jiang/ArtFL.git.CCS CONCEPTS• Computing methodologies → Machine learning.

The demand for fully on-device health monitoring is huge and urgent. However, deploying Large Foundation Models conventionally relies on cloud-based computing services, which poses privacy concerns. Driven by the belief of delivering personalised healthcare to family members, this study presents the development of an innovative on-device machine learning system, CaringFM. This family caring system utilizes privacy-protecting sensors and an edge-deployed Foundation Model(FM) to offer a convenient and low-cost solution for chronic disease prediction and health condition monitoring at home. In particular, CaringFM provides general health suggestions and personalized medical information while ensuring high privacy by processing and preserving all data locally.

LiDAR technology holds vast potential across various sectors, including robotics, autonomous driving, and urban planning. However, the performance of current LiDAR sensors is hindered by limited field of view (FOV), low resolution, and lack of flexible focusing capability. We introduce αLiDAR, an innovative LiDAR system that employs controllable actuation to provide a panoramic FOV, high resolution, and adaptable scanning focus. The core concept of αLiDAR is to expand the operational freedom of a LiDAR sensor through the incorporation of a controllable, active rotational mechanism. This modification allows the sensor to scan previously inaccessible blind spots and focus on specific areas of interest in an adaptive manner. By modeling uncertainties in LiDAR rotation process and estimating point-wise uncertainty, αLiDAR can correct point cloud distortions resulted from significant rotation. In addition, by optimizing LiDAR's rotation trajectory, αLiDAR can swiftly adapt to dynamic areas of interest. We developed several prototypes of αLiDAR and conducted comprehensive evaluations in various indoor and outdoor real-world scenarios. Our results demonstrate that αLiDAR achieves centimeter-level pose estimation accuracy, with an average latency of only 37 ms. In two typical LiDAR applications, αLiDAR significantly enhances 3D mapping accuracy, coverage, and density by 8.5×, 2×, and 1.6× respectively, compared to conventional LiDAR sensors. Additionally, αLiDAR's adaptive rotation improves the effective sensing distance by 1.8× and increases the number of perceived objects by 1.9×. A video demonstration of αLiDAR's in action in real world is available at https://youtu.be/x4zc_I_xTaw. The code is available at https://github.com/HViktorTsoi/alpha_lidar.

We present αLiDAR, an innovative LiDAR system that incorporates a controllable active rotational mechanism to broaden the field of view (FOV), enhance resolution, and provide adaptable focusing. This system addresses the inherent limitations of traditional LiDAR sensors, such as narrow FOV, low resolution, and lack of flexible focusing capability. By scanning blind spots and dynamically focusing on areas of interest, αLiDAR significantly surpasses conventional LiDAR sensors. Our prototypes, tested under varied real-world conditions, have demonstrated marked improvements in typical LiDAR applications. Specifically, αLiDAR enhances 3D mapping accuracy, coverage, and density by factors of 8.5, 2, and 1.6, respectively. Furthermore, the adaptive rotational mechanism of αLiDAR extends the effective sensing distance by 1.8× and increases object detection by 1.9×. To see αLiDAR in action, visit our video demonstration at https://youtu.be/x4zc_I_xTaw. Both the hardware and software implementations of αLiDAR are open-sourced at https://github.com/HViktorTsoi/alpha_lidar.

Large Language Models (LLMs) have shown significant potential in performing inferences on various tasks using heterogeneous sensors with minimal human intervention. Despite their promise, challenges such as high inference overhead and limitations on resource-constrained edge devices remain. Additionally, model hallucinations, particularly those arising from cognitive biases when interpreting numerical data, hinder performance. This work introduces a novel technique, embedding interpolation, to enhance LLMs' understanding of sensor measurements and mitigate inference overhead on edge devices. By computing embeddings through pre-computed boundary embeddings instead of directly from the input, we improve efficiency and accuracy. The effective-ness of this approach is demonstrated through visualizations with image generation models.

Alzheimer's Disease (AD) and related dementia are a growing global health challenge due to the aging population. In this paper, we present ADMarker, the first end-to-end system that integrates multi-modal sensors and new federated learning algorithms for detecting multidimensional AD digital biomarkers in natural living environments. ADMarker features a novel three-stage multi-modal federated learning architecture that can accurately detect digital biomarkers in a privacy-preserving manner. Our approach collectively addresses several major real-world challenges, such as limited data labels, data heterogeneity, and limited computing resources. We built a compact multi-modality hardware system and deployed it in a four-week clinical trial involving 91 elderly participants. The results indicate that ADMarker can accurately detect a comprehensive set of digital biomarkers with up to 93.8% accuracy and identify early AD with an average of 88.9% accuracy. ADMarker offers a new platform that can allow AD clinicians to characterize and track the complex correlation between multidimensional interpretable digital biomarkers, demographic factors of patients, and AD diagnosis in a longitudinal manner.

Recently, smart roadside infrastructure (SRI) has demonstrated the potential of achieving fully autonomous driving systems. To explore the potential of infrastructure-assisted autonomous driving, this paper presents the design and deployment of Soar, the first end-to-end SRI system specifically designed to support autonomous driving systems. Soar consists of both software and hardware components carefully designed to overcome various system and physical challenges. Soar can leverage the existing operational infrastructure like street lampposts for a lower barrier of adoption. Soar adopts a new communication architecture that comprises a bi-directional multi-hop I2I network and a downlink I2V broadcast service, which are designed based on off-the-shelf 802.11ac interfaces in an integrated manner. Soar also features a hierarchical DL task management framework to achieve desirable load balancing among nodes and enable them to collaborate efficiently to run multiple data-intensive autonomous driving applications. We deployed a total of 18 Soar nodes on existing lampposts on campus, which have been operational for over two years. Our real-world evaluation shows that Soar can support a diverse set of autonomous driving applications and achieve desirable real-time performance and high communication reliability. Our findings and experiences in this work offer key insights into the development and deployment of next-generation smart roadside infrastructure and autonomous driving systems.

On-device Deep Neural Network (DNN) training has been recognized as crucial for privacy-preserving machine learning at the edge. However, the intensive training workload and limited onboard computing resources pose significant challenges to the availability and efficiency of model training. While existing works address these challenges through native resource management optimization, we instead leverage our observation that edge environments usually comprise a rich set of accompanying trusted edge devices with idle resources beyond a single terminal. We propose Asteroid, a distributed edge training system that breaks the resource walls across heterogeneous edge devices for efficient model training acceleration. Asteroid adopts a hybrid pipeline parallelism to orchestrate distributed training, along with a judicious parallelism planning for maximizing throughput under certain resource constraints. Furthermore, a fault-tolerant yet lightweight pipeline replay mechanism is developed to tame the device-level dynamics for training robustness and performance stability. We implement Asteroid on heterogeneous edge devices with both vision and language models, demonstrating up to 12.2× faster training than conventional parallelism methods and 2.1× faster than state-of-the-art hybrid parallelism methods through evaluations. Furthermore, Asteroid can recover training pipeline 14× faster than baseline methods while preserving comparable throughput despite unexpected device exiting and failure.

Alzheimer's disease and related dementia has emerged as a global health challenge due to aging population. Expressed Emotion (EE) is a widely-used medical measure of family emotional environment of patients with caregivers. We present EmoMarker, a multi-modal sensor detection system for dyadic digital biomarkers of EE in dementia patients' homes. EmoMarker consists of a privacy-preserving depth camera and a microphone to extracts interpretable dyadic (i.e., motor and acoustic) digital biomarkers of the interaction between patients and caregivers and predict the scores of Family Altitude Scale, an assessment tool for measuring the emotional climate of families. We have deployed our system in 99 elder people's homes and achieved 81.13% prediction accuracy in preliminary results.

Ultra-wideband (UWB) technology, known for its precise distance measurement capabilities, is widely utilized in indoor localization and ranging applications. However, recent UWB systems that operate within dense and dynamic networks face challenges in real-time performance evaluation and experimental data collection, especially in infrastructure-less scenarios. To address this, we present a mobile UWB testbed, which consists of a scalable ranging system, a data collection pipeline, and a mesh-based control network. This testbed facilitates UWB-related research such as ranging protocol design, as well as implementing and evaluating applications such as social interaction analysis and contact tracing.

Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Chong Meng, Shuaiqiang Wang, Zhicong Cheng, Zhaochun Ren, Dawei Yin. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Dongsheng Zhu, Daniel Tang, Weidong Han, Jinghui Lu, Yukun Zhao, Guoliang Xing, Junfeng Wang, Dawei Yin. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Fusing Radar and Lidar sensor data can fully utilize their complementary advantages and provide more accurate reconstruction of the surrounding for autonomous driving systems. Surround Radar/Lidar can provide 360° view sampling with the minimal cost, which are promising sensing hardware solutions for autonomous driving systems. However, due to the intrinsic physical constraints, the rotating speed of surround Radar, and thus the frequency to generate Radar data frames, is much lower than surround Lidar. Existing Radar/Lidar fusion methods have to work at the low frequency of surround Radar, which cannot meet the high responsiveness requirement of autonomous driving systems. This paper develops techniques to fuse surround Radar/Lidar with working frequency only limited by the faster surround Lidar instead of the slower surround Radar, based on widely-used object detection model called MVDNet. The basic idea of our approach is simple: we let MVDNet work with temporally unaligned data from Radar/Lidar, so that fusion can take place at any time when a new Lidar data frame arrives, instead of waiting for the slow Radar data frame. However, directly applying MVDNet to temporally unaligned Radar/Lidar data greatly degrades its object detection accuracy. The key information revealed in this paper is that we can achieve high output frequency with little accuracy loss by enhancing the training procedure to explore the temporal redundancy in MVDNet so that it can tolerate the temporal unalignment of input data. We explore several different ways of training enhancement and compare them quantitatively with experiments.

Resistance training is widely incorporated in exercise programs, including in-home fitness and rehabilitation. However, improper motion patterns and muscle stimulation can undermine the safety of the subjects, making precise monitoring essential. Existing solutions primarily focus on correcting motion patterns with difficulties assessing muscle contraction levels. In this work, we introduce MyoTrainer, which provides muscle-aware motion descriptions and personalized feedback in natural language. Taking a person's exercise video as input, MyoTrainer first utilizes pose estimation models to capture motion sequences in real-time. A GCN-Former model has been developed for fine-grained motion analysis, which includes action recognition, incorrect movement pattern detection, and muscle contraction intensity estimation. Additionally, MyoTrainer integrates fitness and physiotherapeutic domain knowledge to deliver personalized, professional feedback. Extensive evaluations show that our system outperforms existing solutions in all recognition tasks and a survey indicates 88.9% of users find the generated feedback to be beneficial.

Drowning poses a significant threat to human life, making underwater human activity monitoring necessary for pool management. However, it is challenging for existing methods to recognize the aquatic activities of diverse users continuously without privacy concerns. In this paper, we propose a novel sonar-based pool monitoring system that localizes swimmers and recognizes pool activities. Our system is deployed and evaluated in a real-world pool, outperforming baselines in terms of activity recognition.

Despite the extensive use of sensors enabling intelligent applications, the complementary potential of co-existing sensor systems is often not fully utilized, limiting more advanced applications. This paper introduces a novel solution using Large Language Models (LLMs) to coordinate sensor systems for handling complex user queries. It defines a sensor language for sensor systems, including vocabulary set and grammar rules, analogous to natural language components, enabling LLMs to translate user intentions into sensor coordination plans. Preliminary results show that our approach significantly outperforms the existing solution at plan generation, execution and response generation stages.

Federated learning in multi-modal systems faces challenges due to modality heterogeneity, where edge devices have different sensor setups. Labeling multi-modal data is labor-intensive and impractical, leading to the label scarcity issue on edge clients. This paper presents a novel semi-supervised federated learning framework to address these issues. It uses complementary data, like RGB images and depth sensors, with a pseudo-labeling algorithm to improve cross-modal learning. Applied to the human action recognition task, the framework outperforms baselines. It enables efficient federated learning, handling labeling difficulties and missing modalities, offering robust performance in real-world scenarios.

Tracking interpersonal distances is essential for real-time social distancing management and ex-post contact tracing to prevent spreads of contagious diseases. Bluetooth neighbor discovery has been employed for such purposes in combating COVID-19, but does not provide satisfactory spatiotemporal resolutions. This paper presents ImmTrack, a system that uses a millimeter wave radar and exploits the inertial measurement data from user-carried smartphones or wearables to track interpersonal distances. By matching the movement traces reconstructed from the radar and inertial data, the pseudo identities of the inertial data can be transferred to the radar sensing results in the global coordinate system. The re-identified, radar-sensed movement trajectories are then used to track interpersonal distances. In a broader sense, ImmTrack is the first system that fuses data from millimeter wave radar and inertial measurement units for simultaneous user tracking and re-identification. Evaluation with up to 27 people in various indoor/outdoor environments shows ImmTrack’s decimeters-seconds spatiotemporal accuracy in contact tracing, which is similar to that of the privacy-intrusive camera surveillance and significantly outperforms the Bluetooth neighbor discovery approach.

Recent years have witnessed the emergence of a new class of cooperative edge systems in which a large number of edge nodes can collaborate through local peer-to-peer connectivity. In this paper, we propose CoEdge, a novel cooperative edge system that can support concurrent data/compute-intensive deep learning (DL) models for distributed real-time applications such as city-scale traffic monitoring and autonomous driving. First, CoEdge includes a hierarchical DL task scheduling framework that dispatches DL tasks to edge nodes based on their computational profiles, communication overhead, and real-time requirements. Second, CoEdge can dramatically increase the execution efficiency of DL models by batching sensor data and aggregating the inferences of the same model. Finally, we propose a new edge containerization approach that enables an edge node to execute concurrent DL tasks by partitioning the CPU and GPU workloads into different containers. We extensively evaluate CoEdge on a self-deployed smart lamppost testbed on a university campus. Our results show that CoEdge can achieve up to reduction on deadline missing rate compared to baselines.

HD map is a key enabling technology towards fully autonomous driving. We propose VI-Map, the first system that leverages roadside infrastructure to enhance real-time HD mapping for autonomous driving. The core concept of VI-Map is to exploit the unique cumulative observations made by roadside infrastructure to build and maintain an accurate and current HD map. This HD map is then fused with on-vehicle HD maps in real time, resulting in a more comprehensive and up-to-date HD map. By extracting concise bird-eye-view features from infrastructure observations and utilizing vectorized map representations, VI-Map incurs low compute and communication overhead. We conducted end-to-end evaluations of VI-Map on a real-world testbed and a simulator. Experiment results show that VI-Map can construct decentimeter-level (up to 0.3 m) HD maps and achieve real-time (up to a delay of 42 ms) map fusion between driving vehicles and roadside infrastructure. This represents a significant improvement of 2.8× and 3× in map accuracy and coverage compared to the state-of-the-art online HD mapping approaches. A video demo of VI-Map on our real-world testbed is available at https://youtu.be/p2RO65R5Ezg.

In recent years, the deployment of unmanned vehicle delivery services has increased unprecedentedly, leading to a need for enhanced security due to the risk of leaving high-value packages to an unauthorized third party during pickup or delivery. Existing authentication methods such as QR code and one-time password are inadequate, as they are susceptible to attacks and provide only one-way authentication. This paper, for the first time to our best knowledge, proposes Wave-for-Safe (W4S) --- a novel mutual authentication system that utilizes multi-modal sensors on both the user's smartphone and the unmanned vehicle. W4S uses random hand-waving by the legitimate user to achieve robust authentication by obtaining highly correlated sensory data measured by the Inertial Measurement Unit (IMU) in the smartphone and sensors in the unmanned vehicle (e.g., mmWave radar and camera). We propose several novel methods to overcome challenges such as heterogeneous data processing, asynchronization, and imitating attacks. The prototype is implemented on an unmanned vehicle and various smartphones, and evaluation in different real-world scenarios shows that W4S achieves an equal error rate below 0.013 against various attacks.

Multi-modal sensing systems are increasingly prevalent in real-world applications such as health monitoring and autonomous driving. Most multi-modal learning approaches need to access users' raw data, which poses significant concerns to users' privacy. Federated learning (FL) provides a privacy-aware distributed learning framework. However, current FL approaches have not addressed the unique challenges of heterogeneous multi-modal FL systems, such as modality heterogeneity and significantly longer training delay. In this paper, we propose Harmony, a new system for heterogeneous multi-modal federated learning. Harmony disentangles the multi-modal network training in a novel two-stage framework, namely modality-wise federated learning and federated fusion learning. By integrating a novel balance-aware resource allocation mechanism in modality-wise FL and exploiting modality biases in federated fusion learning, Harmony improves the model accuracy under non-i.i.d. data distributions and speeds up system convergence. We implemented Harmony on a real-world multi-modal sensor testbed deployed in the homes of 16 elderly subjects for Alzheimer's Disease monitoring. Our evaluation on the testbed and three large-scale public datasets of different applications show that, Harmony outperforms by up to 46.35% accuracy over state-of-the-art baselines and saves up to 30% training delay.

Sensing in low-light and dark environments has a wide range of applications. However, existing sensing technologies suffer several major challenges, such as excessive noise and low resolution. This paper proposes Mozart - a new mobile sensing system that leverages off-the-shelf Time-of-Flight (ToF) depth cameras to generate high-resolution and rich-in-texture maps for applications in dark scenarios. The design of Mozart is based on our key observation that the phase components of ToF measurements can be manipulated to expose texture information. Through in-depth analysis of the physical reflection model, we show that the textures can be exposed and enhanced using highly compute-efficient phase manipulation functions. By exploiting the physics texture models, we propose an autoencoder-based unsupervised learning approach that can automatically learn efficient representations from phase components to generate high-resolution maps. We implemented Mozart on several Android smartphone models1, and an edge testbed with standalone ToF camera platforms for various applications in the dark. The results show that Mozart can work in real time and delivers significant improvement over existing sensing technologies. Therefore, Mozart offers a low-cost, high-performance sensing technology for next-generation applications in the dark.

To achieve reliable and precise scene understanding, autonomous vehicles typically incorporate multiple sensing modalities to capitalize on their complementary attributes. However, existing cross-modal 3D detectors do not fully utilize the image domain information to address the bottleneck issues of the LiDAR-based detectors. This paper presents a new cross-modal 3D object detector, namely UPIDet, which aims to unleash the potential of the image branch from two aspects. First, UPIDet introduces a new 2D auxiliary task called normalized local coordinate map estimation. This approach enables the learning of local spatial-aware features from the image modality to supplement sparse point clouds. Second, we discover that the representational capability of the point cloud backbone can be enhanced through the gradients backpropagated from the training objectives of the image branch, utilizing a succinct and effective point-to-pixel module. Extensive experiments and ablation studies validate the effectiveness of our method. Notably, we achieved the top rank in the highly competitive cyclist class of the KITTI benchmark at the time of submission. The source code is available at https://github.com/Eaphan/UPIDet.

Deep Learning (DL) models have been widely deployed on IoT devices with the help of advancements in DL algorithms and chips. However, the limited resources of edge devices make these on-device DL models hard to be generalizable to diverse environments and tasks. Although the recently emerged foundation models (FMs) show impressive generalization power, how to effectively leverage the rich knowledge of FMs on resource-limited edge devices is still not explored. In this paper, we propose EdgeFM, a novel edge-cloud cooperative system with open-set recognition capability. EdgeFM selectively uploads unlabeled data to query the FM on the cloud and customizes the specific knowledge and architectures for edge models. Meanwhile, EdgeFM conducts dynamic model switching at run-time taking into account both data uncertainty and dynamic network variations, which ensures the accuracy always close to the original FM. We implement EdgeFM using two FMs on two edge platforms. We evaluate EdgeFM on three public datasets and two self-collected datasets. Results show that EdgeFM can reduce the end-to-end latency up to 3.2x and achieve 34.3% accuracy increase compared with the baseline.

Many applications such as autonomous driving and augmented reality, require the concurrent running of multiple deep neural networks (DNN) that poses different levels of real-time performance requirements. However, coordinating multiple DNN tasks with varying levels of criticality on edge GPUs remains an area of limited study. Unlike server-level GPUs, edge GPUs are resource-limited and lack hardware-level resource management mechanisms for avoiding resource contention. Therefore, we propose Miriam, a contention-aware task coordination framework for multi-DNN inference on edge GPU. Miriam consolidates two main components, an elastic-kernel generator, and a runtime dynamic kernel coordinator, to support mixed critical DNN inference. To evaluate Miriam, we build a new DNN inference benchmark based on CUDA with diverse representative DNN workloads. Experiments on two edge GPU platforms show that Miriam can increase system throughput by 92% while only incurring less than 10% latency overhead for critical tasks, compared to state of art baselines.

We present TensorBind, a novel approach aimed at unifying different hardware architectures for compilation optimization. Our proposed framework establishes an embedding space to seamlessly bind diverse hardware platforms together. By leveraging this unified representation, TensorBind enables efficient tensor program optimization techniques across a wide range of hardware platforms. We provide experimental results demonstrating the essentiality and adaptability of TensorBind in translating tensor program optimization records across multiple hardware architectures, thus revolutionizing compilation optimization strategies and facilitating the development of high-performance compilation systems over heterogeneous devices.

Wi-Fi sensing systems leverage wireless signals from widely deployed Wi-Fi devices to realize sensing for a broad range of applications. However, current Wi-Fi sensing systems heavily rely on the channel state information (CSI) to learn the signal propagation characteristics, while the availability of CSI is highly dependent on specific Wi-Fi chipsets. Through a city-scale measurement, we discover that the availability of CSI is extremely limited in operational Wi-Fi devices. In this work, we propose a new wireless sensing system called BeamSense that exploits the compressed beamforming reports (CBR). Due to the extensive support of transmit beamforming in operational Wi-Fi devices, CBR is commonly accessible and hence enables a ubiquitous sensing capability. BeamSense adopts a novel multi-path estimation algorithm that can efficiently and accurately map bidirectional CBR to a multi-path channel based on intrinsic fingerprints. We implement BeamSense on several prevalent models of Wi-Fi devices and evaluated its performance with microbenchmarks and three representative Wi-Fi sensing applications. The results show that BeamSense is capable of enabling existing CSI-based sensing algorithms to work with CBR with high sensing accuracy and improved generalizability.

This paper considers a system in which multiple users send periodically generated status information to a common access point (AP) over a collision channel. To avoid high overhead, there is no time synchronization and no feedback information from the AP to indicate whether a transmission is successful or not. The performance metric that we focus on is the age-of-information (AoI), which represents the freshness of the status information received at the AP. For this model, we propose a sequence based MAC scheme in which each user is pre-assigned a periodic sequence to schedule transmissions. This scheme guarantees each user at least one successful packet transmission within a sequence period, in the absence of time synchronization and feedback information from the AP. To the best of our knowledge, this is the first study investigating AoI performance under a sequence based MAC scheme. We derive the closed-form expressions for average AoI, average peak AoI and average age penalty under the sequence based scheduling. Besides, we derive several critical properties of the sequences to optimize the AoI performance. Comparison results show that our proposed sequence scheme outperforms slotted ALOHA and framed ALOHA in various settings.

Federated Learning (FL) has recently received significant interest, thanks to its capability of protecting data privacy. However, existing FL paradigms yield unsatisfactory performance for a wide class of human activity recognition (HAR) applications, since they are oblivious to the intrinsic relationship between data of different users. We propose ClusterFL, a clustering-based federated learning system that can provide high model accuracy and low communication overhead for HAR applications. ClusterFL features a novel clustered multi-task federated learning framework that minimizes the empirical training loss of multiple learned models while automatically capturing the intrinsic clustering relationship among the nodes. We theoretically prove the convergence of proposed FL framework for non-convex and strongly convex models and provide the guidance on selection of hyper-parameters for achieving such convergence. Based on the learned cluster relationship, ClusterFL can efficiently drop the nodes that converge slower or have little correlations with others in each cluster, significantly speeding up the convergence while maintaining the accuracy performance. We evaluate the performance of ClusterFL on an NVIDIA edge testbed using four new HAR datasets collected from 145 users. The results show that ClusterFL outperforms several state-of-the-art FL paradigms in terms of overall accuracy and can save more than 50% communication overhead.

Drowning is a major cause of unintentional deaths in swimming pools. Most swimming pools hire lifeguards for continuous surveil-lance, which is labor-intensive and hence unfeasible for small private pools. The existing unmanned surveillance solutions like camera array requires non-trivial installations, only work in certain conditions (e.g., with adequate ambient lighting), or raise privacy concerns. This demo presents SwimSonar, the first practical drowning detection system based on underwater sonar. SwimSonar employs an active ultrasonic sonar and features a novel sonar scanning strategy that balances the time and accuracy. Lastly, SwimSonar leverages a deep neural network for accurate drowning detection. Our experiments in real swimming pools show that the system achieves 88 % classification accuracy with a scan time of 1.5 seconds.

Federated Learning (FL) is an emerging learning paradigm that enables the collaborative learning of different nodes without ex-posing the raw data. However, a critical challenge faced by the current federated learning algorithms in real-world applications is the long-tailed data distribution, i.e., in both local and global views, the numbers of classes are often highly imbalanced. This would lead to poor model accuracy on some rare but vital classes, e.g., those related to safety in health and autonomous driving applications. In this paper, we propose BalanceFL, a federated learning frame-work that can robustly learn both common and rare classes from a long-tailed real-world dataset, addressing both the global and local data imbalance at the same time. Specifically, instead of letting nodes upload a class-drifted model trained on imbalanced private data, we design a novel local update scheme that rectifies the class imbalance, forcing the local model to behave as if it were trained on ideal uniform distributed data. To evaluate the performance of BalanceFL, we first adapt two public datasets to the long-tailed federated learning setting, and then collect a real-life IMU dataset for action recognition, which includes more than 10,000 data sam-ples and naturally exhibits the global long tail effect and the local imbalance. On all of these three datasets, BalanceFL outperforms state-of-the-art federated learning approaches by a large margin.

Human activity recognition (HAR) is a key enabling technology for a wide range of emerging applications. Although multimodal sensing systems are essential for capturing complex and dynamic human activities in real-world settings, they bring several new challenges including limited labeled multimodal data. In this paper, we propose Cosmo, a new system for contrastive fusion learning with small data in multimodal HAR applications. Cosmo features a novel two-stage training strategy that leverages both unlabeled data on the cloud and limited labeled data on the edge. By integrating novel fusion-based contrastive learning and quality-guided attention mechanisms, Cosmo can effectively extract both consistent and complementary information across different modalities for efficient fusion. Our evaluation on a cloud-edge testbed using two public datasets and a new multimodal HAR dataset shows that Cosmo delivers significant improvement over state-of-the-art baselines in both recognition accuracy and convergence delay.

Infrastructure-assisted autonomous driving is an emerging paradigm that expects to significantly improve the driving safety of autonomous vehicles. The key enabling technology for this vision is to fuse LiDAR results from the roadside infrastructure and the vehicle to improve the vehicle's perception in real time. In this work, we propose VIPS, a novel lightweight system that can achieve decimeter-level and real-time (up to 100 ms) perception fusion between driving vehicles and roadside infrastructure. The key idea of VIPS is to exploit highly efficient matching of graph structures that encode objects' lean representations as well as their relationships, such as locations, semantics, sizes, and spatial distribution. Moreover, by leveraging the tracked motion trajectories, VIPS can maintain the spatial and temporal consistency of the scene, which effectively mitigates the impact of asynchronous data frames and unpredictable communication/compute delays. We implement VIPS end-to-end based on a campus smart lamppost testbed. To evaluate the performance of VIPS under diverse situations, we also collect two new multi-view point cloud datasets using the smart lamppost testbed and an autonomous driving simulator, respectively. Experiment results show that VIPS can extend the vehicle's perception range by 140% within 58 ms on average, and delivers a 4X improvement in perception fusion accuracy and 47X data transmission saving over existing approaches. A video demo of VIPS based on the lamppost dataset is available at https://youtu.be/zW4oi_EWOu0.

We present a demonstration of an enhanced Time-of-Flight (ToF) depth system named HiToF, which can expose high-resolution textures from captured depth maps. By design, a ToF camera can easily capture the depth maps of a scene while largely omitting the corresponding texture information, which is often critical for the performance of many depth applications. HiToF is developed to address this issue by generating enhanced depth maps with high-resolution textures. The key idea is to manipulate the phase components used in the measurement of time-of-flight for the received IR light. In this demo, we showcase our implementation using off-the-shelf ToF cameras and engage audience with an interactive experience in various scenarios, which illustrates the system's effectiveness in improving the performance of ToF cameras in depth applications.

With the development of Internet of Things (IoT), wireless communication networks and Artificial Intelligence (AI), more and more real-time applications such as online games and autonomous driving have emerged. However, due to limited computing power and battery capacity, it has become increasingly difficult for local user devices to take on the full range of computing tasks under tight timing constraints. The emerging Mobile Edge Computing (MEC) technology is widely considered to be an important technology for achieving ultra-low latency. However, most of the existing work is focused on non-splittable computation tasks. In fact, data partitioning-oriented applications can be split into multiple subtasks for parallel processing. In this paper, we study the partial computation offloading of multiple detachable tasks in MEC networks, focusing on minimizing the total user device latency in the multi-MEC multi-user scenarios. Considering the dynamic partitioning of tasks, we adopt the barrel theory to construct a linear system of equations to find the optimal solutions and propose an approach for distributed computation offloading based on numerical methods. The simulation results show that the proposed algorithm can reduce the average user device latency by 31 % compared with the binary offloading method.

Traffic camera is one of the most ubiquitous traffic facilities, providing high coverage of complex, accident-prone road sections such as intersections. This work leverages traffic cameras to improve the perception and localization performance of autonomous vehicles at intersections. In particular, vehicles can expand their range of perception by matching the images captured by both the traffic cameras and on-vehicle cameras. Moreover, a traffic camera can match its images to an existing high-definition map (HD map) to derive centimeter-level location of the vehicles in its field of view. To this end, we propose AutoMatch - a novel system for real-time image registration, which is a key enabling technology for traffic camera-assisted perception and localization of autonomous vehicles. Our key idea is to leverage landmark keypoints of distinctive structures such as ground signs at intersections to facilitate image registration between traffic cameras and HD maps or vehicles. By leveraging the strong structural characteristics of ground signs, AutoMatch can extract very few but precise landmark keypoints for registration, which effectively reduces the communication/compute overhead. We implement AutoMatch on a testbed consisting of a self-built autonomous car, drones for surveying and mapping, and real traffic cameras. In addition, we collect two new multi-view traffic image datasets at intersections, which contain images from 220 real operational traffic cameras in 22 cities. Experimental results show that AutoMatch achieves pixel-level image registration accuracy within 88 milliseconds, and delivers an 11.7× improvement in accuracy, 1.4× speedup in compute time, and 17.1× data transmission saving over existing approaches.

Smart traffic is an emerging research area gaining more attention due to a class of emerging applications such as autonomous driving. Most smart traffic scenarios are outdoors, which are hard to collect traffic data and build demanding sensing systems. In this work, an indoor smart traffic testbed with an F1TENTH autonomous driving vehicle is built, allowing the collection of traffic datasets under different scenarios and performing various smart traffic tasks. This novel data collection system and collected dataset can help research teams build various smart traffic systems and evaluate indoor smart traffic datasets. The collected traffic light dataset is publicly available at the link1.

In recent years, Deep Neural Network (DNN) has been increasingly adopted by a wide range of time-critical applications running on edge platforms with heterogeneous multiprocessors. To meet the stringent timing requirements of these applications, heterogeneous CPU and GPU resources must be efficiently utilized for the inference of multiple DNN models. Such a cross-processor real-time DNN inference paradigm poses major challenges due to the inherent performance imbalance among different processors and the lack of real-time support for cross-processor inference from existing deep learning frameworks. In this work, we propose a new system named BlastNet that exploits duo-block - a new model inference abstraction to support highly efficient cross-processor real-time DNN inference. Each duo-block has a dual model structure, enabling efficient fine-grained inference alternatively across different processors. BlastNet employs a novel block-level Neural Architecture Search (NAS) technique to generate duo-blocks, which accounts for computing characteristics and communication overhead. The duo-blocks are optimized at design time and then dynamically scheduled to achieve high resource utilization of heterogeneous CPU and GPU at runtime. BlastNet is implemented on an indoor autonomous driving platform and three popular edge platforms. Extensive results show that BlastNet achieves 35.07 % less deadline missing rate with a mere 1.63% of model accuracy loss.

AI applications powered by deep learning are increasingly running on edge devices. Meanwhile, many real-world IoT applications demand multiple real-time tasks to run on the same device, for example, to achieve both object tracking and image segmentation simultaneously on an augmented reality glass. However, the current solutions can not yet support such multi-tenant real-time DNN inference on edge devices. Techniques such as on-device model compression trade inference accuracy for speed, while traditional DNN compilers mainly focus on single-tenant DNN model optimization. To fill this gap, we propose Aaron, which leverages DNN compiling techniques to accelerate multi-DNN inference on edge GPU based on compile-time kernel adaptation with no accuracy loss. Aaron integrates both DNN graph and kernel optimization to maximize on-device parallelism and minimize contention brought by concurrent inference.

Narrowband Internet of Things (NB-IoT) standardized by the 3GPP has attracted significant attention since its appearance. It provides extended coverage, high capacity, reduced device processing complexity, and low-power consumption to meet the requirements of a wide range of IoT applications. In particular, NB-IoT is expected to bring IoT devices prolonged lifetime up to ten years. Radio access (RA) plays a key role in the total power consumption of NB-IoT devices. Specifically, the enhanced coverage levels (ECLs) configure the user equipment (UE) with different random-access resources and power consumption during packet transmissions. In this article, we examine the ECL selection strategies for reducing the power consumption of NB-IoT. We develop two testbeds to conduct extensive field measurements related to ECL selection. Based on the measurement results, we analyze the key issues in the ECL selection process. Then, we propose an adaptive RA approach for UE, which includes two novel strategies for predictive ECL selection and opportunistic packet transmission. Evaluations show that, with the configuration of ECL selected by our adaptive approach, the UE can reduce the radio power consumption up to 36% while maintaining the same block error rate (BLER) during uploading under real-world settings.

Recent years have witnessed the increasing penetration of wireless charging base stations in the workplace and public areas, such as airports and cafeterias. Such an emerging wireless charging infrastructure has presented opportunities for new indoor localization and identification services for mobile users. In this paper, we present QID, the first system that can identify a Qi-compliant mobile device during wireless charging in real-time. QID extracts features from the clock oscillator and control scheme of the power receiver and employs light-weight algorithms to classify the device. QID adopts a 2-dimensional motion unit to emulate a variety of multi-coil designs of Qi, which allows for fine-grained device fingerprinting. Our results show that QID achieves high recognition accuracy. With the prevalence of public wireless charging stations, our results also have important implications for mobile user privacy.

Narrowband-Internet of Things (NB-IoT) is an emerging cellular communication technology designed for low-power wide-area applications. Cell selection determines the channel of user device and hence is an important issue in cellular networks. In this article, we make the first attempt to examine and optimize the cell selection in NB-IoT networks by field measurement. We conduct measurements at 30 different locations which involve five typical application scenarios of NB-IoT. Two kinds of NB-IoT modules and two network operators are also involved in the measurements. We find four potential issues on the cell selection of the User Equipment (UE) through the measurements. We propose an adaptive cell selection approach to optimize the cell selection of UE. The simulation test based on real-world measurement data shows that the cell selected by the adaptive approach can improve the coverage level and reduce the power consumption for UE.

Infrastructure-assisted autonomous driving is an emerging paradigm that aims to make affordable autonomous vehicles a reality. A key technology for realizing this vision is real-time point cloud registration which allows a vehicle to fuse the 3D point clouds generated by its own LiDAR and those on roadside infrastructures such as smart lampposts, which can deliver increased sensing range, more robust object detection, and centimeter-level navigation. Unfortunately, the existing methods for point cloud registration assume two clouds to share a similar perspective and large overlap, which result in significant delay and inaccuracy in real-world infrastructure-assisted driving settings. This paper proposes VI-Eye - the first system that can align vehicle-infrastructure point clouds at centimeter accuracy in real-time. Our key idea is to exploit traffic domain knowledge by detecting a set of key semantic objects including road, lane lines, curbs, and traffic signs. Based on the inherent regular geometries of such semantic objects, VI-Eye extracts a small number of saliency points and leverage them to achieve real-time registration of two point clouds. By allowing vehicles and infrastructures to extract the semantic information in parallel, VI-Eye leads to a highly scalable architecture for infrastructure-assisted autonomous driving. To evaluate the performance of VI-Eye, we collect two new multiview LiDAR point cloud datasets on an indoor autonomous driving testbed and a campus smart lamppost testbed, respectively. They contain total 915 point cloud pairs and cover three roads of 1.12km. Experiment results show that VI-Eye achieves centimeter-level accuracy within around 0.2s, and delivers a 5X improvement in accuracy and 2X speedup over state-of-the-art baselines.

Federated Learning (FL) has recently received significant interests thanks to its capability of protecting data privacy. However, existing FL paradigms yield unsatisfactory performance for a wide class of human activity recognition (HAR) applications since they are oblivious to the intrinsic relationship between data of different users. We propose ClusterFL, a similarity-aware federated learning system that can provide high model accuracy and low communication overhead for HAR applications. ClusterFL features a novel clustered multi-task federated learning framework that maximizes the training accuracy of multiple learned models while automatically capturing the intrinsic clustering relationship among the data of different nodes. Based on the learned cluster relationship, ClusterFL can efficiently drop out the nodes that converge slower or have little correlation with other nodes in each cluster, significantly speeding up the convergence while maintaining the accuracy performance. We evaluate the performance of ClusterFL on an NVIDIA edge testbed using four new HAR datasets collected from total 145 users. The results show that, ClusterFL outperforms several state-of-the-art FL paradigms in terms of overall accuracy, and save more than 50% communication overhead at the expense of negligible accuracy degradation.

Recent years have witnessed the increasing popularity of mobile applications, e.g., virtual reality, unmanned driving, which are generally computation-intensive and latency-sensitive, posing a major challenge for resource-limited user equipment (UE). Mobile edge computing (MEC) has been proposed as a promising approach to alleviate the problem, by offloading mobile tasks to the edge server (ES) deployed in close proximity to UE. However, most existing task offloading algorithms are primarily based on centralized scheduling, which could suffer from the ‘curse of dimensionality’ in large MEC environments. To address this issue, this paper proposes a fully distributed task offloading approach based on multi-agent deep reinforcement learning, whose critic and actor neural networks are trained under the assistance of global and local network states, respectively. In addition, we design a model parameter aggregation mechanism, along with a normalized fine-tuned reward function, to further improve the learning efficiency of the training process. Simulation results show that our proposed approach could achieve substantial performance improvements over baseline approaches.

Recent years have witnessed an emerging class of real-time applications, e.g., autonomous driving, in which resource-constrained edge platforms need to execute a set of real-time mixed Deep Learning (DL) tasks concurrently. Such an application paradigm poses major challenges due to the huge compute workload of deep neural network models, diverse performance requirements of different tasks, and the lack of real-time support from existing DL frameworks. In this paper, we present RT-mDL, a novel framework to support mixed real-time DL tasks on edge platform with heterogeneous CPU and GPU resource. RT-mDL aims to optimize the mixed DL task execution to meet their diverse real-time/accuracy requirements by exploiting unique compute characteristics of DL tasks. RT-mDL employs a novel storage-bounded model scaling method to generate a series of model variants, and systematically optimizes the DL task execution by joint model variants selection and task priority assignment. To improve the CPU/GPU utilization of mixed DL tasks, RT-mDL also includes a new priority-based scheduler which employs a GPU packing mechanism and executes the CPU/GPU tasks independently. Our implementation on an F1/10 autonomous driving testbed shows that, RT-mDL can enable multiple concurrent DL tasks to achieve satisfactory real-time performance in traffic light detection and sign recognition. Moreover, compared to state-of-the-art baselines, RT-mDL can reduce deadline missing rate by 40.12% while only sacrificing 1.7% model accuracy.

Deep learning has been increasingly applied to improve human activity recognition (HAR) accuracy and reduce the human efforts of handcrafted feature extractions. Federated Learning (FL) is an emerging learning paradigm that enables the collaborative learning of a global model without exposing users' raw data. However, existing FL approaches yield unsatisfactory HAR performance as they fail to dynamically aggregate models according to the statistical diversity of users' data. In this paper, we propose FedDL, a novel federated learning system for HAR that can capture the underlying user relationships and apply them to learn personalized models for different users dynamically. Specifically, we design a dynamic layer sharing scheme that learns the similarity among users' model weights to form the sharing structure and merges models accordingly in an iterative, bottom-up layer-wise manner. FedDL merges local models based on the dynamic sharing scheme, significantly speeding up the convergence while maintaining high accuracy. We have implemented FedDL and evaluated using a new data set we collected using LiDAR and four public real-world datasets involving 178 users in total. The results show that FedDL outperforms several state-of-the-art FL paradigms in terms of model accuracy (by more than 15%), converging rate (by more than 70%), and communication overhead (about 30% reduction). Moreover, the testing results on the datasets of different scales show that FedDL has high scalability and hence can be deployed for large-scale real-world applications.

Time-of-flight (ToF) depth cameras have been increasingly adopted in various real-world applications, e.g., used with RGB cameras for advanced computer vision tasks like 3-D mapping or deployed alone in privacy-sensitive applications such as sleep monitoring. In this paper, we propose UltraDepth, the first system that can expose high-resolution texture from depth maps captured by off-the-shelf ToF cameras, simply by introducing a distorting IR source. The exposed texture information can significantly augment depth-based applications. Moreover, such a capability can be used to launch privacy attacks, which poses a major concern due to the prominence of ToF cameras. To design UltraDepth, we present an in-depth analysis on the impact of the distorting IR light on the distance measurement. We further show that, the reflection properties (reflectivity and incidence angle) of the objects will be encoded in the distorted depth map and hence can be leveraged to reveal texture of objects in UltraDepth. We then propose two practical implementations of UltraDepth, i.e., reflection-based and external IR-based implementations. Our extensive real-world experiments show that, the depth maps output by UltraDepth achieve 89.06%, 99.33%, 81.25% mean accuracy in object detection, face recognition and character recognition, respectively, which offers over 10x improvement over the ordinary depth maps and even approaches the performance of RGB and IR images in a number of scenarios. The findings of this work provide key insights for new research on depth-related computer vision and security of depth sensing devices.

No abstract available.

Continuous and accurate respiratory volume monitoring is crucial in many healthcare-related applications. Traditional respiratory volume monitoring approaches involve obtrusive devices that are uncomfortable for long-term monitoring, while unobtrusive approaches mainly focus on sensing the respiratory rate, which is insufficient for many healthcare-related applications. In this article, we present radio-frequency respiratory volume monitoring (RF-RVM), an unobtrusive system to sense the respiratory volume based on commercial off-the-shelf (COTS) RFID devices. Specifically, RF-RVM continuously collects the temporal phase information from tags attached to the chest and abdomen to extract the chest displacement and abdomen displacement caused by respiration. Then, we assess the respiratory volume by training a backpropagation neural network model to correlate chest and abdomen displacements and respiratory volume. We use a reference tag attached under the user's neck to eliminate the noise caused by slight movements of the upper body during respiration. We implement and evaluate RF-RVM based on COTS RFID devices. The experimental results show that RF-RVM can continuously monitor user's respiratory volume with an average accuracy of 94.52% for leave-one-session-out cross-validation and 91.96% for leave-one-record-out cross-validation based on a data set sampled from 20 volunteers.

Nowadays, field-programmable gate array (FPGA) devices have been widely used in various fields. However, modules of circuits to be executed on FPGAs are placed within rectangular reconfigurable regions (RRs) with current floorplanners, leading to internal fragments, and lower utilization of resources. To address this, a dynamic description model of RRs and the corresponding floorplanner named dynamic union partial reconfiguration floorplan (DUPRFloor) are proposed in this article. The RR dynamic description modeling adds an anchor within a rectangular RR to reduce internal fragments. In this way, the modeling can represent both rectangular and nonrectangular shapes. Then, to find the optimal anchor, a clipping method is devised by constraining width and height of the candidate region. Finally, the mixed-integer linear programming (MILP) is used to optimize an objective function which considers the resources utilization and communication costs to obtain a desirable floorplanning result. The proposed method has been validated by simulation on three kinds of devices. And experimental results show that reconfigurable resources can be saved as much as 19.16% compared to rectangular modeling method. The DUPRFloor is also validated on the Microelectronics Center of North Carolina standard benchmark data sets. Results show that DUPRFloor can reduce 18.65% global wire length at most with almost the same execution time compared to state-of-the-art algorithms. Our approach is tested on a FPGA implemented software-defined radio (SDR) and reduced 29.41% wasted configurable frames, and to the overall design, 2% configurable frames are saved at most.

Recent years have seen a widespread deployment of NB-IoT networks for massive machine-to-machine communication in the emerging 5G era. Unfortunately, the key aspects of NB-IoT networks, such as radio access performance and power consumption have not been well-understood due to lack of effective tools and closed nature of operational cellular infrastructure. In this paper, we develop NB-Scope - the first hardware NB-IoT diagnostic tool that supports fine-grained fusion of power and protocol traces. We then conduct a large-scale field measurement study consisting of 30 nodes deployed at over 1,200 locations in 4 regions during a period of three months. Our in-depth analysis of the collected 49 GB traces showed that NB-IoT nodes yield significantly imbalanced energy consumption in the wild, up to a ratio of 75:1, which may lead to short battery lifetime and frequent network partition. Such a high performance variance can be attributed to several key factors including diverse network coverage levels, long tail power profile, and excessive control message repetitions. We then explore the optimization of NB-IoT base station settings on a software-defined eNodeB testbed, and suggest several important design aspects that can be considered by future NB-IoT specifications and chipsets.

Heart rate (HR) estimation based on photoplethysmography (PPG) signals has been widely adopted in wrist-worn devices. However, the motion artifacts caused by the user’s physical activities make it difficult to get the accurate HR estimation from contaminated PPG signals. Although many signal processing methods have been proposed to address this challenge, they are often highly optimized for specific scenarios, making them impractical in real-world settings where a user may perform a wide range of physical activities. In this article, we propose DeepHeart, a new HR estimation approach that features deep-learning-based denoising and spectrum-analysis-based calibration. DeepHeart generates clean PPG signals from electrocardiogram signals based on a training data set. Then a set of denoising convolutional neural networks (DCNNs) are trained with the contaminated PPG signals and their corresponding clean PPG signals. Contaminated PPG signals are then denoised by an ensemble of DCNNs and a spectrum-analysis-based calibration is performed to estimate the final HR. We evaluate DeepHeart on the IEEE Signal Processing Cup training data set with 12 records collected during various physical activities. DeepHeart achieves an average absolute error of 1.61 beats per minute (bpm), outperforming a state-of-the-art deep learning approach (4 bpm) and a classical signal processing approach (2.34 bpm).

Narrowband-Internet of Things (NB-IoT) is an emerging cellular communication technology designed for low power wide area applications. Cell selection determines the channel of user device and hence is an important issue in cellular networks. In this paper, we take the first attempt to examine and optimize the the cell selection in NB-IoT networks by field measurement. We conduct measurements at 30 different locations which involve 5 typical application scenarios of NB-IoT. Two kinds of NB-IoT modules and two network operators are also involved in the measurements. We find three potential issues on the cell selection of the User Equipment (UE) through the measurements. We propose an adaptive cell selection approach to optimize the cell selection of UE. The simulation test based on real-world measurement data shows that the cell selected by the adaptive approach can improve the coverage level and reduce the power consumption for UE.

Recent years have seen a widespread deployment of NB-IoT networks for massive machine-to-machine communication in the emerging 5G era. Unfortunately, the key aspects of NB-IoT networks, such as radio access performance and power consumption have not been well-understood due to lack of effective tools and closed nature of operational cellular infrastructure. In this paper, we develop NB-Scope - the first hardware NB-IoT diagnostic tool that supports fine-grained fusion of power and protocol traces. We then conduct a large-scale field measurement study consisting of 30 nodes deployed at over 1,200 locations in 3 regions during a period of three months. Our in-depth analysis of the collected 49 GB traces showed that NB-IoT nodes yield significantly imbalanced energy consumption in the wild, up to a ratio of 75:1, which may lead to short battery lifetime and frequent network partition. Such a high performance variance can be attributed to several key factors including diverse network coverage levels, long tail power profile, and excessive control message repetitions. We then explore the optimization of NB-IoT base station settings on a software-defined eNodeB testbed, and suggest several important design aspects that can be considered by future NB-IoT specifications and chipsets.

No abstract available.

Driving while distracted or losing alertness significantly increases the risk of traffic accident. The emerging Internet of Things (IoT) systems for smart driving hold the promise of significantly reducing road accidents. In particular, detecting unsafe hand motions and warning the driver using smart sensors can improve the driver’s alertness and skill. However, due to the impact of the vehicle’s movement and the significant variation across different driving environments, detecting the position of the driver’s hand is challenging. This article presents SafeWatch—a system based on smartwatches and smartphones that detects the driver’s unsafe behaviors in a real-time manner. SafeWatch infers driver’s hand position based on several important features, such as the posture of the driver’s forearm and the vibration on the smartwatch. SafeWatch employs a novel adaptive training algorithm that keeps updating the training data set at run-time based on inferred hand positions in certain driving conditions. The evaluation with 75 real driving trips from six subjects shows that SafeWatch has a high accuracy over 97.0% for both recall and precision in detection of the unsafe hand positions when the condition lasts for more than 6.0 s , as well as over 97.1% recall and over 91.0% precision in detection of the unsafe hand movements when it lasts for more than 2.5 s . The relative position of the hand to the steering wheel also reveals some detailed driving habits, like the type of steering method.

By learning from the existing family mealtime activities, family members can be motivated to make the positive changes towards better relationships, which are important for the physical and mental health of children. Moreover, the details of family mealtime activities provide rich information for study in sociology and culture. This paper presents FamilyLog - a practical system to log family mealtime activities using smartphones and smartwatches. FamilyLog automatically detects and logs details of activities during the mealtime, including occurrence and duration of meal, conversations, participants, TV viewing, etc., in an unobtrusive manner. Based on the sensor data collected from real families, we carefully design robust yet lightweight signal features from a set of complex activities during the meal, including clattering sound, arm gestures of eating, human voice, TV sound, etc. Moreover, FamilyLog opportunistically fuses data from built-in sensors of multiple mobile devices available in a family with a CRFs-based classifier. To evaluate the real-world performance of FamilyLog, we perform extensive experiments that consist of 77 days of sensor data from 37 subjects in 8 families with children. FamilyLog can detect those events with high accuracy across different families and home environments.

The design trade-offs of transceiver hardware are crucial to the performance of wireless systems. In this paper, we present an in-depth study to characterize the surprisingly notable systemic impacts of low-pass filter (LPF) design, which is a small yet indispensable component used for shaping spectrum and rejecting interference. Using a bottom-up approach, we examine how signal-level distortions caused by the trade-off of LPF design propagate to the upper-layers of wireless communication, reshaping bit error patterns and degrading link performance of today's 802.11 systems. Moreover, we propose a novel algorithm that harnesses LPF defects for improving video streaming, which substantially enhances video quality in mobile environments.

The quality of sleep is an important factor in maintaining a healthy life style. A great deal of work has been done for designing sleep monitoring systems. However, most of existing solutions bring invasion to users more or less due to the exploration of the accelerometer sensor inside the device. This article presents iSleep—a practical system to monitor people’s sleep quality using off-the-shelf smartphone. iSleep uses the built-in microphone of the smartphone to detect the events that are closely related to sleep quality, and infers quantitative measures of sleep quality. iSleep adopts a lightweight decision-tree-based algorithm to classify various events. For two-user scenario, iSleep differentiates the events of two users either when two phones can collaborate with each other or when two phones cannot communicate with each other. The experimental results show that iSleep achieves consistently above 90% accuracy for event classification in a variety of different settings in one-user scenario and above 92% accuracy for distinguishing users in two-user scenario. By providing a fine-grained sleep profile that depicts details of sleep-related events, iSleep allows the user to track the sleep efficiency over time and relate irregular sleep patterns to possible causes.

PPG-based heart rate estimation has been widely adopted in wrist-worn devices. However, the motion artifacts caused by the user's physical activities make it difficult to get the accurate HR estimation from contaminated PPG signals. Although many signal processing methods have been proposed to address this challenge, they are often highly optimized for specific scenarios (e.g., running or biking), making them impractical in real-world settings where a user may perform a wide range of physical activities. In this paper, we propose DeepHeart, a new HR estimation approach that features deep-learning-based denoising and spectrum-analysis-based calibration. DeepHeart generates clean PPG signals from ECG signals based on a training data set. Then a denoising convolutional neural network (DnCNN) is trained with the contaminated PPG signals and their corresponding clean PPG signals. Contaminated PPG signals are then denoised by the DnCNN and a spectrum-analysis-based calibration is performed to estimate the final HR. We evaluate DeepHeart on the IEEE Signal Processing Cup (SPC) training data set with 12 records collected during various physical activities. DeepHeart achieves an average absolute error of 1.98 bpm, outperforming two state-of-the-art methods TROIKA and Deep PPG.

With the prevalence of personal Bluetooth devices, potential breach of user privacy has been an increasing concern. To date, sniffing Bluetooth traffic has been widely considered an extremely intricate task due to Bluetooth's indiscoverable mode, vendor-dependent adaptive hopping behavior, and the interference in the open 2.4 GHz band. In this paper, we present BlueEar-a practical Bluetooth traffic sniffer. BlueEar features a novel dual-radio architecture where two Bluetooth-compliant radios coordinate with each other on learning the hopping sequence of indiscoverable Bluetooth networks, predicting adaptive hopping behavior, and mitigating the impacts of RF interference. We built a prototype of BlueEar to sniff on Bluetooth classic traffic. Experiment results show that BlueEar can maintain a packet capture rate higher than 90% consistently in real-world environments, where the target Bluetooth network exhibits diverse hopping behaviors in the presence of dynamic interference from coexisting 802.11 devices. In addition, we discuss the privacy implications of the BlueEar system, and present a practical countermeasure that effectively reduces the packet capture rate of the sniffer to 20%. The proposed countermeasure can be easily implemented on Bluetooth master devices while requiring no modification to slave devices such as keyboards and headsets.

Cooperative relaying in cognitive radio networks offers time and space diversity and thereby, provides an effective technique to improve spectrum utilization. Most of the existing research has been focused on adopting this technique in single hop communication which may not fully exploit the benefits of cooperative transmissions. Recently, multi-hop cooperative relaying has been explored in routing path formation between primary transmitter-receiver pairs. In this approach, each user handles one cooperation request at a time and takes part in at most one routing path, which makes the model unscalable and limits the benefits of cooperation. Also, primary users dictate the cooperation terms with no or limited involvement from the participating secondary users. This model, therefore, cannot capture the dynamics when both the primary and secondary users require to make cooperation decisions considering the tradeoffs of multiple offers. As a result, the essence of multi-hop cooperative relaying cannot be realized from the existing approach and calls for further investigation. In this work, we consider a network of coexisting primary and secondary users in which their intention to improve throughput via mutual cooperation is formulated as an overlapping coalition formation game. Based on the analysis of the game, we devise mcRoute, a distributed multi-hop coalition based cooperative routing and scheduling algorithm that forms stable coalitions satisfying users' mutual interest. A primary user constructs its routing path in the form of a coalition with secondary users relaying its packet. A secondary user takes part in one or more coalitions by relaying corresponding primary packets and accessing their channels for its own transmission. Finally, we analyze the performance of the algorithm through extensive numerical simulations.

Recently a class of new in-vehicle technologies based on off-the-shelf mobile devices have been developed to improve driver safety and driving experience. For instance, smartwatches are utilized to monitor driving performance and detect possible secondary tasks of drivers such as texting, operating the in-vehicle infotainment, or eating. However, a key challenge for these systems is to track the real-time attitude of mobile devices in a driving vehicle. This demo presents a novel system called Real-time Attitude and Motion Tracking (RAMT) that can enable a mobile device to accurately learn the coordinate system of the moving vehicle, and hence track the attitude and the motion of the device in real time.

Real-time image-based object tracking from live video is of great importance for several smart city applications like surveillance, intelligent traffic management and autonomous driving. Although recent deep learning systems can achieve satisfactory tracking performance, they incur significant compute overhead, which prevents them from wide adoption on resource-constrained IoT platforms. In this demonstration, we present an Edge Computing system for Real-time object Tracking (ECRT) for resource-constrained devices. The key feature of our system is that it intelligently partitions compute-intensive tasks such as inferencing a convolutional neural network(CNN) into two parts, which are executed locally on an IoT device and/or on the edge server. Moreover, ECRT can minimize the power consumption of IoT devices while taking into consideration the dynamic network environment and user requirement on end to end delay.

Volcano tomography provides valuable information concerning the internal structure of a volcano. During times of increased volcano activities, real-time images of the interior would allow seismologists to better understand volcanic dynamics. However, real-time tomography requires spatial coverage of the volcano utilizing large number of seismic sensors. The current broadband seismic sensors used in volcanology are expensive and difficult to install in large quantities. In this paper, we present the design and deployment of the first wireless sensor network that is capable of in-network processing of seismic signals and performing real-time volcano tomography. Specifically, our system design addresses several major challenges in real-time volcano tomography, including high data intensity and compute overhead, dynamic nature of seismic activities, and extended operational lifetime. We systematically analyze the system delay and lifetime, and present a dynamic scheme that assigns compute tasks to different system tiers to meet energy and delay constraints. Lastly, we present learned lessons from deploying the system on two volcanoes in Ecuador and Chile in 2012 and 2015. Our study shows the feasibility of achieving in-network volcanic event detection and real-time tomography using a sensor network that is two orders of magnitude less expensive than traditional seismic equipment.

The design trade-offs of transceiver hardware are crucial to the performance of wireless systems. In this paper, we present an in-depth study to characterize the surprisingly notable systemic impacts of low-pass filter (LPF) design, which is a small yet indispensable component used for shaping spectrum and rejecting interference. Using a bottom-up approach, we examine how signal-level distortions caused by the trade-off of LPF design propagate to the upper-layers of wireless communication, reshaping bit error patterns and degrading link performance of today's 802.11 systems. Moreover, we propose a novel algorithm that harnesses LPF defects for improving video streaming, which substantially enhances video quality in mobile environments.

We propose a kernel-level energy profiling tool KLEP that can work with diverse APIs of Android. KLEP addresses the challenges of the tail energy problem and the complex interrelation between hardware components in the device energy consumption profile. KLEP collects energy-sensitive events in the kernel and measures real energy consumption of the device at the same time, and employs a LSTM neural-network-based model for energy profiling. The preliminary results show that the curves profiled by KLEP can match the actual energy consumption with low error and overhead.

Research has shown that family mealtime plays a critical role in establishing good relationships among family members and maintaining their physical and mental health. In particular, regularly eating dinner as a family significantly reduces prevalence of obesity. However, American families with children spend only 1 hour on family meals while three hours watching TV on an average work day. Fine-grained activity-logging is proven effective for increasing self-awareness and motivating people to modify their life styles for improved wellness. This paper presents FamilyLog - a practical system to log family mealtime activities using smartphones and smartwatches. FamilyLog automatically detects and logs details of activities during the mealtime, including occurrence and duration of meal, conversations, participants, TV viewing etc., in an unobtrusive manner. Based on the sensor data collected from real families, we carefully design robust yet lightweight signal features from a set of complex activities during the meal, including clattering sound, arm gestures of eating, human voice, TV sound, etc. Moreover, FamilyLog opportunistically fuses data from built-in sensors of multiple mobile devices available in a family through an HMM-based classifier. To evaluate the real-world performance of FamilyLog, we perform extensive experiments that consist of 77 days of sensor data from 37 subjects in 8 families with children. Our results show that FamilyLog can detect those events with high accuracy across different families and home environments.

Tracking heart rate for fitness using wrist-type wearables is challenging, because of the significant noise caused by intensive wrist movements. In this paper, we present FitBeat - a lightweight system that enables accurate heart rate tracking on wrist-type wearables during intensive exercises. Unlike existing approaches that rely on computation- intensive signal processing, FitBeat integrates and augments standard filter and spectral analysis tool, which achieves comparable accuracy while significantly reducing computational overhead. FitBeat integrates contact sensing, motion sensing and simple spectral analysis algorithms to suppress various error sources. We implement FitBeat on a COTS smartwatch, and evaluate the performance of FitBeat for typical workouts of different intensities, including walking, running and riding. Experimental results involving 10 subjects show that the average error of FitBeat is around 4 beats per minute, which improves heart rate accuracy of the default heart rate tracker of Moto 360 by 10x.

Owing to the rich processing, multi-modal sensing, and versatile networking capabilities, smartphones are increasingly used to build data-intensive embedded sensing applications. However, various challenges must be systematically addressed before smartphones can be used as a generic embedded sensing platform, including high power consumption, lack of real-time functionality, and user-friendly embedded programming support. This paper presents ORBIT, a smartphone-based platform for data-intensive embedded sensing applications. ORBIT features a tiered architecture, in which a smartphone can interface to an energy-efficient peripheral board and/or a cloud service. ORBIT as a platform addresses the shortcomings of current smartphones while utilizing their strengths. ORBIT provides a profile-based task partitioning that allows it to intelligently dispatch the processing tasks among the tiers to minimize the system power consumption. ORBIT also provides a data processing library that includes two mechanisms namely adaptive delay-quality trade-off and data partitioning via multi-threading to optimize resource usage. Moreover, ORBIT supplies an annotation-based programming API for developers that significantly simplifies the application development and provides programming flexibility. Extensive microbenchmark evaluation and three case studies including seismic sensing, visual tracking using an ORBIT robot, and multi-camera 3D reconstruction, validate the generic design of ORBIT.

Appliance-level power usage monitoring may help conserve electricity in homes. Several existing systems achieve this goal by exploiting appliances’ power usage signatures identified in labor-intensive in situ training processes. Recent work shows that autonomous power usage monitoring can be achieved by supplementing a smart meter with distributed sensors that detect the working states of appliances. However, sensors must be carefully installed for each appliance, resulting in a high installation cost. This article presents Supero —the first ad hoc sensor system that can monitor appliance power usage without supervised training. By exploiting multisensor fusion and unsupervised machine learning algorithms, Supero can classify the appliance events of interest and autonomously associate measured power usage with the respective appliances. Our extensive evaluation in five real homes shows that Supero can estimate the energy consumption with errors less than 7.5%. Moreover, nonprofessional users can quickly deploy Supero with considerable flexibility.

With the prevalence of personal Bluetooth devices, potential breach of user privacy has been an increasing concern. To date, sniffing Bluetooth traffic has been widely considered an extremely intricate task due to Bluetooth's indiscoverable mode, vendor-dependent adaptive hopping behavior, and the interference in the open 2.4 GHz band. In this paper, we present BlueEar -a practical Bluetooth traffic sniffer. BlueEar features a novel dual-radio architecture where two Bluetooth-compliant radios coordinate with each other on learning the hopping sequence of indiscoverable Bluetooth networks, predicting adaptive hopping behavior, and mitigating the impacts of RF interference. Experiment results show that BlueEar can maintain a packet capture rate higher than 90% consistently in real-world environments, where the target Bluetooth network exhibits diverse hopping behaviors in the presence of dynamic interference from coexisting Wi-Fi devices. In addition, we discuss the privacy implications of the BlueEar system, and present a practical countermeasure that effectively reduces the packet capture rate of the sniffer to 20%. The proposed countermeasure can be easily implemented on the Bluetooth master device while requiring no modification to slave devices like keyboards and headsets.

Our study reveals that facilitating warm launch of just five smartphone applications is extremely expensive, using up to 36 percent of memory. Further investigation of 20 popular applications indicates that rich multimedia applications have high heap usage and go above allowed boundaries, up to 5.63 times more heap than guaranteed by the system, and may cause crashes and erroneous behaviors. Therefore, we present iRAM, a personalized system that maintains optimal heap size limits to avoid crashes, efficiently maximizes free memory levels, and cleans low-priority processes to reduce application delays. The evaluation on memory hungry applications indicates that iRAM reduces application crashes by up to 14 percent, and reduces launch delays by up to 78.2 percent. In addition, the results confirm that iRAM increases free memory levels by up to 4.8 times. This performance gain comes with 3.5 percent of CPU overhead and 0.9 percent of power overhead.

Monitoring aquatic debris is of great interest to the ecosystems, marine life, human health, and water transport. This paper presents the design and implementation of SOAR-a vision-based surveillance robot system that integrates an off-the-shelf Android smartphone and a gliding robotic fish for debris monitoring in relatively calm waters. SOAR features real-time debris detection and coverage-based rotation scheduling algorithms. The image processing algorithms for debris detection are specifically designed to address the unique challenges in aquatic environments. The rotation scheduling algorithm provides effective coverage for sporadic debris arrivals despite camera's limited angular view. Moreover, SOAR is able to dynamically offload compute-intensive processing tasks to the cloud for battery power conservation. We have implemented a SOAR prototype and conducted extensive experimental evaluation. The results show that SOAR can accurately detect debris in the presence of various environment and system dynamics, and the rotation scheduling algorithm enables SOAR to capture debris arrivals with reduced energy consumption.

Wireless sensor networks (WSNs) are increasingly deployed for mission-critical applications such as emergency management and health care, which impose stringent requirements on the communication performance of WSNs. To support these applications, it is crucial to model and measure the effect of wireless interference, which is the major factor that limits WSN performance. Accurate modeling and measurement of interference faces two key challenges. First, as shown in our experimental results, interference yields considerable spatial and temporal variations of WSN performance, which poses a major challenge for measurement at rum-time. Second, in the unlicensed band, the communication of WSN is interfered by coexisting wireless devices such as smartphones and laptops equipped with 802.11 radios, which lead to cross-technology interference that are difficult to characterize due to the heterogeneous PHY. To tackle these challenges, this paper presents a novel accuracy-aware approach to interference modeling and measurement for WSNs. First, we propose a new regression-based interference model and analytically characterize its accuracy based on statistics theory. Second, we develop a novel protocol called accuracy-aware interference measurement for measuring the proposed interference model with assured accuracy at run time. Third, building on interference modeling, we propose an algorithm that accurately forecasts the performance of WSNs in the presence of cross-technology interference. Our extensive experiments on a testbed of 17 TelosB motes show that the proposed approaches achieve high accuracy of interference modeling and WSN performance forecasting with significantly lower overhead than state-of-the-art approaches.

2D barcodes have enjoyed a significant penetration rate in mobile applications. This is largely due to the extremely low barrier to adoption-almost every camera-enabled smartphone can scan 2D barcodes. As an alternative to NFC technology, 2D barcodes have been increasingly used for security-sensitive mobile applications including mobile payments and personal identification. However, the security of barcode-based communication in mobile applications has not been systematically studied. Due to the visual nature, 2D barcodes are subject to eavesdropping when they are displayed on the smartphone screens. On the other hand, the fundamental design principles of 2D barcodes make it difficult to add security features. In this paper, we propose SBVLC-a secure system for barcode-based visible light communication (VLC) between smartphones. We formally analyze the security of SBVLC based on geometric models and propose physical security enhancement mechanisms for barcode communication by manipulating screen view angles and leveraging user-induced motions. We then develop three secure data exchange schemes that encode information in barcode streams. These schemes are useful in many security-sensitive mobile applications including private information sharing, secure device pairing, and contactless payment. SBVLC is evaluated through extensive experiments on both Android and iOS smartphones.

Monitoring aquatic environment is of great interest to the ecosystem, marine life, and human health. This article presents the design and implementation of Samba—an aquatic surveillance robot that integrates an off-the-shelf Android smartphone and a robotic fish to monitor harmful aquatic processes such as oil spills and harmful algal blooms. Using the built-in camera of the smartphone, Samba can detect spatially dispersed aquatic processes in dynamic and complex environment. To reduce the excessive false alarms caused by the nonwater area (e.g., trees on the shore), Samba segments the captured images and performs target detection in the identified water area only. However, a major challenge in the design of Samba is the high energy consumption resulted from continuous image segmentation. We propose a novel approach that leverages the power-efficient inertial sensors on smartphones to assist image processing. In particular, based on the learned mapping models between inertial and visual features, Samba uses real-time inertial sensor readings to estimate the visual features that guide image segmentation, significantly reducing the energy consumption and computation overhead. Samba also features a set of lightweight and robust computer vision algorithms, which detect harmful aquatic processes based on their distinctive color features. Last, Samba employs a feedback-based rotation control algorithm to adapt to spatiotemporal development of the target aquatic process. We have implemented a Samba prototype and evaluated it through extensive field experiments, lab experiments, and trace-driven simulations. The results show that Samba can achieve a 94% detection rate, a 5% false alarm rate, and a lifetime up to nearly 2 months.

As one of the most popular exercises, running is accomplished through a tight cooperation between the respiratory and locomotor systems. Research has suggested that a proper running rhythm -- the coordination between breathing and strides -- helps improve exercise efficiency and postpone fatigue. This paper presents RunBuddy -- the first smartphone-based system for continuous running rhythm monitoring. RunBuddy is designed to be a convenient and unobtrusive exercise feedback system, and only utilizes commodity devices including smartphone and Bluetooth headset. A key challenge in designing RunBuddy is that the sound of breathing typically has very low intensity and is susceptible to interference. To reliably measure running rhythm, we propose a novel approach that integrates ambient sensing based on accelerometer and microphone, and a physiological model called Locomotor Respiratory Coupling (LRC), which indicates possible ratios between the stride and breathing frequencies. We evaluate RunBuddy through experiments involving 13 subjects and 39 runs. Our results show that, by leveraging the LRC model, RunBuddy correctly measures the running rhythm for indoor/outdoor running 92:7% of the time. Moreover, RunBuddy also provides detailed physiological profile of running that can help users better understand their running process and improve exercise self-efficacy.

Prior studies show that repairing partially corrupted packets, instead of retransmitting them in their entirety, holds potential in improving the performance of 802.11 networks. However, the efficiency of existing packet recovery approaches is severely limited by various overhead associated to redundant transmission and repeated channel contention. In this paper, we propose CodeRepair, a practical coding-based protocol that recovers partially corrupted 802.11 packets without these pains. The design of CodeRepair is based on two novel ideas. First, CodeRepair pushes the limit of 802.11 PHY to piggyback parities in the padded bits of OFDM, obviating the need of transmitting extra information for error correction. Second, CodeRepair corrects errors at the PHY layer, which is significantly more efficient than traditional link-layer approaches. This is due to the fact that a single coded bit usually affects the decoding of a group of data bits in 802.11 convolutional code. As a result, CodeRepair can salvage a partially corrupted packet by correcting a small number of erroneous coded bits using the padded parities. To reduce computational cost of error recovery, CodeRepair employs single parity code for correcting coded bit errors. We propose several techniques to augment the error correcting capability of single parity code without compromising its computation efficiency. Our evaluation shows that CodeRepair recovers an average of 34% partially corrupted packets, and improves the end-to-end link goodput by 59% on lossy 802.11 links.

This paper presents VINCE - a novel visible light sensing design for smartphone-based Near Field Communication (NFC) systems. VINCE encodes information as different brightness levels of smartphone screens, while receivers capture the light signal via light sensors. In contrast to RF technologies, the direction and distance of such a Visible Light Communication (VLC) link can be easily controlled, preserving communication privacy and security. As a result, VINCE can be used in a wide range of NFC applications such as contactless payments and device pairing. We experimentally profile the impact of screen brightness levels and refresh rates of smartphones, and then use the results to guide the design of light intensity encoding scheme of VINCE. We adopt several signal processing techniques and empirically derive a model to deal with the significant variation of received light intensity caused by noises and low screen refresh rates. To improve the communication reliability, VINCE adopts a feedback-based retransmission scheme, and dynamically adjusts the number of encoding brightness levels based on the current light channel condition. We also derive an analytical model that characterizes the relation among the distance, SNR (Signal to Noise Ratio), and BER (Bit Error Rate) of VINCE. Our design and theoretical model are validated via extensive evaluations using a hardware implementation of VINCE on Android smartphones and the Arduino platform.

Network coding (NC) based opportunistic routing has been well studied, but the impact of routing diversity on the performance of NC-based routing remains largely unexplored. Towards understanding the importance of routing diversity in NC-based routing, we study the problems of estimating and minimizing the data delivery cost in NC-based routing. In particular, we propose an analytical framework for estimating the total number of packet transmissions for NC-based routing in arbitrary topologies. We design a greedy algorithm that minimizes the total transmission cost of NC-based routing and determines the corresponding forwarder set for each node. We prove the optimality of this algorithm and show that 1) nodes on the shortest path may not always be favored when selecting forwarders for NC-based routing and 2)the minimal cost of NC-based routing is upper-bounded by the cost of shortest path routing. Based on the greedy, optimal algorithm, we design and implement ONCR, a distributed minimal cost NC-based routing protocol. Using the NetEye sensor testbed, we comparatively study the performance of ONCR and existing approaches such as the single path routing protocol CTP and the NC-based opportunistic routing protocols MORE and CodeOR. Results show that ONCR achieves close to 100% delivery reliability while having the lowest delivery cost among all the protocols and 25-28% less than the second best protocol CTP. This low delivery cost also enables ONCR to achieve the highest network goodput, i.e., about two-fold improvement over MORE and CodeOR. Our findings demonstrate the significance of optimizing data forwarding diversity in NC-based routing for data delivery reliability, efficiency, and goodput.

Owing to the rich processing, multi-modal sensing, and versatile networking capabilities, smartphones are increasingly used to build data-intensive embedded sensing applications. However, various challenges must be systematically addressed before smartphones can be used as a generic embedded sensing platform, including high power consumption, lack of real-time functionality and user-friendly embedded programming support. This paper presents ORBIT, a smartphone-based platform for data-intensive embedded sensing applications. ORBIT features a tiered architecture, in which a smartphone can interface to an energy-efficient peripheral board and/or a cloud service. ORBIT as a platform addresses the shortcomings of current smartphones while utilizing their strengths. ORBIT provides a profile-based task partitioning allowing it to intelligently dispatch the processing tasks among the tiers to minimize the system power consumption. ORBIT also provides a data processing library that includes two mechanisms namely adaptive delay/quality trade-off and data partitioning via multi-threading to optimize resource usage. Moreover, ORBIT supplies an annotation based programming API for developers that significantly simplifies the application development and provides programming flexibility. Extensive microbenchmark evaluation and two case studies including seismic sensing and multi-camera 3D reconstruction, validate the generic design of ORBIT.

Monitoring aquatic environment is of great interest to the ecosystem, marine life, and human health. This paper presents the design and implementation of Samba -- an aquatic surveillance robot that integrates an off-the-shelf Android smartphone and a robotic fish to monitor harmful aquatic processes such as oil spill and harmful algal blooms. Using the built-in camera of on-board smartphone, Samba can detect spatially dispersed aquatic processes in dynamic and complex environments. To reduce the excessive false alarms caused by the non-water area (e.g., trees on the shore), Samba segments the captured images and performs target detection in the identified water area only. However, a major challenge in the design of Samba is the high energy consumption resulted from the continuous image segmentation. We propose a novel approach that leverages the power-efficient inertial sensors on smartphone to assist the image processing. In particular, based on the learned mapping models between inertial and visual features, Samba uses real-time inertial sensor readings to estimate the visual features that guide the image segmentation, significantly reducing energy consumption and computation overhead. Samba also features a set of lightweight and robust computer vision algorithms, which detect harmful aquatic processes based on their distinctive color features. Lastly, Samba employs a feedback-based rotation control algorithm to adapt to spatiotemporal evolution of the target aquatic process. We have implemented a Samba prototype and evaluated it through extensive field experiments, lab experiments, and trace-driven simulations. The results show that Samba can achieve 94% detection rate, 5% false alarm rate, and a lifetime up to nearly two months.

The smartphone has become an important part of our daily lives. However, the user experience is still far from being optimal. In particular, despite the rapid hardware upgrades, current smartphones often suffer various unpredictable delays during operation, e.g., when launching an app, leading to poor user experience. In this paper, we investigate the behavior of reads and writes in smartphones. We conduct the first large-scale measurement study on the Android I/O delay using the data collected from our Android application running on 2611 devices within nine months. Among other factors, we observe that reads experience up to 626% slowdown when blocked by concurrent writes for certain workloads. Additionally, we show the asymmetry of the slowdown of one I/O type due to another, and elaborate the speedup of concurrent I/Os over serial ones. We use this obtained knowledge to design and implement a system prototype called SmartIO that reduces the application delay by prioritizing reads over writes, and grouping them based on assigned priorities. SmartIO issues I/Os with optimized concurrency parameters. The system is implemented on the Android platform and evaluated extensively on several groups of popular applications. The results show that our system reduces launch delays by up to 37.8%, and run-time delays by up to 29.6%.

This paper presents the design and implementation of a novel user-initiated indoor localization system called QiLoc. QiLoc is a simple yet effective way to accurately locate and identify occupants of Qi-compatible devices inside buildings. The system is composed of QiLoc Stations and a QiLoc Server. A QiLoc Station is usually embedded inside or under a desk/table. By using the Qi wireless charging protocol, it can extract the unique ID of a charging device, thus locating the occupant. The QiLoc Server maintains the location information of all occupants, and provides a set of APIs via standard web services such as location, ad hoc group membership, and authentication. Using these primitives, QiLoc enables various smart home applications. We have deployed QiLoc in an office building and implemented a number of applications, including a smartphone app that identifies people in the same physical space, and a Windows-based notification system that provides live updates of colleagues' locations. We also demonstrate how QiLoc can enhance human interaction and productivity by integrating with industrial solutions like calendar, instant messaging, and email systems.

Clock synchronization is critical for many WSNs due to the need of inter-node coordination and collaborative information processing. Existing protocols based on message passing achieve satisfactory clock synchronization accuracy, however, incur prohibitively high overhead especially in large-scale networks. In this paper, we propose a new clock synchronization approach called ROCS which exploits the radio data system (RDS) from FM radio stations. First, we design a new hardware FM receiver that can extract a periodic pulse from FM broadcasts, referred to as RDS clock. We then conduct a large-scale measurement study of RDS clock in our lab for a period of six days and on a vehicle driving through a metropolitan area of over 40km2. Our results show that RDS clock is highly stable and hence is a viable means to calibrate the clocks of large-scale city-wide sensor networks. To reduce the high power consumption of FM receiver, ROCS adaptively calibrates the native clock via the RDS clock. We implement ROCS in TinyOS on our hardware FM receiver and a TelosB-compatible WSN platform. Our extensive experiments using a 12-node testbed and our driving measurement traces show that ROCS achieves accurate and precise clock synchronization with low power consumption.

Data centers have become a critical computing infrastructure in the era of cloud computing. Temperature monitoring and forecasting are essential for preventing server shutdowns because of overheating and improving a data center’s energy efficiency. This article presents a novel cyber-physical approach for temperature forecasting in data centers, one that integrates Computational Fluid Dynamics (CFD) modeling, in situ wireless sensing, and real-time data-driven prediction. To ensure forecasting fidelity, we leverage the realistic physical thermodynamic models of CFD to generate transient temperature distribution and calibrate it using sensor feedback. Both simulated temperature distribution and sensor measurements are then used to train a real-time prediction algorithm. As a result, our approach reduces not only the computational complexity of online temperature modeling and prediction, but also the number of deployed sensors, which enables a portable, noninvasive thermal monitoring solution that does not rely on the infrastructure of a monitored data center. We extensively evaluated the proposed system on a rack of 15 servers and a testbed of five racks and 229 servers in a small-scale production data center. Our results show that our system can predict the temperature evolution of servers with highly dynamic workloads at an average error of 0.52○C, within a duration up to 10 minutes. Moreover, our approach can reduce the required number of sensors by 67% while maintaining desirable prediction fidelity.

One of the key challenges facing wireless sensor networks (WSNs) is extending network lifetime due to sensor nodes having limited power supplies. Extending WSN lifetime is complicated because nodes often experience differential power consumption. For example, nodes closer to the sink in a given routing topology transmit more data and thus consume power more rapidly than nodes farther from the sink. Inspired by the huddling behavior of emperor penguins where the penguins take turns on the cold extremities of a penguin “huddle”, we propose mobile node rotation, a new method for using low-cost mobile sensor nodes to address differential power consumption and extend WSN lifetime. Specifically, we propose to rotate the nodes through the high power consumption locations. We propose efficient algorithms for single and multiple rounds of rotations. Our extensive simulations show that mobile node rotation can extend WSN topology lifetime by more than eight times on average which is significantly better than existing alternatives.

Despite the widespread use of Bluetooth technology, identity management of Bluetooth devices remains a significant challenge because the MAC address and name of Bluetooth device are easy to forge. In this paper, we present BlueID - a practical system that identifies Bluetooth devices by fingerprinting their clocks. Previous approaches to clock fingerprinting exclusively rely on the timestamps carried by packet headers, which can be easily spoofed by hacking the user-space device driver. In comparison, BlueID performs clock fingerprinting based on the temporal feature of Bluetooth frequency hopping, which is impossible to forge without a customized baseband. Due to the proprietary nature of chipset firmware that implements baseband on commodity Bluetooth devices, BlueID will significantly raise the bar of identity spoofing. Moreover, BlueID employs simple yet efficient techniques to detect and differentiate low power Bluetooth transmissions from a distance, making it suitable for mobile applications like energy efficient localization and tracking. BlueID is implemented on a low cost wireless development platform and extensively evaluated based on 56 commodity devices. We show that BlueID can detect Bluetooth radios from 100m away, and identify different devices with high accuracy, short delay, and low computational overhead. Although this paper focuses on Bluetooth, the design of BlueID is general and can be applied to other frequency hopping based wireless systems.

The increasing availability of inexpensive off-the-shelf 802.11 hardware has made it possible to deploy access points (APs) densely to ensure the coverage of complex enterprise environments such as business and college campuses. However, dense AP deployment often leads to increased level of wireless contention, resulting in low system throughput. A promising approach to address this issue is to enable the transmission concurrency of exposed terminals in which two senders lie in the range of one another but do not interfere each other's receiver. However, existing solutions ignore the rate diversity of 802.11 and hence cannot fully exploit concurrent transmission opportunities in a WLAN. In this paper, we presentTRACK-TransmissionRateAdaptation forColliding linKs, a novel protocol for harnessing exposed terminals with a rate adaptation approach in enterprise WLANs. Using measurement-based channel models, TRACK can optimize the bit rates of concurrent links to improve system throughput while maintaining link fairness. Our extensive experiments on a testbed of 17 nodes show that TRACK improves system throughput by up to 67% and 35% over 802.11 CSMA and conventional approaches of harnessing exposed terminals.

As an alternative to NFC technology, 2D barcodes have been increasingly used for security-sensitive applications including payments and personal identification. However, the security of barcode-based communication in mobile applications has not been systematically studied. Due to the visual nature, 2D barcodes are subject to eavesdropping when they are displayed on the screen of a smartphone. On the other hand, the fundamental design principles of 2D barcodes make it difficult to add security features. In this paper, we propose SBVLC - a secure system for barcode-based visible light communication (VLC) between smartphones. We formally analyze the security of SBVLC based on geometric models and propose physical security enhancement mechanisms for barcode communication by manipulating screen view angles and leveraging user-induced motions. We then develop two secure data exchange schemes. These schemes are useful in many security-sensitive mobile applications including private information sharing, secure device pairing, and mobile payment. SBVLC is evaluated through extensive experiments on both Android and iOS smartphones.

No abstract available.

The smartphone has become an important part of our daily lives. However, the user experience is still far from being optimal. In particular, despite the rapid hardware upgrades, current smartphones often suffer various unpredictable delays during operation, e.g., when launching an application, leading to poor user experience. This video features our study of storage impact on smartphone application delay. We conduct the first large-scale measurement study on the I/O delay of Android using the data collected from our application running on 1009 devices within 130 days. We observe that Android devices spend up to 58% of their CPU active time waiting for storage I/Os to complete. This negatively affects the smartphone's overall application performance, and results in slow response time. Further investigation, among others, reveals that reads experience up to a 626% slowdown in the presence of concurrent writes. The obtained knowledge is used to design and implement a system called SmartIO that reduces the application delay by prioritizing reads over writes, and grouping them based on assigned priorities. SmartIO is implemented on the Android platform and evaluated extensively on several groups of popular applications. The results from the 20 researched applications demonstrate that SmartIO reduces launch delays by up to 37.8%, and run-time delays by up to 29.6%.

The Near Field Communication (NFC) technology is gaining increasing popularity among mobile users. However, as a relatively new and developing technology, NFC may also introduce security threats that make mobile devices vulnerable to various malicious attacks. This work presents the first system study on the feasibility of and defense again passive NFC eavesdropping. Our experiments show that commodity NFC-enabled mobile devices can be eavesdropped from up to 240 cm away, which is at least an order of magnitude of the intended NFC communication distance. This finding challenges the general perception that NFC is largely immune to eavesdropping because of its short working range. We then present the design of a hardware security system called nShield. With a small form factor, nShield can be attached to the back of mobile devices to attenuate the signal strength against passive eavesdropping. At the same time, the absorbed RF energy is scavenged by nShield for its perpetual operation. nShield intelligently determines the right attenuation level that is just enough to sustain reliable data communication. We implement a prototype of nShield, and evaluate its performance via extensive experiments. Our results show that nShield has low power consumption (23 uW), can harvest significant amount of power (55 mW), and adaptively attenuates the signal strength of NFC in a variety of realistic settings, while only introducing insignificant delay (up to 2.2 s).

Current data centers often adopt conservative and static settings for cooling and air circulation systems, leading to excessive energy consumption. This paper presents the design and evaluation of PTEC -- a system for predictive thermal and energy control in data centers. PTEC leverages the server built-in sensors and monitoring utilities, as well as a wireless sensor network, to monitor both the cyber and physical status of a data center. By predicting the temperature evolution of a data center in real time, PTEC finds the temperature set points, the cold air supply rates, and the speeds of server internal fans to minimize the expected total energy consumption of cooling and circulation systems. Moreover, PTEC enforces the upper bounds on server inlet temperatures and their temporal variations to prevent server overheating and reduce server hardware failure rate. We evaluated PTEC on a hardware test bed consisting of 15 servers and a total of 23 temperature and power sensors, as well as through Computational Fluid Dynamics (CFD) simulations based on real data traces collected from a data center with 229 servers. The experimental results show that PTEC can reduce the cooling and circulation energy consumption by more than 30%, compared with baseline thermal control strategies.

Time synchronization is a fundamental service for wireless sensor networks (WSNs). Although a number of message passing protocols can achieve satisfactory synchronization accuracy, they suffer poor scalability and high transmission overhead. An alternative approach is to utilize the global time references such as those induced by GPS and timekeeping radios. However, they require the hardware receiver to decode the out of band clock signal, which introduces extra cost and design complexity. This paper proposes a novel WSN time synchronization approach by exploiting the existing Wi-Fi infrastructure. Our approach leverages the fact that 802.15.4 sensors and Wi-Fi nodes often occupy the same or overlapping radio frequency bands in the 2.4 GHz unlicensed spectrum. As a result, a 802.15.4 node can detect and synchronize to the periodic beacons broadcasted by Wi-Fi access points (APs). A key advantage of our approach is that, due to the long communication range of Wi-Fi, a large number of 802.15.4 sensors can synchronize clock rates to the same beacons without any message exchange. This paper makes several key contributions. First, we experimentally characterize the spatial and temporal characteristics of Wi-Fi beacons in an enterprise Wi-Fi network consisting of over 50 APs deployed in a 300,000 square foot office building. Motivated by our measurement results, we design a novel synchronization protocol called WizSync. WizSync employs digital signal processing (DSP) techniques to detect periodic Wi-Fi beacons and use them to calibrate the frequency of native clocks. WizSync can intelligently predict the clock skew and adaptively schedules nodes to sleep to conserve energy. We implement WizSync in TinyOS 2.1.1 and conduct extensive evaluation on a testbed consisting of 19 TelosB motes. Our results show that WizSync can achieve an average synchronization error of 0.12 milliseconds over a period of 10 days with radio power consumption of 50.9 microwatts/node.

Water resources and aquatic ecosystems are facing increasing threats from climate change, improper waste disposal, and oil spill incidents. It is of great interest to deploy mobile sensors to detect and monitor certain diffusion processes (e.g., chemical pollutants) that are harmful to aquatic environments. In this paper, we propose an accuracy-aware diffusion process profiling approach using smart aquatic mobile sensors such as robotic fish. In our approach, the robotic sensors collaboratively profile the characteristics of a diffusion process including source location, discharged substance amount, and its evolution over time. In particular, the robotic sensors reposition themselves to progressively improve the profiling accuracy. We formulate a novel movement scheduling problem that aims to maximize the profiling accuracy subject to the limited sensor mobility and energy budget. We develop an efficient greedy algorithm and a more complex near-optimal radial algorithm to solve the problem. We conduct extensive simulations based on real data traces of GPS localization errors, robotic fish movement, and wireless communication. The results show that our approach can accurately profile dynamic diffusion processes under tight energy budgets. Moreover, a preliminary evaluation based on the implementation on TelosB motes validates the feasibility of deploying our profiling algorithms on mote-class robotic sensor platforms.

Wi-Fi networks have enjoyed an unprecedent penetration rate in recent years. However, due to the limited coverage, existing Wi-Fi infrastructure only provides intermittent connectivity for mobile users. Once leaving the current network coverage, Wi-Fi clients must actively discover new Wi-Fi access points (APs), which wastes the precious energy of mobile devices. Although several solutions have been proposed to address this issue, they either require significant modifications to existing network infrastructures or rely on context information that is not available in unknown environments. In this work, we develop a system called ZiFithat utilizes ZigBee radios to identify the existence of Wi-Fi networks through unique interference signatures generated by Wi-Fi beacons. We develop a new digital signal processing algorithm called common multiple folding (CMF) that accurately amplifies periodic beacons in Wi-Fi interference signals. ZiFi also adopts a constant false alarm rate (CFAR) detector that can minimize the false negative (FN) rate of Wi-Fi beacon detection while satisfying the user-specified upper bound on false positive (FP) rate. We have implemented ZiFi on two platforms, a Linux netbook integrating a TelosB mote through the USB interface, and a Nokia N73 smartphone integrating a ZigBee card through the miniSD interface. Our experiments show that, under typical settings, ZiFi can detect Wi-Fi APs with high accuracy (<;5 percent total FP and FN rate), short delay (~780 ms), and little computation overhead.

Wireless sensor network applications, such as those for natural disaster warning, vehicular traffic monitoring, and surveillance, have stringent accuracy requirements for detecting or classifying events and demand long system lifetimes. Through quantitative study, we show that existing event detection approaches are challenged to explore the sensing capability of a deployed system and choose the right sensors to meet user-specified accuracy. Event detection systems are also challenged to provide a generic system that efficiently adapts to environmental dynamics and works easily with a range of applications, machine learning approaches, and sensor modalities. Consequently, we propose Watchdog, a modality-agnostic event detection framework that clusters the right sensors to meet user-specified detection accuracy during runtime while significantly reducing energy consumption. Watchdog can use different machine learning techniques to learn the sensing capability of a heterogeneous sensor deployment and meet accuracy requirements. To address environmental dynamics and ensure energy savings, Watchdog wakes up and puts to sleep sensors as needed to meet user-specified accuracy. Through evaluation with real vehicle detection trace data and a building traffic monitoring testbed of IRIS motes, we demonstrate the superior performance of Watchdog over existing solutions in terms of meeting user-specified detection accuracy, energy savings, and environmental adaptability.

Recent years have witnessed the pilot deployments of audio or low-rate video wireless sensor networks for a class of mission-critical applications including search-and-rescue, security surveillance, and disaster management. In this article, we report the design and implementation of Quality-aware Voice Streaming (QVS) for wireless sensor networks. QVS is built upon SenEar, a new sensor hardware platform we developed for high-bandwidth wireless audio communication. QVS comprises several novel components, which include an empirical model for online voice-quality evaluation and control, dynamic voice compression/duplication adaptation for lossy wireless links, and distributed stream admission control that exploits network capacity for rate allocation. We have extensively tested QVS on a 20-node network deployment. Our experimental results show that QVS delivers satisfactory voice quality under a range of realistic settings while achieving high network capacity utilization.

Monitoring important aquatic processes like harmful algal blooms is of increasing interest to public health, ecosystem sustainability, marine biology, and aquaculture industry. This article presents a novel approach to spatiotemporal aquatic field reconstruction using inexpensive, low-power mobile sensing platforms called robotic fish . Robotic fish networks are a typical example of cyber-physical systems where the design of cyber components (sensing, communication, and information processing) must account for inherent physical dynamics of the robots and the aquatic environment. Our approach features a rendezvous-based mobility control scheme where robotic fish collaborate in the form of a swarm to sense the aquatic environment in a series of carefully chosen rendezvous regions. We design a novel feedback control algorithm that maintains the desirable level of wireless connectivity for a sensor swarm in the presence of significant environment and system dynamics. Information-theoretic analysis is used to guide the selection of rendezvous regions so that the spatiotemporal field reconstruction accuracy is maximized subject to the limited sensor mobility. The effectiveness of our approach is validated via implementation on sensor hardware and extensive simulations based on real data traces of water surface temperature field and on-water ZigBee wireless communication.

Tomography imaging, applied to seismology, requires a new, decentralized approach if high resolution calculations are to be performed in a sensor network configuration. The real-time data retrieval from a network of large-amount wireless seismic nodes to a central server is virtually impossible due to the sheer data amount and resource limitations. In this paper, we present a distributed multi-resolution evolving tomography algorithm for processing data and inverting volcano tomography in the network, while avoiding costly data collections and centralized computations. The new algorithm distributes the computational burden to sensor nodes and performs real-time tomography inversion under the constraints of network resources. We implemented and evaluated the system design in the CORE emulator. The experiment results validate that our proposed algorithm not only balances the computation load, but also achieves low communication cost and high data loss-tolerance.

Indoor localization becomes increasingly important as context-aware applications gain popularity in mobile users. A promising approach for indoor localization is to leverage the pervasive WiFi infrastructure via fingerprinting-based inference. However, a WiFi device must frequently scan for WiFi signals during localization, leading to high power consumption. Moreover, switching to the scanning mode introduces inevitable disruptions to data communication of WiFi interface. This paper presents a new indoor localization system called ZiFind that exploits the cross-technology interference in the unlicensed 2.4 GHz frequency spectrum. ZiFind utilizes low-power ZigBee interface to collect WiFi interference signals and adopts digital signal processing techniques to extract unique signatures as fingerprints for localization. To deal with the noise in the fingerprints, we design a new learning algorithm called R-KNN that can improve the accuracy of localization by assigning different weights to fingerprint features according to their importance. We implement ZiFind on TelosB motes and evaluate its performance through extensive experiments in a 16,000 ft2office building floor consisting of 28 rooms. Our results show that ZiFind leads to significant power saving compared with existing approaches based on WiFi interface, and yields satisfactory localization accuracy in a range of realistic settings.

Recent years have witnessed pilot deployments of inexpensive wireless sensor networks (WSNs) for active volcano monitoring. This paper studies the problem of picking arrival times of primary waves (i.e., P-phases) received by seismic sensors, one of the most critical tasks in volcano monitoring. Two fundamental challenges must be addressed. First, it is virtually impossible to download the real-time high-frequency seismic data to a central station for P-phase picking due to limited wireless network bandwidth. Second, accurate P-phase picking is inherently computation-intensive, and is thus prohibitive for many low-power sensor platforms. To address these challenges, we propose a new P-phase picking approach for hierarchical volcano monitoring WSNs where a large number of inexpensive sensors are used to collect fine-grained, real-time seismic signals while a small number of powerful coordinator nodes process collected data and pick accurate P-phases. We develop a suite of new in-network signal processing algorithms for accurate P-phase picking, including lightweight signal pre-processing at sensors, sensor selection at coordinators as well as signal compression and reconstruction algorithms. Testbed experiments and extensive simulations based on real data collected from a volcano show that our approach achieves accurate P-phase picking while only 16% of the sensor data are transmitted.

In this paper, we present the design and implementation of Nemo -- a practical in situ power metering system for wireless sensor networks. Nemo features a new circuit design called shunt resistor switch that can dynamically adjust the resistance of shunt resistors based on the current load. This allows Nemo to achieve a wide dynamic current range and high measurement accuracy. Nemo transmits real-time power measurements to the host node solely through the power line, by modulating the current load and the supply voltage. This feature leads to a noninvasive, plug & play design that allows Nemo to be easily installed on existing mote platforms without physical wiring or soldering. We have implemented a prototype of Nemo and conducted extensive experimental evaluation. Our results show that Nemo can transmit high-throughput measurement data to the host through voltage/current load modulation. Moreover, it has satisfactory measurement fidelity over a wide range of operating conditions. In particular, Nemo yields a dynamic measurement range of 250,000:1, which is 2.5X and 7X that of two state-of-the-art sensor network power meter systems, while only incurring an average measurement error of 1.34%. We also use a case study to demonstrate that Nemo is able to track the highly dynamic sleep current consumption of TelosB motes, which has important implications for the design of low duty-cycle sensor networks that operate in dynamic environments.

This demo abstract presents the design and the demonstration plan of Nemo -- a practical in situ power metering system for wireless sensor networks. Nemo features a new circuit design called shunt resistor switch that can dynamically adjust the resistance of shunt resistors based on the current load. This allows Nemo to achieve a wide dynamic current range and high measurement accuracy. Nemo transmits real-time power measurement to the host node solely through the power line, by modulating the current load and the supply voltage. This feature leads to a noninvasive, plug & play design that allows Nemo to be easily installed on existing mote platforms without physical wiring or soldering. We describe the set-up of demonstration and outline our demonstration plan.

In many body sensor network (BSN) applications, such as activity recognition for assisted living residents or physical fitness assessment of a sports team, users spend a significant amount of time with one another while performing many of the same activities. We exploit this physical proximity with Remora, a smartphone-based Body Sensor Network activity recognition system which shares sensing resources among neighboring BSNs. Compared to other resource sharing approaches, Remora provides both increased accuracy and significant energy savings. To increase classification accuracy, Remora BSNs share sensors by overhearing neighbors' sensor data transmissions. When sharing, fewer on-body sensors are needed to achieve high accuracy, resulting in energy savings by turning off unneeded sensors. To save phone energy, neighboring BSNs share classifiers: only one classifier is active at a time classifying activities for all neighbors. Remora addresses three major challenges of sharing with physical neighbors: 1) Sharing only when the energy benefit outweighs the cost, 2) Finding and utilizing the shared sensors and classifiers which produce the best combination of accuracy improvement and energy savings, and 3) Providing a lightweight and collaborative classification approach, without the use of a backend server, which adapts to the dynamics of available neighbors. In a two week evaluation with 6 subjects, we show that Remora provides up to a 30% accuracy increase while extending phone battery lifetime by over 65%.

Error correction is a fundamental problem in wireless system design as wireless links often suffer high bit error rate due to the effects of signal attenuation, multipath fading and interference. This paper presents a new cross-layer solution called LEAD to improve the performance of existing channel decoders. While the traditional wisdom of cross-layer design is to exploit physical layer information at upper-layers, LEAD represents a paradigm shift in that it leverages upper-layer protocol signatures to improve the performance of physical layer channel decoding. The approach of LEAD is motivated by two key insights. First, channel codes can correct more errors when the values of some bits, which we refer to as {\em pilots}, are known before decoding. Second, some header fields of upper-layer protocols are often fixed or highly biased toward certain values. These distinctive bit pattern signatures can thus be exploited as pilots to assist channel decoding. To realize this idea, we first characterize bit bias in real-life network traffic, and develop an efficient algorithm to extract pilot bits with assured prediction accuracy. We then propose a decoding framework to allow existing channel decoders to effectively exploit extracted pilots. We implement LEAD on GNURadio/USRP platform and evaluate its performance by replaying real-life packet traces on a testbed of 12 USRP links. Our results show that LEAD significantly improve wireless link performance, while incurring very low overhead. Specifically, LEAD reduces more than 90\% bit errors for 48.9\% packets, and improves the end-to-end link throughput by 1.43x to 1.93x over existing error correction schemes.

As a key technology of home area networks in smart grids, fine-grained power usage monitoring may help conserve electricity. Several existing systems achieve this goal by exploiting appliances' power usage signatures identified in labor-intensive in situ training processes. Recent work shows that autonomous power usage monitoring can be achieved by supplementing a smart meter with distributed sensors that detect the working states of appliances. However, sensors must be carefully installed for each appliance, resulting in high installation cost. This paper presents Supero - the first ad hoc sensor system that can monitor appliance power usage without supervised training. By exploiting multisensor fusion and unsupervised machine learning algorithms, Supero can classify the appliance events of interest and autonomously associate measured power usage with the respective appliances. Our extensive evaluation in five real homes shows that Supero can estimate the energy consumption with errors less than 7.5%. Moreover, non-professional users can quickly deploy Supero with considerable flexibility.

802.11-based wireless LANs (WLANs) have become an important communication infrastructure for today's pervasive computing applications. Nevertheless, WLAN users often experience various performance issues such as highly variable signal quality. To diagnose such transient service degradations and plan for future network upgrades, it is essential to closely monitor the performance of a WLAN and collect user statistics. This paper proposes a new WLAN performance monitoring approach motivated by the fact that many low-power wireless technologies such as ZigBee and Bluetooth co-exist with WLAN in the same open radio spectrum and are capable of sensing Received Signal Strength (RSS) of 802.11 transmissions. We have developed a ZigBee-based WLAN monitoring system called WizNet. Powered by batteries, ZigBee sensors of WizNet can be deployed in large quantities to monitor the spatial performance of a WLAN in long periods of time. By adopting digital signal processing techniques, WizNet automatically identifies 802.11 signals from ZigBee RSS measurements and associates them with wireless access points. To ensure the monitoring fidelity, WizNet accounts for the significant differences in ZigBee and WLAN radios, such as bandwidth and susceptibility to multipath and frequency-selective fading. A simple yet accurate linear estimator derived from a signal propagation model is used to infer the access points' signal to noise ratio (SNR). Moreover, WizNet can measure the congestion level of the channel and detect rogue APs. WizNet can also collect WLAN client statistics and classify device models based on RSS signatures of 802.11 access point scans. We have implemented WizNet in TinyOS 2.x and extensively evaluated its performance on a wireless testbed. Our results over a period of 140 hours show that WizNet can accurately capture the spatial and temporal performance variability of a large-scale production WLAN.

Tomography imaging, applied to seismology, requires a new, decentralized approach if high resolution calculations are to be performed in a sensor network configuration. The real-time data retrieval from a network of large-amount wireless seismic nodes to a central server is virtually impossible due to the sheer data amount and resource limitations. In this paper, we present a distributed multi-resolution evolving tomography algorithm for processing data and inverting volcano tomography in the network, while avoiding costly data collections and centralized computations. The new algorithm distributes the computational burden to sensor nodes and performs real-time tomography inversion under the constraints of network resources. We implemented and evaluated the system design in the CORE emulator. The experiment results validate that our proposed algorithm not only balances the computation load, but also achieves low communication cost and high data loss tolerance.1

The quality of sleep is an important factor in maintaining a healthy life style. To date, technology has not enabled personalized, in-place sleep quality monitoring and analysis. Current sleep monitoring systems are often difficult to use and hence limited to sleep clinics, or invasive to users, e.g., requiring users to wear a device during sleep. This paper presents iSleep -- a practical system to monitor an individual's sleep quality using off-the-shelf smartphone. iSleep uses the built-in microphone of the smartphone to detect the events that are closely related to sleep quality, including body movement, couch and snore, and infers quantitative measures of sleep quality. iSleep adopts a lightweight decision-tree-based algorithm to classify various events based on carefully selected acoustic features, and tracks the dynamic ambient noise characteristics to improve the robustness of classification. We have evaluated iSleep based on the experiment that involves 7 participants and total 51 nights of sleep, as well the data collected from real iSleep users. Our results show that iSleep achieves consistently above 90% accuracy for event classification in a variety of different settings. By providing a fine-grained sleep profile that depicts details of sleep-related events, iSleep allows the user to track the sleep efficiency over time and relate irregular sleep patterns to possible causes.

Wireless Sensor Networks (WSNs) are increasingly used in data-intensive applications such as microclimate monitoring, precision agriculture, and audio/video surveillance. A key challenge faced by data-intensive WSNs is to transmit all the data generated within an application's lifetime to the base station despite the fact that sensor nodes have limited power supplies. We propose using low-cost disposable mobile relays to reduce the energy consumption of data-intensive WSNs. Our approach differs from previous work in two main aspects. First, it does not require complex motion planning of mobile nodes, so it can be implemented on a number of low-cost mobile sensor platforms. Second, we integrate the energy consumption due to both mobility and wireless transmissions into a holistic optimization framework. Our framework consists of three main algorithms. The first algorithm computes an optimal routing tree assuming no nodes can move. The second algorithm improves the topology of the routing tree by greedily adding new nodes exploiting mobility of the newly added nodes. The third algorithm improves the routing tree by relocating its nodes without changing its topology. This iterative algorithm converges on the optimal position for each node given the constraint that the routing tree topology does not change. We present efficient distributed implementations for each algorithm that require only limited, localized synchronization. Because we do not necessarily compute an optimal topology, our final routing tree is not necessarily optimal. However, our simulation results show that our algorithms significantly outperform the best existing solutions.

Wireless rechargeable sensor networks (WRSNs) have emerged as an alternative to solving the challenges of size and operation time posed by traditional battery-powered systems. In this paper, we study a WRSN built from the industrial wireless identification and sensing platform (WISP) and commercial off-the-shelf RFID readers. The paper-thin WISP tags serve as sensors and can harvest energy from RF signals transmitted by the readers. This kind of WRSNs is highly desirable for indoor sensing and activity recognition and is gaining attention in the research community. One fundamental question in WRSN design is how to deploy readers in a network to ensure that the WISP tags can harvest sufficient energy for continuous operation. We refer to this issue as the energy provisioning problem. Based on a practical wireless recharge model supported by experimental data, we investigate two forms of the problem: point provisioning and path provisioning. Point provisioning uses the least number of readers to ensure that a static tag placed in any position of the network will receive a sufficient recharge rate for sustained operation. Path provisioning exploits the potential mobility of tags (e.g., those carried by human users) to further reduce the number of readers necessary: mobile tags can harvest excess energy in power-rich regions and store it for later use in power-deficient regions. Our analysis shows that our deployment methods, by exploiting the physical characteristics of wireless recharging, can greatly reduce the number of readers compared with those assuming traditional coverage models.

Volcano monitoring is of great interest to public safety and scientific explorations. However, traditional volcanic instrumentation such as broadband seismometers are expensive, power hungry, bulky, and difficult to install. Wireless sensor networks (WSNs) offer the potential to monitor volcanoes on unprecedented spatial and temporal scales. However, current volcanic WSN systems often yield poor monitoring quality due to the limited sensing capability of low-cost sensors and unpredictable dynamics of volcanic activities. In this article, we propose a novel quality-driven approach to achieving real-time, distributed, and long-lived volcanic earthquake detection and timing. By employing novel in-network collaborative signal processing algorithms, our approach can meet stringent requirements on sensing quality (i.e., low false alarm/missing rate, short detection delay, and precise earthquake onset time) at low power consumption. We have implemented our algorithms in TinyOS and conducted extensive evaluation on a testbed of 24 TelosB motes as well as simulations based on real data traces collected during 5.5 months on an active volcano. We show that our approach yields near-zero false alarm/missing rate, less than one second of detection delay, and millisecond precision earthquake onset time while achieving up to six-fold energy reduction over the current data collection approach.

Wireless sensor networks are typically composed of low-cost sensors that are deeply integrated in physical environments. As a result, the sensing performance of a wireless sensor network is inevitably undermined by biases in imperfect sensor hardware and the noises in data measurements. Although a variety of calibration methods have been proposed to address these issues, they often adopt the device-level approach that becomes intractable for moderate-to large-scale networks. In this article, we propose a two-tier system-level calibration approach for a class of sensor networks that employ data fusion to improve the sensing performance. In the first tier of our calibration approach, each sensor learns its local sensing model from noisy measurements using an online algorithm and only transmits a few model parameters. In the second tier, sensors' local sensing models are then calibrated to a common system sensing model. Our approach fairly distributes computation overhead among sensors and significantly reduces the communication overhead of calibration compared with the device-level approach. Based on this approach, we develop an optimal model calibration scheme that maximizes the target detection probability of a sensor network under bounded false alarm rate. Our approach is evaluated by both experiments on a testbed of TelosB motes and extensive simulations based on synthetic datasets as well as data traces collected in a real vehicle detection experiment. The results demonstrate that our system-level calibration approach can significantly boost the detection performance of sensor networks in scenarios with low signal-to-noise ratios.

It is well known that periodically putting nodes into sleep can effectively save energy in wireless sensor networks at the cost of increased communication delays. However, most existing work mainly focuses on the static sleep scheduling, which cannot guarantee the desired delay when the network conditions change dynamically. In many applications with user-specified end-to-end delay requirements, the duty cycle of every node should be tuned individually at runtime based on the network conditions to achieve the desired end-to-end delay guarantees and energy efficiency. In this article, we propose DutyCon, a control theory-based dynamic duty-cycle control approach. DutyCon decomposes the end-to-end delay guarantee problem into a set of single-hop delay guarantee problems along each data flow in the network. We then formulate the single-hop delay guarantee problem as a dynamic feedback control problem and design the controller rigorously, based on feedback control theory, for analytic assurance of control accuracy and system stability. DutyCon also features a queuing delay adaptation scheme that adapts the duty cycle of each node to unpredictable incoming packet rates, as well as a novel energy-balancing approach that extends the network lifetime by dynamically adjusting the delay requirement allocated to each hop. Our empirical results on a hardware testbed demonstrate that DutyCon can effectively achieve the desired trade-off between end-to-end delay and energy conservation. Extensive simulation results also show that DutyCon outperforms two baseline sleep scheduling protocols by having more energy savings while meeting the end-to-end delay requirements.

Multi-channel communications can effectively reduce channel competition and interference in a wireless sensor network and thus achieve increased throughput and improved end-to-end delay guarantees with reduced power consumption. However, existing work relies only on a small number of orthogonal channels, resulting in degraded performance when a large number of data flows need to be transmitted on different channels. In this article, we conduct empirical studies to investigate interferences among overlapping channels. Our results show that overlapping channels can also be utilized for improved real-time performance if the node transmission power is carefully configured. In order to minimize the overall transmission power consumption of a network with multiple data flows under end-to-end delay constraints, we formulate a constrained optimization problem to configure the transmission power level of every node and assign overlapping channels to different data flows. Since the optimization problem has an exponential computational complexity, we then present a heuristic algorithm designed based on simulated annealing to find a suboptimal solution. Our extensive empirical results on a 33-mote testbed demonstrate that our algorithm achieves better real-time performance and less power consumption than two baselines, including a scheme using only orthogonal channels.

Recent studies have shown that a significant portion of the total energy consumption of many data centers is caused by the inefficient operation of their cooling systems. Without effective thermal monitoring with accurate location information, the cooling systems often use unnecessarily low temperature set points to overcool the entire room, resulting in excessive energy consumption. Sensor network technology has recently been adopted for data-center thermal monitoring because of its nonintrusive nature for the already complex data center facilities and robustness to instantaneous CPU or disk activities. However, existing solutions place sensors in a simplistic way without considering the thermal dynamics in data centers, resulting in unnecessarily degraded hot server detection probability. In this paper, we first formulate the problems of sensor placement for hot server detection in a data center as constrained optimization problems in two different scenarios. We then propose a novel placement scheme based on computational fluid dynamics (CFD) to take various factors, such as cooling systems and server layout, as inputs to analyze the thermal conditions of the data center. Based on the CFD analysis in various server overheating scenarios, we apply data fusion and advanced optimization techniques to find a near-optimal sensor placement solution, such that the probability of detecting hot servers is significantly improved. Our empirical results in a real server room demonstrate the detection performance of our placement solution. Extensive simulation results in a large-scale data center with 32 racks also show that the proposed solution outperforms several commonly used placement solutions in terms of detection probability.

Water resources and aquatic ecosystems are facing increasing threats from climate change, improper waste disposal, and oil spill incidents. It is of great interest to deploy mobile sensors to detect and monitor certain diffusion processes (e.g., chemical pollutants) that are harmful to aquatic environments. In this paper, we propose an accuracy-aware diffusion process profiling approach using smart aquatic mobile sensors such as robotic fish. In our approach, the robotic sensors collaboratively profile the characteristics of a diffusion process including source location, discharged substance amount, and its evolution over time. In particular, the robotic sensors reposition themselves to progressively improve the profiling accuracy. We formulate a novel movement scheduling problem that aims to maximize the profiling accuracy subject to limited sensor mobility and energy budget. We develop an efficient greedy algorithm and a more complex near-optimal radial algorithm to solve the problem. We conduct extensive simulations based on real data traces of robotic fish movement and wireless communication. The results show that our approach can accurately profile dynamic diffusion processes under tight energy budgets. Moreover, a preliminary evaluation based on the implementation on TelosB motes validates the feasibility of deploying our movement scheduling algorithms on mote-class robotic sensor platforms.

This paper presents COBRA - a visible light communication (VLC) system for off-the-shelf smartphones. COBRA encodes information into specially designed 2D color barcodes and streams them between screen and camera of smartphones. Due to the directionality and short range of visible light, COBRA can preserve user privacy and security in many near field communication scenarios such as opportunistic data exchange between smartphones. We develop a new 2D color barcode for COBRA that is optimized for streaming between small-size screen and low-speed camera of smartphones. COBRA adapts the size and layout of code blocks in streamed barcodes to deal with the significant image blur in mobile environments, and adopts new image processing techniques to achieve real-time barcode stream decoding. Our approach is evaluated through extensive experiments on Android smartphones.

No abstract available.

Data centers have become a critical computing infrastructure in the era of cloud computing. Temperature monitoring and forecasting are essential for preventing overheating-induced server shutdowns and improving a data center's energy efficiency. This paper presents a novel cyber-physical approach for temperature forecasting in data centers, which integrates Computational Fluid Dynamics (CFD) modeling, in situ wireless sensing, and real-time data-driven prediction. To ensure the forecasting fidelity, we leverage the realistic physical thermodynamic models of CFD to generate transient temperature distribution and calibrate it using sensor feedback. Both simulated temperature distribution and sensor measurements are then used to train a real-time prediction algorithm. As a result, our approach significantly reduces the computational complexity of online temperature modeling and prediction, which enables a portable, noninvasive thermal monitoring solution that does not rely on the infrastructure of monitored data center. We extensively evaluated our system on a rack of 15 servers and a test bed of five racks and 229 servers in a production data center. Our results show that our system can predict the temperature evolution of servers with highly dynamic workloads at an average error of 0.52C, within a duration up to 10 minutes.

Monitoring important aquatic processes like harmful algal blooms is of increasing interest to public health, ecosystem sustainability, marine biology, and aquaculture industry. This paper presents a novel approach to spatiotemporal aquatic field reconstruction using inexpensive, low-power, mobile sensing platforms called robotic fish. Robotic fish networks are a typical example of Cyber-Physical Systems where the design of cyber components (sensing, communication, and information processing) must account for inherent physical dynamics of the robots and the aquatic environment. Our approach features a rendezvous-based mobility control scheme where robotic fish collaborate in the form of a swarm to sense the aquatic environment in a series of carefully chosen rendezvous regions. We design a novel feedback control algorithm that maintains the desirable level of wireless connectivity for a sensor swarm in the presence of significant environment and system dynamics. Information-theoretic analysis is used to guide the selection of rendezvous regions so that the spatiotemporal field reconstruction accuracy is maximized subject to the limited sensor mobility. The effectiveness of our approach is validated via implementation on sensor hardware and extensive simulations based on real data traces of water surface temperature field and on-water ZigBee wireless communication.

Many Cyber-Physical Systems (CPS) are composed of low-cost devices that are deeply integrated with physical environments. As a result, the performance of a CPS system is inevitably undermined by various physical uncertainties , which include stochastic noises, hardware biases, unpredictable environment changes, and dynamics of the physical process of interest. Traditional solutions to these issues (e.g., device calibration and collaborative signal processing) work in an open-loop fashion and hence often fail to adapt to the uncertainties after system deployment. In this article, we propose an adaptive system-level calibration approach for a class of CPS systems whose primary objective is to detect events or targets of interest. Through collaborative data fusion, our calibration approach features a feedback control loop that exploits system heterogeneity to mitigate the impact of aforementioned uncertainties on the system performance. In contrast to existing heuristic-based solutions, our control-theoretical calibration algorithm can ensure provable system stability and convergence. We also develop a routing algorithm for fusion-based multihop CPS systems that is robust to communication unreliability and delay. Our approach is evaluated by both experiments on a testbed of Tmotes as well as extensive simulations based on data traces gathered from a real vehicle detection experiment. The results demonstrate that our calibration algorithm enables a CPS system to maintain the optimal sensing performance in the presence of various system and environmental dynamics.

Efficient estimation of global information is a common requirement for many wireless sensor network applications. Examples include counting the number of nodes alive in the network and measuring the scale of physically correlated events. These tasks must be accomplished at extremely low overhead due to the severe resource limitation of sensor nodes, which poses a challenge for large-scale sensor networks. In this paper, we develop a novel protocol FLAKE to efficiently and accurately estimate the global information of large-scale sensor networks based on the sparse sampling theory. Specially, FLAKE disseminates a small number of messages called seeds to the network and issues a query about which nodes receive a seed. The number of nodes that have the information of interest can be estimated by counting the seeds disseminated, the nodes queried, and the nodes that receive a seed. FLAKE can be easily implemented in a distributed manner due to its simplicity. Moreover, desirable tradeoffs can be achieved between the accuracy of estimation and the system overhead. Our simulations show that FLAKE significantly outperforms several existing schemes on accuracy, delay, and message overhead.

Recent research shows that significant energy saving can be achieved in mobility-enabled wireless sensor networks (WSNs) that visit sensor nodes and collect data from them via short-range communications. However, a major performance bottleneck of such WSNs is the significantly increased latency in data collection due to the low movement speed of mobile base stations. To address this issue, we propose a rendezvous-based data collection approach in which a subset of nodes serve as rendezvous points that buffer and aggregate data originated from sources and transfer to the base station when it arrives. This approach combines the advantages of controlled mobility and in-network data caching and can achieve a desirable balance between network energy saving and data collection delay. We propose efficient rendezvous design algorithms with provable performance bounds for mobile base stations with variable and fixed tracks, respectively. The effectiveness of our approach is validated through both theoretical analysis and extensive simulations.

Wireless sensor networks (WSNs) have been increasingly available for critical applications such as security surveillance and environmental monitoring. An important performance measure of such applications is sensing coverage that characterizes how well a sensing field is monitored by a network. Although advanced collaborative signal processing algorithms have been adopted by many existing WSNs, most previous analytical studies on sensing coverage are conducted based on overly simplistic sensing models (e.g., the disc model) that do not capture the stochastic nature of sensing. In this paper, we attempt to bridge this gap by exploring the fundamental limits of coverage based on stochastic data fusion models that fuse noisy measurements of multiple sensors. We derive the scaling laws between coverage, network density, and signal-to-noise ratio (SNR). We show that data fusion can significantly improve sensing coverage by exploiting the collaboration among sensors when several physical properties of the target signal are known. In particular, for signal path loss exponent of (typically between 2.0 and 5.0), ρf= O(ρd1-1/k, where ρfand ρdare the densities of uniformly deployed sensors that achieve full coverage under the fusion and disc models, respectively. Moreover, data fusion can also reduce network density for regularly deployed networks and mobile networks where mobile sensors can relocate to fill coverage holes. Our results help understand the limitations of the previous analytical results based on the disc model and provide key insights into the design of WSNs that adopt data fusion algorithms. Our analyses are verified through extensive simulations based on both synthetic data sets and data traces collected in a real deployment for vehicle detection.

Recent years have seen the growing deployments of Cyber-Physical Systems (CPSs) in many mission-critical applications such as security, civil infrastructure, and transportation. These applications often impose stringent requirements on system sensing fidelity and timeliness. However, existing approaches treat these two concerns in isolation and hence are not suitable for CPSs where system fidelity and timeliness are dependent on each other because of the tight integration of computational and physical resources. In this paper, we propose a holistic approach called Fidelity-Aware Utilization Controller (FAUC) for Wireless Cyber-physical Surveillance (WCS) systems that combine low-end sensors with cameras for large-scale ad hoc surveillance in unplanned environments. By integrating data fusion with feedback control, FAUC can enforce a CPU utilization upper bound to ensure the system's real-time schedulability although CPU workloads vary significantly at runtime because of stochastic detection results. At the same time, FAUC optimizes system fidelity and adjusts the control objective of CPU utilization adaptively in the presence of variations of target/noise characteristics. We have implemented FAUC on a small-scale WCS testbed consisting of TelosB/Iris motes and cameras. Moreover, we conduct extensive simulations based on real acoustic data traces collected in a vehicle surveillance experiment. The testbed experiments and the trace-driven simulations show that FAUC can achieve robust fidelity and real-time guarantees in dynamic environments.

Low-power Wireless Networks (LWNs) have become increasingly available for mission-critical applications such as security surveillance and disaster response. In particular, emerging low-power wireless audio platforms provide an economical solution for ad hoc voice communication in emergency scenarios. In this paper, we develop a system called Adaptive Stream Multicast (ASM) for voice communication over multihop LWNs. ASM is composed of several novel components specially designed to deliver robust voice quality for multiple sinks in dynamic environments: 1) an empirical model to automatically evaluate the voice quality perceived at sinks based on current network condition; 2) a feedback-based Forward Error Correction (FEC) scheme where the source can adapt its coding redundancy ratio dynamically in response to the voice quality variation at sinks; 3) a Tree-based Opportunistic Routing (TOR) protocol that fully exploits the broadcast opportunities on a tree based on novel forwarder selection and coordination rules; and 4) a distributed admission control algorithm that ensures the voice quality guarantees when admitting new voice streams. ASM has been implemented on a low-power hardware platform and extensively evaluated through experiments on a test bed of 18 nodes. The experiment results show that ASM can achieve satisfactory multicast voice quality in dynamic environments while incurring low-communication overhead.

Wireless Sensor Networks (WSNs) are increasingly available for mission-critical applications such as emergency management and health care. To meet the stringent requirements on communication performance, it is crucial to understand the complex wireless interference among sensor nodes. Recent empirical studies suggest that the packet-level interference model, also referred to as the packet reception ratio (PRR) versus SINR model or PRR-SINR model, offers significantly improved realism than other simplistic models such as the disc model. However, as shown in our experimental results, the PRR-SINR model yields considerable spatial and temporal variations in reality, which poses a major challenge for accurate measurement at run time. This paper presents a novel accuracy-aware approach to interference modeling and measurement for WSNs. First, we propose a new regression-based PRR-SINR model and analytically characterize its accuracy based on statistics theory. Second, we develop a novel protocol called accuracy-aware interference measurement (AIM) for measuring the proposed PRR-SINR model with assured accuracy at run time. AIM also adopts new clock calibration and in-network aggregation techniques to reduce the overhead of interference measurement. Our extensive experiments on a 17-node testbed of TelosB motes show that AIM achieves high accuracy of PRR-SINR modeling with significantly lower overhead than state of the art approaches.

Recent studies have shown that a significant portion of the total energy consumption of many data centers is caused by the inefficient operation of their cooling systems. Without effective thermal monitoring with accurate location information, the cooling systems often use unnecessarily low temperature set points to over cool the entire room, resulting in excessive energy consumption. Sensor network technology has recently been adopted for data-center thermal monitoring because of its non-intrusive nature for the already complex data center facilities and robustness to instantaneous CPU or disk activities. However, existing solutions place sensors in a simplistic way without considering the thermal dynamics in data centers, resulting in unnecessarily degraded hot server detection probability. In this paper, we first formulate the problem of sensor placement for hot server detection in a data center as a constrained optimization problem. We then propose a novel placement scheme based on Computational Fluid Dynamics (CFD) to take various factors, such as cooling systems and server layout, as inputs to analyze the thermal conditions of the data center. Based on the CFD analysis in various server overheating scenarios, we apply data fusion and advanced optimization techniques to find a near-optimal sensor placement solution, such that the probability of detecting hot servers is significantly improved. Our empirical results in a real server room demonstrate the detection performance of our placement solution. Extensive simulation results also show that the proposed solution outperforms a commonly used placement solution in terms of detection probability.

Wireless rechargeable sensor networks (WRSNs) have emerged as an alternative to solving the challenges of size and operation time posed by traditional battery-powered systems. In this paper, we study a WRSN built from the industrial wireless identification and sensing platform (WISP) and commercial off-the-shelf RFID readers. The paper-thin WISP tags serve as sensors and can harvest energy from RF signals transmitted by the readers. This kind of WRSNs is highly desirable for indoor sensing and activity recognition, and is gaining attention in the research community. One fundamental question in WRSN design is how to deploy readers in a network to ensure that the WISP tags can harvest sufficient energy for continuous operation. We refer to this issue as the energy provisioning problem. Based on a practical wireless recharge model supported by experimental data, we investigate two forms of the problem: point provisioning and path provisioning. Point provisioning uses the least number of readers to ensure that a static tag placed in any position of the network will receive a sufficient recharge rate for sustained operation. Path provisioning exploits the potential mobility of tags (e.g., those carried by human users) to further reduce the number of readers necessary: mobile tags can harvest excess energy in power-rich regions and store it for later use in power-deficient regions. Our analysis shows that our deployment methods, by exploiting the physical characteristics of wireless recharging, can greatly reduce the number of readers compared with those assuming traditional coverage models.

Wireless sensor networks for human health monitoring, military surveillance, and disaster warning all have stringent accuracy requirements for detecting or classifying events while maximizing system lifetime. We define meeting such user accuracy requirements as confident sensing. To perform confident sensing and reduce energy, we must address sensing diversity: sensing capability differences among heterogeneous and homogeneous sensors in a specific deployment. We are among the first to explore the impact of sensing diversity on sensor collaboration, exploit diversity for sensing confidence, and apply diversity exploitation for confident sensing coverage. We show that our diversity-exploiting confident coverage problem is NP-hard for any specific deployment and present a practical solution, Wolfpack. Through a distributed and iterative sensor collaboration approach, Wolfpack maximizes a specific deployment's capability to meet user detection requirements and save energy by powering off unneeded nodes. Using real vehicle detection trace data, we demonstrate that Wolfpack provides confident event detection coverage for 30% more detection locations, using 20% less energy than a state of the art approach.

Clock synchronization is critical for Wireless Sensor Networks (WSNs) due to the need of inter-node coordination and collaborative information processing. Although many message passing protocols can achieve satisfactory clock synchronization accuracy, they incur prohibitively high overhead when the network scales to more than tens of nodes. An alternative approach is to take advantage of the global time reference induced by existing infrastructures including GPS, timekeeping radio stations, or power grid. However, high power consumption and geographic constraints present them from being widely adopted in WSNs. In this paper, we propose ROCS, a new clock synchronization approach exploiting the Radio Data System (RDS) of FM radios. First, we design a new hardware FM receiver that can extract a periodic pulse from FM broadcasts, referred to as RDS clock. We then conduct a large-scale measurement study of RDS clock in our lab for a period of six days and on a vehicle driving through a metropolitan area of over 40 $km^2$. Our results show that RDS clock is highly stable and hence is a viable means to calibrate the clocks of large-scale city-wide sensor networks. To reduce the high power consumption of FM receiver, ROCS intelligently predicts the time error due to drift, and adaptively calibrates the native clock via the RDS clock. We implement ROCS in TinyOS on our hardware FM receiver and a TelosB-compatible WSN platform. Our extensive experiments using a 12-node testbed and our driving measurement traces show that ROCS achieves accurate and precise clock synchronization with low power consumption.

(1) Institute of Software, Chinese Academy of Sciences, China; (2) Graduate University, Chinese Academy of Sciences, China; (3) Department of Computer Science and Engineering, Michigan State University, United States

Time synchronization is a fundamental service for Wireless Sensor Networks (WSNs). This paper proposes a novel WSN time synchronization approach by exploiting the existing Wi-Fi infrastructure. Our approach leverages the fact that ZigBee sensors and Wi-Fi nodes often occupy the same or overlapping radio frequency bands in the 2.4 GHz unlicensed spectrum. As a result, a ZigBee node can detect and synchronize to the periodic beacons broadcasted by Wi-Fi access points (APs). We experimentally characterize the spatial and temporal characteristics of Wi-Fi beacons in an enterprise Wi-Fi network consisting of over 50 APs deployed in a 300,000 square foot office building. Motivated by our measurement results, we design a novel synchronization protocol called WizSync. WizSync employs advanced Digital Signal Processing (DSP) techniques to detect periodic Wi-Fi beacons and use them to calibrate the frequency of native clocks. WizSync can intelligently predict the clock skew and adaptively schedules nodes to sleep to conserve energy. We implement WizSync in TinyOS 2.1x and conduct extensive evaluation on a testbed consisting of 19 TelosB motes. Our results show that WizSync can achieve an average synchronization error of 0.12 milliseconds over a period of 10 days with radio power consumption of 50.9 microwatts/node.

The vast array of small wireless sensors is a boon to body sensor network applications, especially in the context awareness and activity recognition arena. However, most activity recognition deployments and applications are challenged to provide personal control and practical functionality for everyday use. We argue that activity recognition for mobile devices must meet several goals in order to provide a practical solution: user friendly hardware and software, accurate and efficient classification, and reduced reliance on ground truth. To meet these challenges, we present PBN: Practical Body Networking. Through the unification of TinyOS motes and Android smartphones, we combine the sensing power of on-body wireless sensors with the additional sensing power, computational resources, and user-friendly interface of an Android smartphone. We provide an accurate and efficient classification approach through the use of ensemble learning. We explore the properties of different sensors and sensor data to further improve classification efficiency and reduce reliance on user annotated ground truth. We evaluate our PBN system with multiple subjects over a two week period and demonstrate that the system is easy to use, accurate, and appropriate for mobile devices.

Recent years have witnessed the wide adoption of the RFID technology in many important application domains including logistics, inventory, retailing, public transportation, and security. Though RFID tags (transponders) can be passive, the high power consumption of RFID readers (interrogators) has become a critical issue as handheld and mobile readers are increasingly available in pervasive computing environments. Moreover, high transmission power aggravates interference, complicating the deployment and operation of RFID systems. In this paper, we present an energy-efficient RFID inventory algorithm called Automatic Power Stepping (APS). The design of APS is based on extensive empirical study on passive tags, and takes into consideration several important details such as tag response states and variable slot lengths. APS dynamically estimates the number of tags to be read, incrementally adjusts the transmission power level to use sufficient but not excessive power for communication, and consequently reduces both the energy consumption for reading a set of tags and the possibility of collisions. We design APS to be compatible with the current Class-1 Generation-2 RFID standards so that a reader running APS can interact with existing commercial tags without modification. We have implemented APS both on an NI RFID testing platform and in a high-fidelity simulator. The evaluation shows that APS can save more than 60% energy used by RFID readers while maintaining comparable performance on the read rate.

In Distributed Real-Time Systems (DRTSs), computation and communication are the main operations contained in activities. The timeliness of activities depends on that of computations and communications. Furthermore, the timeliness of computations relies on that of communications, and vice versa. Hence, the interplay between computation and communication is inherently a key factor in determining the timeliness of activities. This paper proposes a class of general utility functions under the utility accrual model UAM+to capture and characterize this interplay. Accordingly, a technique called Dynamic Deadline Adjustment (DDA) is proposed to fully explore such interplay and help resource managers proceed toward utility accrual. An online algorithm called IDRSA, which integrates the DDA technique, is developed to perform resource scheduling for DRTSs. IDRSA adopts a two-level scheduling framework to decompose resource scheduling into subprocesses and distribute them to processing nodes so as to reduce the cost of resource scheduling through parallel processing. In addition, IDRSA incorporates a new data structure called testing interval tree to effectively reduce the costs of schedulability tests for tasks and messages. Simulation results reveal the effectiveness of IDRSA, especially when the load of computation is heavy and/or the interplay between computation and communication is tight.

As many radio chips used in today’s sensor mote hardware can work at different frequencies, several multichannel communication protocols have recently been proposed to improve network throughput and reduce packet loss for wireless sensor networks. However, existing work cannot utilize multiple channels to provide explicit guarantees for application-specified end-to-end communication delays, which are critical to many real-time applications such as surveillance and disaster response. In this article, we propose MCRT, a multichannel real-time communication protocol that features a flow-based channel allocation strategy. Because of the small number of orthogonal channels available in current mote hardware, MCRT allocates channels to network partitions formed based on many-to-one data flows. To achieve bounded end-to-end communication delay for every data flow, the channel allocation problem has been formulated as a constrained optimization problem and proven to be NP-complete. We then present the design of MCRT, which includes a channel allocation algorithm and a real-time packet forwarding strategy. Extensive simulation results based on a realistic radio model and empirical results on a real hardware testbed of Tmote nodes both demonstrate that MCRT can effectively utilize multiple channels to reduce the number of deadlines missed in end-to-end communications. Our results also show that MCRT outperforms a state-of-the-art real-time protocol and two baseline multichannel communication schemes.

Mission-critical target detection imposes stringent performance requirements for wireless sensor networks, such as high detection probabilities and low false alarm rates. Data fusion has been shown as an effective technique for improving system detection performance by enabling efficient collaboration among sensors with limited sensing capability. Due to the high cost of network deployment, it is desirable to place sensors at optimal locations to achieve maximum detection performance. However, for sensor networks employing data fusion, optimal sensor placement is a nonlinear and nonconvex optimization problem with prohibitively high computational complexity. In this paper, we present fast sensor placement algorithms based on a probabilistic data fusion model. Simulation results show that our algorithms can meet the desired detection performance with a small number of sensors while achieving up to seven-fold speedup over the optimal algorithm.

Real-time detection is an important requirement of many mission-critical wireless sensor network applications such as battlefield monitoring and security surveillance. Due to the high network deployment cost, it is crucial to understand and predict the real-time detection capability of a sensor network. However, most existing real-time analyses are based on overly simplistic sensing models (e.g., the disc model) that do not capture the stochastic nature of detection. In practice, data fusion has been adopted in a number of sensor systems to deal with sensing uncertainty and enable efficient collaboration among resource-limited sensors. However, real-time performance analysis of sensor networks designed based on data fusion has received little attention. In this paper, we bridge this gap by investigating the fundamental real-time detection performance of large-scale sensor networks under stochastic sensing models. In particular, we consider two basic data fusion schemes, i.e., value fusion and decision fusion. Our results show that data fusion is effective in achieving stringent performance requirements such as short detection delay and low false alarm rates. Moreover, value fusion and decision fusion are suitable for low and high signal-to-noise ratio scenarios, respectively. Our results help understand the impact of data fusion and provide important guidelines for the design of real-time wireless sensor networks for intrusion detection. Our analyses are verified through extensive simulations based on both synthetic data sets and data traces collected in a real deployment for vehicle detection. The results show that data fusion can reduce the network density by about 60 percent compared with the disc model while detecting any intruder within one detection period at a false alarm rate lower than five percent.

Recent years have witnessed the increasing adoption of ZigBee technology for performance-sensitive applications such as wireless patient monitoring in hospitals. However, operating in unlicensed ISM bands, ZigBee devices often yield unpredictable throughput and packet delivery ratio due to the interference from ever increasing WiFi hotspots in 2.4 GHz band. Our empirical results show that, although WiFi traffic contains abundant white space, the existing coexistence mechanisms such as CSMA are surprisingly inadequate for exploiting it. In this paper, we propose a novel approach that enables ZigBee links to achieve assured performance in the presence of heavy WiFi interference. First, based on statistical analysis of real-life network traces, we present a Pareto model to accurately characterize the white space in WiFi traffic. Second, we analytically model the performance of a ZigBee link in the presence of WiFi interference. Third, based on the white space model and our analysis, we develop a new ZigBee frame control protocol called WISE, which can achieve desired trade-offs between link throughput and delivery ratio. Our extensive experiments on a testbed of 802.11 netbooks and 802.15.4 TelosB motes show that, in the presence of heavy WiFi interference, WISE achieves 4× and 2× performance gains over B-MAC and a recent reliable transmission protocol, respectively, while only incurring 10.9% and 39.5% of their overhead.

Interference modeling is crucial for the performance of numerous WSN protocols such as congestion control, link/channel scheduling, and reliable routing. In particular, understanding and mitigating interference becomes increasingly important for Wireless Sensor Networks (WSNs) as they are being deployed for many data-intensive applications such as structural health monitoring. However, previous works have widely adopted simplistic interference models that fail to capture the wireless realities such as probabilistic packet reception performance. Recent studies suggested that the physical interference model (i.e., PRR-SINR model) is significantly more accurate than existing interference models. However, existing approaches to physical interference modeling exclusively rely on the use of active measurement packets, which imposes prohibitively high overhead to bandwidth-limited WSNs. In this paper, we propose the passive interference measurement (PIM) approach to tackle the complexity of accurate physical interference characterization. PIM exploits the spatiotemporal diversity of data traffic for radio performance profiling and only needs to gather a small amount of statistics about the network. We evaluate the efficiency of PIM through extensive experiments on both a 13-node and a 40-node testbeds of TelosB motes. Our results show that PIM can achieve high accuracy of PRR-SINR modeling with significantly lower overhead compared with the active measurement approach.

Wireless sensor networks (WSNs) are typically composed of low-cost sensors that are deeply integrated with physical environments. As a result, the sensing performance of a WSN is inevitably undermined by various physical uncertainties, which include stochastic sensor noises, unpredictable environment changes and dynamics of the monitored phenomenon. Traditional solutions (e.g., sensor calibration and collaborative signal processing) work in an open-loop fashion and hence fail to adapt to these uncertainties after system deployment. In this paper, we propose an adaptive system-level calibration approach for a class of sensor networks that employ data fusion to improve system sensing performance. Our approach features a feedback control loop that exploits sensor heterogeneity to deal with the aforementioned uncertainties in calibrating system performance. In contrast to existing heuristic based solutions, our control-theoretical calibration algorithm can ensure provable system stability and convergence. We also systematically analyze the impacts of communication reliability and delay, and propose an optimal routing algorithm that minimizes the impact of packet loss on system stability. Our approach is evaluated by both experiments on a testbed of Tmotes as well as extensive simulations based on data traces gathered from a real vehicle detection experiment. The results demonstrate that our calibration algorithm enables a network to maintain the optimal detection performance in the presence of various system and environmental dynamics.

Efficient estimation of global information is a common requirement for many wireless sensor network applications. Examples include counting the number of nodes alive in the network and measuring the scale of physically correlated events. These tasks must be accomplished at extremely low overhead due to the severe resource limitation of sensor nodes, which poses a challenge for large-scale sensor networks. In this paper, we develop a novel protocol called FLAKE that can efficiently and accurately estimate the global information of large-scale sensor networks based on the sparse sampling theory. Specially, FLAKE disseminates a small number of messages called seeds to the network and issues a query about which nodes receive a seed. The number of nodes that have the information of interest can be estimated by counting the seeds disseminated, the nodes queried, and the nodes that receive a seed. FLAKE can be easily implemented in a distributed manner due to its simplicity. Moreover, desirable trade-offs can be achieved between the accuracy of estimation and the system overhead. Our simulations show that FLAKE significantly outperforms several existing schemes on accuracy, delay and message overhead.

It is well known that periodically putting nodes into sleep can effectively save energy in wireless sensor networks, at the cost of increased communication delays. However, most existing work mainly focuses on static sleep scheduling, which cannot guarantee the desired delay when the network conditions change dynamically. In many applications with user-specified end-to-end delay requirements, the duty cycle of every node should be tuned individually at runtime based on the network conditions to achieve the desired end-to-end delay guarantees and energy efficiency. In this paper, we propose DutyCon, a control theory-based dynamic duty cycle control approach. DutyCon decomposes the end-to-end delay guarantee problem into a set of single-hop delay guarantee problems along each data flow in the network. We then formulate the single-hop delay guarantee problem as a dynamic feedback control problem and design the controller rigorously, based on feedback control theory, for analytic assurance of control accuracy and system stability. DutyCon also features a queuing delay adaptation scheme that adapts the duty cycle of each node to unpredictable packet rates, as well as a novel energy balancing approach that extends the network lifetime by dynamically adjusting the delay requirement allocated to each hop. Our empirical results on a hardware testbed demonstrate that DutyCon can effectively achieve the desired tradeoff between end-to-end delay and energy conservation. Extensive simulation results also show that DutyCon outperforms two baseline sleep scheduling protocols by having more energy savings while meeting the end-to-end delay requirements.

Recently, the availability of numerous low-cost robotic units (e.g., Packbot, Robomote, and Khepera) has made it possible to massively deploy mobile sensors in a network and use them in a disposable manner. It has been shown that the controlled mobility offered by sensors can be exploited to improve the energy efficiency of a network. In this paper, we study a new problem called max-data mobile relay configuration (MMRC) that finds the positions of a set of mobile sensors, referred to as relays, that maximize the total amount of data gathered by the network during its lifetime. Different from previous controlled mobility approaches, we account for several characteristics of existing practical mobile sensing platforms including limited mobility and the high energy consumption of locomotion. We show that the MMRC problem is surprisingly complex even for a trivial network topology due to the joint consideration of the energy consumption of both wireless communication and mechanical locomotion. We present optimal MMRC algorithms and practical distributed implementations for several important network topologies. Our extensive simulations based on realistic energy models of existing mobile sensing platforms show that our approach can increase the data gathering capacity by a factor of at least 2 in most scenarios. Moreover, our distributed algorithms converge quickly and incur low messaging overhead.

Recent years have witnessed the emergence of numerous new Internet services for mobile users. Supporting mobile applications via public WiFi networks has received significant research attention due to the drastic increase of penetration rate of 802.11-based networks. Nevertheless, recent empirical studies showed that unplanned WiFi networks cannot provide satisfactory Quality of Service for interactive mobile applications due to intermittent network connectivity. In this paper, we exploit realistic mobility characteristics of users to deploy WiFi Access Points (APs) for continuous service for mobile users. We study two AP deployment problems that aim to maximize the continuous user coverage and to minimize the AP deployment cost, respectively. Both problems are formulated based on mobility graphs that capture the statistical mobility patterns of users. We prove that both problems are NP-hard. We develop several optimal and approximation algorithms with provable performance bounds for different topologies of mobility graphs. The effectiveness of our approaches is validated by extensive simulations using real user mobility traces.

WiFi networks have enjoyed an unprecedent penetration rate in recent years. However, due to the limited coverage, existing WiFi infrastructure only provides intermittent connectivity for mobile users. Once leaving the current network coverage, WiFi clients must actively discover new WiFi access points (APs), which wastes the precious energy of mobile devices. Although several solutions have been proposed to address this issue, they either require significant modifications to existing network infrastructures or rely on context information that is not available in unknown environments. In this work, we develop a system called ZiFi that utilizes ZigBee radios to identify the existence of WiFi networks through unique interference signatures generated by WiFi beacons. We develop a new digital signal processing algorithm called Common Multiple Folding (CMF) that accurately amplifies periodic beacons in WiFi interference signals. ZiFi also adopts a constant false alarm rate (CFAR) detector that can minimize the false negative (FN) rate of WiFi beacon detection while satisfying the user-specified upper bound on false positive (FP) rate. We have implemented ZiFi on two platforms, a Linux netbook integrating a TelosB mote through the USB interface, and a Nokia N73 smartphone integrating a ZigBee card through the miniSD interface. Our experiments show that, under typical settings, ZiFi can detect WiFi APs with high accuracy (<5% total FP and FN rate), short delay (~780 ms), and little computation overhead

Barrier coverage is a critical issue in wireless sensor networks for various battlefield and homeland security applications. The goal is to effectively detect intruders that attempt to penetrate the region of interest. A sensor barrier is formed by a connected sensor cluster across the entire deployed region, acting as a "trip wire" to detect any crossing intruders. In this paper we study how to efficiently improve barrier coverage using mobile sensors with limited mobility. After the initial deployment, mobile sensors can move to desired locations and connect with other sensors in order to create new barriers. However, simply moving sensors to form a large local cluster does not necessarily yield a global barrier. This global nature of barrier coverage makes it a challenging task to devise effective sensor mobility schemes. Moreover, a good sensor mobility scheme should efficiently improve barrier coverage under the constraints of available mobile sensors and their moving range. We first explore the fundamental limits of sensor mobility on barrier coverage and present a sensor mobility scheme that constructs the maximum number of barriers with minimum sensor moving distance. We then present an efficient algorithm to compute the existence of barrier coverage with sensors of limited mobility, and examine the effects of the number of mobile sensors and their moving ranges on the barrier coverage improvement. Both the analytical results and performance of the algorithms are evaluated via extensive simulations.

Recent years have witnessed the wide adoption of the RFID technology in many important application domains including logistics, inventory, retailing, public transportation, and security. Though RFID tags (transponders) can be passive, the high power consumption of RFID readers (interrogators) has become a critical issue as handheld and mobile readers are increasingly available in pervasive computing environments. Moreover, high transmission power aggravates interference, complicating the deployment and operation of RFID systems. In this paper, we present an energy-efficient RFID inventory algorithm called Automatic Power Stepping (APS). The design of APS is based on extensive empirical study on passive tags, and takes into consideration several important details such as tag response states and variable slot lengths. APS dynamically estimates the number of tags to be read, incrementally adjusts power level to use sufficient but not excessive power for communication, and consequently reduces both the energy consumption for reading a set of tags and the possibility of collisions. We design APS to be compatible with the current Class-1 Generation-2 RFID standards and hence a reader running APS can interact with existing commercial tags without modification. We have implemented APS both on the NI RFID testing platform and in a high-fidelity simulator. The evaluation shows that APS can save more than 60% energy used by RFID readers.

Many mission-critical applications such as military surveillance, human health monitoring, and obstacle detection in autonomous vehicles impose stringent requirements for event detection accuracy and demand long system lifetimes. Through quantitative study, we show that traditional approaches to event detection have difficulty meeting such requirements. Specifically, they cannot explore the detection capability of a deployed system and choose the right sensors, homogeneous or heterogeneous, to meet user specified detection accuracy. They also cannot dynamically adapt the detection capability to runtime observations to save energy. Therefore, we are motivated to propose Watchdog, a modality-agnostic event detection framework that clusters the right sensors to meet user specified detection accuracy during runtime while significantly reducing energy consumption. Through evaluation with vehicle detection trace data and a building traffic monitoring testbed of IRIS motes, we demonstrate the superior performance of Watchdog over existing solutions in terms of meeting user specified detection accuracy and energy savings.

Recent years have seen the growing deployments of Cyber-Physical Systems (CPSs) in many mission-critical applications such as security, civil infrastructure, and transportation. These applications often impose stringent requirements on system sensing fidelity and timeliness. However, existing approaches treat these two concerns in isolation and hence are not suitable for CPSs where system fidelity and timeliness are dependent of each other because of the tight integration of computational and physical resources. In this paper, we propose a holistic approach called Fidelity-Aware Utilization Controller (FAUC) for Wireless Cyber-physical Surveillance (WCS) systems that combine low-end sensors with cameras for large-scale ad hoc surveillance in unplanned environments. By integrating data fusion with feedback control, FAUC can enforce a CPU utilization upper bound to ensure the system's real-time schedulability although CPU workloads vary significantly at runtime because of stochastic detection results. At the same time, FAUC optimizes system fidelity and adjusts the control objective of CPU utilization adaptively in the presence of variations of target/noise characteristics. We have implemented FAUC on a small-scale WCS testbed consisting of TelosB/Iris motes and cameras. Our extensive experiments on light and acoustic target detection show that FAUC can achieve robust fidelity and real-time guarantees in dynamic environments.

Low-power Wireless Networks (LWNs) have become increasingly available for mission-critical applications such as security surveillance and disaster response. In particular, emerging low-power wireless audio platforms provide an economical solution for ad hoc voice communication in emergency scenarios. In this paper, we develop a system called Adaptive Stream Multicast (ASM) for voice communication over multi-hop LWNs. ASM is composed of several novel components specially designed to deliver robust voice quality for multiple sinks in dynamic environments: 1) an empirical model to automatically evaluate the voice quality perceived at sinks based on current network condition, 2) a feedback-based Forward Error Correction scheme where the source can adapt its coding redundancy ratio dynamically in response to the voice quality variation at sinks, 3) a Tree-based Opportunistic Routing (TOR) protocol that fully exploits the broadcast opportunities on a tree based on novel forwarder selection and coordination rules, and 4) a distributed admission control algorithm that ensures the voice quality guarantees when admitting new voice streams. ASM has been implemented on a low-power hardware platform and extensively evaluated through experiments on a testbed of 18 nodes.

Volcano monitoring is of great interest to public safety and scientific explorations. However, traditional volcanic instrumentation such as broadband seismometers are expensive, power-hungry, bulky, and difficult to install. Wireless sensor networks (WSNs) offer the potential to monitor volcanoes at unprecedented spatial and temporal scales. However, current volcanic WSN systems often yield poor monitoring quality due to the limited sensing capability of low-cost sensors and unpredictable dynamics of volcanic activities. Moreover, they are designed only for short-term monitoring due to the high energy consumption of centralized data collection. In this paper, we propose a novel quality-driven approach to achieving real-time, in-situ, and long-lived volcanic earthquake detection. By employing novel in-network collaborative signal processing algorithms, our approach can meet stringent requirements on sensing quality (low false alarm/missing rate and precise earthquake onset time) at low power consumption. We have implemented our algorithms in TinyOS and conducted extensive evaluation on a testbed of 24 TelosB motes as well as simulations based on real data traces collected during 5.5 months on an active volcano. We show that our approach yields near-zero false alarm/missing rate and less than one second of detection delay while achieving up to 6-fold energy reduction over the current data collection approach.

Wireless sensor networks are typically composed of low-cost sensors that are deeply integrated in physical environments. As a result, the sensing performance of a wireless sensor network is inevitably undermined by biases in imperfect sensor hardware and the noises in data measurements. Although a variety of calibration methods have been proposed to address these issues, they often adopt the device-level approach that becomes intractable for moderate- to large-scale networks. In this paper, we propose a two-tier system-level calibration approach for a class of sensor networks that employ data fusion to improve the sensing performance. In the first tier of our calibration approach, each sensor learns its local sensing model from noisy measurements using an online algorithm and only transmits a few model parameters. In the second tier, sensors' local sensing models are then calibrated to a common system sensing model. Our approach fairly distributes computation overhead among sensors and significantly reduces the communication overhead of calibration. Based on this approach, we develop an optimal model calibration scheme that maximizes the target detection probability of a sensor network under bounded false alarm rate. Our approach is evaluated by both experiments on a testbed of TelosB motes and extensive simulations based on data traces collected in a real vehicle detection experiment. The results demonstrate that our system-level calibration approach can significantly boost the detection performance of sensor networks in the scenarios with low signal-to-noise ratios.

Wireless Sensor Networks (WSNs) represent a new generation of networked embedded systems that must achieve long lifetimes on scarce amounts of energy. Since radio communication accounts for the primary source of power drain in these networks, a large number of different radio power management protocols have been proposed. However, the lack of operating system support for flexibly integrating them with a diverse set of applications and network platforms has made them difficult to use. This article focuses on providing link layer support toward realizing a unified power management architecture (UPMA) for WSNs. In contrast to existing monolithic approaches, we provide (i) a set of standard interfaces that separate link layer power management protocols from common MAC level functionality, (ii) an architectural framework that allows applications to easily swap out different power-management protocols depending on its needs, and (iii) a mechanism for coordinating multiple applications with different power management requirements. We have implemented our approach on both the Mica2 and Telosb radio drivers in TinyOS-2.0, the second generation of the de facto standard operating system for WSNs. Microbenchmark results show that our approach can coordinate the power-management requirements of multiple applications in a platform independent fashion while incurring negligible overhead.

Recent years have witnessed the deployments of wireless sensor networks in a class of mission-critical applications such as object detection and tracking. These applications often impose stringent Quality-of-Service requirements including high detection probability, low false alarm rate, and bounded detection delay. Although a dense all-static network may initially meet these Quality-of-Service requirements, it does not adapt to unpredictable dynamics in network conditions (e.g., coverage holes caused by death of nodes) or physical environments (e.g., changed spatial distribution of events). This paper exploits reactive mobility to improve the target detection performance of wireless sensor networks. In our approach, mobile sensors collaborate with static sensors and move reactively to achieve the required detection performance. Specifically, mobile sensors initially remain stationary and are directed to move toward a possible target only when a detection consensus is reached by a group of sensors. The accuracy of final detection result is then improved as the measurements of mobile sensors have higher Signal-to-Noise Ratios after the movement. We develop a sensor movement scheduling algorithm that achieves near-optimal system detection performance under a given detection delay bound. The effectiveness of our approach is validated by extensive simulations using the real data traces collected by 23 sensor nodes.

Service-oriented wireless sensor networks have recently been proposed to provide an integrated platform, where new applications can be rapidly developed through flexible service composition. In wireless sensor networks, sensors are periodically switched into the sleep mode for energy saving. This, however, will cause the unavailability of nodes, which, in turn, incurs disruptions to the service compositions requested by the applications. Thus, it is desirable to maintain enough active sensors in the system to provide each required service at any time in order to achieve dependable service compositions for various applications. In this paper, we study the cross-layer sleep scheduling design, which aims to prolong the network lifetime while satisfying the service availability requirement at the application layer. We formally define the problem, prove that the problem is NP-hard, and develop two approximation algorithms based on the LP relaxation and one efficient reordering heuristic algorithm. The proposed work will enhance the dependability of the service composition in service-oriented wireless sensor networks.

Many wireless sensor networks require sufficient sensing coverage over long periods of time. To conserve energy, a coverage maintenance protocol achieves desired coverage by activating only a subset of nodes, while allowing the others to sleep. Existing coverage maintenance protocols are often designed based on simplistic sensing models that do not capture the stochastic nature of distributed sensing. We propose a new sensing coverage model based on the distributed detection theory, which captures two important characteristics of sensor networks, i.e., probabilistic detection by individual sensors and data fusion among sensors. We then present three coverage maintenance protocols that can meet the specified event detection probability and false alarm rate. The centralized protocol only activates a small number of sensors, but introduces extremely long coverage configuration delay. The Se-Grid protocol reduces the configuration time by dividing the network into separate fusion groups, but increases the number of active sensors due to the lack of collaboration among sensors in different groups. In contrast, by coordinating overlapping fusion groups, the Co-Grid protocol can effectively reduce the number of active sensors and the coverage configuration time. The advantages of Co-Grid have been validated through simulations and benchmark results on Mica2 motes.

We investigate the problem of delay constrained maximal information collection for CSMA-based wireless sensor networks. We study how to allocate the maximal allowable transmission delay at each node, such that the amount of information collected at the sink is maximized and the total delay for the data aggregation is within the given bound. We formulate the problem by using dynamic programming and propose an optimal algorithm for the optimal assignment of transmission attempts. Based on the analysis of the optimal solution, we propose a distributed greedy algorithm. It is shown to have a similar performance as the optimal one.

Wireless sensor networks (WSNs) deployed for mission-critical applications face the fundamental challenge of meeting stringent spatiotemporal performance requirements using nodes with limited sensing capacity. Although advance network planning and dense node deployment may initially achieve the required performance, they often fail to adapt to the unpredictability and variability of physical reality. This paper explores efficient use of mobile sensors to address limitations of static WSNs for target detection. We propose a data-fusion-based detection model that enables static and mobile sensors to effectively collaborate in target detection. An optimal sensor movement scheduling algorithm is developed to minimize the total moving distance of sensors while achieving a set of spatiotemporal performance requirements including high detection probability, low system false alarm rate, and bounded detection delay. The effectiveness of our approach is validated by extensive simulations based on real data traces collected by 23 sensor nodes.

Recent years have witnessed the pilot deployments of audio or low-rate video wireless sensor networks for a class of mission-critical applications including search and rescue, security surveillance, and disaster management. In this paper, we report the design and implementation of Quality-aware Voice Streaming (QVS) for wireless sensor networks. QVS is built upon SenEar, a new sensor hardware platform we developed for high-bandwidth wireless audio communication. QVS comprises several novel components, which include an empirical model for online voice quality evaluation and control, dynamic voice compression/duplication adaptation for lossy wireless links, and distributed stream admission control that exploits network capacity for rate allocation. We have extensively tested QVS on a 20-node network deployment. Our experimental results show that QVS delivers satisfactory voice quality under a range of realistic settings while achieving high network capacity utilization.

This paper presents C-MAC, a new MAC protocol designed to achieve high-throughput bulk communication for data-intensive sensing applications. C-MAC exploits concurrent wireless channel access based on empirical power control and physical interference models. Nodes running C-MAC estimate the level of interference based on the physical signal-to-interference-plus-noise-ratio (SINR) model and adjust the transmission power accordingly for concurrent channel access. C-MAC employs a block-based communication mode that not only amortizes the overhead of channel assessment, but also improves the probability that multiple nodes within the interference range of each other can transmit concurrently. C-MAC has been implemented in TinyOS-1.x and extensively evaluated on Tmote nodes. Our experiments show that C-MAC significantly outperforms the state-of-art CSMA protocol in TinyOS with respect to system throughput, delay and energy consumption.

Recently, wireless sensor networks (WSNs) have become increasingly available for data-intensive applications such as micro-climate monitoring, precision agriculture, and audio/video surveillance. A key challenge faced by data-intensive WSNs is to transmit the sheer amount of data generated within an application's lifetime to the base station despite the fact that sensor nodes have limited power supplies such as batteries or small solar panels. In this paper, we propose to use low-cost disposable mobile relays to reduce the energy consumption of data-intensive WSNs. Different from previous work, our approach does not require complex motion planning of mobile nodes, and hence can be implemented on a number of low-cost mobile sensor platforms. Moreover, we integrate the energy consumption due to both mobility and wireless transmissions into a holistic optimization framework. The optimal relay configuration is shown to depend on both the positions of nodes and the amount of data to be sent. We develop two algorithms that iteratively refine the configuration of mobile relays and converge to the optimal solution. These algorithms have efficient distributed implementations that do not require explicit synchronization. Our simulation results based on realistic energy models obtained from existing mobile and static sensor platforms show that our algorithms significantly outperform the best existing solutions.

Recently, mobility-assisted data collection has been proposed to prolong network lifetime. However, the upper bound of throughput capacity in such mobility-assisted data collection models has not been studied. In this work, we first derive the upper bound of throughput capacity when a mobile sink is available for data collection. Given the traveling speed of the mobile sink and the required delay deadline, we analyze the necessary conditions (e.g., optimal number of clusters, minimum buffer size at each cache, and minimum traveling distance) to achieve the maximum throughput capacity. Our analysis shows that 3W/4n per-node throughput capacity can be achieved at a low traveling speed, which is 3 times of the throughput capacity in a static WSN. We further extend the analysis to the case where multiple mobile devices can assist in data collection. We show that 4 mobile relays are enough to achieve the upper bound of throughput capacity O(W/n). Finally, we derive the throughput capacity under existing mobility-assisted data collection models.

Wireless sensor networks (WSNs) have been increasingly available for critical applications such as security surveil-lance and environmental monitoring. An important per-formance measure of such applications is sensing coverage that characterizes how well a sensing field is monitored by a network. Although advanced collaborative signal process-ing algorithms have been adopted by many existing WSNs, most previous analytical studies on sensing coverage are con-ducted based on overly simplistic sensing models (e.g., the disc model) that do not capture the stochastic nature of sens-ing. In this paper, we attempt to bridge this gap by explor-ing the fundamental limits of coverage based on stochastic data fusion models that fuse noisy measurements of multi-ple sensors. We derive the scaling laws between coverage, network density, and signal-to-noise ratio (SNR). We show that data fusion can significantly improve sensing coverage by exploiting the collaboration among sensors. In particu-lar, for signal path loss exponent of k (typically between 2.0 and 5.0), ρf = O(ρ1−1/kd), where ρf and ρd are the densi-ties of uniformly deployed sensors that achieve full coverage under the fusion and disc models, respectively. Our results help understand the limitations of the previous analytical re-sults based on the disc model and provide key insights into the design of WSNs that adopt data fusion algorithms. Our analyses are verified through extensive simulations based on both synthetic data sets and data traces collected in a real deployment for vehicle detection.

Real-time detection is an important requirement of many mission-critical wireless sensor network applications such as battlefield monitoring and security surveillance. Due to the high network deployment cost, it is crucial to understand and predict the real-time detection capability of a sensor network. However, most existing real-time analyses are based on overly simplistic sensing models (e.g., the disc model) that do not capture the stochastic nature of detection. In practice, data fusion has been adopted in a number of sensor systems to deal with sensing uncertainty and enable the collaboration among sensors. However, real-time performance analysis of sensor networks designed based on data fusion has received little attention. In this paper, we bridge this gap by investigating the fundamental real-time detection performance of large-scale sensor networks under stochastic sensing models. Our results show that data fusion is effective in achieving stringent performance requirements such as short detection delay and low false alarm rates, especially in the scenarios with low signal-to-noise ratios (SNRs). Data fusion can reduce the network density by about 60% compared with the disc model while detecting any intruder within one detection period at a false alarm rate lower than 2%. In contrast, the disc model is only suitable when the SNR is sufficiently high. Our results help understand the impact of data fusion and provide important guidelines for the design of real-time wireless sensor networks for intrusion detection.

Multi-channel design has received significant attention for low-power wireless networks (LWNs), such as 802.15.4-based wireless sensor networks, due to its potential of mitigating interference and improving network capacity. However, recent studies reveal that the number of orthogonal channels available on commodity wireless platforms is small, which significantly hinders the performance of existing multi-channel protocols. A promising solution is to explore the use of partially overlapping channels for communications. However, this approach faces several key challenges such as increased inter-channel interference and significantly higher overhead of channel measurement. In this paper, we systematically study the inter-channel interference and its impact on link capacity and the performance of multi-channel protocols in LWNs. First, we develop empirical models for characterizing inter-channel signal attenuation based on experiments on TelosB motes. We then propose a novel measurement algorithm which can significantly reduce the overhead of multi-channel interference measurement by exploiting the spectral power density (SPD) of the transmitter. Finally, we apply our interference models to both link capacity analysis and channel assignment protocols. Our extensive experiments on a testbed of 30 TelosB motes show that our interference measurement algorithm has an average error of 2.95%. Our results also demonstrate that multi-channel protocols for LWNs can significantly benefit from using overlapping channels.

In-situ data collection for mobile wireless sensor network deployments has received little study, such as in the case of floating sensor networks for storm surge and innundation monitoring. We demonstrate through quantitative study that traditional approaches to routing in mobile environments do not work well due to volatile topology changes. Consequently, we propose Sidewinder, a predictive data forwarding protocol for mobile wireless sensor networks. Like a heat-seeking missile, data packets are guided towards a sink node with increasing accuracy as packets approach the sink. Different from conventional sensor network routing protocols, Sidewinder continuously predicts the current sink location based on distributed knowledge of sink mobility among nodes in a multi-hop routing process. Moreover, the continuous sink estimation is scaled and adjusted to perform with resource-constrained wireless sensors. Our design is implemented with nesC and evaluated in TOSSIM. The performance evaluation demonstrates that Sidewinder significantly outperforms state-of-the-art solutions in packet delivery ratio, time delay, and energy efficiency.

Recent years have seen the deployments of wireless sensor networks (WSNs) in a variety of applications to gather the information about physical environments. A key requirement of many data-gathering WSNs is to deliver the information about dynamic physical phenomena to users at multiple temporal resolutions. In this paper, we propose a novel solution called theMinimumIncrementalDisseminationTree(MIDT) for dynamic multiresolution data dissemination in WSNs. MIDT includes an online tree construction algorithm with an analytical performance bound and two lightweight tree adaptation heuristics for handling data requests with dynamic temporal resolutions. Our simulations based on realistic settings of Mica2 motes show that MIDT outperforms several typical data dissemination schemes. The two tree adaptation heuristics can effectively maintain desirable energy efficiency of the dissemination tree while reducing the overhead of tree reconfigurations under representative traffic patterns in WSNs.

Limitation on available resources is a major challenge in wireless sensor networks. Due to high rates of unexpected node/link failures, robust data delivery through multiple hops also becomes a critical issue. In this article we present a state-free gradient-based forwarding (SGF) protocol to address these challenges. Nodes running SGF do not maintain states of neighbors or network topology and thus can scale to very large networks. Without using routing tables, SGF builds a cost field called gradient that provides each node the direction to forward data. The maintenance of gradient is purely driven by data transmissions and hence incurs little overhead. To adapt to transient channel variations and topology changes, the forwarder of a routing node is selected opportunistically among multiple candidate nodes through a distributed contention process. Simulation results show that SGF achieves significant energy savings and outperforms several existing data forwarding protocols in terms of packet delivery ratio and end-to-end delay.

Abstract Many wireless sensor networks must sustain long lifetimes on limited energy resources. Two major approaches, transmission power control and sleep scheduling, have been proposed to reduce the radio power consumption in the transmission state and the idle state, respectively. In this paper, we first review existing transmission power control and sleep scheduling approaches and then describe a Unified Radio Power Management framework for the design and implementation of holistic radio power management solutions in wireless sensor networks. It has two key components: (1) a novel optimization approach called Minimum Power Configuration that minimizes the aggregate radio power consumption of all ratio states and (2) a Unified Power Management Architecture (UPMA) that aims to support the flexible cross‐layer integration of different power management strategies. A novel feature of UPMA is that it enables cross‐layer coordination and joint optimization of different power management strategies that exist at multiple network layers. Copyright © 2008 John Wiley & Sons, Ltd.

Wireless sensor networks (WSNs) deployed for mission-critical applications face the fundamental challenge of meeting stringent spatiotemporal performance requirements using nodes with limited sensing capacity. Although advance network planning and dense node deployment may initially achieve the required performance, they often fail to adapt to the unpredictability of physical reality. This paper explores efficient use of mobile sensors to address the limitations of static WSNs in target detection. We propose a data fusion model that enables static and mobile sensors to effectively collaborate in target detection. An optimal sensor movement scheduling algorithm is developed to minimize the total moving distance of sensors while achieving a set of spatiotemporal performance requirements including high detection probability, low system false alarm rate and bounded detection delay. The effectiveness of our approach is validated by extensive simulations based on real data traces collected by 23 sensor nodes.

Recent years have witnessed the deployments of wireless sensor networks in a class of mission-critical applications such as object detection and tracking. These applications often impose stringent QoS requirements including high detection probability, low false alarm rate and bounded detection delay. Although a dense all-static network may initially meet these QoS requirements, it does not adapt to unpredictable dynamics in network conditions (e.g., coverage holes caused by death of nodes) or physical environments (e.g., changed spatial distribution of events). This paper exploits reactive mobility to improve the target detection performance of wireless sensor networks. In our approach, mobile sensors collaborate with static sensors and move reactively to achieve the required detection performance. Specifically, mobile sensors initially remain stationary and are directed to move toward a possible target only when a detection consensus is reached by a group of sensors. The accuracy of final detection result is then improved as the measurements of mobile sensors have higher signal-to-noise ratios after the movement. We develop a sensor movement scheduling algorithm that achieves near-optimal system detection performance within a given detection delay bound. The effectiveness of our approach is validated by extensive simulations using the real data traces collected by 23 sensor nodes.

Recent research shows that significant energy saving can be achieved in wireless sensor networks with a mobile base station that collects data from sensor nodes via short-range communications. However, a major performance bottleneck of such WSNs is the significantly increased latency in data collection due to the low movement speed of mobile base stations. To address this issue, we propose a rendezvous-based data collection approach in which a subset of nodes serve as the rendezvous points that buffer and aggregate data originated from sources and transfer to the base station when it arrives. This approach combines the advantages of controlled mobility and in-network data caching and can achieve a desirable balance between network energy saving and data collection delay. We propose two efficient rendezvous design algorithms with provable performance bounds for mobile base stations with variable and fixed tracks, respectively. The effectiveness of our approach is validated through both theoretical analysis and extensive simulations.

Minimizing energy consumption is a key issue in designing real-time applications on wireless embedded systems. While a lot of work has been done to manage energy consumption on single processor real-time system, few work addresses network-wide energy consumption management for real-time tasks. Moreover, existing work on network-wide energy consumption assumes that the underlying network is always connected, which is not consistent with the practice in which wireless nodes often turn off their network interfaces in asleep schedule to reduce energy consumption. In this paper, we propose solutions to minimize network-wide energy consumption for real-time tasks with precedence constraints executing on wireless embedded systems. Our solutions take the radio sleep scheduling of wireless nodes into account when adjusting the execution modes of processors. We also propose a runtime dynamic energy management scheme to further reduce energy consumption while guaranteeing the timing constraint. The experiments show that our approach significantly reduces total energy consumption compared with the previous work.

Mission-critical target detection imposes stringent performance requirements for wireless sensor networks, such as high detection probabilities and low false alarm rates. Data fusion has been shown as an effective technique for improving system detection performance by enabling efficient collaboration among sensors with limited sensing capability. Due to the high cost of network deployment, it is desirable to place sensors at optimal locations to achieve maximum detection performance. However, for sensor networks employing data fusion, optimal sensor placement is a non-linear optimizationproblem with prohibitive computational complexity. In this paper, we present fast sensor placement algorithms based on a probabilistic data fusion model.Simulation results show that our algorithms can meet the desired detection performance with a small number of sensors while achieving up to 7-fold speedup over the optimal algorithm.

Cyber-physical systems (CPS) is a new generation of engineered systems that integrate physical systems with the capability of networked computing and control. Real-time video capture and communication is expected to be an important function in many cyber-physical systems that involve camera-equipped mobile phones. In this paper, we present AnySense, a network architecture that supports video communication between 3G phones and Internet hosts in cyber-physical systems. AnySense implements transcoding of video streams between the Internet and circuit-switched 3G cellular networks, and is transparent to 3G service providers. AnySense can support a class of ubiquitous cyber-physical systems that require video-based information collection and sharing. A prototype of AnySense has been built and a video demo is available at http://www.anyserver.org/.

Recent research shows that significant energy saving can be achieved in wireless sensor networks by using mobile elements (MEs) capable of carrying data mechanically. However, the low movement speed of MEs hinders their use in data-intensive sensing applications with temporal constraints. To address this issue, we propose a rendezvous-based approach in which a subset of nodes serve as the rendezvous points (RPs) that buffer data originated from sources and transfer to MEs when they arrive. RPs enable MEs to collect a large volume of data at a time without traveling long distances, which can achieve a desirable balance between network energy saving and data collection delay. We develop two rendezvous planning algorithms, RP-CP and RP-UG. RP-CP finds the optimal RPs when MEs move along the data routing tree while RP-UG greedily chooses the RPs with maximum energy saving to travel distance ratios. We design the rendezvous-based data collection protocol that facilitates reliable data transfers from RPs to MEs in presence of significant unexpected delays in ME movement and network communication. Our approach is validated through extensive simulations.

Wireless sensor networks (WSNs) introduce new challenges to topology control due to the prevalence of lossy links. We propose a new topology control formulation for lossy WSNs that captures the stochastic nature of lossy links and quantifies the worst-case path quality in a network. We develop a novel localized scheme called Configurable Topology Control (CTC). The key feature of CTC is its capability of flexibly configuring the topology of a lossy WSN to achieve desired path quality bounds in a localized fashion. Furthermore, CTC can incorporate different control strategies (per-node/per-link) and optimization criteria. Simulations using a realistic radio model of Mica2 motes show that CTC significantly outperforms a representative traditional topology control algorithm called LMST in terms of both communication performance and energy efficiency. Our results demonstrate the importance of incorporating lossy links of WSNs in the design of topology control algorithms.

Radio power management is of paramount concern in wireless sensor networks that must achieve long lifetimes on scarce amounts of energy. While a multitude of power management protocols have been proposed in the past, the lack of system support for flexibly integrating them with a diverse set of applications and network platforms has made them diffcult to use. Instead of proposing yet another power management protocol, this paper focuses on providing link layer support towards realizing a Unified Power Management Architecture (UPMA) for flexible radio power management in wireless sensor networks. In contrast to the monolithic approaches adopted by existing power management solutions, we provide (1) a set of standard interfaces that allow different power management protocols existing at the link layer to be easily implemented on top of common MAC level functionality, (2) an architectural framework for enabling these protocols to be easily swapped in and out depending on the needs of the applications that require them, and (3) a mechanism for coordinating the existence of multiple applications, each of which may have different requirements for the same underlying power management protocol. We have implemented these features on the Mica2 and Telosb radio stacks in TinyOS-2.0. Microbenchmark results demonstrate that the separation of power management from MAC level functionality incurs a negligible decrease in performance when compared to existing monolithic implementations. Two case studies show that the power management requirements of multiple applications can be easily coordinated, sometimes even resulting in better power savings than any one of them can achieve individually.

Recently, several storage-centric wireless sensor networks (WSNs) have been developed to store massive sensor data in the network. A crucial task of these networks is to disseminate useful information to the users at dynamic temporal resolutions. We formulate the problem of dynamic multi-resolution data dissemination in this paper. We propose a novel solution called the Minimum Incremental Dissemination Tree (MIDT) that includes an online tree construction algorithm with analytical performance bound and two lightweight tree adaptation heuristics for handling data requests with dynamic temporal resolutions. Our simulations show that MIDT outperforms several typical data dissemination schemes. The two tree adaptation heuristics can effectively maintain desirable energy efficiency of the dissemination tree while minimizing the tree adaption overhead under representative traffic patterns in WSNs.

Research shows that significant energy saving can be achieved in wireless sensor networks by using mobile elements (MEs) capable of carrying data mechanically. However, the low movement speed of MEs hinders their use in data-intensive sensing applications with temporal constraints. To address this issue, we propose a rendezvous-based approach in which a subset of nodes serve as the rendezvous points (RPs) that buffer data originated from sources and transfer to MEs when they arrive. RPs enable MEs to collect a large volume of data at a time without traveling long distances, which can achieve a desirable balance between network energy saving and data collection delay. We develop two rendezvous planning algorithms, RP-CP and RP-UG. RP-CP finds the optimal RPs when MEs move along the data routing tree while RP-UG greedily chooses the RPs with maximum energy saving to travel distance ratios. We design the rendezvous-based data collection protocol that facilitates reliable data transfers from RPs to MEs in presence of significant unexpected delays in ME movement and network communication. Our approach is validated through extensive simulations.

This article proposes the minimum power configuration (MPC) approach to power management in wireless sensor networks. In contrast to earlier research that treats different radio states (i.e., transmission/reception/idle) in isolation, MPC integrates them in a joint optimization problem that depends on both the set of active nodes and the transmission power. We propose four approximation algorithms with provable performance bounds and two practical routing protocols. Simulations based on realistic radio models show that the MPC approach can conserve more energy than existing minimum power routing and topology control protocols. Furthermore, it can flexibly adapt to network workload and radio platforms.

Many wireless sensor network applications must resolve the inherent conflict between energy efficient communication and the need to achieve desired quality of service such as end-to-end communication delay. To address this challenge, we propose the Real-time Power-Aware Routing (RPAR) protocol, which achieves application-specified communication delays at low energy cost by dynamically adapting transmission power and routing decisions. RPAR features a power-aware forwarding policy and an efficient neighborhood manager that are optimized for resource-constrained wireless sensors. Moreover, RPAR addresses important practical issues in wireless sensor networks, including lossy links, scalability, and severe memory and bandwidth constraints. Simulations based on a realistic radio model of MICA2 motes show that RPAR significantly reduces the number of deadlines missed and energy consumption compared to existing real-time and energy-efficient routing protocols.

No abstract available.

MOTIVATION: The identification of signatures of positive selection can provide important insights into recent evolutionary history in human populations. Current methods mostly rely on allele frequency determination or focus on one or a small number of candidate chromosomal regions per study. With the availability of large-scale genotype data, efficient approaches for an unbiased whole genome scan are becoming necessary. METHODS: We have developed a new method, the whole genome long-range haplotype test (WGLRH), which uses genome-wide distributions to test for recent positive selection. Adapted from the long-range haplotype (LRH) test, the WGLRH test uses patterns of linkage disequilibrium (LD) to identify regions with extremely low historic recombination. Common haplotypes with significantly longer than expected ranges of LD given their frequencies are identified as putative signatures of recent positive selection. In addition, we have also determined the ancestral alleles of SNPs by genotyping chimpanzee and gorilla DNA, and have identified SNPs where the non-ancestral alleles have risen to extremely high frequencies in human populations, termed 'flipped SNPs'. Combining the haplotype test and the flipped SNPs determination, the WGLRH test serves as an unbiased genome-wide screen for regions under putative selection, and is potentially applicable to the study of other human populations. RESULTS: Using WGLRH and high-density oligonucleotide arrays interrogating 116 204 SNPs, we rapidly identified putative regions of positive selection in three populations (Asian, Caucasian, African-American), and extended these observations to a fourth population, Yoruba, with data obtained from the International HapMap consortium. We mapped significant regions to annotated genes. While some regions overlap with genes previously suggested to be under positive selection, many of the genes have not been previously implicated in natural selection and offer intriguing possibilities for further study. AVAILABILITY: the programs for the WGLRH algorithm are freely available and can be downloaded at http://www.affymetrix.com/support/supplement/WGLRH_program.zip.

Greedy geographic routing is an attractive localized routing scheme for wireless sensor networks due to its efficiency and scalability. However, greedy geographic routing may fail due to routing voids on random network topologies. We study greedy geographic routing in an important class of wireless sensor networks (e.g., surveillance or object tracking systems) that provide sensing coverage over a geographic area. Our analysis and simulation results demonstrate that an existing geographic routing algorithm, greedy forwarding (GF), can successfully find short routing paths based on local states in sensing-covered networks. In particular, we derive theoretical upper bounds on the network dilation of sensing-covered networks under GF. We also propose a new greedy geographic routing algorithm called Bounded Voronoi Greedy Forwarding (BVGF) that achieves path dilation lower than 4.62 in sensing-covered networks as long as the communication range is at least twice the sensing range. Furthermore, we extend GF and BVGF to achieve provable performance bounds in terms of total number of transmissions and reliability in lossy networks.

This paper presents MobiQuery, a spatiotemporal query service that allows mobile users to periodically gather information from their surrounding areas through a wireless sensor network. A key advantage of MobiQuery lies in its capability to meet stringent spatiotemporal performance constraints crucial to many applications. These constraints include query latency, data freshness and fidelity, and changing query areas due to user mobility. A novel just-in-time prefetching algorithm enables MobiQuery to maintain robust spatiotemporal guarantees even when nodes operate under extremely low duty cycles. Furthermore, it significantly reduces the storage cost and network contention caused by continuous queries from mobile users. We validate our approach through both theoretical analysis and simulation results under a range of realistic settings.

Many mission-critical applications require spatiotemporal data services for mobile users or objects. Examples include distributed object tracking and fire monitoring by firefighters. To support such applications, wireless sensor networks must satisfy a set of stringent spatiotemporal constraints despite having low network duty cycles and scarce resources. We have developed two new wake-up and topology maintenance protocols, directional tree maintenance (DTM) and omnidirectional tree creation (OTC), to support spatiotemporal services in mobile environments. A key feature of our protocols is that they provide robust spatiotemporal performance while maintaining low overhead and energy consumption. Our simulations showed that both DTM and OTC can successfully deliver over 85% of query results to a mobile user within desired spatiotemporal constraints, even when the sleep schedule is as long as 15 s, the user changes direction every minute, and the location error is as high as 10 m. The benefits of our protocols have been validated through theoretical analysis and empirical results on a testbed of Mica2 motes.

This paper proposes the minimum power configuration (MPC) approach to energy conservation in wireless sensor networks. In sharp contrast to earlier research that treats topology control, power-aware routing, and sleep management in isolation, MPC integrates them as a joint optimization problem in which the power configuration of a network consists of a set of active nodes and the transmission powers of the nodes. We show through analysis that the minimum power configuration of a network is inherently dependent on the data rates of sources. We propose several approximation algorithms with provable performance bounds compared to the optimal solution, and a practical Minimum Power Configuration Protocol (MPCP) that can dynamically (re)configure a network to minimize the energy consumption based on current data rates. Simulations based on realistic radio models of the Mica2 motes show that MPCP can conserve significantly more energy than existing minimum power routing and topology control protocols.

An effective approach for energy conservation in wireless sensor networks is scheduling sleep intervals for extraneous nodes while the remaining nodes stay active to provide continuous service. For the sensor network to operate successfully, the active nodes must maintain both sensing coverage and network connectivity. Furthermore, the network must be able to configure itself to any feasible degree of coverage and connectivity in order to support different applications and environments with diverse requirements. This article presents the design and analysis of novel protocols that can dynamically configure a network to achieve guaranteed degrees of coverage and connectivity. This work differs from existing connectivity or coverage maintenance protocols in several key ways. (1) We present a Coverage Configuration Protocol (CCP) that can provide different degrees of coverage requested by applications. This flexibility allows the network to self-configure for a wide range of applications and (possibly dynamic) environments. (2) We provide a geometric analysis of the relationship between coverage and connectivity. This analysis yields key insights for treating coverage and connectivity within a unified framework; in sharp contrast to several existing approaches that address the two problems in isolation. (3) We integrate CCP with SPAN to provide both coverage and connectivity guarantees. (4) We propose a probabilistic coverage model and extend CCP to provide probabilistic coverage guarantees. We demonstrate the capability of our protocols to provide guaranteed coverage and connectivity configurations through both geometric analysis and extensive simulations.

Wireless sensor networks often face the critical challenge of sustaining long-term operation on limited battery energy. Coverage maintenance protocols can effectively prolong network lifetime by maintaining sufficient sensing coverage over a region using a small number of active nodes while scheduling the others to sleep. We present a novel distributed coverage maintenance protocol called the Coordinating Grid (Co-Grid). In contrast to existing coverage maintenance protocols which are based on simpler detection models, Co-Grid adopts a distributed detection model based on data fusion that is more consistent with many distributed sensing applications. Co-Grid organizes the network into coordinating fusion groups located on overlapping virtual grids. Through coordination among neighboring fusion groups, Co-Grid can achieve comparable number of active nodes as a centralized algorithm, while reducing the network (re-)configuration time by orders of magnitude. Co-Grid is especially suitable for large and energy-constrained sensor networks that require quick (re-)configuration in response to node failures and environmental changes. We validate our claims by both theoretical analysis and simulations.

Greedy geographic routing is attractive in wireless sensor networks due to its efficiency and scalability. However, greedy geographic routing may incur long routing paths or even fail due to routing voids on random network topologies. We study greedy geographic routing in an important class of wireless sensor networks that provide sensing coverage over a geographic area (e.g., surveillance or object tracking systems). Our geometric analysis and simulation results demonstrate that existing greedy geographic routing algorithms can successfully find short routing paths based on local states in sensing-covered networks. In particular, we derive theoretical upper bounds on the network dilation of sensing-covered networks under greedy geographic routing algorithms. Furthermore, we propose a new greedy geographic routing algorithm called Bounded Voronoi Greedy Forwarding (BVGF) that allows sensing-covered networks to achieve an asymptotic network dilation lower than 4:62 as long as the communication range is at least twice the sensing range. Our results show that simple greedy geographic routing is an effective routing scheme in many sensing-covered networks.

General purpose middleware has been shown to be effective off-the-shelf, in meeting diverse functional requirements for a wide range of distributed systems. However, middleware customization is necessary for many networked embedded systems because of the resource constraints in the networked nodes. We demonstrate that reduced middleware footprint can be achieved while maintaining real-time properties of applications running on such systems. We also give evidence that empirical measurement using a representative application is crucial to guide (1) selection of feature subsets from general purpose middleware and (2) trade-offs among different dimensions of design metrics including real-time, footprint, and portability.

No abstract available.

An effective approach for energy conservation in wireless sensor networks is scheduling sleep intervals for extraneous nodes, while the remaining nodes stay active to provide continuous service. For the sensor network to operate successfully, the active nodes must maintain both sensing coverage and network connectivity. Furthermore, the network must be able to configure itself to any feasible degrees of coverage and connectivity in order to support different applications and environments with diverse requirements. This paper presents the design and analysis of novel protocols that can dynamically configure a network to achieve guaranteed degrees of coverage and connectivity. This work differs from existing connectivity or coverage maintenance protocols in several key ways: 1) We present a Coverage Configuration Protocol (CCP) that can provide different degrees of coverage requested by applications. This flexibility allows the network to self-configure for a wide range of applications and (possibly dynamic) environments. 2) We provide a geometric analysis of the relationship between coverage and connectivity. This analysis yields key insights for treating coverage and connectivity in a unified framework: this is in sharp contrast to several existing approaches that address the two problems in isolation. 3) Finally, we integrate CCP with SPAN to provide both coverage and connectivity guarantees. We demonstrate the capability of our protocols to provide guaranteed coverage and connectivity configurations, through both geometric analysis and extensive simulations.