Effective sensor data processing is critical for cyber-physical and IoT systems but often requires specialized expertise. While Large Language Models (LLMs) show promise as autonomouscopilotsfor sensor processing, their capabilities remain underexplored. We introduce SensorBench, the first comprehensive benchmark for evaluating LLMs across diverse real-world sensor datasets and tasks. SensorBench evaluates three paradigms for leveraging LLMs in sensing tasks: Tool-Augmented Coding (TAC), Standalone Coding (SAC), and Direct Answer (DA). We evaluate 8 leading LLM variants, including 2 Large Reasoning Models (LRMs) and 2 domain-specific LLMs, providing a structured reference for absolute performance, latency, and resource requirements. Our analysis reveals that: (1) TAC significantly outperforms SAC and DA; (2) LLMs excel at simple tasks but consistently underperform domain experts on compositional tasks requiring parameter tuning and multi-step reasoning. (3) The reasoning mechanism introduced in LRMs does not yield substantial performance gains. To improve the performance, we explore four prompting strategies and fine-tuning approaches (using our newly released sensor-processing corpus). The results show that self-verification prompting proves most effective, outperforming other methods simultaneously in 48% of tasks, while fine-tuning yields marginal gains. Our analysis suggests that more sophisticated interaction frameworks, such as signal-level self-verification, may bridge the gap to human expert-level performance. This benchmark provides a foundation for evaluating and improving LLMs in sensing applications1.

We propose a neuro-symbolic approach for learning causal complex event models from multi-source data, integrating causal discovery and temporal logic. Given resource constraints, we employ signal-level fusion by averaging the data from different antennas of the same WiFi receiver, followed by downsampling to reduce computational overhead. We consider a dataset of WiFi Channel State Information capturing human activities alongside video data from which we extract atomic symbolic activities such as “moving the upper arm.” The extracted symbolic information is processed through LPCMCI (Latent PCMCI). This causal discovery method extends PCMCI (Peter and Clark Momentary Conditional Independence) to handle latent dependencies across multiple time steps while mitigating false discoveries due to auto-correlations. The resulting causal structure is then translated into a temporal logic formula, which serves as a symbolic constraint in a neuro-symbolic learning pipeline. To efficiently process and learn from these structured constraints under resource limitations, we leverage Spiking Neural Networks, which offer energy-efficient computation while preserving temporal dynamics.

The increasing deployment of deep neural networks (DNNs) in cyber-physical systems (CPS) enhances perception fidelity, but imposes substantial computational demands on execution platforms, posing challenges to real-time control deadlines. Traditional distributed CPS architectures typically favor on-device inference to avoid network variability and contention-induced delays on remote platforms. However, this design choice places significant energy and computational demands on the local hardware. In this work, we revisit the assumption that cloud-based inference is intrinsically unsuitable for latency-sensitive control tasks. We demonstrate that, when provisioned with high-throughput compute resources, cloud platforms can effectively amortize network and queueing delays, enabling them to match or surpass on-device performance for real-time decision-making. Specifically, we develop a formal analytical model that characterizes distributed inference latency as a function of the sensing frequency, platform throughput, network delay, and task-specific safety constraints. We instantiate this model in the context of emergency braking for autonomous driving and validate it through extensive simulations using real-time vehicular dynamics. Our empirical results identify concrete conditions under which cloud-based inference adheres to safety margins more reliably than its on-device counterpart. These findings challenge prevailing design strategies and suggest that the cloud is not merely a feasible option, but often the preferred inference location for distributed CPS architectures. In this light, the cloud is not as distant as traditionally perceived; in fact, it is closer than it appears.

Today's Internet of Things (IoT) has evolved from simple sensing and actuation devices to those with embedded processing and intelligent services, enabling rich collaborations between users and their devices. However, enabling such collaboration becomes challenging when transient devices need to interact with host devices in temporarily visited environments. In such cases, fine-grained access control policies are necessary to ensure secure interactions; however, manually implementing them is often impractical for non-expert users. Moreover, at run-time, the system must automatically configure the devices and enforce such fine-grained access control rules. Additionally, the system must address the heterogeneity of devices.

Spatiotemporal reasoning plays a key role in Cyber-Physical Systems (CPS). Despite advances in Large Language Models (LLMs) and Large Reasoning Models (LRMs), their capacity to reason about complex spatiotemporal signals remains underexplored. This paper proposes a hierarchical SpatioTemporal reAsoning benchmaRK, STARK, to systematically evaluate LLMs across three levels of reasoning complexity: state estimation (e.g., predicting field variables, localizing and tracking events in space and time), spatiotemporal reasoning over states (e.g., inferring spatial-temporal relationships), and world-knowledge-aware reasoning that integrates contextual and domain knowledge (e.g., intent prediction, landmark-aware navigation). We curate 26 distinct spatiotemporal tasks with diverse sensor modalities, comprising 14,552 challenges where models answer directly or by Python Code Interpreter. Evaluating 3 LRMs and 8 LLMs, we find LLMs achieve limited success in tasks requiring geometric reasoning (e.g., multilateration or triangulation), particularly as complexity increases. Surprisingly, LRMs show robust performance across tasks with various levels of difficulty, often competing or surpassing traditional first-principle-based methods. Our results show that in reasoning tasks requiring world knowledge, the performance gap between LLMs and LRMs narrows, with some LLMs even surpassing LRMs. However, the LRM o3 model continues to achieve leading performance across all evaluated tasks, a result attributed primarily to the larger size of the reasoning models. STARK motivates future innovations in model architectures and reasoning paradigms for intelligent CPS by providing a structured framework to identify limitations in the spatiotemporal reasoning of LLMs and LRMs.

Multimodal deep learning systems are deployed in dynamic scenarios due to the robustness afforded by multiple sensing modalities. Nevertheless, they struggle with varying compute resource availability (due to multi-tenancy, device heterogeneity, etc.) and fluctuating quality of inputs (from sensor feed corruption, environmental noise, etc.). Statically provisioned multimodal systems cannot adapt when compute resources change over time, while existing dynamic networks struggle with strict compute budgets. Additionally, both systems often neglect the impact of variations in modality quality. Consequently, modalities suffering substantial corruption may needlessly consume resources better allocated towards other modalities. We propose ADMN, a layer-wise Adaptive Depth Multimodal Network capable of tackling both challenges - it adjusts the total number of active layers across all modalities to meet compute resource constraints, and continually reallocates layers across input modalities according to their modality quality. Our evaluations showcase ADMN can match the accuracy of state-of-the-art networks while reducing up to 75% of their floating-point operations.

Synthesizing radio-frequency (RF) data given the transmitter and receiver positions, e.g., received signal strength indicator (RSSI), is critical for wireless networking and sensing applications, such as indoor localization. However, it remains challenging due to complex propagation interactions, including reflection, diffraction, and scattering. State-of-the-art neural radiance field (NeRF)-based methods achieve high-fidelity RF data synthesis but are limited by long training times and high inference latency. We introduce GSRF, a framework that extends 3D Gaussian Splatting (3DGS) from the optical domain to the RF domain, enabling efficient RF data synthesis. GSRF realizes this adaptation through three key innovations: First, it introduces complex-valued 3D Gaussians with a hybrid Fourier–Legendre basis to model directional and phase-dependent radiance. Second, it employs orthographic splatting for efficient ray–Gaussian intersection identification. Third, it incorporates a complex-valued ray tracing algorithm, executed on RF-customized CUDA kernels and grounded in wavefront propagation principles, to synthesize RF data in real time. Evaluated across various RF technologies, GSRF preserves high-fidelity RF data synthesis while achieving significant improvements in training efficiency, shorter training time, and reduced inference latency.

In this paper, we explore how humans communicate with teammates from an explainable-AI perspective, comparing how they interact with both human and AI-controlled robot teammates under a number of different strategies in scenarios such as disaster relief. We find that while humans do adapt their communication based on which strategy they are following, they consistently communicate differently with AI teammates than human teammates, tending to give explicit orders to the former while sending more vague messages with implicit understanding to the later. However, we also find that modern Large Language Models (LLMs) are capable of understanding the explainability intent of messages to the same level as humans, implying that such differences may not be required if LLMs are fully integrated into AI agents.

The integration of Large Language Models (LLMs) with mobile devices is set to transform mental health care accessibility and quality. This paper introduces MindGuard, an autonomous LLM agent that utilizes mobile sensor data and engages in proactive, personalized conversations while ensuring user privacy through local processing. Unlike traditional mental health AI tools, MindGuard enables real-time, context-aware interventions by dynamically adapting to users' emotional and physiological states. The real-world implementation demonstrates its effectiveness with the ultimate goal of creating an accessible, scalable, and personalized mental healthcare ecosystem for anyone with smart mobile devices.

Detecting context shifts in human experience is critical for applications in cognitive modeling, human-AI interaction, and adaptive neurotechnology. However, formalizing and identifying these shifts in real-world settings remains challenging due to annotation inconsistencies, data sparsity, and the multimodal nature of human perception.

Multimodal sensing systems are increasingly prevalent in various real-world applications. Most existing multimodal learning approaches heavily rely on training with a large amount of synchronized, complete multimodal data. However, such a setting is impractical in real-world IoT sensing applications where data is typically collected by distributed nodes with heterogeneous data modalities, and is also rarely labeled. In this paper, we propose MMBind, a new data binding approach for multimodal learning on distributed and heterogeneous IoT data. The key idea of MMBind is to construct a pseudo-paired multimodal dataset for model training by binding data from disparate sources and incomplete modalities through a sufficiently descriptive shared modality. We also propose a weighted contrastive learning approach to handle domain shifts among disparate data, coupled with an adaptive multimodal learning architecture capable of training models with heterogeneous modality combinations. Evaluations on ten real-world multi-modal datasets highlight that MMBind outperforms state-of-the-art baselines under varying degrees of data incompleteness and domain shift, and holds promise for advancing multimodal foundation model training in IoT applications1.

Many emerging IoT devices are mobile, enabling them to visit new environments and networks beyond their home networks. Mobile devices often have to interact and collaborate with users and their devices, which belong to the different administrative environments they are temporarily visiting. In this paper, we envision a system for seamless collaboration among transient devices in IoT environments. The system is based on zero-conf collaboration and allows for fine-grained access control. Our proposed design supports hardware-independent interfaces and supports a large number of devices.

Effective communication and sensing in next-generation wireless technologies require resource-intensive site surveys for data collection. These surveys capture key RF characteristics at various positions, including the Received Signal Strength Indicator (RSSI) and spatial spectrum (RSSI measured from all directions around the receiver). An alternative approach is Radio-Frequency (RF) signal spatial propagation modeling, which predicts received signals based on transceiver positions. Existing Neural Radiance Field (NeRF)-based methods exhibit a fundamental trade-off between scalability and fidelity. To address this challenge, we explore leveraging 3D Gaussian Splatting, an advanced technique for real-time image synthesis of 3D scenes from arbitrary camera poses. This work develops RFSPM, an end-to-end 3D Gaussian distribution-based framework for scalable RF signal Spatial Propagation Modeling. We evaluate RFSPM in spatial spectrum synthesis, demonstrating its learning efficiency compared to NeRF-based methods.

Standard multimodal self-supervised learning (SSL) algorithms regard cross-modal synchronization as implicit supervisory labels during pretraining, thus posing high requirements on the scale and quality of multimodal samples. These constraints significantly limit the performance of sensing intelligence in IoT applications, as the heterogeneity and the non-interpretability of time-series signals result in abundant unimodal data but scarce high-quality multimodal pairs. This paper proposes InfoMAE, a cross-modal alignment framework that tackles the challenge of multimodal pair efficiency under the SSL setting by facilitating efficient cross-modal alignment of pretrained unimodal representations. InfoMAE achieves efficient cross-modal alignment with limited data pairs through a novel information theory-inspired formulation that simultaneously addresses distribution-level and instance-level alignment. Extensive experiments on two real-world IoT applications are performed to evaluate InfoMAE's pairing efficiency to bridge pretrained unimodal models into a cohesive joint multimodal model. InfoMAE enhances downstream multimodal tasks by over 60% with significantly improved multimodal pairing efficiency. It also improves unimodal task accuracy by an average of 22%.

The integration of the Internet of Things (IoT) and modern Artificial Intelligence (AI) has given rise to a new paradigm known as the Artificial Intelligence of Things (AIoT). In this survey, we provide a systematic and comprehensive review of AIoT research. We examine AIoT literature related to sensing, computing, and networking & communication, which form the three key components of AIoT. In addition to advancements in these areas, we review domain-specific AIoT systems that are designed for various important application domains. We have also created an accompanying GitHub repository, where we compile the papers included in this survey: https://github.com/AIoT-MLSys-Lab/AIoT-Survey. This repository will be actively maintained and updated with new research as it becomes available. As both IoT and AI become increasingly critical to our society, we believe that AIoT is emerging as an essential research field at the intersection of IoT and modern AI. It is our hope that this survey will serve as a valuable resource for those engaged in AIoT research and act as a catalyst for future explorations to bridge gaps and drive advancements in this exciting field.

Teams of embodied AI-enabled agents are critical for applications in extreme and highly dynamic environments. Developing robust controllers for such agents requires a deep understanding of the challenges encountered when attempting to coordinate and synchronize their individual perception-cognition-communication-action (PCCA) loops for team-wide mission objectives. We introduce a framework to explore the coordination of the PCCA loops across multiple agents in a new simulated physical environment designed to explore collaboration in each PCCA stage. This environment tasks teams of agents with the correct disposal of dangerous objects in an area and forces careful coordination of sensing, communication, movement, and manipulation actions by providing spatially-bounded communication, incorporating situations that require concerted effort by groups of agents, and introducing uncertainty into agents’ sensing capabilities. We provide a set of heuristic controllers, an offline oracle model, and an initial exploration of a Reward Machine-based controller that learns its policies from training. Together these approaches serve to provide insights into the complexity of the multi-agent PCCA loop coordination problem. The multiagent PCCA simulation environment, which supports AI and human-controlled agents, and the code for various agent controllers are available at https://github.com/nesl/AI-Collab.

Wi-Fi devices, akin to passive radars, can discern human activities within indoor settings due to the human body’s interaction with electromagnetic signals. Current Wi-Fi sensing applications predominantly employ data-driven learning techniques to associate the fluctuations in the physical properties of the communication channel with the human activity causing them. However, these techniques often lack the desired flexibility and transparency. This paper introduces DeepProbHAR, a neuro-symbolic architecture for Wi-Fi sensing, providing initial evidence that Wi-Fi signals can differentiate between simple movements, such as leg or arm movements, which are integral to human activities like running or walking. The neuro-symbolic approach affords gathering such evidence without needing additional specialised data collection or labelling. The training of DeepProbHAR is facilitated by declarative domain knowledge obtained from a camera feed and by fusing signals from various antennas of the Wi-Fi receivers. DeepProbHAR achieves results comparable to the state-of-the-art in human activity recognition. Moreover, as a by-product of the learning process, DeepProbHAR generates specialised classifiers for simple movements that match the accuracy of models trained on finely labelled datasets, which would be particularly costly.

Localization is a critical technology for various applications ranging from navigation and surveillance to assisted living. Localization systems typically fuse information from sensors viewing the scene from different perspectives to estimate the target location while also employing multiple modalities for enhanced robustness and accuracy. Recently, such systems have employed end-to-end deep neural models trained on large datasets due to their superior performance and ability to handle data from diverse sensor modalities. However, such neural models are often trained on data collected from a particular set of sensor poses (i.e., locations and orientations). During real-world deployments, slight deviations from these sensor poses can result in extreme inaccuracies. To address this challenge, we introduce FlexLoc, which employs conditional neural networks to inject node perspective information to adapt the localization pipeline. Specifically, a small subset of model weights are derived from node poses at run time, enabling accurate generalization to unseen perspectives with minimal additional overhead. Our evaluations on a multimodal, multi-view indoor tracking dataset showcase that FlexLoc improves the localization accuracy by almost 50% in the zero-shot case (no calibration data available) compared to the baselines. The source code of FlexLoc is available in https://github.com/nesl/FlexLoc.

Mobile devices with dynamic refresh rate (DRR) switching displays have recently become increasingly common. For power optimization, these devices switch to lower refresh rates when idling, and switch to higher refresh rates when the content displayed requires smoother transitions. However, the security and privacy vulnerabilities of DRR switching have not been investigated properly. In this paper, we propose a novel attack vector called RefreshChannels that exploits DRR switching capabilities for mobile device attacks. Specifically, we first create a covert channel between two colluding apps that are able to stealthily share users' private information by modulating the data with the refresh rates, bypassing the OS sandboxing and isolation measures. Second, we further extend its applicability by creating a covert channel between a malicious app and either a phishing webpage or a malicious advertisement on a benign webpage. Our extensive evaluations on five popular mobile devices from four different vendors demonstrate the effectiveness and widespread impacts of these attacks. Finally, we investigate several countermeasures, such as restricting access to refresh rates, and find they are inadequate for thwarting RefreshChannels due to DDR's unique characteristics.

Machine learning at the extreme edge has enabled a plethora of intelligent, time-critical, and remote applications. However, deploying interpretable artificial intelligence systems that can perform high-level symbolic reasoning and satisfy the underlying system rules and physics within the tight platform resource constraints is challenging. In this paper, we introduce TinyNS, the first platform-aware neurosymbolic architecture search framework for joint optimization of symbolic and neural operators. TinyNS provides recipes and parsers to automatically write microcontroller code for five types of neurosymbolic models, combining the context awareness and integrity of symbolic techniques with the robustness and performance of machine learning models. TinyNS uses a fast, gradient-free, black-box Bayesian optimizer over discontinuous, conditional, numeric, and categorical search spaces to find the best synergy of symbolic code and neural networks within the hardware resource budget. To guarantee deployability, TinyNS talks to the target hardware during the optimization process. We showcase the utility of TinyNS by deploying microcontroller-class neurosymbolic models through several case studies. In all use cases, TinyNS outperforms purely neural or purely symbolic approaches while guaranteeing execution on real hardware.

We present a method for inferring dense depth from a camera image and a sparse noisy radar point cloud. We first describe the mechanics behind mmWave radar point cloud formation and the challenges that it poses, i.e. ambiguous elevation and noisy depth and azimuth components that yields incorrect positions when projected onto the image, and how existing works have overlooked these nuances in camera-radar fusion. Our approach is motivated by these mechanics, leading to the design of a network that maps each radar point to the possible surfaces that it may project onto in the image plane. Unlike existing works, we do not process the raw radar point cloud as an erroneous depth map, but query each raw point independently to associate it with likely pixels in the image – yielding a semi-dense radar depth map. To fuse radar depth with an image, we propose a gated fusion scheme that accounts for the confidence scores of the correspondence so that we selectively combine radar and camera embeddings to yield a dense depth map. We test our method on the NuScenes benchmark and show a 10.3% improvement in mean absolute error and a 9.1% improvement in root-mean-square error over the best method. Code: https://github.com/nesl/radar-camera-fusion-depth.

Wi-Fi devices can effectively be used as passive radar systems that sense what happens in the surroundings and can even discern human activity. We propose, for the first time, a principled architecture which employs Variational Auto-Encoders for estimating a latent distribution responsible for generating the data, and Evidential Deep Learning for its ability to sense out-of-distribution activities. We verify that the fused data processed by different antennas of the same Wi-Fi receiver results in increased accuracy of human activity recognition compared with the most recent benchmarks, while still being informative when facing out-of-distribution samples and enabling semantic interpretation of latent variables in terms of physical phenomena. The results of this paper are a first contribution toward the ultimate goal of providing a flexible, semantic characterisation of black-swan events, i.e., events for which we have limited to no training data.

Precision agricultural robots require high-resolution navigation solutions. In this paper, we introduce a robust neural-inertial sequence learning approach to track such robots with ultra-intermittent GNSS updates. First, we propose an ultra-lightweight neural-Kalman filter that can track agricultural robots within 1.4 m (1.4–5.8× better than competing techniques), while tracking within 2.75 m with 20 mins of GPS outage. Second, we introduce a user-friendly video-processing toolbox to generate high-resolution (±5 cm) position data for fine-tuning pre-trained neural-inertial models in the field. Third, we introduce the first and largest (6.5 hours, 4.5 km, 3 phases) public neural-inertial navigation dataset for precision agricultural robots. The dataset, toolbox, and code are available at: https://github.com/nesl/agrobot.

This paper proposes a novel contrastive learning framework, called FOCAL, for extracting comprehensive features from multimodal time-series sensing signals through self-supervised training. Existing multimodal contrastive frameworks mostly rely on the shared information between sensory modalities, but do not explicitly consider the exclusive modality information that could be critical to understanding the underlying sensing physics. Besides, contrastive frameworks for time series have not handled the temporal information locality appropriately. FOCAL solves these challenges by making the following contributions: First, given multimodal time series, it encodes each modality into a factorized latent space consisting of shared features and private features that are orthogonal to each other. The shared space emphasizes feature patterns consistent across sensory modalities through a modal-matching objective. In contrast, the private space extracts modality-exclusive information through a transformation-invariant objective. Second, we propose a temporal structural constraint for modality features, such that the average distance between temporally neighboring samples is no larger than that of temporally distant samples. Extensive evaluations are performed on four multimodal sensing datasets with two backbone encoders and two classifiers to demonstrate the superiority of FOCAL. It consistently outperforms the state-of-the-art baselines in downstream tasks with a clear margin, under different ratios of available labels. The code and self-collected dataset are available at https://github.com/tomoyoshki/focal.

Visual object tracking has seen significant progress in recent years. However, the vast majority of this work focuses on tracking objects within the image plane of a single camera and ignores the uncertainty associated with predicted object locations. In this work, we focus on the geospatial object tracking problem using data from a distributed camera network. The goal is to predict an object’s track in geospatial coordinates along with uncertainty over the object’s location while respecting communication constraints that prohibit centralizing raw image data. We present a novel single-object geospatial tracking data set that includes high-accuracy ground truth object locations and video data from a network of four cameras. We present a modeling framework for addressing this task including a novel backbone model and explore how uncertainty calibration and fine-tuning through a differentiable tracker affect performance.

Wearable devices like smartwatches and smart wristbands have gained substantial popularity in recent years. However, their small interfaces create inconvenience and limit computing functionality. To fill this gap, we propose ViWatch, which enables robust finger interactions under deployment variations, and relies on a single IMU sensor that is ubiquitous in COTS smartwatches. To this end, we design an unsupervised Siamese adversarial learning method. We built a real-time system on commodity smartwatches and tested it with over one hundred volunteers. Results show that the system accuracy is about 97% over a week. In addition, it is resistant to deployment variations such as different hand shapes, finger activity strengths, and smartwatch positions on the wrist. We also developed a number of mobile applications using our interactive system and conducted a user study where all participants preferred our un-supervised approach to supervised calibration. The demonstration of ViWatch is shown at https://youtu.be/N5-ggvy2qfI.

Detecting complex events from subsymbolic data streams (such as images, audio recordings or videos) is a challenging problem, as traditional symbolic approaches cannot be used to process subsymbolic data, and neural-only approaches usually require larger amounts of training data than available. In this paper, we present DeepProbCEP, a Complex Event Processing (CEP) approach designed with four objectives: (i) allowing the use of subsymbolic data as an input, (ii) retaining flexibility and modularity in the definition of complex event rules, (iii) limiting the cost of obtaining training data and (iv) being robust against adversarial conditions. DeepProbCEP archives this by using a neuro-symbolic approach, which combines the neural and symbolic approaches to allow training with sparse data. This is made possible through the injection of human knowledge. In this paper, we demonstrate that DeepProbCEP outperforms other state-of-the-art approaches when training using sparse data. We also show that DeepProbCEP is robust in different adversarial settings. Finally, DeepProbCEP’s flexibility is demonstrated by showing it can be used to process both images and audio as input.

Recent years have seen the increasing attention and popularity of federated learning (FL), a distributed learning framework for privacy and data security. However, by its fundamental design, federated learning is inherently vulnerable to model poisoning attacks: a malicious client may submit the local updates to influence the weights of the global model. Therefore, detecting malicious clients against model poisoning attacks in federated learning is useful in safety-critical tasks.However, existing methods either fail to analyze potential malicious data or are computationally restrictive. To overcome these weaknesses, we propose a robust federated learning method where the central server learns a supervised anomaly detector using adversarial data generated from a variety of state-of-the-art poisoning attacks. The key idea of this powerful anomaly detector lies in a comprehensive understanding of the benign update through distinguishing it from the diverse malicious ones. The anomaly detector would then be leveraged in the process of federated learning to automate the removal of malicious updates (even from unforeseen attacks).Through extensive experiments, we demonstrate its effectiveness against backdoor attacks, where the attackers inject adversarial triggers such that the global model will make incorrect predictions on the poisoned samples. We have verified that our method can achieve 99.0% detection AUC scores while enjoying longevity as the model converges. Our method has also shown significant advantages over existing robust federated learning methods in all settings. Furthermore, our method can be easily generalized to incorporate newly-developed poisoning attacks, thus accommodating ever-changing adversarial learning environments.

With advancement of recent network and chip technologies, IoT devices are becoming smarter with increasing compute power, bandwidth, and storage available on the device. This enables intelligent decision making and information transferring on the devices and unleashes the power of AIoT (Artificial Intelligence of Things) that supports applications such as smart city/agriculture/manufacturing/health care and self-driving scenarios.

This paper presents AdaMask, a machine-centric video streaming framework for remote deep neural network (DNN) inference. The objective is to optimize the accuracy of downstream DNNs, offloaded to a remote machine, by adaptively changing video compression control knobs at runtime. Our main contributions are twofold. First, we propose frame masking as an effective mechanism to reduce the bandwidth consumption of video stream, which only preserves regions that potentially contain objects of interest. Second, we design a new adaptation algorithm that achieves the Pareto-optimal tradeoff between accuracy and bandwidth by controlling the masked portions of frames together with conventional H.264 control knobs (eg. resolution). Through extensive evaluations on three sensing scenarios (dash camera, traffic surveillance, and drone), frame masking saves the bandwidth by up to 65% with < 1% accuracy degradation, and AdaMask improves the accuracy by up to 14% over the baselines against the network dynamics.

Sensing and data collection platforms are the crucial components of high-quality datasets that can fuel advancements in research. However, such platforms usually are ad-hoc designs and are limited in sensor modalities. In this paper, we discuss our experience designing and deploying a multi-modal multi-node sensor data collection platform that can be utilized for various data collection tasks. The main goal of this platform is to create a modality-rich data collection platform suitable for Internet of Things (IoT) applications with easy reproducibility and deployment, which can accelerate data collection and downstream research tasks.

Video scene analysis is a well-investigated area where researchers have devoted efforts to detect and classify people and objects in the scene. However, real-life scenes are more complex: the intrinsic states of the objects (e.g., machine operating states or human vital signals) are often overlooked by vision-based scene analysis. Recent work has proposed a radio frequency (RF) sensing technique, wireless vibrometry, that employs wireless signals to sense subtle vibrations from the objects and infer their internal states. We envision that the combination of video scene analysis with wireless vibrometry form a more comprehensive understanding of the scene, namely "rich scene analysis". However, the RF sensors used in wireless vibrometry only provide time series, and it is challenging to associate these time series data with multiple real-world objects. We propose a real-time RF-vision sensor fusion system, Capricorn, that efficiently builds a cross-modal correspondence between visual pixels and RF time series to better understand the complex natures of a scene. The vision sensors in Capricorn model the surrounding environment in 3D and obtain the distances of different objects. In the RF domain, the distance is proportional to the signal time-of-flight (ToF), and we can leverage the ToF to separate the RF time series corresponding to each object. The RF-vision sensor fusion in Capricorn brings multiple benefits. The vision sensors provide environmental contexts to guide the processing of RF data, which helps us select the most appropriate algorithms and models. Meanwhile, the RF sensor yields additional information that is originally invisible to vision sensors, providing insight into objects' intrinsic states. Our extensive evaluations show that Capricorn real-timely monitors multiple appliances' operating status with an accuracy of 97%+ and recovers vital signals like respirations from multiple people. A video (https://youtu.be/b-5nav3Fi78) demonstrates the capability of Capricorn.

Intelligent systems commonly employ vision sensors like cameras to analyze a scene. Recent work has proposed a wireless sensing technique, wireless vibrometry, to enrich the scene analysis generated by vision sensors. Wireless vibrometry employs wireless signals to sense subtle vibrations from the objects and infer their internal states. However, it is difficult for pure Radio-Frequency (RF) sensing systems to obtain objects' visual appearances (e.g., object types and locations), especially when an object is inactive. Thus, most existing wireless vibrometry systems assume that the number and the types of objects in the scene are known. The key to getting rid of these presumptions is to build a connection between wireless sensor time series and vision sensor images. We present Capricorn, a vision-guided wireless vibrometry system. In Capricorn, the object type information from vision sensors guides the wireless vibrometry system to select the most appropriate signal processing pipeline. The object tracking capability in computer vision also helps wireless systems efficiently detect and separate vibrations from multiple objects in real time.

Printers have become ubiquitous in modern office spaces, and their placement in these spaces been guided more by accessibility than security. Due to the proximity of printers to places with potentially high-stakes information, the possible misuse of these devices is concerning. We present a previously unexplored covert channel that effectively uses the sound generated by printers with inkjet technology to exfiltrate arbitrary sensitive data (unrelated to the apparent content of the document being printed) from an air-gapped network. We also discuss a series of defense techniques that can make these devices invulnerable to covert manipulation. The proposed covert channel works by malware installed on a computer with access to a printer, injecting certain imperceptible patterns into all documents that applications on the computer send to the printer. These patterns can control the printing process without visibly altering the original content of a document, and generate acoustic signals that a nearby acoustic recording device, such as a smartphone, can capture and decode. To prove and analyze the capabilities of this new covert channel, we carried out tests considering different types of document layouts and distances between the printer and recording device. We achieved a bit error ratio less than 5% and an average bit rate of approximately 0.5 bps across all tested printers at distances up to 4 m, which is sufficient to extract tiny bits of information.

Public release of wrist-worn motion sensor data is growing. They enable and accelerate research in developing new algorithms to passively track daily activities, resulting in improved health and wellness utilities of smartwatches and activity trackers. But, when combined with sensitive attribute inference attack and linkage attack via re-identification of the same user in multiple datasets, undisclosed sensitive attributes can be revealed to unintended organizations with potentially adverse consequences for unsuspecting data contributing users. To guide both users and data collecting researchers, we characterize the re-identification risks inherent in motion sensor data collected from wrist-worn devices in users' natural environment. For this purpose, we use an open-set formulation, train a deep learning architecture with a new loss function, and apply our model to a new data set consisting of 10 weeks of daily sensor wearing by 353 users. We find that re-identification risk increases with an increase in the activity intensity. On average, such risk is 96% for a user when sharing a full day of sensor data.

Owing to a need for low latency data accesses, emerging IoT and mobile applications commonly require distributed data stores (e.g., key-value or KV stores) to operate entirely at the network's edge. Unfortunately, existing KV stores employ randomized data placement policies (e.g., consistent hashing) that ignore the client mobility and resulting variance in client-server latencies that are inherent to edge applications---the effect is largely suboptimal and inefficient data placement. We present Portkey, a distributed KV store that dynamically adapts data placement according to time-varying client mobility and data access patterns. The key insight with Portkey is to lean into the inherent mobility and prioritize rapid but approximate placement decisions over delayed optimal ones. Doing so enables the efficient tracking of client-server latencies despite edge resource constraints, and the use of greedy placement heuristics that are self-correcting over short timescales. Results with a realistic autonomous vehicle dataset and two small-scale deployments reveal that Portkey reduces average and tail request latency by 21-82% and 26-77% compared to existing placement strategies.

The increased availability of on-body sensors gives researchers access to rich time-series data, many of which are related to human health conditions. Sharing such data can allow cross-institutional collaborations that create advanced data-driven models to make inferences on human well-being. However, such data are usually considered privacy-sensitive, and publicly sharing this data may incur significant privacy concerns. In this work, we seek to protect clinical time-series data against membership inference attacks, while maximally retaining the data utility. We achieve this by adding an imperceptible noise to the raw data. Known as adversarial perturbations, the noise is specially trained to force a deep learning model to make inference mistakes (in our case, mispredicting user identities). Our preliminary results show that our solution can better protect the data from membership inference attacks than the baselines, while succeeding in all the designed data quality checks.

With advancement of recent network and chip technologies, IoT devices are becoming smarter with increasing compute power, bandwidth, and storage available on the device. This enables intelligent decision making and information transferring on the devices and unleashes the power of AIoT (Artificial Intelligence of Things) that supports scenarios such as smart city/agriculture/manufacturing/health care and self-driving scenarios. The AIoT Workshop is a forum for researchers, scientists, engineers, and practitioners to share and learn AI powered IoT solutions. The AIoT is a multi-disciplinary area, which include but not limited to IoT, AI/ML, embedded systems, and networking. The 4th AIoT workshop will be hosted virtually in conjunction with the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2021). The workshop program consists of keynote(s), invited talks, accepted technical paper presentations, as well as an indoor location competition panel.

Tuning hyperparameters for machine learning algorithms is a tedious task, one that is typically done manually. To enable automated hyperparameter tuning, recent works have started to use techniques based on Bayesian optimization. However, to practically enable automated tuning for large scale machine learning training pipelines, significant gaps remain in existing libraries, including lack of abstractions, fault tolerance, and flexibility to support scheduling on any distributed computing framework. To address these challenges, we present Mango, a Python library for parallel hyperparameter tuning. Mango enables the use of any distributed scheduling framework, implements intelligent parallel search strategies, and provides rich abstractions for defining complex hyperparameter search spaces that are compatible with scikit-learn. Mango is comparable in performance to Hyperopt [1], another widely used library. Mango is available open-source [2] and is currently used in production at Arm Research to provide state-of-art hyperparameter tuning capabilities.

Explaining the inner workings of deep neural network models have received considerable attention in recent years. Researchers have attempted to provide human parseable explanations justifying why a model performed a specific classification. Although many of these toolkits are available for use, it is unclear which style of explanation is preferred by end-users, thereby demanding investigation. We performed a cross-analysis Amazon Mechanical Turk study comparing the popular state-of-the-art explanation methods to empirically determine which are better in explaining model decisions. The participants were asked to compare explanation methods across applications spanning image, text, audio, and sensory domains. Among the surveyed methods, explanation-by-example was preferred in all domains except text sentiment classification, where LIME's method of annotating input text was preferred. We highlight qualitative aspects of employing the studied explainability methods and conclude with implications for researchers and engineers that seek to incorporate explanations into user-facing deployments.

An ability to detect, classify, and locate complex acoustic events can be a powerful tool to help smart systems build context-awareness, e.g., to make rich inferences about human behaviors in physical spaces. Conventional methods to measure acoustic signals employ microphones as sensors. As signals from multiple acoustic sources are blended during propagation to a sensor, such methods impose a dual challenge of separating the signal for an acoustic event from background noise and from other acoustic events of interest. Recent research has proposed using radio-frequency (RF) signals, e.g., Wi-Fi and millimeter-wave (mmWave), to sense sound directly from source vibrations. Whereas these works allow separating an acoustic event from background noise, they cannot monitor multiple sound sources simultaneously. In this paper, we present UWHear, a system that simultaneously recovers and separates sounds from multiple sources. Unlike previous works using continuous-wave RF, UWHear employs Impulse Radio Ultra-Wideband (IR-UWB) technology, in order to construct an enhanced audio sensing system tackling the above challenges. Further, IR-UWB radios can penetrate light building materials, which enables UWHear to operate in some non-line-of-sight (NLOS) conditions. In addition to providing a theoretical guarantee for audio recovery using RF pulses, we also implement an audio sensing prototype exploiting a commercial-off-the-shelf IR-UWB radar. Our experiments show that UWHear can effectively separate the content of two speakers that are placed only 25cm apart. UWHear can also capture and separate multiple sounds and vibrations of household appliances while being immune to non-target noise coming from other directions.

Despite the remarkable success in a broad set of sensing applications, state-of-the-art deep learning techniques struggle with complex reasoning tasks across a distributed set of sensors. Unlike recognizing transient complex activities (e.g., human activities such as walking or running) from a single sensor, detecting more complex events with larger spatial and temporal dependencies across multiple sensors is extremely difficult, e.g., utilizing a hospital's sensor network to detect whether a nurse is following a sanitary protocol as they traverse from patient to patient. Training a more complicated model requires a larger amount of data-which is unrealistic considering complex events rarely happen in nature. Moreover, neural networks struggle with reasoning about serial, aperiodic events separated by large quantities in the spatial-temporal dimensions.

We propose a new clock synchronization architecture for systems under time transfer attacks. Facilitated by a feedforward control with feedback trim --based clock adjustment, coupled with packet filtering and frequency shaping techniques, our proposed architecture bounds the clock errors in the presence of a powerful network attacker capable of attacking packets between a master and a client. A key advantage is consistent measurements, timely coordination, and synchronized actuation in distributed systems. In contrast, current time synchronization architectures behave poorly under attacks due to assumptions that the network is benign and delays are symmetric. The usage of feedback controllers aggravates poor performance. We provide an architecture that is indifferent to delays and eases the integration to traditional protocols. We implement a delay attack--resistant precision time protocol and validate the results on a hardware-supported testbed.

Pervasive sensing has enabled continuous monitoring of user physiological state through mobile and wearable devices, allowing for large scale user studies to be conducted, such as those found in mHealth. However, current mHealth studies are limited in their ability of allowing users to express their privacy preferences on the data they share across multiple entities involved in a research study. In this work, we present mPolicy, a privacy policy language for study participants to express the context-aware and data-handling policies needed for mHealth. In addition, we provide a privacy-adaptive policy creation mechanism for byproduct data (such as motion inferences). Lastly, we create a software library called privLib for implementing parsing, enforcement, and policy creation on byproduct data for mPolicy. We evaluate the latency overhead of these operations, and discuss future improvements for scaling to realistic mHealth scenarios.

Deep neural networks are vulnerable to adversarial examples, even in the black-box setting, where the attacker is restricted solely to query access. Existing black-box approaches to generating adversarial examples typically require a significant number of queries, either for training a substitute network or performing gradient estimation. We introduce GenAttack, a gradient-free optimization technique that uses genetic algorithms for synthesizing adversarial examples in the black-box setting. Our experiments on different datasets (MNIST, CIFAR-10, and ImageNet) show that GenAttack can successfully generate visually imperceptible adversarial examples against state-of-the-art image recognition models with orders of magnitude fewer queries than previous approaches. Against MNIST and CIFAR-10 models, GenAttack required roughly 2,126 and 2,568 times fewer queries respectively, than ZOO, the prior state-of-the-art black-box attack. In order to scale up the attack to large-scale high-dimensional ImageNet models, we perform a series of optimizations that further improve the query efficiency of our attack leading to 237 times fewer queries against the Inception-v3 model than ZOO. Furthermore, we show that GenAttack can successfully attack some state-of-the-art ImageNet defenses, including ensemble adversarial training and non-differentiable or randomized input transformations. Our results suggest that evolutionary algorithms open up a promising area of research into effective black-box attacks.

The state-of-art models for speech synthesis and voice conversion are capable of generating synthetic speech that is perceptually indistinguishable from bonafide human speech. These methods represent a threat to the automatic speaker verification (ASV) systems. Additionally, replay attacks where the attacker uses a speaker to replay a previously recorded genuine human speech are also possible. We present our solution for the ASVSpoof2019 competition, which aims to develop countermeasure systems that distinguish between spoofing attacks and genuine speeches. Our model is inspired by the success of residual convolutional networks in many classification tasks. We build three variants of a residual convolutional neural network that accept different feature representations (MFCC, Log-magnitude STFT, and CQCC) of input. We compare the performance achieved by our model variants and the competition baseline models. In the logical access scenario, the fusion of our models has zero t-DCF cost and zero equal error rate (EER), as evaluated on the development set. On the evaluation set, our model fusion improves the t-DCF and EER by 25% compared to the baseline algorithms. Against physical access replay attacks, our model fusion improves the baseline algorithms t-DCF and EER scores by 71% and 75% on the evaluation set, respectively.

An accurate sense of elapsed time is essential for the safe and correct operation of hardware, software, and networked systems. Unfortunately, an adversary can manipulate the system's time and violate causality, consistency, and scheduling properties of underlying applications. Although cryptographic techniques are used to secure data, they cannot ensure time security as securing a time source is much more challenging, given that the result of inquiring time must be delivered in a timely fashion. In this paper, we first describe general attack vectors that can compromise a system's sense of time. To counter these attacks, we propose a secure time architecture, TIMESEAL that leverages a Trusted Execution Environment (TEE) to secure time-based primitives. While CPU security features of TEEs secure code and data in protected memory, we show that time sources available in TEE are still prone to OS attacks. TIMESEAL puts forward a high-resolution time source that protects against the OS delay and scheduling attacks. Our TIMESEAL prototype is based on Intel SGX and provides sub-millisecond (msec) resolution as compared to 1-second resolution of SGX trusted time. It also securely bounds the relative time accuracy to msec under OS attacks. In essence, TIMESEAL provides the capability of trusted timestamping and trusted scheduling to critical applications in the presence of a strong adversary. It delivers all temporal use cases pertinent to secure sensing, computing, and actuating in networked systems.

Modern smartphones and smartwatches are equipped with inertial sensors (accelerometer, gyroscope, and magnetometer) that can be used for Human Activity Recognition (HAR) to infer tasks such as daily activities, transportation modes and, gestures. HAR requires collecting raw inertial sensor values and training a machine learning model on the collected data. The challenge in this approach is that the models are trained for specific devices and device configurations whereas, in reality, the set of devices carried by a person may vary over time. Ideally, one would like activity inferencing to be robust of this variation and provide accurate predictions by making opportunistic use of information from available devices. Moreover, the devices may be located at different parts of the body (e.g. pocket, left and right wrist), may have different sets of sensors (e.g. a smartwatch may not have gyroscope while a smartphone might), and may differ in sampling frequencies. In this paper, we provide a solution which makes use of the information from available devices while being robust to their variations. Instead of training an end-to-end model for every permutation of device combinations and configurations, we propose a scalable deep learning based solution in which each device learns its own sensor fusion model that maps the raw sensor values to a shared low dimensional latent space which we call the 'SenseHAR'-a virtual activity sensor. The virtual sensor has the same format and similar behavior regardless of the subset of devices, sensor's availability, sampling rate, or a device's location. This would help machine learning engineers to develop their application specific (e.g., from gesture recognition to activities of daily life) models in a hardware-agnostic manner based on this virtual activity sensor. Our evaluations show that an application model trained on SenseHAR achieves the state of the art accuracies of 95.32%, 74.22% and 93.13% on PAMAP2, Opportunity(gestures) and our collected datasets respectively.

Deep Neural Networks (DNNs) deliver state-of-the-art performance in many image recognition and understanding applications. However, despite their outstanding performance, these models are black-boxes and it is hard to understand how they make their decisions. Over the past few years, researchers have studied the problem of providing explanations of why DNNs predicted their results. However, existing techniques are either obtrusive, requiring changes in model training, or suffer from low output quality. In this paper, we present a novel method, NeuroMask, for generating an interpretable explanation of classification model results. When applied to image classification models, NeuroMask identifies the image parts that are most important to classifier results by applying a mask that hides/reveals different parts of the image, before feeding it back into the model. The mask values are tuned by minimizing a properly designed cost function that preserves the classification result and encourages producing an interpretable mask. Experiments using state-of-art Convolutional Neural Networks for image recognition on different datasets (CIFAR-10 and ImageNet) show that NeuroMask successfully localizes the parts of the input image which are most relevant to the DNN decision. By showing a visual quality comparison between NeuroMask explanations and those of other methods, we find NeuroMask to be both accurate and interpretable.

Deep learning models typically make inferences over transient features of the latent space, i.e., they learn data representations to make decisions based on the current state of the inputs over short periods of time. Such models would struggle with state-based events, or complex events, that are composed of simple events with complex spatial and temporal dependencies. In this paper, we propose DeepCEP, a framework that integrates the concepts of deep learning models with complex event processing engines to make inferences across distributed, multimodal information streams with complex spatial and temporal dependencies. DeepCEP utilizes deep learning to detect primitive events. A user can define a complex event to be detected as a particular sequence or pattern of primitive events as well as any other logical predicates that constrain the definition of such an event. The integration of human logic not only increases robustness and interpretability, but also greatly reduces the amount of training data required. Further, we demonstrate how the uncertainty of a model can be propagated throughout the complex event detection pipeline. Finally, we enumerate the future directions of research enabled by DeepCEP. In particular, we detail how an end-to-end training model for complex event processing with deep learning may be realized.

Machine learning has enabled many interesting applications and is extensively being used in big data systems. The popular approach - training machine learning models in frameworks like Tensorflow, Pytorch and Keras - requires movement of data from database engines to analytical engines, which adds an excessive overhead on data scientists and becomes a performance bottleneck for model training. In this demonstration, we give a practical exhibition of a solution for the enablement of distributed machine learning natively inside database engines. During the demo, the audience will interactively use Python APIs in Jupyter Notebooks to train multiple linear regression models on synthetic regression datasets and neural network models on vision and sensory datasets directly inside Teradata SQL Engine.

Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify.In the image domain, these perturbations are often virtually indistinguishable to human perception, causing humans and state-of-the-art models to disagree.However, in the natural language domain, small perturbations are clearly perceptible, and the replacement of a single word can drastically alter the semantics of the document.Given these challenges, we use a black-box population-based optimization algorithm to generate semantically and syntactically similar adversarial examples that fool well-trained sentiment analysis and textual entailment models with success rates of 97% and 70%, respectively.We additionally demonstrate that 92.3% of the successful sentiment analysis adversarial examples are classified to their original label by 20 human annotators, and that the examples are perceptibly quite similar.Finally, we discuss an attempt to use adversarial training as a defense, but fail to yield improvement, demonstrating the strength and diversity of our adversarial examples.We hope our findings encourage researchers to pursue improving the robustness of DNNs in the natural language domain.

In this paper we provide a critical analysis with metrics that will inform guidelines for designing distributed systems for Collective Situational Understanding (CSU). CSU requires both collective insight-i.e., accurate and deep understanding of a situation derived from uncertain and often sparse data and collective foresight-i.e., the ability to predict what will happen in the future. When it comes to complex scenarios, the need for a distributed CSU naturally emerges, as a single monolithic approach not only is unfeasible: it is also undesirable. We therefore propose a principled, critical analysis of AI techniques that can support specific tasks for CSU to derive guidelines for designing distributed systems for CSU.

Recent advances in Machine Learning (ML) have profoundly changed many detection, classification, recognition and inference tasks. Given the complexity of the battlespace, ML has the potential to revolutionise how Coalition Situation Understanding is synthesised and revised. However, many issues must be overcome before its widespread adoption. In this paper we consider two - interpretability and adversarial attacks. Interpretability is needed because military decision-makers must be able to justify their decisions. Adversarial attacks arise because many ML algorithms are very sensitive to certain kinds of input perturbations. In this paper, we argue that these two issues are conceptually linked, and insights in one can provide insights in the other. We illustrate these ideas with relevant examples from the literature and our own experiments.

An upcoming frontier for distributed computing might literally save lives in future military operations. In civilian scenarios, significant efficiencies were gained from interconnecting devices into networked services and applications that automate much of everyday life from smart homes to intelligent transportation. The ecosystem of such applications and services is collectively called the Internet of Things (IoT). Can similar benefits be gained in a military context by developing an IoT for the battlefield? This paper describes unique challenges in such a context as well as potential risks, mitigation strategies, and benefits.

Thanks to the adoption of more sensors in the automotive industry, context-aware Advanced Driver Assistance Systems (ADAS) become possible. On one side, a common thread in ADAS applications is to focus entirely on the context of the vehicle and its surrounding vehicles leaving the human (driver) context out of consideration. On the other side, and due to the increasing sensing capabilities in mobile phones and wearable technologies, monitoring complex human context becomes feasible which paves the way to develop driver-in-the-loop context-aware ADAS that provide personalized driving experience. In this paper, we propose Sentio1; a Reinforcement Learning based algorithm to enhance the Forward Collision Warning (FCW) system leading to Driver-in-the-Loop FCW system. Since the human driving preference is unknown a priori, varies between different drivers, and moreover, varies across time for the same driver, the proposed Sentio algorithm needs to take into account all these variabilities which are not handled by the standard reinforcement learning algorithms. We verified the proposed algorithm against several human drivers. Our evaluation, across distracted human drivers, shows a significant enhancement in driver experience---compared to standard FCW systems---reflected by an increase in the driver safety by 94.28%, an improvement in the driving experience by 20.97%, a decrease in the false negatives from 55.90% down to 3.26%, while adding less than 130 ms runtime execution overhead.

As the density of wireless, resource-constrained sensors grows, so does the need to choreograph their actions across both time and space. Recent advances in ultra-wideband RF communication have enabled accurate packet timestamping, which can be used to precisely synchronize time. Location may be further estimated by timing signal propagation, but this requires additional communication overhead to mitigate the effect of relative clock drift. This additional communication lowers overall channel efficiency and increases energy consumption. This article describes a novel approach to simultaneously localizing and time synchronizing networked mobile devices. An Extended Kalman Filter is used to estimate all devices’ positions and clock errors, and packet timestamps serve as measurements that constrain time and overall network geometry. By inspection of the uncertainty in our state estimate, we can adapt the number of messages sent in each communication round to balance accuracy with communication cost. This reduces communication overhead, which decreases channel congestion and power consumption compared to traditional time of arrival and time difference of arrival localization techniques. We demonstrate the performance and efficiency of our approach using a real network of custom RF devices and mobile quadrotors.

The use of smartphones and wearables as sensing devices has created innumerable context inference apps including a class of workout tracking apps. Workout data generated by mobile tracking apps can assist both users and physicians in achieving better health care, rehabilitation, and self-motivation. Previous approaches impose extra burdens on users by requiring users to select types of exercises or to start/stop sessions. In this paper, we propose MiLift, a practical end-to-end workout tracking system that performs automatic segmentation to remove user burdens. MiLift uses commercial off-the-shelf smartwatches to accurately and efficiently track both cardio and weightlifting workouts without manual inputs from users. For weightlifting tracking, MiLift supports both machine-based and free weight exercises, and proposes a lightweight repetition detection algorithm to ensure efficiency. A research study of 22 users shows that MiLift can achieve above 90 percent average precision and recall for cardio workout classification, weightlifting session detection, and weightlifting type classification. MiLift can also count repetitions of weightlifting exercises with an average error of 1.12 reps (out of an average of 9.65). Our empirical app study on a Moto 360 watch suggests that MiLift can extend watch battery lives by up to 8.25χ (19.13h) compared with previous approaches.

We present Timestamp Order Preserving (TOP), a replicated state machine (RSM) protocol that exploits the synchrony of networks to provide high performance. TOP uses physical timestamp of synchronized clock as a consistent total order to achieve consensus. It keeps estimating the bounds of network latency and offset of synchronized clock to deduce the commit time for each operation. It adopts speculative processing and reconciliation techniques to improve performance. To demonstrate its advantages, we implement a key-value data store that uses TOP for data replication. Through evaluations in a geo-deployed testbed, by comparing it with Primary-Copy and Quorum-Replication protocols, we demonstrate that TOP has a similar commit latency with a higher sustainable throughput. In addition, it processes operations in the order of submission timestamp, which provides a stricter form of consistency.

Light emanations from flat-panel displays are a side channel hinting towards the displayed content. Optical eavesdropping requires sensors in the proximity of such displays, necessitating physical access to the the target's environment. This requirement may be eliminated by exploiting the light sensor on the target's mobile device, though there are significant challenges. Such sensors measure one-dimensional light intensity, provide no chromatic information, and have very low sampling rate (normally up to 10Hz). In this paper, we demonstrate that in spite of these challenges, it is possible - based on intensity measurements from a mobile device's light sensor - to make quality inferences regarding the displayed content. We do so by selecting features of measured light that capture information related to transitions between samples. Such features are resilient to ambient noise. In our experiments, involving over 60 hours of collected data and 140 movie clips, we were able to (i) classify content into categories (game, movie, etc) with approximately 90% and 70% accuracy for two-class and four-class classification, respectively; and (ii) identify specific movies or TV programs being played with > 85% accuracy. These findings suggest that access to raw light-sensor readings, which can currently be done without special access controls, may carry nontrivial security ramifications.

This work considers that multiple mobile clients offload various continuous sensing applications with end-to-end delay constraints, to a cluster of machines as the server. Contention for shared computing resources on a server can result in delay degradation and application malfunction. We present ATOMS (Accurate Timing prediction and Offloading for Mobile Systems), a framework to mitigate multi-tenant resource contention and to improve delay using a two-phase Plan-Schedule approach. The planning phase includes methods to predict future workloads from all clients, to estimate contention, and to devise offloading schedule to reduce contention. The scheduling phase dispatches arriving offloaded workload to the server machine that minimizes contention, based on the running workloads on each machine.

Situational understanding (SU) requires a combination of insight - the ability to accurately perceive an existing situation - and foresight - the ability to anticipate how an existing situation may develop in the future. SU involves information fusion as well as model representation and inference. Commonly, heterogenous data sources must be exploited in the fusion process: often including both hard and soft data products. In a coalition context, data and processing resources will also be distributed and subjected to restrictions on information sharing. It will often be necessary for a human to be in the loop in SU processes, to provide key input and guidance, and to interpret outputs in a way that necessitates a degree of transparency in the processing: systems cannot be “black boxes”. In this paper, we characterize the Coalition Situational Understanding (CSU) problem in terms of fusion, temporal, distributed, and human requirements. There is currently significant interest in deep learning (DL) approaches for processing both hard and soft data. We analyze the state-of-the-art in DL in relation to these requirements for CSU, and identify areas where there is currently considerable promise, and key gaps.

This demo abstract presents PrOLoc, a localization system that combines partially homomorphic encryption with a new way of structuring the localization problem to enable efficient and accurate computation of a target's location while preserving the privacy of the observers.

Aided by advances in sensors and algorithms, systems for localizing and tracking target objects or events have become ubiquitous in recent years. Most of these systems operate on the principle of fusing measurements of distance and/or direction to the target made by a set of spatially distributed observers using sensors that measure signals such as RF, acoustic, or optical. The computation of the target's location is done using multilateration and multiangulation algorithms, typically running at an aggregation node that, in addition to the distance/direction measurements, also needs to know the observers' locations. This presents a privacy risk for an observer that does not trust the aggregation node or other observers and could in turn lead to lack of participation. For example, consider a crowd-sourced sensing system where citizens are required to report security threats, or a smart car, stranded with a malfunctioning GPS, sending out localization requests to neighboring cars - in both cases, observer (i.e., citizens and cars respectively) participation can be increased by keeping their location private. This paper presents PrOLoc, a localization system that combines partially homomorphic encryption with a new way of structuring the localization problem to enable efficient and accurate computation of a target's location without requiring observers to make public their locations or measurements. Moreover, and unlike previously proposed perturbation based techniques, PrOLoc is also resilient to malicious active false data injection attacks. We present two realizations of our approach, provide rigorous theoretical guarantees, and also compare the performance of each against traditional methods. Our experiments on real hardware demonstrate that PrOLoc yields location estimates that are accurate while being at least 500x faster than state-of-art secure function evaluation techniques.

Through the last decade, we have witnessed a surge of Internet of Things (IoT) devices, and with that a greater need to choreograph their actions across both time and space. Although these two problems, namely time synchronization and localization, share many aspects in common, they are traditionally treated separately or combined on centralized approaches that results in an inefficient use of resources, or in solutions that are not scalable in terms of the number of IoT devices. Therefore, we propose D-SLATS, a framework comprised of three different and independent algorithms to jointly solve time synchronization and localization problems in a distributed fashion. The first two algorithms are based mainly on the distributed Extended Kalman Filter (EKF) whereas the third one uses optimization techniques. No fusion center is required, and the devices only communicate with their neighbors. The proposed methods are evaluated on custom Ultra-Wideband communication Testbed and a quadrotor, representing a network of both static and mobile nodes. Our algorithms achieve up to three microseconds time synchronization accuracy and 30 cm localization error.

The development and validation studies of new multisensory biomarkers and sensor-triggered interventions requires collecting raw sensor data with associated labels in the natural field environment. Unlike platforms for traditional mHealth apps, a software platform for such studies needs to not only support high-rate data ingestion, but also share raw high-rate sensor data with researchers, while supporting high-rate sense-analyze-act functionality in real-time. We present mCerebrum, a realization of such a platform, which supports high-rate data collections from multiple sensors with realtime assessment of data quality. A scalable storage architecture (with near optimal performance) ensures quick response despite rapidly growing data volume. Micro-batching and efficient sharing of data among multiple source and sink apps allows reuse of computations to enable real-time computation of multiple biomarkers without saturating the CPU or memory. Finally, it has a reconfigurable scheduler which manages all prompts to participants that is burden- and context-aware. With a modular design currently spanning 23+ apps, mCerebrum provides a comprehensive ecosystem of system services and utility apps. The design of mCerebrum has evolved during its concurrent use in scientific field studies at ten sites spanning 106,806 person days. Evaluations show that compared with other platforms, mCerebrum's architecture and design choices support 1.5 times higher data rates and 4.3 times higher storage throughput, while causing 8.4 times lower CPU usage.

Today large amount of data is generated by cities. Many of the datasets are openly available and are contributed by different sectors, government bodies and institutions. The new data can affect our understanding of the issues faced by cities and can support evidence based policies. However usage of data is limited due to difficulty in assimilating data from different sources. Open datasets often lack uniform structure which limits its analysis using traditional database systems. In this paper we present Citadel, a data hub for cities. Citadel's goal is to support end to end knowledge discovery cyber-infrastructure for effective analysis and policy support. Citadel is designed to ingest large amount of heterogeneous data and supports multiple use cases by encouraging data sharing in cities. Our poster presents the proposed features, architecture, implementation details and initial results.

Tagging animals with sensors is a powerful approach to acquire critical information about the behavioural ecology of free-living animals, which ultimately can provide data to inform best practice in conservation efforts. Sensor tags for such deployments need long lifetimes and incorporate multiple sensors, especially location because space use can contextualize behavior. The tag size needs to be minimal so as not to affect the activities of the animal. Aquatic animals in particular present challenges due to lack of wireless communication and water-proofing. Taking these points into consideration we have designed Aquamote: an ultra-low power, tiny sensor tag (20 x 29 mm2) which integrates accelerometer, gyroscope, magnetometer, depth sensor, GPS and BLE. Our poster will showcase the performance of AquaMote and highlight our design decisions to reduce its size and power consumption.

Differential privacy concepts have been successfully used to protect anonymity of individuals in population-scale analysis. Sharing of mobile sensor data, especially physiological data, raise different privacy challenges, that of protecting private behaviors that can be revealed from time series of sensor data. Existing privacy mechanisms rely on noise addition and data perturbation. But the accuracy requirement on inferences drawn from physiological data, together with well-established limits within which these data values occur, render traditional privacy mechanisms inapplicable. In this work, we define a new behavioral privacy metric based on differential privacy and propose a novel data substitution mechanism to protect behavioral privacy. We evaluate the efficacy of our scheme using 660 hours of ECG, respiration, and activity data collected from 43 participants and demonstrate that it is possible to retain meaningful utility, in terms of inference accuracy (90%), while simultaneously preserving the privacy of sensitive behaviors.

Having a shared and accurate sense of time is critical to distributed Cyber-Physical Systems (CPS) and the Internet of Things (IoT). Thanks to decades of research in clock technologies and synchronization protocols, it is now possible to measure and synchronize time across distributed systems with unprecedented accuracy. However, applications have not benefited to the same extent due to limitations of the system services that help manage time, and hardware-OS and OS-application interfaces through which timing information flows to the application. Due to the importance of time awareness in a broad range of emerging applications, running on commodity platforms and operating systems, it is imperative to rethink how time is handled across the system stack. We advocate the adoption of a holistic notion of Quality of Time (QoT) that captures metrics such as resolution, accuracy, and stability. Building on this notion we propose an architecture in which the local perception of time is a controllable operating system primitive with observable uncertainty, and where time synchronization balances applications' timing demands with system resources such as energy and bandwidth. Our architecture features an expressive application programming interface that is centered around the abstraction of a timeline - a virtual temporal coordinate frame that is defined by an application to provide its components with a shared sense of time, with a desired accuracy and resolution. The timeline abstraction enables developers to easily write applications whose activities are choreographed across time and space. Leveraging open source hardware and software components, we have implemented an initial Linux realization of the proposed timeline-driven QoT stack on a standard embedded computing platform. Results from its evaluation are also presented.

Truth discovery is an important technique for enabling reliable crowdsourcing applications. It aims to automatically discover the truths from possibly conflicting crowdsourced claims. Most existing truth discovery approaches focus oncategoricalapplications, such as image classification. They use the accuracy, i.e., rate of exactly correct claims, to capture the reliability of participants. As a consequence, they are not effective for truth discovery inquantitativeapplications, such as percentage annotation and object counting, where similarity rather than exact matching between crowdsourced claims and latent truths should be considered. In this paper, we propose two unsupervised Quantitative Truth Finders (QTFs) for truth discovery in quantitative crowdsourcing applications. One QTF explores an additive model and the other explores a multiplicative model to capture different relationships between crowdsourced claims and latent truths in different classes of quantitative tasks. These QTFs naturally incorporate the similarity between variables. Moreover, they use the bias and the confidence instead of the accuracy to capture participants’ abilities in quantity estimation. These QTFs are thus capable of accurately discovering quantitative truths in particular domains. Through extensive experiments, we demonstrate that these QTFs outperform other state-of-the-art approaches for truth discovery in quantitative crowdsourcing applications and they are also quite efficient.

The ubiquity of smartphones has led to the emergence of mobile crowdsourcing tasks such as the detection of spatial events when smartphone users move around in their daily lives. However, the credibility of those detected events can be negatively impacted by unreliable participants with low-quality data. Consequently, a major challenge in mobile crowdsourcing is truth discovery, i.e., to discover true events from diverse and noisy participants’ reports. This problem is uniquely distinct from its online counterpart in that it involves uncertainties in both participants’mobilityandreliability. Decoupling these two types of uncertainties through location tracking will raise severe privacy and energy issues, whereas simply ignoring missing reports or treating them as negative reports will significantly degrade the accuracy of truth discovery. In this paper, we propose two new unsupervised models, i.e., Truth finder for Spatial Events (TSE) and Personalized Truth finder for Spatial Events (PTSE), to tackle this problem. In TSE, we model location popularity, location visit indicators, truths of events, and three-way participant reliability in a unified framework. In PTSE, we further model personal location visit tendencies. These proposed models are capable of effectively handling various types of uncertainties and automatically discovering truths without any supervision or location tracking. Experimental results on both real-world and synthetic datasets demonstrate that our proposed models outperform existing state-of-the-art truth discovery approaches in the mobile crowdsourcing environment.

To enable reliable crowdsourcing applications, it is of great importance to develop algorithms that can automatically discover the truths from possibly noisy and conflicting claims provided by various information sources. In order to handle crowdsourcing applications involving big or streaming data, a desirable truth discovery algorithm should not only beeffective, but also bescalable. However, with respect to quantitative crowdsourcing applications such as object counting and percentage annotation, existing truth discovery algorithms are not simultaneously effective and scalable. They either address truth discovery in categorical crowdsourcing or perform batch processing that does not scale. In this paper, we propose new parallel and streaming truth discovery algorithms for quantitative crowdsourcing applications. Through extensive experiments on real-world and synthetic datasets, we demonstrate that 1) both of them are quite effective, 2) the parallel algorithm can efficiently perform truth discovery on large datasets, and 3) the streaming algorithm processes data incrementally, and it can efficiently perform truth discovery both on large datasets and in data streams.

Embedded sensing systems are pervasively used in life- and security-critical systems such as those found in airplanes, automobiles, and healthcare. Traditional security mechanisms for these sensors focus on data encryption and other post-processing techniques, but the sensors themselves often remain vulnerable to attacks in the physical/analog domain. If an adversary manipulates a physical/analog signal prior to digitization, no amount of digital security mechanisms after the fact can help. Fortunately, nature imposes fundamental constraints on how these analog signals can behave. This work presents PyCRA, a physical challenge-response authentication scheme designed to protect active sensing systems against physical attacks occurring in the analog domain. PyCRA provides secure active sensing by continually challenging the surrounding environment via random but deliberate physical probes. By analyzing the responses to these probes, the system is able to ensure that the underlying physics involved are not violated, providing an authentication mechanism that not only detects malicious attacks but provides resilience against them. We demonstrate the effectiveness of PyCRA in detecting and mitigating attacks through several case studies using two sensing systems: (1) magnetic sensors like those found on gear and wheel speed sensors in robotics and automotive, and (2) commercial Radio Frequency Identification (RFID) tags used in many security-critical applications. In doing so, we evaluate both the robustness and the limitations of the PyCRA security scheme, concluding by outlining practical considerations as well as further applications for the proposed authentication mechanism.

Fusion of infrastructure-based pedestrian tracking systems and embedded sensors on mobile devices holds promise for providing accurate positioning in large public buildings. However, privacy concerns regarding handling of sensitive user location data potentially disrupt the adoption of such systems. This paper presents AnonyCast, a novel privacy-aware mechanism for delivering precise location information measured by crowd-tracking systems to individual pedestrians' smartphones. AnonyCast uses sparsely placed Bluetooth Low Energy transmitters to advertise location-dependent, time-varying keys. Using location measurements, AnonyCast estimates a subset of keys that each pedestrian's phone receives along its path. By combining a cryptography scheme called CP-ABE with a novel greedy algorithm for key selection, it encrypts each path before publishing, allowing users to decrypt only their own trajectories. The results from field experiments show that AnonyCast delivers accurate locations over 84% of time, bounding probability of unauthorized access to one's location below 1%.

Information about quantitative characteristics in local businesses and services, such as the number of people waiting in line in a cafe and the number of available fitness machines in a gym, is important for informed decision, crowd management and event detection. In this paper, we investigate the potential of leveraging crowds as sensors to report such quantitative characteristics and investigate how to recover the true quantity values from noisy crowdsourced information. Through experiments, we find that crowd sensors have both bias and variance in quantity sensing, and task difficulties impact the sensing accuracy. Based on these findings, we propose an unsupervised probabilistic model to jointly assess task difficulties, ability of crowd sensors and true quantity values. Our model differs from existing categorical truth finding models as ours is specifically designed to tackle quantitative truth. In addition to devising an efficient model inference algorithm in a batch mode, we also design an even faster online version for handling streaming data. Experimental results in various scenarios demonstrate the effectiveness of our model.

Context-awareness is the ability of software systems to sense and adapt to their physical environment. Many contemporary mobile applications adapt to changing locations, connectivity states, available computational and energy resources, and proximity to other users and devices. Nevertheless, there is little systematic support for context-awareness in contemporary mobile operating systems. Because of this, application developers must build their own context-awareness adaptation engines, dealing directly with sensors and polluting application code with complex adaptation decisions. In this paper, we introduce CAreDroid, which is a framework that is designed to decouple the application logic from the complex adaptation decisions in Android context-aware applications. In this framework, developers are required- only-to focus on the application logic by providing a list of methods that are sensitive to certain contexts along with the permissible operating ranges under those contexts. At run time, CAreDroid monitors the context of the physical environment and intercepts calls to sensitive methods, activating only the blocks of code that best fit the current physical context. CAreDroid is implemented as part of the Android runtime system. By pushing context monitoring and adaptation into the runtime system, CAreDroid eases the development of context-aware applications and increases their efficiency. In particular, case study applications implemented using CAre-Droid are shown to have: (1) at least half lines of code fewer and (2) at least 10× more efficient in execution time compared to equivalent context-aware applications that use only standard Android APIs.

Mobile sensor data-to-knowledge (MD2K) was chosen as one of 11 Big Data Centers of Excellence by the National Institutes of Health, as part of its Big Data-to-Knowledge initiative. MD2K is developing innovative tools to streamline the collection, integration, management, visualization, analysis, and interpretation of health data generated by mobile and wearable sensors. The goal of the big data solutions being developed by MD2K is to reliably quantify physical, biological, behavioral, social, and environmental factors that contribute to health and disease risk. The research conducted by MD2K is targeted at improving health through early detection of adverse health events and by facilitating prevention. MD2K will make its tools, software, and training materials widely available and will also organize workshops and seminars to encourage their use by researchers and clinicians.

Increasing hardware variability in newer integrated circuit fabrication technologies has caused corresponding power variations on a large scale. These variations are particularly exaggerated for idle power consumption, motivating the need to mitigate the effects of variability in systems whose operation is dominated by long idle states with periodic active states. In systems where computation is severely limited by anemic energy reserves and where a long overall system lifetime is desired, maximizing the quality of a given application subject to these constraints is both challenging and an important step toward achieving high-quality deployments. This work describes VaRTOS, an architecture and corresponding set of operating system abstractions that provide explicit treatment of both idle and active power variations for tasks running in real-time operating systems. Tasks in VaRTOS express elasticity by exposing individual knobs —shared variables that the operating system can tune to adjust task quality and, correspondingly, task power, maximizing application utility both on a per-task and on a system-wide basis. We provide results regarding online learning of instance-specific sleep power, active power, and task-level power expenditure on simulated hardware with demonstrated effects for several prototypical applications. Our results on networked sensing applications, which are representative of a broader category of applications that VaRTOS targets, show that VaRTOS can reduce variability-induced energy expenditure errors from over 70% in many cases to under 2% in most cases and under 5% in the worst case.

The ubiquity of smartphones has led to the emergence of mobile crowdsourcing tasks such as the detection of spatial events when smartphone users move around in their daily lives. However, the credibility of those detected events can be negatively impacted by unreliable participants with low-quality data. Consequently, a major challenge in quality control is to discover true events from diverse and noisy participants' reports. This truth discovery problem is uniquely distinct from its online counterpart in that it involves uncertainties in both participants' mobility and reliability. Decoupling these two types of uncertainties through location tracking will raise severe privacy and energy issues, whereas simply ignoring missing reports or treating them as negative reports will significantly degrade the accuracy of the discovered truth. In this paper, we propose a new method to tackle this truth discovery problem through principled probabilistic modeling. In particular, we integrate the modeling of location popularity, location visit indicators, truth of events and three-way participant reliability in a unified framework. The proposed model is thus capable of efficiently handling various types of uncertainties and automatically discovering truth without any supervision or the need of location tracking. Experimental results demonstrate that our proposed method outperforms existing state-of-the-art truth discovery approaches in the mobile crowdsourcing environment.

We present the CISpaces framework, a collaborative virtual space for intelligence analysts for the elaboration of information to explain a situation. CISpaces supports the analysis of conflicting information in collaboration exploiting argumentation schemes to structure and share analyses, crowd-sourcing to collect information and provenance to establish the credibility of hypotheses.

Sensors of diverse capabilities and modalities, carried by us or deeply embedded in the physical world, have invaded our personal, social, work, and urban spaces. Our relationship with these sensors is a complicated one. On the one hand, these sensors collect rich data that are shared and disseminated, often initiated by us, with a broad array of service providers, interest groups, friends, and family. Embedded in this data is information that can be used to algorithmically construct a virtual biography of our activities, revealing intimate behaviors and lifestyle patterns. On the other hand, we and the services we use, increasingly depend directly and indirectly on information originating from these sensors for making a variety of decisions, both routine and critical, in our lives. The quality of these decisions and our confidence in them depend directly on the quality of the sensory information and our trust in the sources. Sophisticated adversaries, benefiting from the same technology advances as the sensing systems, can manipulate sensory sources and analyze data in subtle ways to extract sensitive knowledge, cause erroneous inferences, and subvert decisions. The consequences of these compromises will only amplify as our society increasingly complex human-cyber-physical systems with increased reliance on sensory information and real-time decision cycles.Drawing upon examples of this two-faceted relationship with sensors in applications such as mobile health and sustainable buildings, this talk will discuss the challenges inherent in designing a sensor information flow and processing architecture that is sensitive to the concerns of both producers and consumer. For the pervasive sensing infrastructure to be trusted by both, it must be robust to active adversaries who are deceptively extracting private information, manipulating beliefs and subverting decisions. While completely solving these challenges would require a new science of resilient, secure and trustworthy networked sensing and decision systems that would combine hitherto disciplines of distributed embedded systems, network science, control theory, security, behavioral science, and game theory, this talk will provide some initial ideas. These include an approach to enabling privacy-utility trade-offs that balance the tension between risk of information sharing to the producer and the value of information sharing to the consumer, and method to secure systems against physical manipulation of sensed information.

The broad spectrum of interconnected sensors and actuators, available on various mobile devices and smartphones and collectively defined as the Internet of Things (IoT), have evolved into platforms with ability to both collect personal sensory data and also change the users' immediate environment. The continuous streams of richly annotated sensory data on these IoT devices have also enabled the emergence of a new class of context-aware apps that use the data to infer user context and accordingly customize their responses in real-time. However, this growth in the number of apps has not been complemented with adequate system support on the IoT devices resulting in monolithic apps that each implement and execute their own customized sensing pipelines. In this paper, we outline our vision of a sensing stack, akin to a networking stack, that can facilitate the development and execution of context-aware apps on IoT devices. There are several advantages to building a rich sensing stack. First, it allows apps to reuse stages of the sensing pipeline easing their development. Second, the layers of the stack allow for both in- and cross-layer resource optimization. Finally, it allows better control over the shared data as instead of raw-sensor data, higher-level semantic abstractions, such as inferences can now be shared with apps. We describe our initial efforts towards creating the different building blocks of such a sensing stack.

No abstract available.

Commercial and residential buildings are usually instrumented with meters and sensors that are deployed as part of a utility infrastructure installed by companies that provide services such as electricity, water, gas, security, phone, etc. As part of their normal operation, these service providers have direct access to information from the sensors and meters. A concern arises that the sensory information collected by the providers, although coarse-grained, can be subject to analysis that reveals private information about the users of the building. Oftentimes, multiple services are provided by the same company, in which case the potential for leakage of private information increases. Our research seeks to investigate the extent to which easily available sensory information may be used by external service providers to make occupancy-related inferences. Particularly, we focus on inferences from two different sources: motion sensors, which are installed and monitored by security companies, and smart electric meters, which are deployed by electric companies for billing and demand-response management. We explore the motion sensor scenario in a three-person single-family home and the electric meter scenario in a twelve-person university lab. Our exploration with various inference methods shows that sensory information available to service providers can enable them to make undesired occupancy related inferences, such as levels of occupancy or even the identities of current occupants, significantly better than naive prediction strategies that do not make use of sensor information.

Large-scale coordination and control problems in cyber-physical systems are often expressed within the networked optimization model. While significant advances have taken place in optimization techniques, their widespread adoption in practical implementations has been impeded by the complexity of internode coordination and lack of programming support for the same. Currently, application developers build their own elaborate coordination mechanisms for synchronized execution and coherent access to shared resources via distributed and concurrent controller processes. However, they typically tend to be error prone and inefficient due to tight constraints on application development time and cost. This is unacceptable in many CPS applications, as it can result in expensive and often irreversible side-effects in the environment due to inaccurate or delayed reaction of the control system. This article explores the design of a distributed shared memory (DSM) architecture that abstracts the details of internode coordination. It simplifies application design by transparently managing routing, messaging, and discovery of nodes for coherent access to shared resources. Our key contribution is the design of provably correct locality-sensitive synchronization mechanisms that exploit the spatial locality inherent in actuation to drive faster and scalable application execution through opportunistic data parallel operation. As a result, applications encoded in the proposed Hotline Application Programming Framework are error free, and in many scenarios, exhibit faster reactions to environmental events over conventional implementations. Relative to our prior work, this article extends Hotline with a new locality-sensitive coordination mechanism for improved reaction times and two tunable iteration control schemes for lower message costs. Our extensive evaluation demonstrates that realistic performance and cost of applications are highly sensitive to the prevalent deployment, network, and environmental characteristics. This highlights the importance of Hotline, which provides user-configurable options to trivially tune these metrics and thus affords time to the developers for implementing, evaluating, and comparing multiple algorithms.

Although a significant fraction of emerging failure and wearout mechanisms result in intermittent or permanent faults in hardware, their impact (as distinct from transient faults) on software applications has not been well studied. In this paper, we develop a distinguishing application characteristic, referred to as similarity from fundamental circuit-level understanding of the failure mechanisms. We present a mathematical definition and a procedure for similarity computation for practical software applications and experimentally verify the relationship between similarity and fault rate. Leveraging dependence of application robustness on the similarity metric, we present example architecture independent code transformations to reduce similarity and thereby the worst-case fault rate with minimal performance degradation. Our experimental results with arithmetic unit faults show as much as 74% improvement in the worst case fault rate on benchmark kernels, with less than 10% runtime penalty.

We study the competing goals of utility and privacy as they arise when a provider delegates the processing of its personal information to a recipient who is better able to handle this data. We formulate our goals in terms of the inferences which can be drawn using the shared data. A whitelist describes the inferences that are desirable, i.e., providing utility. A blacklist describes the unwanted inferences which the provider wants to keep private. We formally define utility and privacy parameters using elementary information-theoretic notions and derive a bound on the region spanned by these parameters. We provide constructive schemes for achieving certain boundary points of this region. Finally, we improve the region by sharing data over aggregated time slots.

Intelligent personal devices interacting with smart spaces will create a world where your environment itself anticipates and is ready to serve your every need. Many disciplines and technologies are realizing this vision of context awareness, which was science fiction just 25 years ago. In particular, your smartphone will evolve into your “sixth” sense — autonomously alerting you to your environment and notifying your social network of your interests, concerns and goals. Research challenges include ultra-low power always-on sensing and inference technologies, near-zero overhead peer-to-peer networking, and automated reasoning. Models derived from deep learning and knowledge-based reasoners in the proximate cloud will combine to create sentient immersive spaces. Advances in low power silicon, energy harvesting and scavenging will ensure that these smart environments are sustainable. This panel brings together leading experts to tell us about what sixth-sense functions are round the corner and what further breakthroughs are needed to realize this contextual awareness vision.

Microelectronic circuits exhibit increasing variations in performance, power consumption, and reliability parameters across the manufactured parts and across use of these parts over time in the field. These variations have led to increasing use of overdesign and guardbands in design and test to ensure yield and reliability with respect to a rigid set of datasheet specifications. This paper explores the possibility of constructing computing machines that purposely expose hardware variations to various layers of the system stack including software. This leads to the vision of underdesigned hardware that utilizes a software stack that opportunistically adapts to a sensed or modeled hardware. The envisioned underdesigned and opportunistic computing (UnO) machines face a number of challenges related to the sensing infrastructure and software interfaces that can effectively utilize the sensory data. In this paper, we outline specific sensing mechanisms that we have developed and their potential use in building UnO machines.

The increasing use of sensor-derived information from planned, ad-hoc, and/or opportunistically deployed sensor networks provides enhanced visibility to everyday activities and processes, enabling fast-paced data-to-decision in personal, social, civilian, military, and business contexts. The value that information brings to this visibility and ensuing decisions depends on the quality characteristics of the information gathered. In this article, we highlight, refine, and extend upon our past work in the areas of quality and value of information (QoI and VoI) for sensor networks. Specifically, we present and elaborate on our two-layer QoI/VoI definition, where the former relates to context-independent aspects and the latter to context-dependent aspects of an information product. Then, we refine our taxonomy of pertinent QoI and VoI attributes anchored around a simple ontological relationship between the two. Finally, we introduce a framework for scoring and ranking information products based on their VoI attributes using the analytic hierarchy multicriteria decision process, illustrated via a simple example.

Instance and temperature-dependent power variation has a direct impact on quality of sensing for battery-powered long-running sensing applications. We measure and characterize the active and leakage power for an ARM Cortex M3 processor and show that, across a temperature range of 20 -60, there is a 10% variation in active power, and a variation in leakage power. We introduce variability-aware duty cycling methods and a duty cycle (DC) abstraction for TinyOS which allows applications to explicitly specify the lifetime and minimum DC requirements for individual tasks, and dynamically adjusts the DC rates so that the overall quality of service is maximized in the presence of power variability. We show that variability-aware duty cycling yields a improvement in total active time over schedules based on worst case estimations of power, with an average improvement of across a wide variety of deployment scenarios based on the collected temperature traces. Conversely, datasheet power specifications fail to meet required lifetimes by 7%-15%, with an average 37 days short of the required lifetime of 1 year. Finally, we show that a target localization application using variability-aware DC yields a 50% improvement in quality of results over one based on worst case estimations of power consumption.

Smart phones with increased computation and sensing capabilities have enabled the growth of a new generation of applications which are organic and designed to react depending on the user contexts. These contexts typically define the personal, social, work and urban spaces of an individual and are derived from the underlying sensor measurements. The shared context streams therefore embed in them information, which when stitched together can reveal behavioral patterns and possible sensitive inferences, raising serious privacy concerns. In this paper, we propose a model based technique to capture the relationship between these contexts, and better understand the privacy implications of sharing them. We further demonstrate that by using a generative model of the context streams we can simultaneously meet the utility objectives of the context-aware applications while maintaining individual privacy. We present our current implementation which uses offline model learning with online inferencing performed on the smart phone. Preliminary results are presented to provide proof-of-concept of our proposed technique.

Resources in modern computer systems include not only CPU, but also memory, hard disk, bandwidth, etc. To serve multiple users simultaneously, we need to satisfy their requirements in all resource dimensions. Meanwhile, their demands follow a certain distribution and may change over time. Our goal is then to admit as many users as possible to the system without violating the resource capacity more often than a predefined overflow probability. In this paper, we study the problem of allocating multiple resources among a group of users/tasks with stochastic demands. We model it as a stochastic multi-dimensional knapsack problem. We extend and apply the concept of effective bandwidth in order to solve this problem efficiently. Via numerical experiments, we show that our algorithms achieve near-optimal performance with specified overflow probability.

Many mobile sensing applications consider users reporting and accessing sensing data through the Internet. However, WiFi and 3G connectivities are not always available in remote areas. Existing data dissemination schemes for opportunistic networks are not sufficient for sensing applications as sensing context has not been explored. In this work, we present a novel context-aware sensing data dissemination framework for mobile users in a remote sensing field. It maximizes information utility by considering such sensing context as sensing type, locality, time-to-live, mobility and user interests. Different from existing works, the mobile users not only collect sensing data, but also upload data to sensors for information sharing. We develop a context-aware deployment algorithm and a hybrid data exchange mechanism for generic sensors and mobile users. We evaluate our solution by both analysis and simulations, and show that it can provide high information utility for mobile users at low communication overhead.

We present MiDebug, a web-based Integrated Development Environment (IDE) for embedded system programming with in-browser debugging capabilities. This web application greatly reduces the time and effort required for rapid prototyping of microcontroller based devices.

We present DoubleDip, a low power monitoring system for enabling non-intrusive water flow detection. DoubleDip taps into minute thermal gradients in pipes for both replenishing energy reserves and performing low power wakeup. One of the remaining issues with wireless water monitoring in residences and offices is that current solutions require installing sensor nodes with access to electrical wiring or replacing batteries frequently. DoubleDip (DD) significantly extends the lifetime of vibration-based non-intrusive water flow sensors by harvesting thermal energy from hot pipes wherever accessible. DoubleDip requires less than an inch of exposed metal pipe to attach a coupler for gathering sufficient energy to power itself, in some cases, into perpetuity. We observe that water use in homes and offices is incredibly sporadic, making continuous monitoring both impractical and wasteful. Instead, DD puts a thermoelectric harvester into double duty. It uses thermal gradients not only for gathering energy but also for extremely low power (< 1μA) wakeup. In this paper, we describe the DoubleDip design and demonstrate that thermoelectric wakeup is essential for longevity and accuracy. Since DD wakes up from its low power state only when there is a water flow event, it replenishes the energy it uses in sensing and transmitting data by the energy it harvests from the corresponding heat gradient. While DD nodes installed on cold water pipes harvest far less than those installed on hot water pipes, our pilot deployment over four weeks and five locations suggests that thermoelectric wake up is only slightly worse in latency for cold water monitoring and there is sufficient energy harvested from the hot water that it can be shared to extend the lifetime of nearby cold water nodes too.

Continuous collection of sensory information using smartphones and body-worn sensors is now feasible with recent advancement of technologies. Sharing such personal information enables many useful applications such as medical behavioral studies, personal health-care, and participatory sensing. However, sharing such information along with inferences that can be drawn from the data increases user's various privacy concerns. This paper proposes SensorSafe, an application framework that enables users to share adequate amounts of their private data and supports obfuscation of sensitive information to protect user privacy. Our framework provides rule-based sharing with context-awareness and conflicting rule detection. In addition, our framework includes several optimization techniques for database processing of rule-based sharing and data obfuscation. We evaluate the optimization techniques with a large amount of accelerometer data from the fine-grained posture recognition application, which is about 6.25 GB.

We present a long-term and cross-sectional study of a vibration-based water flow rate monitoring system in practical environments and scenarios. In our earlier research, we proved that a water flow monitoring system with vibration sensors is feasible by deploying and evaluating it in a small-scale laboratory setting. To validate the proposed system, the system was deployed in existing environments—two houses and a public restroom—and in two different laboratory test settings. With the collected data, we first demonstrate various aspects of the system's performance, including sensing stability, sensor node lifetime, the stability of autonomous sensor calibration, time to adaptation, and deployment complexity. We then discuss the practical challenges and lessons from the full-scale deployments. The evaluation results show that our water monitoring solution is a practical, quick-to-deploy system with a less than 5% average flow estimation error.

Wireless neural recording systems are subject to stringent power consumption constraints to support long-term recordings and to allow for implantation inside the brain. In this paper, we propose using a combination of on-chip detection of action potentials ("spikes") and compressive sensing (CS) techniques to reduce the power consumption of the neural recording system by reducing the power required for wireless transmission. We empirically verify that spikes are compressible in the wavelet domain and show that spikes from different neurons acquired from the same electrode have subtly different sparsity patterns or supports. We exploit the latter fact to further enhance the sparsity by incorporating a union of these supports learned over time into the spike recovery procedure. We show, using extra cellular recordings from human subjects, that this mechanism improves the SNDR of the recovered spikes over conventional basis pursuit recovery by up to 9.5 dB (6 dB mean) for the same number of CS measurements. Though the compression ratio in our system is contingent on the spike rate at the electrode, for the datasets considered here, the mean ratio achieved for 20-dB SNDR recovery is improved from 26:1 to 43:1 using the learned union of supports.

Wearable sensors are revolutionizing healthcare and science by enabling capture of physiological, psychological, and behavioral measurements in natural environments. However, these seemingly innocuous measurements can be used to infer potentially private behaviors such as stress, conversation, smoking, drinking, illicit drug usage, and others. We conducted a study to assess how concerned people are about disclosure of a variety of behaviors and contexts that are embedded in wearable sensor data. Our results show participants are most concerned about disclosures of conversation episodes and stress - inferences that are not yet widely publicized. These concerns are mediated by temporal and physical context associated with the data and the participant's personal stake in the data. Our results provide key guidance on the extent to which people understand the potential for harm and data characteristics researchers should focus on to reduce the perceived harm from such datasets.

Instance and temperature-dependent leakage power variability is already a significant issue in contemporary embedded processors, and one which is expected to increase in importance with scaling of semiconductor technology. We measure and characterize this leakage power variability in current microprocessors, and show that variability aware duty cycle scheduling produces 7.1× improvement in sensing quality for a desired lifetime. In contrast, pessimistic estimations of power consumption leave 61% of the energy untapped, and datasheet power specifications fail to meet required lifetimes by 14%. Finally, we introduce a duty cycle abstraction for TinyOS that allows applications to explicitly specify lifetime and minimum duty cycle requirements for individual tasks, and dynamically adjusts duty cycle rates so that overall quality of service is maximized in the presence of power variability.

Large-scale coordination and control problems in sensor/actuator networks are often expressed within the networked optimization model. While significant advances have taken place in both first- and higher-order optimization techniques, their widespread adoption in practical implementations has been hindered by a lack of adequate programming and evaluation support. This motivates the two major contributions of this paper. First, we extend the distributed programming framework proposed in with a synchronization primitive to implement different versions of the subgradient technique and perform extensive evaluation with varying deployment and algorithmic parameters. Second, the insights - obtained by observing the variability in practical metrics such as response time and incurred message cost - lead us to exploit the spatial locality inherent in these large-scale actuator control applications, and propose a novel consensus algorithm applied to the subgradient method. We show using simulations that there is at least 99% improvement in response time and the message cost is reduced by more than 90% over prior consensus based algorithms.

With the popularity and advancements of smart phones, mobile users can interact with the sensing facilities and exchange information with other wireless devices in the environment by short range communications. Opportunistic exchange has recently been suggested in similar contexts; yet we show strong evidence that, in our application, opportunistic exchange would lead to insufficient data availability and extremely high communication overheads due to inadequate or excessive human contacts in the environment. In this paper, we present OppSense, a novel design to provide efficient opportunistic information exchange for mobile phone users in sensing field with data repositories that tackles the fundamental availability and overhead issues. Our design differs from conventional opportunistic information exchange in that it can provide mobile phone users guaranteed opportunities for information exchange regardless the number of users and contacts in different environments. Through both analysis and simulations, we show that the deployment of data repositories plays a key role in the overall system optimization. We demonstrate that the placement of data repositories is equivalent to a connected K-coverage problem, and an elegant heuristic solution considering the mobility of users exists. We evaluate our proposed framework and algorithm with real mobile traces. Extensive simulations demonstrate that data repositories can effectively enhance the data availability up to 41% in low contact environment and significantly reduce the communication overheads to only 28% compared to opportunistic information exchange in high contact environment.

Bicycling is an affordable, environmentally friendly alternative transportation mode to motorized travel. A common task performed by bikers is to find good routes in an area, where the quality of a route is based on safety, efficiency, and enjoyment. Finding routes involves trial and error as well as exchanging information between members of a bike community. Biketastic is a platform that enriches this experimentation and route sharing process making it both easier and more effective. Using a mobile phone application and online map visualization, bikers are able to document and share routes, ride statistics, sensed information to infer route roughness and noisiness, and media that documents ride experience. Biketastic was designed to ensure the link between information gathering, visualization, and bicycling practices. In this paper, we present architecture and algorithms for route data inferences and visualization. We evaluate the system based on feedback from bicyclists provided during a two-week pilot.

Detailed diagnostic data is a prerequisite for debugging problems and understanding runtime performance in distributed wireless embedded systems. Severe bandwidth limitations, tight timing constraints, and limited program text space hinder the application of standard diagnostic tools within this domain. This work introduces the Log Instrumentation Specification (LIS), which provides a high level logging interface to developers and is able to create extremely compact diagnostic logs. LIS uses a token scoping technique to aggressively compact identifiers that are packed into bit aligned log buffers. LIS is evaluated in the context of recording call traces within a network of wireless sensor nodes. Our evaluation shows that logs generated using LIS require less than 50% of the bandwidth utilized by alternate logging mechanisms. Through microbench-marking of a complete LIS implementation for the TinyOS operating system, we demonstrate that LIS can comfortably fit onto low-end embedded systems. By significantly reducing log bandwidth, LIS enables extraction of a more complete picture of runtime behavior from distributed wireless embedded systems.

The rapid adoption of mobile devices that are able to capture and transmit a wide variety of sensing modalities (media and location) has enabled a new data collection paradigm - participatory sensing. Participatory sensing initiatives organize individuals to gather sensed information using mobile devices through cooperative data collection. A major factor in the success of these data collection projects is sustained, high quality participation. However, since data capture requires a time and energy commitment from individuals, incentives are often introduced to motivate participants. In this work, we investigate the use of micro-payments as an incentive model. We define a set of metrics that can be used to evaluate the effectiveness of incentives and report on findings from a pilot study using various micro-payment schemes in a university campus sustainability initiative.

Data loss in wireless sensing applications is inevitable and while there have been many attempts at coping with this issue, recent developments in the area of Compressive Sensing (CS) provide a new and attractive perspective. Since many physical signals of interest are known to be sparse or compressible, employing CS, not only compresses the data and reduces effective transmission rate, but also improves the robustness of the system to channel erasures. This is possible because reconstruction algorithms for compressively sampled signals are not hampered by the stochastic nature of wireless link disturbances, which has traditionally plagued attempts at proactively handling the effects of these errors. In this paper, we propose that if CS is employed for source compression, then CS can further be exploited as an application layer erasure coding strategy for recovering missing data. We show that CS erasure encoding (CSEC) with random sampling is efficient for handling missing data in erasure channels, paralleling the performance of BCH codes, with the added benefit of graceful degradation of the reconstruction error even when the amount of missing data far exceeds the designed redundancy. Further, since CSEC is equivalent to nominal oversampling in the incoherent measurement basis, it is computationally cheaper than conventional erasure coding. We support our proposal through extensive performance studies.

We present Virtual High-resolution Time (VHT), a power-proportional time-keeping service that offers a baseline power draw of a low-speed clock (e.g. 32 kHz crystal), but provides the time resolution that only a higher frequency clock could offer (e.g. 8 MHz crystal), and scales essentially linearly with access (i.e. the "reading" and "writing" of the clock). We achieve this performance by revisiting a basic assumption in the design of time-keeping systems -- that to achieve a given time-stamping resolution, a free-running timebase of equivalent frequency is needed. We show that this assumption is false and argue that the dependence is not on usage (i.e. whether on or off) but rather on access (i.e. reading and writing). Therefore, it is possible to duty cycle the free-running timebase itself, and augment it with a lower-frequency, temperature-compensated one, which achieves comparable resolution, at a fraction of the power, for typical workloads. The key technical challenge lies in duty cycling the fast clock and synchronizing the fast and slow clocks. To assess the viability of the approach, we explore how VHT could be implemented on several different platform architectures, and to study the power/performance tradeoff, we characterize VHT on one particular architecture in detail. Our results show power-proportional operation with a 10x improvement in average power and a synchronization accuracy exceeding 1 μs at duty cycles below 0.1%.

Extending the lifetime of wireless sensor networks requires energy-conserving operations such as duty-cycling. However, such operations may impact the effectiveness of high fidelity real-time sensing tasks, such as object tracking, which require high accuracy and short response times. In this paper, we quantify the influence of different duty-cycle schemes on the efficiency of bearings-only object tracking. Specifically, we use the Maximum Likelihood localization technique to analyze the accuracy limits of object location estimates under different response latencies considering variable network density and duty-cycle parameters. Moreover, we study the tradeoffs between accuracy and response latency under various scenarios and motion patterns of the object. We have also investigated the effects of different duty-cycled schedules on the tracking accuracy using acoustic sensor data collected at Aberdeen Proving Ground, Maryland, by the U.S. Army Research Laboratory (ARL).

Continuously understanding a user's location context in colloquial terms and the paths that connect the locations unlocks many opportunities for emerging applications. While extensive research effort has been made on efficiently tracking a user's raw coordinates, few attempts have been made to efficiently provide everyday contextual information about these locations as places and paths. We introduce SensLoc, a practical location service to provide such contextual information, abstracting location as place visits and path travels from sensor signals. SensLoc comprises of a robust place detection algorithm, a sensitive movement detector, and an on-demand path tracker. Based on a user's mobility, SensLoc proactively controls active cycle of a GPS receiver, a WiFi scanner, and an accelerometer. Pilot studies show that SensLoc can correctly detect 94% of the place visits, track 95% of the total travel distance, and still only consume 13% of energy than algorithms that periodically collect coordinates to provide the same information.

To achieve more accurate global time synchronization, this paper argues for decoupling the clock distribution network from the routing tree in a multihop wireless network. We find that both flooding and routing-integrated time synchronization rapidly propagate node-level errors (typically due to temperature fluctuations) across the network. Therefore, we propose that a node chooses synchronization neighbors that offer the greatest frequency stability. We propose two methods to estimate a neighbor’s stability. The first approach selects the neighbor whose Frequency Error Variance, or simply FEV, is smallest with respect to the local clock. The second approach selects the neighbor that reports the lowest FEV relative to its synchronization parent. We also propose the node-level time-variance FEV as an additive metric for selecting more stable clock trees than either naïve flooding or routing-integrated time synchronization can provide. We incorporate these techniques into FTSP, a widelyused time synchronization protocol, and show that the mean error in global time significantly improved (by a factor of five) when some nodes are warmed and others are not.

The 25 papers in this special issue focus on simple wireless sensor networking solutions.

Driven by the confluence between the need to collect data about people's physical, physiological, psychological, cognitive, and behavioral processes in spaces ranging from personal to urban and the recent availability of the technologies that enable this data collection, wireless sensor networks for healthcare have emerged in the recent years. In this review, we present some representative applications in the healthcare domain and describe the challenges they introduce to wireless sensor networks due to the required level of trustworthiness and the need to ensure the privacy and security of medical data. These challenges are exacerbated by the resource scarcity that is inherent with wireless sensor network platforms. We outline prototype systems spanning application domains from physiological and activity monitoring to large-scale physiological and behavioral studies and emphasize ongoing research challenges.

I would like to extend to all our readers a warm welcome to a new year, and take this opportunity to comment on the state of our journal and various changes that have taken place. TMC is overall in robust health, and is clearly the journal of choice for researchers in mobile computing, as demonstrated by a high impact factor that has grown rapidly in recent years. It is now the third most cited Computer Society journal, and is ranked among the top 10 journals in information systems and telecommunications. Perhaps the most significant trend for TMC during the last year or two has been a significant increase in the number of submissions, from around 400 in 2007 to close to 550 during the past year. Needless to say, this has brought challenges for the editorial process, with the peer review burden increasing quite significantly, a discernible decrease in average submission quality, and an increase of papers in newer areas that were not as well represented on the Editorial Board. Furthermore, the furious pace with which related conferences have grown means that getting an adequate number of high quality reviewers in a timely fashion has become much harder. While we continue to do well in average submission-to-decision and submission-to-publication times, there are an increasing number of papers that have seen unacceptable delays, and a gradual build up of backlog that is concerning. To address these issues, we have been taking many steps. First, we are becoming more hard-nosed about papers that are on the periphery of TMC’s scope. In previous years, as TMC was growing, there was a desire to accommodate these papers, but that is a luxury that we are increasingly unable to afford. For example, submissions in areas such as sensor networks and lower layers that do not offer a substantive treatment of TMC-relevant topics such as computing, location/ context awareness, mobility, higher layer networking, energy management, etc., will increasingly be administratively rejected, and the authors will be recommended to send their papers to journals more focused in those areas. Second, we are subjecting papers to a higher degree of initial scrutiny to determine whether it is worthwhile to devote resources for a full review, and administratively rejecting papers of doubtful quality upfront. I expect this reliance on initial screening will only increase as we get an increasing number of papers that clearly have not been vetted through conferences and are not at all ready for an archival journal review. Third, we have aggressively expanded the size of the Editorial Board to reduce the burden on individual Associate Editors, and also added expertise in areas where we receive more papers. In this context, it is my pleasure to introduce the following new Associate Editors who were added in recent months: Suman Banerjee, Levente Buttyan, Thomas Hou, Mary Ann Ingram, Neal Patwari, Konstantinos Psounis, Lili Qiu, Andreas Terzis, and Qian Zhang. Collectively, they bring expertise in sensor networks, delay-tolerant networks, security and privacy, wireless internetworking, crosslayer and physical-layer issues, cognitive networks, localization mechanisms, and mobility management. I would like to thank them for agreeing to serve on the Editorial Board. Their biographies are included on the following pages. In addition, I would also like to take this opportunity to thank our departing Associate Editors whose terms finished during 2009 or who stepped down for personal reasons: Robert Istepanian, Rohit Negi, Stephan Weiss, and Suresh Singh. All four of these Associate Editors contributed immensely to TMC, and I truly appreciate their dedicated service during their tenures. One area where the journal still needs to improve is in better reflecting the diversity of mobile computing. Papers submitted to TMC continue to be dominantly in mobile and wireless networking, and we would like to see more papers on systems issues of mobile computing, such as OS support, energy management, novel platforms, human factors, applications, etc. In an attempt to reinforce that TMC is actively soliciting such papers, in the coming months, there will be a special section of top papers drawn from MobiSys 2009. I would really like to encourage authors to submit results from their best systems research as papers to TMC. We have a number of well-regarded systems researchers on the Editorial Board who will provide a thoughtful and fair review and decision process, which, unlike the up/down decisions at conferences, provides for a meaningful dialogue between the authors and the reviewers that often helps improve the paper. Last, I would like to thank our readers and authors for their continued support of TMC, and also the members of the Editorial Board, the Steering Committee, and the Computer Society staff for their help in making this journal succeed. Please feel free to send me your feedback and suggestions about the journal and its direction. I look forward to hearing from you.

R we have added to the editorial board several new Associate Editors to a) fi ll gaps left by Associate Editors whose terms expired, b) strengthen expertise in areas where we are now receiving a large number of papers, and c) add editorial board members from countries that are not well represented. It is my pleasure to introduce these new Associate Editors: Amotz Bar-Noy, Mun Choon Chan, Tamer ElBatt, Dennis Goeckel, Marco Gruteser, Yunhao Liu, Petri Mahonen, and Cormac Sreenan. Together, these Associate Editors bring expertise in areas such as localization, cognitive networks, security, experimental systems, theoretical foundations, and mobile networks. Furthermore, these new members represent many different countries—Egypt, Germany, Ireland, Singapore, Taiwan, and the United States—thus tremendously enhancing the geographical diversity. I would like to thank them for agreeing to serve on the editorial board. Their biographies are included below. Additionally, I would like to thank several Associate Editors whose terms ended or who stepped down for personal reasons in recent months. These include Tarek Abdelzaher, Marco Conti, Mark Corner, Ravi Prakash, Krithi Ramamritham, Ram Ramanathan, Jie Wu, and Wei Ye. These AEs brought unique expertise to the journal, which we will miss. I would like to express my appreciation for their dedicated service in recent years. I would like to thank our readers and authors for their continued support of TMC, and also the members of the Editorial Board, the Steering Committee, and the Computer Society staff for their help in making this journal succeed. TMC continues to be the most desirable and cited journal in mobile and wireless computing and networking, and this would not be possible without all this support. Please feel free to send me your feedback and suggestions about the journal and its direction. I look forward to hearing from you.

As mobile phones advance in functionality and capability, they are being used for more than just communication. Increasingly, these devices are being employed as instruments for introspection into habits and situations of individuals and communities. Many of the applications enabled by this new use of mobile phones rely on contextual information. The focus of this work is on one dimension of context, the transportation mode of an individual when outside. We create a convenient (no specific position and orientation setting) classification system that uses a mobile phone with a built-in GPS receiver and an accelerometer. The transportation modes identified include whether an individual is stationary, walking, running, biking, or in motorized transport. The overall classification system consists of a decision tree followed by a first-order discrete Hidden Markov Model and achieves an accuracy level of 93.6% when tested on a dataset obtained from sixteen individuals.

The efficiency of the time synchronization service in wireless sensor networks is tightly connected to the design of the radio, the quality of the clocking hardware, and the synchronization algorithm employed. While improvements can be made on all levels of the system, over the last few years most work has focused on the algorithmic level to minimize message exchange and in radio architectures to provide accurate time-stamping mechanisms. Surprisingly, the influences of the underlying clock system and its impact on the overall synchronization accuracy has largely been unstudied. In this work, we investigate the impact of the clocking subsystem on the time synchronization service and address, in particular, the influence of changes in environmental temperature on clock drift in highly duty-cycled wireless sensor nodes. We also develop formulas that help the system architect choose the optimal resynchronization period to achieve a given synchronization accuracy. We find that the synchronization accuracy has a two region behavior. In the first region, the synchronization accuracy is limited by quantization error, while int he second region changes in environmental temperature impact the achievable accuracy. We verify our analytic results in simulation and real hardware experiments.

A key prerequisite for residential energy conservation is knowing when and where energy is being spent. Unfortunately, the current generation of energy reporting devices only provide partial and coarse grained information or require expensive professional installation. This limitation stems from the presumption that calculating per-appliance consumption requires per-appliance current measurements. However, since appliances typically emit measurable signals when they are consuming energy, we can estimate their consumption using indirect sensing. This paper presents ViridiScope, a fine-grained power monitoring system that furnishes users with an economical, self-calibrating tool that provides power consumption of virtually every appliance in the home. ViridiScope uses ambient signals from inexpensive sensors placed near appliances to estimate power consumption, thus no in-line sensor is necessary. We use a model-based machine learning algorithm that automates the sensor calibration process. Through experiments in a real house, we show that ViridiScope can estimate the end-point power consumption within 10% error.

Cellular and Wi-Fi networks now form a global substrate that provides billions of mobile phone users with consistent, location-aware communication and multimedia data access. On this substrate is emerging a new class of mobile phone applications that use the phones location, image and acoustic sensors, and enable people to choose what to sense and when to share data about themselves and their surroundings. Peoples' natural movement through and among living, work, and ldquothirdrdquo spaces, provides spatial and temporal coverage for these modalities, the character of which is impossible to achieve through embedded instrumentation alone. This paper proposes a network service architecture for participatory sensing, describing challenges in (1) network coordination services enabling applications to efficiently select, incentivize and task mobile users based on measures of coverage, capabilities and interests; (2) attestation mechanisms to enable data consumers to assign trust to the data they access; and (3) participatory privacy regulation mechanisms used by data contributors to control what data they share.

Compressive Sensing (CS) is a recently developed mechanism that allows signal acquisition and compression to be performed in one inexpensive step so that the sampling process itself produces a compressed version of the signal. This significantly improves systemic energy efficiency because the average sampling rate can be considerably reduced and explicit compression eliminated.

Biochemical activities in sewer pipes generate various volatile substances that lead to several serious problems such as malodor complaints and lawsuits, concrete and metal corrosion, increased operational costs, and health risks. Frequent inspections are critical to maintain sewer health, yet are extremely expensive given the extent of the sewer system and the "unfriendliness" of the environment. In this paper we propose SewerSnort, a low cost, unmanned, fully automated in-sewer gas monitoring system. A sensor float is introduced at the upstream station and drifts to the end pumping station, collecting location tagged gas measurements. The retrieved SewerSnort provides an accurate gas exposure profile to be used for preventive maintenance and/or repair. The key innovations of SewerSnort are the fully automated, end-to-end monitoring solution and the low energy self localizing strategy. From the implementation standpoint, the key enablers are the float mechanical design that fits the sewer constraints and the embedded sensor design that matches the float form factor and complies with the tight energy constraints. Experiments based on a dry land emulator demonstrate the feasibility of the SewerSnort concept, in particular, the localization technique and the embedded sensor design.

In this demonstration, we will present three key technologies we recently developed to improve time synchronization accuracy in sensor networks: (1) Temperature Driven Time Synchronization, (2) Low-Power Sub-μSecond Time Synchronization, and (3) Low-Power FPGA implementation of a High-Low Timer.

The six articles in this special issue focus on the technology and applications of body area networking.

This tutorial presents a detailed study of sensor faults that occur in deployed sensor networks and a systematic approach to model these faults. We begin by reviewing the fault detection literature for sensor networks. We draw from current literature, our own experience, and data collected from scientific deployments to develop a set of commonly used features useful in detecting and diagnosing sensor faults. We use this feature set to systematically define commonly observed faults, and provide examples of each of these faults from sensor data collected at recent deployments.

Time keeping and synchronization are important services for networked and embedded systems. High quality timing information allows embedded network nodes to provide accurate time-stamping, fast localization, efficient duty cycling schedules, and other basic but essential functions - all of which are required for low power operation.

Water is nature's most precious resource and growing demand is pushing fresh water supplies to the brink of non-renewability. New technological and social initiatives that enhance conservation and reduce waste are needed. Providing consumers with fine-grained real-time information has yielded benefits in conservation of power and gasoline. Extending this philosophy to water conservation, we introduce a novel water monitoring system, NAWMS, that similarly empowers users.

This poster introduces an application-specific trace log compression mechanism targeted for execution on wireless sensor network nodes. Trace logs capture sequences of significant events executed on a node to provide visibility into the system. The application-specific compression mechanism exploits static program control flow knowledge to automate insertion of trace statements that capture trace data in a concise form. Initial evaluation reveals that these compressed trace logs, when generated, consume just over a fifth of the space required by standard trace logging techniques.

Time synchronization is an essential service in distributed computing and control systems. It is used to enable tasks such as synchronized data sampling and accurate time-of-flight estimation, which can be used to locate nodes. The deviation in nodes' knowledge of time and inter-node resynchronization rate are affected by three sources of time stamping errors: network wireless communication delays, platform hardware and software delays, and environment-dependent frequency drift characteristics of the clock source. The focus of this work is on the last source of error, the clock source, which becomes a bottleneck when either required time accuracy or available energy budget and bandwidth (and thus feasible resynchronization rate) are too stringent. Traditionally, this has required the use of expensive clock sources (such as temperature compensation using precise sensors and calibration models) that are not cost-effective in low-end wireless sensor nodes. Since the frequency of a crystal is a product of manufacturing and environmental parameters, we describe an approach that exploits the subtle manufacturing variation between a pair of inexpensive oscillators placed in close proximity to algorithmically compensate for the drift produced by the environment. The algorithm effectively uses the oscillators themselves as a sensor that can detect changes in frequency caused by a variety of environmental factors. We analyze the performance of our approach using behavioral models of crystal oscillators in our algorithm simulation. Then we apply the algorithm to an actual temperature dataset collected at the James Wildlife Reserve in Riverside County, California, and test the algorithms on a waveform generator based testbed. The result of our experiments show that the technique can effectively improve the frequency stability of an inexpensive uncompensated crystal 5 times with the potential for even higher gains in future implementations.

Inspired by unidirectional error detecting codes that are used in situations where only one kind of bit errors are possible (e.g., it is possible to change a bit "0" into a bit "1", but not the contrary), we propose integrity codes (I-codes) for a radio communication channel, which enable integrity protection of messages exchanged between entities that do not hold any mutual authentication material (i.e. public keys or shared secret keys). The construction of I-codes enables a sender to encode any message such that if its integrity is violated in transmission over a radio channel, the receiver is able to detect it. In order to achieve this, we rely on the physical properties of the radio channel and on unidirectional error detecting codes. We analyze in detail the use of I-codes on a radio communication channel and we present their implementation on a wireless platform as a "proof of concept". We further introduce a novel concept called "authentication through presence", whose broad applications include broadcast authentication, key establishment and navigation signal protection. We perform a detailed analysis of the security of our coding scheme and we show that it is secure within a realistic attacker model.

Time synchronization is critical in sensor networks at many layers of their design. It enables better duty-cycling of the radio, accurate and secure localization, beamforming, and other collaborative signal processing tasks. These benefits make time-synchronization protocols a prime target of malicious adversaries who want to disrupt the normal operation of a sensor network. In this article, we analyze attacks on existing time synchronization protocols for wireless sensor networks and we propose a secure time synchronization toolbox to counter these attacks. This toolbox includes protocols for secure pairwise and group synchronization of nodes that either lie in the neighborhood of each other or are separated by multiple hops. We provide an in-depth analysis of the security and the energy overhead of the proposed protocols. The efficiency of these protocols has been tested through an experimental study on Mica2 motes.

In this work, we propose and analyze a new approach for securing localization and location verification in wireless networks based on hidden and mobile base stations. Our approach enables secure localization with a broad spectrum of localization techniques: ultrasonic or radio, based on received signal strength or signal time of flight. Through several examples we show how this approach can be used to secure node-centric and infrastructure-centric localization schemes. We further show how this approach can be applied to secure localization in mobile ad-hoc and sensor networks.

I is a tremendous honor for me to be selected as the next Editor-in-Chief (EIC) of such a prestigious and well-regarded journal as the IEEE Transactions on Mobile Computing (TMC). I am grateful to the Steering Committee for giving me this opportunity to shape the premiere publication forum in my fi eld. This appointment is even more pleasant as becoming the EIC is a sort of coming home event since my fi rst editorial board experience was as an Associate Editor for this journal when it was formed. TMC has been tremendously fortunate to have Tom La Porta and Nitin H. Vaidya as its fi rst two EICs. Together with the Steering Committee and IEEE Computer Society’s publications staff, Tom and Nitin nurtured this young journal through its formative years as it grew from a quarterly journal to a monthly one with one of the highest impact factors. Measures instituted by them, such as increased publication frequency, more pages, and the availability of preprints and rapid posts, have kept the turnaround time low and thus maintained TMC’s relevance in a fast moving fi eld with competition from selective conferences viewed by some as “journal equivalent.” Clearly, following in their footsteps is a challenge. Where should TMC go next? As I have pondered this question in recent weeks, my thoughts keep coming back to one aspect of TMC: the composition of the community—readers and authors—that it currently attracts and serves. Mobile computing is an inherently multidisciplinary area involving intellectual activities not just from networking but also from computing, embedded systems, low-power circuits, wireless signal processing, and applications. Indeed, some of the greatest advances in this fi eld have been driven by innovations from these other areas. It was precisely this multidisciplinary aspect of mobile computing that attracted me to the fi eld some 15 years ago as a fresh graduate from the University of California, Berkeley, trained in embedded systems and VLSI signal processing but surrounded by networking researchers at Bell Labs. Despite the diversity of topics that are in its scope, TMC remains very much a networking-centered journal in the types of papers it currently attracts. My goal is to take the journal in a direction where it refl ects the multidisciplinary nature of mobile computing and becomes the premier journal that covers all aspects of mobile computing. Building on TMC’s mobile and wireless networking core, I hope to also attract a critical mass of contributors and readers from computing, embedded systems, low-power circuits, and wireless signal processing. In the coming months, I shall certainly take concrete steps in reaching this goal, such as expanding the editorial board to include experts in applications, systems, circuits, and signal-processing aspects. My hope is to attract authors from these fi elds and to be able to provide meaningful and fair reviews for their papers. However, to really make TMC refl ect the diversity of mobile computing, we need help from you, our readers and authors. Many of you have broader research activities in mobile computing, and I invite you to submit excellent papers on systems and other aspects. Please help spread this word as well, and in return we promise your papers the timely and high-quality reviews and short decision cycle that TMC is well known for. Last, the success of any journal is built primarily on four groups of people: the contributors, the reviewers, the associate editors, and the publications staff. I would like to thank all of them and express my sincere appreciation for the support they have given to TMC under my predecessors. I look forward to continuing this relationship and receiving your suggestions and ideas for making TMC more valuable for our research community.

The IEEE Transactions on Mobile Computing (TMC) is now in its seventh year, and has established an excellent reputation in the research community as the journal of choice for mobile computing. The quality and quantity of submissions that TMC receives are indicative of this. However, as I noted in my previous editorial, TMC remains very much a networking-centered journal in the types of papers it currently attracts. We hope to address this by attracting papers that reflect other aspects of mobile computing, such as novel platforms, energy management, operating systems extensions, user interfaces, deployment experience, low-power circuits, and signal processing. Having associate editors with expertise and high visibility in these areas is important to attracting papers from researchers in these fields and to be able to provide them with meaningful and fair reviews. It is with great pleasure that I therefore welcome 13 new associate editors to TMC's editorial board, many of whom are well-regarded researchers in these new areas, while the others strengthen our networking core. The new associate editors are: Saurabh Bagchi, Mark Corner, Sanjay K. Jha, Bhaskar Krishnamachari, Margaret Martonosi, Radha Poovendran, Anand Raghunathan, Ram Ramanathan, Paolo Santi, Tajana Simunic Rosing, Alex C. Snoeren, Wade Trappe, and Nalini Venkatasubramanian. Their biographies are provided.

Sensor network technology promises a vast increase in automatic data collection capabilities through efficient deployment of tiny sensing devices. The technology will allow users to measure phenomena of interest at unprecedented spatial and temporal densities. However, as with almost every data-driven technology, the many benefits come with a significant challenge in data reliability. If wireless sensor networks are really going to provide data for the scientific community, citizen-driven activism, or organizations which test that companies are upholding environmental laws, then an important question arises: How can a user trust the accuracy of information provided by the sensor network? Data integrity is vulnerable to both node and system failures. In data collection systems, faults are indicators that sensor nodes are not providing useful information. In data fusion systems the consequences are more dire; the final outcome is easily affected by corrupted sensor measurements, and the problems are no longer visibly obvious. In this article, we investigate a generalized and unified approach for providing information about the data accuracy in sensor networks. Our approach is to allow the sensor nodes to develop a community of trust. We propose a framework where each sensor node maintains reputation metrics which both represent past behavior of other nodes and are used as an inherent aspect in predicting their future behavior. We employ a Bayesian formulation, specifically a beta reputation system, for the algorithm steps of reputation representation, updates, integration and trust evolution. This framework is available as a middleware service on motes and has been ported to two sensor network operating systems, TinyOS and SOS. We evaluate the efficacy of this framework using multiple contexts: (1) a lab-scale test bed of Mica2 motes, (2) Avrora simulations, and (3) real data sets collected from sensor network deployments in James Reserve.

Many embedded systems contain resource constrained microcontrollers where applications, operating system components and device drivers reside within a single address space with no form of memory protection. Programming errors in one application can easily corrupt the state of the operating system and other applications on the microcontroller. In this paper we propose a system that provides memory protection in tiny embedded processors. Our system consists of a software run-time working with minimal low-cost architectural extensions to the processor core that prevents corruption of state by buggy applications. We restrict memory accesses and control flow of applications to protection domains within the address space. The software run-time consists of a Memory map: a flexible and efficient data structure that records ownership and layout information of the entire address space. Memory map checks are done for store instructions by hardware accelerators that significantly improve the performance of our system. We preserve control flow integrity by maintaining a Safe stack that stores return addresses in a protected memory region. Cross domain function calls are redirected through a software based jump table. Enhancements to the microcontroller call and return instructions use the jump table to track the current active domain. We have implemented our scheme on a VHDL model of ATMEGA103 microcontroller. Our evaluations show that embedded applications can enjoy the benefits of memory protection with minimal impact on performance and a modest increase in the area of the microcontroller.

Many sensor nodes contain resource constrained microcontrollers where user level applications, operating system components, and device drivers share a single address space with no form of hardware memory protection. Programming errors in one application can easily corrupt the state of the operating system or other applications. In this paper, we propose Harbor, a memory protection system that prevents many forms of memory corruption. We use software based fault isolation ("sandboxing") to restrict application memory accesses and control flow to protection domains within the address space. A flexible and efficient memory map data structure records ownership and layout information for memory regions; writes are validated using the memory map. Control flow integrity is preserved by maintaining a safe stack that stores return addresses in a protected memory region. Run-time checks validate computed control flow instructions. Cross domain calls perform low-overhead control transfers between domains. Checks are introduced by rewriting an application's compiled binary. The sand-boxed result is verified on the sensor node before it is admitted for execution. Harbor's fault isolation properties depend only on the correctness of this verifier and the Harbor runtime. We have implemented and tested Harbor on the SOS operating system. Harbor detected and prevented memory corruption caused by programming errors in application modules that had been in use for several months. Harbor's overhead, though high, is less than that of application-specific virtual machines, and reasonable for typical sensor workloads.

We present the design and implementation of the Illuminator, a preliminary sensor network-based intelligent light control system for entertainment and media production. Unlike most sensor network applications, which focus on sensing alone, a distinctive aspect of Illuminator is that it closes the loop from light sensing to lighting control. We describe the Illuminator's design requirements, system architecture, algorithms, implementation and experimental results. To satisfy the high-performance light sensing requirements of entertainment and media production applications, the system uses the Illumimote, which is a multi-modal and high fidelity light sensor module well-suited to wireless sensor networks. The Illuminator system is a toolset to characterize the illumination profile of a deployed set of fixed position lights, generate desired lighting effects for moving targets (actors, scenic elements, etc.) based on user constraints expressed in a formal language, and assist in the set up of lights to achieve the same illumination profile in multiple venues. After characterizing deployed lights, the Illuminator computes at run-time optimal light settings to achieve a user-specified actuation profile using an optimization framework based on a genetic algorithm Uniquely, it can use deployed sensors to incorporate changing ambient lighting conditions and moving targets into actuation. With experimental results, we demonstrate that the Illuminator handles various high-level user's constraints and generates optimal light actuation profile. These results suggest that our system should support entertainment and media production applications.

No abstract available.

Although there is growing interest in the use of physical testbeds to evaluate the performance of applications and protocols for sensor platforms, such studies also encounter significant challenges that include the lack of scalability and repeatability, as well as the inability to represent a diverse set of operational scenarios. On the other hand, simulators can typically address the preceding problems but of-ten lack the high degree of fidelity available to the analysts with physical testbeds. In this paper, we present the design and implementation of SenQ - an accurate and scalable evaluation framework for sensor networks that effectively addresses the preceding challenges. In particular, SenQ integrates sensor network operating systems with a very high-fidelity simulation of wireless networks such that sensor network applications and protocols can be executed, without modifications, in a repeatable manner under a diverse set of scalable environments. SenQ extends beyond the existing suite of simulators and emulators in four key aspects: first, it supports emulation of sensor network applications and protocols in an efficient and exible manner; second, it provides an efficient set of models of diverse sensing phenomena; third, it provides accurate models of both battery power and clock drift effect which have been shown to have a significant impact on sensor network studies; and finally it provides an efficient kernel that allows it to run experiments that provide substantial scalability in both the spatial and temporal contexts.

The rapid adoption of mobile phones by society over the last decade and the increasing ability to capture, classifying, and transmit a wide variety of data (image, audio, and location) have enabled a new sensing paradigm - where humans carrying mobile phones can act as sensor systems. Human-in-the-loop sensor systems raise many new challenges in areas of sensor data quality assessment, mobility and sampling coordination, and user interaction procedures.

Power management is an important concern in sensor networks, because a tethered energy infrastructure is usually not available and an obvious concern is to use the available battery energy efficiently. However, in some of the sensor networking applications, an additional facility is available to ameliorate the energy problem: harvesting energy from the environment. Certain considerations in using an energy harvesting source are fundamentally different from that in using a battery, because, rather than a limit on the maximum energy, it has a limit on the maximum rate at which the energy can be used. Further, the harvested energy availability typically varies with time in a nondeterministic manner. While a deterministic metric, such as residual battery, suffices to characterize the energy availability in the case of batteries, a more sophisticated characterization may be required for a harvesting source. Another issue that becomes important in networked systems with multiple harvesting nodes is that different nodes may have different harvesting opportunity. In a distributed application, the same end-user performance may be achieved using different workload allocations, and resultant energy consumptions at multiple nodes. In this case, it is important to align the workload allocation with the energy availability at the harvesting nodes. We consider the above issues in power management for energy-harvesting sensor networks. We develop abstractions to characterize the complex time varying nature of such sources with analytically tractable models and use them to address key design issues. We also develop distributed methods to efficiently use harvested energy and test these both in simulation and experimentally on an energy-harvesting sensor network, prototyped for this work.

Wireless networks have historically considered support for mobile elements's an extra overhead. However, recent research has provided the means by which a network can take advantage of mobile elements. Particularly in the case of wireless sensor networks, mobile elements can be deliberately built into the system to improve the lifetime of the network and act as mechanical carriers of data. The mobile element, whose mobility is controlled, visits the nodes to collect their data before their buffers are full. In general, the spatio-temporal dynamics of the sensed phenomenon may require sensor nodes to collect samples at different rates, in which case, some nodes need to be visited more frequently than others. This work formulates the problem of scheduling the mobile element in the network so that there is no data loss due to buffer overflow. The problem is shown to be NP-complete and an integer-linear-programming formulation is given. Finally, some computationally practical algorithms for a single mobile and for the case of multiple mobiles are presented and their performances compared

Motion may be used in sensor networks to change the network configuration for improving the sensing performance. We consider the problem of controlling motion in a distributed manner for a mobile sensor network for a specific form of motion capability. Mobility itself may have a high resource overhead, hence we exploit motility , a constrained form of mobility, which has very low overheads but provides significant reconfiguration potential. We present an architecture that allows each node in the network to learn the medium and phenomenon characteristics. We describe a quantitative metric for sensing performance that is concretely tied to real sensor and medium characteristics, rather than assuming an abstract range based model. The problem of determining the desirable network configuration is expressed as an optimization of this metric. We present a distributed optimization algorithm which computes a desirable network configuration, and adapts it to environmental changes. The relationship of the proposed algorithm to simulated annealing and incremental subgradient descent based methods is discussed. A key property of our algorithm is that convergence to a desirable configuration can be proved even though no global coordination is involved. A network protocol to implement this algorithm is discussed, followed by simulations and experiments on a laboratory test bed.

Energy harvesting offers a promising alternative to solve the sustainability limitations arising from battery size constraints in sensor networks. Several considerations in using an environmental energy source are fundamentally different from using batteries. Rather than a limit on the total energy, harvesting transducers impose a limit on the instantaneous power available. Further, environmental energy availability is often highly variable and a deterministic metric such as residual battery capacity is not available to characterize the energy source. The different nodes in a sensor network may also have different energy harvesting opportunities. Since the same end-user performance may be achieved using different workload allocations at multiple nodes, it is important to adapt the workload allocation to the spatio-temporal energy availability profile in order to enable energy-neutral operation of the network. This paper describes power management techniques for such energy harvesting sensor networks. Platform design considerations as well as power scaling techniques at the node-level and network-level are described.

In-situ reconfiguration of software is indispensable in embedded networked sensing systems. It is required for re-tasking a deployed network, fixing bugs, introducing new features and tuning the system parameters to the operating environment. We present a system that supports software recon-figuration in embedded sensor networks at multiple levels. The system architecture is based on an operating system consisting of a fixed tiny static kernel and binary modules that can be dynamically inserted, updated or removed. On top of the operating system is a command interpreter, implemented as a dynamically extensible virtual machine, that can execute high-level scripts written in portable byte code. Any binary module dynamically inserted into the operating systems can register custom extensions in the virtual machine interpreter, thus allowing the high-level scripts executed by the virtual machine to efficiently access services exported by a module, such as tuning module parameters. Together these system mechanisms permit the exibility of selecting the most appropriate level of reconfiguration. In addition to detailing the system architecture and the design choices, the paper presents a systematic analysis of exibility versus cost tradeoffs provided by these mechanisms.

Abstract — Until recently, the problem of localization in wireless networks has been mainly studied in a nonadversarial setting. Only recently, a number of solutions have been proposed that aim to detect and prevent attacks on localization systems. In this work, we propose a new approach to secure localization based on hidden and mobile base stations. Our approach enables secure localization with a broad spectrum of localization techniques: ultrasonic or radio, based on received signal strength or signal time of flight. Through several examples we show how this approach can be used to secure nodecentric and infrastructure-centric localization schemes. We further show how this approach can be applied to secure localization in sensor networks. I.

Harvesting energy from the environment is feasible in many applications to ameliorate the energy limitations in sensor networks. In this paper, we present an adaptive duty cycling algorithm that allows energy harvesting sensor nodes to autonomously adjust their duty cycle according to the energy availability in the environment. The algorithm has three objectives, namely (a) achieving energy neutral operation, i.e., energy consumption should not be more than the energy provided by the environment, (b) maximizing the system performance based on an application utility model subject to the above energy-neutrality constraint, and (c) adapting to the dynamics of the energy source at run-time. We present a model that enables harvesting sensor nodes to predict future energy opportunities based on historical data. We also derive an upper bound on the maximum achievable performance assuming perfect knowledge about the future behavior of the energy source. Our methods are evaluated using data gathered from a prototype solar energy harvesting platform and we show that our algorithm can utilize up to 58% more environmental energy compared to the case when harvesting-aware power management is not used.

In this paper we explore the design of multi-modal MAC for wireless ad hoc and sensor networks that dynamically adapt its behavior in order to minimize the energy to delivery ratio under a wide variety of network loads. The prime motivation is to balance the inherent trade-off between the energy wasted in collisions and the energy expended by collision avoidance handshake mechanisms. Towards this objective, the study goes through two phases. First, we explore the space of MAC modes subject to the constraint that different access schemes can inter-operate. Accordingly, we limit our attention to modes within the non-slotted random access paradigm. Second, we analyze, with the aid of detailed network simulations, the energy performance trade-offs of four variations of the CSMA/CA access scheme. Finally, we shed some light on the problem of dynamically switching between different modes depending on the network loading conditions and application QoS requirements. Initial results reveal interesting observations related to the energy/delivery contribution of channel reservation and single-hop acknowledgment packets under a wide variety of temporal network loads

The resolution at which a sensor network collects data is a crucial parameter of performance since it governs the range of applications that are feasible to be developed using that network. A higher resolution, in most situations, enables more applications and improves the reliability of existing ones. In this paper we discuss a system architecture that uses controlled motion to provide virtual high-resolution in a network of cameras. Several orders of magnitude advantage in resolution may be achieved, depending on tolerable tradeoffs. We discuss several system design choices in the context of our prototype camera network implementation that realizes the proposed architecture. We also mention how some of our techniques may apply to sensors other than cameras. Real world data is collected using our prototype system and used for the evaluation of our proposed methods.

No abstract available.

Inspired by unidirectional error detecting codes that are used in situations where only one kind of bit errors are possible (e.g., it is possible to change a bit "0" into a bit "1", but not the contrary), we propose integrity codes (I-codes) for a radio communication channel, which enable integrity protection of messages exchanged between entities that do not hold any mutual authentication material (i.e. public keys or shared secret keys). The construction of I-codes enables a sender to encode any message such that if its integrity is violated in transmission over a radio channel, the receiver is able to detect it. In order to achieve this, we rely on the physical properties of the radio channel. We analyze in detail the use of I-codes on a radio communication channel and we present their implementation on a Mica2 wireless sensor platform as a "proof of concept". We finally introduce a novel concept called "authentication through presence" that can be used for several applications, including for key establishment and for broadcast authentication over an insecure radio channel. We perform a detailed analysis of the security of our coding scheme and we show that it is secure with respect to a realistic attacker model.

We discuss the use of mobility to enhance network performance for a certain class of applications in sensor networks. A major performance bottleneck in sensor networks is energy since it is impractical to replace the batteries in embedded sensor nodes post-deployment. A significant portion of the energy expenditure is attributed to communications and, in particular, the nodes close to the sensor network gateways used for data collection typically suffer a large overhead as these nodes must relay data from the remaining network. Even with compression and in-network processing to reduce the amount of communicated data, all the processed data must still traverse these nodes to reach the gateway. We discuss a network infrastructure based on the use of controllably mobile elements to reduce the communication energy consumption at the energy constrained nodes and, thus, increase useful network lifetime. In addition, our approach yields advantages in delay-tolerant networks and sparsely deployed networks. We first show how our approach helps reduce energy consumption at battery constrained nodes. Second, we describe our system prototype, which utilizes our proposed approach to improve the energy performance. As part of the prototyping effort, we experienced several interesting design choices and trade-offs that affect system capabilities and performance. We describe many of these design challenges and discuss the algorithms developed for addressing these. In particular, we focus on network protocols and motion control strategies. Our methods are tested using a practical system and do not assume idealistic radio range models or operation in unobstructed environments

Networked infomechanical systems (NIMS) introduces a new actuation capability for embedded networked sensing. By exploiting a constrained actuation method based on rapidly deployable infrastructure, NIMS suspends a network of wireless mobile and fixed sensor nodes in three-dimensional space. This permits run-time adaptation with variable sensing location, perspective, and even sensor type. Discoveries in NIMS environmental investigations have raised requirements for 1) new embedded platforms integrating many diverse sensors with actuators, and 2) advances for in-network sensor data processing. This is addressed with a new and generally applicable processor-preprocessor architecture described in this paper. Also this paper describes the successful integration of R, a powerful statistical computing environment, into the embedded NIMS node platform.

Sustainable operation of battery powered wireless embedded systems (such as sensor nodes) is a key challenge, and considerable research effort has been devoted to energy optimization of such systems. Environmental energy harvesting, in particular solar based, has emerged as a viable technique to supplement battery supplies. However, designing an efficient solar harvesting system to realize the potential benefits of energy harvesting requires an in-depth understanding of several factors. For example, solar energy supply is highly time varying and may not always be sufficient to power the embedded system. Harvesting components, such as solar panels, and energy storage elements, such as batteries or ultracapacitors, have different voltage-current characteristics, which must be matched to each other as well as the energy requirements of the system to maximize harvesting efficiency. Further, battery non-idealities, such as self-discharge and round trip efficiency, directly affect energy usage and storage decisions. The ability of the system to modulate its power consumption by selectively deactivating its sub-components also impacts the overall power management architecture. This paper describes key issues and tradeoffs which arise in the design of solar energy harvesting, wireless embedded systems and presents the design, implementation, and performance evaluation of Heliomote, our prototype that addresses several of these issues. Experimental results demonstrate that Heliomote, which behaves as a plug-in to the Berkeley/Crossbow motes and autonomously manages energy harvesting and storage, enables near-perpetual, harvesting aware operation of the sensor node.

Fidelity is one of the key considerations in data collection schemes for sensor networks. A second important consideration is the energy expense of achieving that fidelity. Data from multiple correlated sensors is collected over multi-hop routes and fused to reproduce the phenomenon. However, the same distortion may be achieved using multiple rate allocations among the correlated sensors. These rate allocations would typically have different energy cost in routing depending on the network topology. We consider the interplay between these two considerations of distortion and energy. First, we describe the various factors that affect this trade-off. Second, we discuss bounds on the achievable performance with respect to this trade-off. Specifically, we relate the network lifetime Ltto the distortion D of the delivered data. Finally, we present low-complexity approximations for the efficient computation of the Lt(D) bound

In this paper we explore the design of multi-modal MAC for wireless ad hoc and sensor networks that dynamically adapt its behavior in order to minimize the energy to delivery ratio under a wide variety of network loads. The prime motivation is to balance the inherent trade-off between the energy wasted in collisions and the energy expended by collision avoidance handshake mechanisms. Towards this objective, the study goes through two phases. First, we explore the space of MAC modes subject to the constraint that different access schemes can inter-operate. Accordingly, we limit our attention to modes within the non-slotted random access paradigm. Second, we analyze, with the aid of detailed network simulations, the energy performance trade-offs of four variations of the CSMA/CA access scheme. Initial results reveal interesting observations related to the energy/delivery contribution of channel reservation and single-hop acknowledgment packets under a wide variety of temporal network loads

Sensor network nodes exhibit characteristics of both embedded systems and general-purpose systems. They must use little energy and be robust to environmental conditions, while also providing common services that make it easy to write applications. In TinyOS, the current state of the art in sensor node operating systems, reusable components implement common services, but each node runs a single statically-linked system image, making it hard to run multiple applications or incrementally update applications. We present SOS, a new operating system for mote-class sensor nodes that takes a more dynamic point on the design spectrum. SOS consists of dynamically-loaded modules and a common kernel, which implements messaging, dynamic memory, and module loading and unloading, among other services. Modules are not processes: they are scheduled cooperatively and there is no memory protection. Nevertheless, the system protects against common module bugs using techniques such as typed entry points, watchdog timers, and primitive resource garbage collection. Individual modules can be added and removed with minimal system interruption. We describe SOS's design and implementation, discuss tradeoffs, and compare it with TinyOS and with the Maté virtual machine. Our evaluation shows that despite the dynamic nature of SOS and its higher-level kernel interface, its long term total usage nearly identical to that of systems such as Matè and TinyOS.

Conserving energy has been known as the most significant problem in all facets of sensor network operation. Particularly in routing, researchers were concerned with the problems of using topology control to achieve power efficient routes, as well as finding alternate routes to extend the lifetime of the network. Current schemes though, fail in one or more of the following areas: 1) provide solutions that treat both the aforementioned problems, 2) provide distributed algorithms, 3) account for the overhead of the algorithms 4) compare the results with theoretically computed optimums, 5) account for the effect of the MAC layer. We propose and evaluate a unicast routing algorithm that exploits the ability of the nodes to transmit at multiple power levels. It can find the optimal power-efficient route between two nodes, with less energy and time overheads than the distributed Bellman-Ford algorithm, as well as use alternate routes to extend the total lifetime of the network, up to 87% of the theoretical optimum lifetime, taking into account all overheads. Furthermore, we address many practical considerations in the context of sensor networks. We built our case around three main pillars: i) a distributed algorithm, ii) extensive evaluation of the overhead of the algorithm, and iii) account for the effect of the MAC layer.

Abstract — The quality of sensing in practical sensor network deployments suffers due to the presence of obstacles in the sensing medium. If such unknown obstacles are present, and the sensor data indicates that no targets of interest are detected, then there is no easy way for the application to distinguish between the cases that there really is no target or that the targets are located in occluded regions. The obstacles may not be known before deployment and may change over time. Hence, it is of interest to develop methods which enable a sensor network to determine the presence and extent of sensing occlusions. We present one such method based on the use of a range sensor to map the obstacles in the medium. A network architecture to support efficient medium mapping facilities is presented, along with several design choices in the acquisition and update of the medium map data. We also present algorithms to rapidly acquire this data and share it among multiple nodes. All algorithms presented are implemented on prototype hardware consisting of an actuated laser and an embedded processing platform. I.

Radio duty cycling has received significant attention in sensor networking literature, particularly in the form of protocols for medium access control and topology management. While many protocols have claimed to achieve significant duty-cycling benefits in theory and simulation, these benefits have often not translated to practice. The dominant factor that prevents the optimal usage of the radio in real deployment settings is time uncertainty between sensor nodes. This paper proposes an uncertainty-driven approach to duty-cycling where a model of long-term clock drift is used to minimize the duty-cycling overhead. First, we use long-term empirical measurements to evaluate and analyze in-depth the interplay between three key parameters that influence long-term synchronization - synchronization rate, history of past synchronization beacons and the estimation scheme. Second, we use this measurement-based study to design a rate-adaptive, energy-efficient long-term time synchronization algorithm that can adapt to changing clock drift and environmental conditions while achieving application-specific precision with very high probability. Finally, we integrate our uncertainty-driven time synchronization scheme with a MAC layer protocol, BMAC, and empirically demonstrate one to two orders of magnitude reduction in the transmit energy consumption at a node with negligible impact on the packet loss rate.

No abstract available.

No abstract available.

Recent technological progress in integrated low power CMOS based imaging devices has led to new type of sensors such as Cyclops. Cyclops is a CMOS image sensor, with reduced complexity and power that allows it to mate with typical sensor network nodes such as Motes. This motivates a new class of sensor networks which exploit vision.In this demonstration we introduce a network of Motes that carry Cyclops sensors. In this network, each individual node can be programmed to perform specific operation on the image. These operations include detecting objects in the scene, detecting edges, reporting histogram of the image, getting the image across the network or getting only particular region of interest.The demonstration showcases the functionality of our network to detect objects. Each node in the network is programmed to detect presence of the objects in its field of view. In addition, users can request the whole image or particular region of interest which the presence of the object has been detected. Performance studies as well as architectural choices will be presented along with outstanding challenges and opportunities.

Despite their increasing sophistication, wireless sensor networks still do not exploit the most powerful of the human senses: vision. Indeed, vision provides humans with unmatched capabilities to distinguish objects and identify their importance. Our work seeks to provide sensor networks with similar capabilities by exploiting emerging, cheap, low-power and small form factor CMOS imaging technology. In fact, we can go beyond the stereo capabilities of human vision, and exploit the large scale of sensor networks to provide multiple, widely different perspectives of the physical phenomena.To this end, we have developed a small camera device called Cyclops that bridges the gap between the computationally constrained wireless sensor nodes such as Motes, and CMOS imagers which, while low power and inexpensive, are nevertheless designed to mate with resource-rich hosts. Cyclops enables development of new class of vision applications that span across wireless sensor network. We describe our hardware and software architecture, its temporal and power characteristics and present some representative applications.

Recently, researchers have proposed a number of ranging and positioning techniques for wireless networks. However, they all studied these techniques in non-adversarial settings. Distance estimation and positioning techniques are, nevertheless, highly vulnerable to attacks from dishonest nodes and malicious attackers. Internal attackers can report false position and distance information in order to cheat on their locations and external attackers can modify the measured positions and distances of wireless nodes. In this work, we demonstrate two attacks on ultrasonic ranging systems: the wormhole attack, by which the attackers reducethe distance measured between two honest nodes, and the pulsedelay attack, by which the attackers enlarge the measured distance. With these attacks, we show that the attackers can arbitrarily modify distances measured with ultrasonic ranging, despite the authentication and integrity protection of the messages used in the ranging protocol. Based on this difference, B estimates its distance to A.

No abstract available.

No abstract available.

Time synchronization is critical to sensor networks at many layers of its design and enables better duty-cycling of the radio, accurate localization, beamforming and other collaborative signal processing. While there has been significant work in sensor network synchronization, measurement based studies have been restricted to very short-term (few minutes) datasets and have focused on obtaining accurate instantaneous synchronization. Long-term synchronization has typically been handled by periodic re-synchronization schemes with beacon intervals of a few minutes based on the assumption that long-term drift is too hard to model and predict. Thus, none of this work exploits the temporally correlated behavior of the clock drift. Yet, there are incredible energy gains to be achieved from better modeling and prediction of long-term drift that can provide bounds on long-term synchronization error across a sensor network. Better synchronization can lead to significantly lower duty-cycles of the radio, simplify signal processing and can enable an order of magnitude greater lifetime than current techniques.We measure, evaluate and analyze in-depth the long-term behavior of synchronization skew and drift on typical Mica sensor nodes and develop an efficient long-term time synchronization protocol. We use four real time data sets gathered over periods of 12-30 hours in different environmental conditions to study the interplay between three key parameters that influence long-term synchronization - synchronization rate, history of past synchronization beacons and the estimation scheme. We use this measurement-based study to design an online adaptive time-synchronization algorithm that can adapt to changing clock drift and environmental conditions while achieving application-specified precision with very high probability. We find that our algorithm achieves between one and two orders of magnitude improvement in energy efficiency over currently available time-synchronization approaches.

Orthogonal Frequency Division Multiple Access (OFDMA) is an emerging multiple access technology. In this paper, we consider OFDMA in the context of fixed wireless networks. This paper addresses the problem of assigning subcarriers and bits to point-to-point wireless links in the presence of cochannel interference and Rayleigh fading. The objective is to minimize the total transmitted power over the entire network while satisfying the data rate requirement of each link. We formulate this problem as a constrained optimization problem and present centralized algorithms. The simulation results show that our approach results in an efficient assignment of subcarriers and transmitter power levels in terms of the energy required for transmitting each bit of information. However, centralized algorithms require knowledge of the entire network topology and channel characteristics of every link. In a practical scenario, that would not be the situation and there is a need for distributed rate allocation algorithms. To address this need, we also present a distributed algorithm for allocating subcarriers and bits in order to satisfy the rate requirements of the links.

Wireless ad hoc sensor networks have recently emerged as a premier research topic. They have great long-term economic potential, ability to transform our lives, and pose many new system-building challenges. Sensor networks also pose a number of new conceptual and optimization problems. Here, we address one of the fundamental problems, namely, coverage. Sensor coverage, in general, answers the questions about the quality of service (surveillance) that can be provided by a particular sensor network. We briefly discuss the definition of the coverage problem from several points of view and formally define the worst and best-case coverage in a sensor network. By combining computational geometry and graph theoretic techniques, specifically the Voronoi diagram and graph search algorithms, we establish the main highlight of the paper - an optimal polynomial time worst and average case algorithm for coverage calculation for homogeneous isotropic sensors. We also present several experimental results and analyze potential applications, such as using best and worst-case coverage information as heuristics to deploy sensors to improve coverage.

Ad hoc localization of wireless sensor nodes is a fundamental problem in wireless sensor networks. Despite the recent proposals for the development of ad hoc localization algorithms, the fundamental behavior in systems using measurements has not been characterized. In this paper, we take a first step toward such a characterization by examining the behavior of error inducing parameters in multihop localization systems in an algorithm independent manner. We first derive the Crame Rao Bound for Gaussian measurement error for multihop localization systems using distance and angular measurements. Later on, we use these bounds on a carefully controlled set of scenarios to study the trends in the error induced by the measurement technology accuracy, network density, beacon node concentration, and beacon uncertainty. By exposing these trends, the goal of this paper is to develop a fundamental understanding of the error behavior that can provide a set of guidelines to be considered during the design and deployment of multihop localization systems.

Wireless networked embedded systems, such as multimedia terminals, sensor nodes, etc., present a rich domain for making energy/performance/quality tradeoffs based on application needs, network conditions, etc. Energy awareness in these systems is the ability to perform tradeoffs between available battery energy and application quality requirements. In this paper, we show how operating system directed dynamic voltage scaling and dynamic power management can provide for such a capability. We propose a real-time scheduling algorithm that uses runtime feedback about application behavior to provide adaptive power-fidelity tradeoffs. We demonstrate our approach in the context of a static priority-based preemptive task scheduler. Simulation results show that the proposed algorithm results in significant energy savings compared to state-of-the-art dynamic voltage scaling schemes with minimal loss in system fidelity. We have implemented our scheduling algorithm into the eCos real-time operating system running on an Intel XScale-based variable voltage platform. Experimental results obtained using this platform confirm the effectiveness of our technique

Multicarrier modulation has established itself as an appealing option for high-performance wireless communication systems. When the channel varies slowly in time, such as in applications with low terminal mobility, adaptive loading further enhances the system performance considerably. However, even with adaptive loading, significant performance fluctuations still occur, which impact the higher layers and the overall system behavior. First, a good understanding of the nature of these fluctuations is crucial to design better higher layers that are able to tolerate or combat this variability. For this reason, we develop a full statistical description of a loaded wireless multicarrier system that accurately characterizes its behavior. Our model abstracts the loaded system as an equivalent single carrier system with flat log-normal fading and has the channel tap as a single parameter. Second, with our model, the physical layer can now be incorporated in an abstracted fashion during simulation, resulting in significant speed-ups. Indeed, the alternative would be to include the entire loaded multicarrier system in these simulations, which is highly time consuming. Third, our statistical model is also important for the mathematical analysis of protocols, which requires knowledge of the physical layer statistics. We derive such statistics for both correlated and uncorrelated Rayleigh fading.

This paper addresses the problem of joint scheduling, power control and rate control while maximizing end-to-end data rates in multi-hop wireless networks. Using a "physical layer" network model that explicitly takes into account interference due to spatial spectrum reuse, we formulate the throughput maximization problem as a mixed integer linear programming problem (MILP). While a MILP based approach yields an optimal solution, it does not scale well to large networks. To address this issue, we also present a computationally efficient water-filling based heuristic. Simulation results, obtained using our heuristic, highlight several capacity related tradeoffs that arise in wireless ad-hoc networks. Prior work only provides either asymptotic results on ad-hoc network capacity or, at best, techniques for computing loose upper bounds for throughput in specific instances of networks.

Actuation ability introduces a fundamentally new design dimension in wireless ad-hoc sensor networks, allowing the network to adaptively reconfigure and repair itself in response to unpredictable run-time dynamics. One of the key network resources in these systems is energy and several uncontrollable factors lead to situations where a certain segment of the network becomes energy constrained before the remaining network. The performance gets limited due to the constrained sections. We argue that in this scenario, instead of rendering the complete network useless, the remaining energy resources should be reorganized to form a new functional topology in the network. We present methods for the network to be aware of its own integrity and use actuation to improve performance when needed. This capability of the system is referred to as "self aware actuation". In this paper, we consider a network where nodes (or a subset of the nodes) have traction ability. The network uses mobility to repair the coverage loss in the area being monitored by it. We present a completely distributed energy aware algorithm (referred to as CO-Fi) for coordinated coverage fidelity maintenance in sensor networks. The energy overheads of mobility are incorporated in the algorithm, thus leaving no hidden costs. Our preliminary analysis shows that CO-Fi can significantly help improve the usable lifetime of these networks.

The capabilities and distributed nature of networked sensors are uniquely suited to the characterization of distributed phenomena in the natural environment. However, environmental characterization by fixed distributed sensors encounters challenges in complex environments. In this paper we describe Networked Infomechanical Systems (NIMS), a new distributed, robotic sensor methodology developed for applications including characterization of environmental structure and phenomena. NIMS exploits deployed infrastructure that provides the benefits of precise motion, aerial suspension, and low energy sustainable operations in complex environments. NIMS nodes may explore a three-dimensional environment and enable the deployment of sensor nodes at diverse locations and viewing perspectives. NIMS characterization of phenomena in a three dimensional space must now consider the selection of sensor sampling points in both time and space. Thus, we introduce a new approach of mobile node adaptive sampling with the objective of minimizing error between the actual and reconstructed spatiotemporal behavior of environmental variables while minimizing required motion. In this approach, the NIMS node first explores as an agent, gathering a statistical description of phenomena using a nested stratified random sampling approach. By iteratively increasing sampling resolution, guided adaptively by the measurement results themselves, this NIMS sampling enables reconstruction of phenomena with a systematic method for balancing accuracy with sampling resource cost in time and motion. This adaptive sampling method is described analytically and also tested with simulated environmental data. Experimental evaluations of adaptive sampling algorithms have also been completed. Specifically, NIMS experimental systems have been developed for monitoring of spatiotemporal variation of atmospheric climate phenomena. A NIMS system has been deployed at a field biology station to map phenomena in a 50m width and 50m span transect in a forest environment. In addition, deployments have occurred in testbed environments allowing additional detailed characterization of sampling algorithms. Environmental variable mapping of temperature, humidity, and solar illumination have been acquired and used to evaluate the adaptive sampling methods reported here. These new methods have been shown to provide a significant advance for efficient mapping of spatially distributed phenomena by NIMS environmental robotics.

The performance of a sensor network may be best judged by the quality of application specific information return. The actual sensing performance of a deployed sensor network depends on several factors which cannot be accounted at design time, such as environmental obstacles to sensing. We propose the use of mobility to overcome the effect of unpredictable environmental influence and to adapt to run time dynamics. Now, mobility with its dependencies such as precise localization and navigation is expensive in terms of hardware resources and energy constraints, and may not be feasible in compact, densely deployed and widespread sensor nodes. We present a method based on low complexity and low energy actuation primitives which are feasible for implementation in sensor networks. We prove how these primitives improve the detection capabilities with theoretical analysis, extensive simulations and real world experiments. The significant coverage advantage recurrent in our investigation justifies our own and other parallel ongoing work in the implementation and refinement of self-actuated systems.

We consider the network information theoretic problem of finding the rate distortion bound when multiple correlated Gaussian sources are present. One of these is the source of interest but some side information from other sources is also transmitted to help reduce the distortion in the reproduction of the first source. The other sources are treated as helpers and are also coded. Special cases of this problem have been solved before, such as when the reproduction is lossless, when the sources are conditionally independent given one of them, or when the number of helpers is limited to one. We consider a generalized version and show that the previously derived expressions fall out as special cases of our bound. Our results can be directly utilized by designers to choose not only how many of the available sources should actually be communicated but also which sources have the highest potential to reduce the distortion.

Computer networks have historically considered support for mobile devices as an extra overhead to be borne by the system. Recently however, researchers have proposed methods by which the network can take advantage of mobile components. We exploit mobility to develop a fluid infrastructure: mobile components are deliberately built into the system infrastructure for enabling specific functionality that is very hard to achieve using other methods. Built-in intelligence helps our system adapt to run time dynamics when pursuing pre-defined performance objectives. Our approach yields significant advantages for energy constrained systems, sparsely deployed networks, delay tolerant networks, and in security sensitive situations. We first show why our approach is advantageous in terms of network lifetime and data fidelity. Second, we present adaptive algorithms that are used to control mobility. Third, we design the communication protocol supporting a fluid infrastructure and long sleep durations on energy-constrained devices. Our algorithms are not based on abstract radio range models or idealized unobstructed environments but founded on real world behavior of wireless devices. We implement a prototype system in which infrastructure components move autonomously to carry out important networking tasks. The prototype is used to validate and evaluate our suggested mobility control methods.

With the advent of tiny networked devices, Mark Weiser's vision of a world embedded with invisible computers is coming to age. Due to their small size and relative ease of deployment, sensor networks have been utilized by zoologists, seismologists and military personnel. In this paper, we investigate the application of sensor networks to the film industry. In particular, we are interested in augmenting film and video footage with sensor data. Unobtrusive sensors are deployed on a film set or in a television studio and on performers. During a filming of a scene, sensor data such as light intensity, color temperature and location are collected and synchronized with each film or video frame. Later, editors, graphics artists and programmers can view this data in synchronization with film and video playback. For example, such data can help define a new level of seamless integration between computer graphics and real world photography. A real-time version of our system would allow sensor data to trigger camera movement and cue special effects. In this paper, we discuss the design and implementation of the first part of our embedded film set environment, the augmented recording system. Augmented recording is a foundational component for the UCLA Hypermedia Studio's research into the use of sensor networks in film and video production. In addition, we have evaluated our system in a television studio.

Wireless networks have historically considered support for mobile elements as an extra overhead. However, recent research has provided means by which network can take advantage of mobile elements. Particularly, in the case of wireless sensor networks, mobile elements are deliberately built into the system to improve the lifetime of the network, and act as mechanical carriers of data. The mobile element, which is controlled, visits the nodes to collect their data before their buffers are full. It may happen that the sensor nodes are sampling at different rates, in which case some nodes need to be visited more frequently than others. We present this problem of scheduling the mobile element in the network, so that there is no data loss due to buffer overflow. We prove that the problem is NP-complete and give an ILP formulation. We give some practical algorithms, and compare their performances.

A key challenge in sensor networks is ensuring the sustainability of the system at the required performance level, in an autonomous manner. Sustainability is a major concern because of severe resource constraints in terms of energy, bandwidth and sensing capabilities in the system. In this paper, we envision the use of a new design dimension to enhance sustainability in sensor networks - the use of controlled mobility. We argue that this capability can alleviate resource limitations and improve system performance by adapting to deployment demands. While opportunistic use of external mobility has been considered before, the use of controlled mobility is largely unexplored. We also outline the research issues associated with effectively utilizing this new design dimension. Two system prototypes are described to present first steps towards realizing the proposed vision.

Monitoring of environmental phenomena with embedded networked sensing confronts the challenges of both unpredictable variability in the spatial distribution of phenomena, coupled with demands for a high spatial sampling rate in three dimensions. For example, low distortion mapping of critical solar radiation properties in forest environments may require two-dimensional spatial sampling rates of greater than 10 samples/m2 over transects exceeding 1000 m2. Clearly, adequate sampling coverage of such a transect requires an impractically large number of sensing nodes. This paper describes a new approach where the deployment of a combination of autonomous-articulated and static sensor nodes enables sufficient spatiotemporal sampling densityo ver large transects to meet a general set of environmental mapping demands.To achieve this we have developed an embedded networked sensor architecture that merges sensing and articulation with adaptive algorithms that are responsive to both variabilityin environmental phenomena discovered bythe mobile sensors and to discrete events discovered byst atic sensors. We begin byde scribing the class of important driving applications, the statistical foundations for this new approach, and task allocation. We then describe our experimental implementation of adaptive, event aware, exploration algorithms, which exploit our wireless, articulated sensors operating with deterministic motion over large areas. Results of experimental measurements and the relationship among sampling methods, event arrival rate, and sampling performance are presented.

No abstract available.

The use of environmental energy is now emerging as a feasible energy source for embedded and wireless computing systems such as sensor networks where manual recharging or replacement of batteries is not practical. However, energy supply from environmental sources is highly variable with time. Further, for a distributed system, the energy available at its various locations will be different. These variations strongly influence the way in which environmental energy is used. We present a harvesting theory for determining performance in such systems. First we present a model for characterizing environmental sources. Second, we state and prove two harvesting theorems that help determine the sustainable performance level from a particular source. This theory leads to practical techniques for scheduling processes in energy harvesting systems. Third, we present our implementation of a real embedded system that runs on solar energy and uses our harvesting techniques. The system adjusts its performance level in response to available resources. Fourth, we propose a localized algorithm for increasing the performance of a distributed system by adapting the process scheduling to the spatio-temporal characteristics of the environmental energy in the distributed system. While our theoretical intuition is based on certain abstractions, all the scheduling methods we present are motivated solely from the experimental behavior and resource constraints of practical sensor networking systems.

As embedded systems are being networked, often wirelessly, an increasingly larger share of their total energy budget is due to the communication. This necessitates the development of power management techniques that address communication subsystems, such as radios, as opposed to computation subsystems, such as embedded processors, to which most of the research effort thus far has been devoted. In this paper, we present techniques for energy efficient packet scheduling and fair queuing in wireless communication systems. Our techniques are based on an extensive slack management approach that dynamically adapts the output rate of the system in accordance with the input packet arrival rate. We use a recently proposed radio power management technique, dynamic modulation scaling (DMS), as a control knob to enable energy-latency trade-offs during wireless packet transmission. We first analyze a single input stream scenario, and describe a rate adaptation technique that results in significantly lower energy consumption (reductions of up to 10 ×), while still bounding the resulting packet delays. By appropriately setting the various parameters of our algorithm, the system can be made to traverse the energy-latency-fidelity trade-off space. We extend our techniques to a multiple input stream scenario, and present E 2 WFQ , an energy efficient version of the weighted fair queuing (WFQ) algorithm for fair packet scheduling. Simulation results show that large energy savings can be obtained through the use of E 2 WFQ , with only a small, bounded increase in worst case packet latency. Further, our results demonstrate that E 2 WFQ does not adversely affect the throughput allocation (and hence, fairness) of WFQ.

Energy related research in wireless ad hoc sensor networks (WASNs) is focusing on energy saving techniques in the application-, protocol-, service-, or hardware-level. Little has been done to manage the finite amount of energy for a given (possibly optimally-designed) set of applications, protocols and hardware. Given multiple candidate applications (i.e., distributed algorithms in a WASN) of different energy costs and different user rewards, how does one manage a finite energy amount? Where does one provide energy, so as to maximize the useful work done (i.e., maximize user rewards)? We formulate the problem at the node-level, by having system-level “hints” from the applications. In order to tackle the central problem we first identify the energy consumption patterns of applications in WASNs, we propose ways for real-time measurements of the energy consumption by individual applications, and we solve the problem of estimating the extra energy consumption that a new application brings to a set of executing applications. Having these tools at our disposal, and by properly abstracting the problem we present an optimal admission control policy and a post-admission policing mechanism at the node-level. The admission policy can achieve up to 48% increase in user rewards compared to the absence of energy management, for a variety of application mixes.

Interconnects have been shown to be a dominant source of energy consumption in modern day System-on-Chip (SoC) designs. With a large (and growing) number of electronic systems being designed with battery considerations in mind, minimizing the energy consumed in on-chip interconnects becomes crucial. Further, the use of nanometer technologies is making it increasingly important to consider reliability issues during the design of SoC communication architectures. Continued supply voltage scaling has led to decreased noise margins, making interconnects more susceptible to noise sources such as crosstalk, power supply noise, radiation induced defects, etc. The resulting transient faults cause the interconnect to behave as an unreliable transport medium for data signals. Therefore, fault tolerant communication mechanisms, such as Automatic Repeat Request (ARQ), Forward Error Correction (FEC), etc., which have been widely used in the networking community, are likely to percolate to the SoC domain.This paper presents a survey of techniques for energy efficient on-chip communication. Techniques operating at different levels of the communication design hierarchy are described, including circuit-level techniques, such as low voltage signaling, architecture-level techniques, such as communication architecture selection and bus isolation, system-level techniques, such as communication based power management and dynamic voltage scaling for interconnects, and network-level techniques, such as error resilient encoding for packetized on-chip communication. Emerging technologies, such as Code Division Multiple Access (CDMA) based buses, and wireless interconnects are also surveyed.

By adapting radio setting, such as the modulation or error code, in response to changes in the wireless channel, the energy consumption of a communication system can be greatly reduced. In paper, we investigate the problem of minimizing the energy for a desired average throughput. When the option of shutting down the radio completely is also considered, deciding on the optimal setting becomes a complex scheduling problem. In this case, we have to take power of both the radio electronics and the power amplifier into account, the relative importance of which has a critical impact on the nature of the adaptation strategy, as we show in this paper. We present a new algorithm that does not require any knowledge of the channel statistics, but instead quickly learns the desired adaptation strategy. It is easy to implement, yet is optimal in terms of energy savings.

Energy constrained systems such as sensor networks can increase their usable lifetimes by extracting energy from their environment. However, environmental energy will typically not be spread homogeneously over the spread of the network. We argue that significant improvements in usable system lifetime can be achieved if the task allocation is aligned with the spatio-temporal characteristics of energy availability. To the best of our knowledge, this problem has not been addressed before. We present a distributed framework for the sensor network to adaptively learn its energy environment and give localized algorithms to use this information for task sharing among nodes. Our framework allows the system to exploit its energy resources more efficiently, thus increasing its lifetime. These gains are in addition to those from utilizing sleep modes and residual energy based scheduling mechanisms. Performance studies for an experimental energy environment show up to 200% improvement in lifetime.

This paper presents techniques for optimizing the energy efficiency of multi-resource, multi-tasking embedded systems. Low power design of individual system resources, such as embedded processors, has been extensively studied in the past. However, system-level techniques, such as those presented in this paper, which exploit the synergy between various system resources, achieve levels of energy efficiency that cannot be obtained by considering individual resources independently. We demonstrate that, in multi-resource embedded systems that concurrently execute multiple applications, there exists a tradeoff between resource management efficiency and resource allocation fairness. By solving the multi-resource energy optimization problem in the context of an embedded sensor system, we show that our techniques enable the system designer to traverse this efficiency-fairness tradeoff space.

Wireless ad hoc sensor networks have emerged as one of the key growth areas for wireless networking and computing technologies. So far these networks/systems have been designed with static and custom architectures for specific tasks, thus providing inflexible operation and interaction capabilities. Our vision is to create sensor networks that are open to multiple transient users with dynamic needs. Working towards this vision, we propose a framework to define and support lightweight and mobile control scripts that allow the computation, communication, and sensing resources at the sensor nodes to be efficiently harnessed in an application-specific fashion. The replication/migration of such scripts in several sensor nodes allows the dynamic deployment of distributed algorithms into the network. Our framework, SensorWare, defines, creates, dynamically deploys, and supports such scripts. Our implementation of SensorWare occupies less than 180Kbytes of code memory and thus easily fits into several sensor node platforms. Extensive delay measurements on our iPAQ-based prototype sensor node platform reveal the small overhead of SensorWare to the algorithms (less than 0.3msec in most high-level operations). In return the programmer of the sensor network receives compactness of code, abstraction services for all of the node's modules, and in-built multi-user support. SensorWare with its features apart from making dynamic programming possible it also makes it easy and efficient without restricting the expressiveness of the algorithms.

The paper describes the system design of Smart Table, a table that can track and identify multiple objects simultaneously when placed on top of its surface. The table has been designed to support a smart problem-solving environment for early childhood education in a project called "Smart Kindergarten". We introduce our technology and present the incorporation of location information and identification provided by Smart Table into context-aware computing applications. In addition, the paper discusses the prototype design, localization algorithm, and the results from final implementation.

With the growing interest in wireless sensor networks, techniques for their systematic analysis design and optimization are essential. Despite numerous research efforts in optimizing hardware, algorithms and protocols for these networks, it remains largely unexplored how these innovations can be all tied together to design a sensor network for a specific practical application. We propose a methodology that starts from four independent quality of service (QoS) parameters and allows the user to completely and unambiguously describe the desired performance, without having to deal with the details of individual devices or protocols. By making appropriate choices in terms of device capabilities and run-time techniques, a design can be positioned in this four-dimensional QoS space. Furthermore, we describe a technique to explore the associated tradeoffs at design time, using both analytical expressions and simulations. To illustrate the benefits of our approach, a design example is worked out, which shows a five fold improvement in network operational lifetime by adapting the event reporting delay.

Wireless ad-hoc sensor networks have caught the fancy of many researchers through the world in a very small span of time. Although notable progress has been made in several key areas, several researchers falter in the way they look at sensor networks i.e. just another kind of wireless ad hoc networks, albeit one composed of energy constrained nodes. A key aspect that makes sensor networks different from traditional networks is their strong link to the physical world. We develop this perspective by studying an interesting class of sensor network applications called aggregation applications.We argue that when a user queries for aggregates like maximum, average etc., he is interested in knowing the statistics of the underlying physical process. In general, this is not equivalent to the statistics gathered from a bunch of nodes. We introduce a distinction between aggregates calculated over a region and aggregates calculated over a discrete set of nodes. We classify them as spatial and nodal aggregates respectively. Till now, researchers have followed the approach of doing nodal aggregation in response to every user query in sensor networks. For a continuous physical process and a random deployment of sensor nodes, which are a norm for sensor networks, the conventional approach of doing nodal aggregation produces inaccurate results.We verify our claim by studying a specific aggregation function namely the average over a region (spatial average) in detail. We use a voronoi-based approach to propose an algorithm for calculating spatial average in sensor networks. The algorithm can work in two modes -- centralized and distributed. The energy and accuracy tradeoff associated with each of the two modes is analyzed in detail. The efficacy of the proposed algorithm is tested over a wide variety of physical processes including real precipitation data of South America acquired over a span of past 50 years. We will show that our algorithm gives a 2-8 times performance gain as compared to the conventional approach of doing nodal aggregation. We further explore the robustness of our algorithm to link failures and channel impairments.Although not 100% accurate, our approach is a simple, efficient and a practical solution for calculating the spatial average. We have implemented our algorithm on Berkeley motes. Our first prototype implementation occupies less than 12K flash ROM and consumes less than 1.6K run-time memory. Our algorithm can be easily integrated with available frameworks for doing aggregation in sensor networks. For a minimal cost, our algorithm can be used to provide a spatial aggregation service on a network of motes.

Wireless ad-hoc sensor networks have emerged as an interesting and important research area in the last few years. The applications envisioned for such networks require collaborative execution of a distributed task amongst a large set of sensor nodes. This is realized by exchanging messages that are time-stamped using the local clocks on the nodes. Therefore, time synchronization becomes an indispensable piece of infrastructure in such systems. For years, protocols such as NTP have kept the clocks of networked systems in perfect synchrony. However, this new class of networks has a large density of nodes and very limited energy resource at every node; this leads to scalability requirements while limiting the resources that can be used to achieve them. A new approach to time synchronization is needed for sensor networks.In this paper, we present Timing-sync Protocol for Sensor Networks (TPSN) that aims at providing network-wide time synchronization in a sensor network. The algorithm works in two steps. In the first step, a hierarchical structure is established in the network and then a pair wise synchronization is performed along the edges of this structure to establish a global timescale throughout the network. Eventually all nodes in the network synchronize their clocks to a reference node. We implement our algorithm on Berkeley motes and show that it can synchronize a pair of neighboring motes to an average accuracy of less than 20ms. We argue that TPSN roughly gives a 2x better performance as compared to Reference Broadcast Synchronization (RBS) and verify this by implementing RBS on motes. We also show the performance of TPSN over small multihop networks of motes and use simulations to verify its accuracy over large-scale networks. We show that the synchronization accuracy does not degrade significantly with the increase in number of nodes being deployed, making TPSN completely scalable.

No abstract available.

Sensor networks are deployed to monitor the physical world and to provide relevant data to the users. An important question in such networks is to estimate the number of sensors required to achieve complete coverage of the desired region. The number of sensors required would depend upon the physical characteristics of the individual sensors as well as the nature of the target. In this paper, we address the problem of finding the critical density of sensors for complete coverage. We use an exposure-based model to find the number of sensors required to cover an area for given sensor and target characteristics. The accuracy of the results is established via simulations.

System-level power management has become a key technique to render modern wireless communication devices economically viable. Despite their relatively large impact on the system energy consumption, power management for radios has been limited to shutdown-based schemes, while processors have benefited from superior techniques based on dynamic voltage scaling (DVS). However, similar scaling approaches that trade-off energy versus performance are also available for radios. To utilize these in radio power management, existing packet scheduling policies have to be thoroughly rethought to make them energy-aware, essentially opening a whole new set of challenges the same way the introduction of DVS did to CPU task scheduling. We use one specific scaling technique, dynamic modulation scaling (DMS), as a vehicle to outline these challenges, and to introduce the intricacies caused by the nonpreemptive nature of packet scheduling and the time-varying wireless channel.

In this paper we introduce novel battery state aware strategies that improve the performance of battery operated systems. In our analysis, we consider the total amount of work done (or service) as the metric instead of considering just the lifetime. Based on our analysis using service curves, we formulate our static approach which generalizes many of the battery aware approaches introduced in the literatures. The results show that the static approach increases the battery utilization by 600% over a non battery aware approach. Furthermore, we show that our dynamic battery state aware approach improves upon the static approach by dynamically adapting the system performance level based on batteryes voltage slope. The results indicate that the dynamic approach achieves improvement of up to 20% over the static approach.

Orthogonal frequency division multiple access (OFDMA) is an emerging multiple access technology. We consider OFDMA in the context of fixed wireless ad hoc networks. We address the problem of assigning subcarriers and bits to point-to-point wireless links in the presence of cochannel interference and Rayleigh fading. The objective is to minimize the total transmitted power over the entire network while satisfying the data rate requirement of each link. We formulate this problem as a constrained optimization problem and present a heuristic algorithm. The simulation results show that our algorithm results in an efficient assignment of subcarriers and transmitter power levels in terms of the energy required for transmitting each bit of information.

We present a low cost wireless microsensor node architecture for distributed computation and sensing in massively distributed embedded systems. Our design focuses on the development of a versatile, low power device to facilitate experimentation and initial deployment of wireless microsensor nodes in deeply embedded systems. This paper provides the details of our architecture and introduces fine-grained node localization as an example application of distributed computation and wireless embedded sensing.

As embedded systems are being networked, often wirelessly, an increasingly larger share of their total energy budget is due to the communication. This necessitates the development of power management techniques that address communication subsystems, such as radios, as opposed to computation subsystems, such as embedded processors, to which most of the research effort thus far has been devoted. In this paper, we present E2WFQ, an energy efficient version of the Weighted Fair Queuing (WFQ) algorithm for packet scheduling in communication systems. We employ a recently proposed radio power management technique, Dynamic Modulation Scaling (DMS), as a control knob to enable energy-latency tradeoffs during wireless packet scheduling. The use of E2WFQ results in an energy aware packet scheduler, which exploits the statistics of the input arrival pattern as well as the variability in packet lengths. Simulation results show that large savings in energy consumption can be obtained through the use of our scheduling scheme, compared to conventional WFQ, with only a small, bounded increase in worst case packet latency.

In wireless sensor networks, energy efficiency is crucial to achieve satisfactory network lifetime. In order to reduce the energy consumption of a node significantly, its radio needs to be turned off. Yet, some nodes have to participate in multi-hop packet forwarding. We tackle this issue by exploiting two degrees of freedom in topology management: the path setup latency and the network density. First, we propose a new technique called Sparse Topology and Energy Management (STEM), which aggressively puts nodes to sleep. It provides a method to wake up nodes only when they need to forward data, where latency is traded off for energy savings. Second, STEM integrates efficiently with existing approaches that leverage the fact that nearby nodes can be equivalent for traffic forwarding. In this case, an increased network density results in more energy savings. We analyze a hybrid scheme, which takes advantage of both setup latency and network density to increase the nodes' lifetime. Our results show improvements of nearly two orders of magnitude compared to sensor networks without topology management.

When designing wireless systems, one of the major challenges is tackling the time depending fading behavior of the channel. To achieve maximum throughput under a power constraint, techniques have been proposed which adapt the modulation on the fly, based on the instantaneous channel condition. However, when the design goal is minimizing the overall energy consumption under a throughput constraint, we have to tackle a more complex scheduling problem. The reason is that energy optimality might require deliberately decreasing the transmission rate at times, if we know that the rate loss can be compensated for in the future when channel conditions are more favorable. We present a solution to the problem of minimum energy scheduling on wireless links by exploiting an analogy with the adaptive bit loading problem in multicarrier systems. Instead of allocating bits across multiple channels with different quality, we formulate the problem as one of allocating bits at the different time instants. The analogy is however not straightforward because one does not know the future, and therefore cannot simply apply conventional bit loading to the time dimension. We devise a new technique that approximates adaptive loading in time, but only depends on the instantaneous channel condition. Our algorithm is simple to implement, and shows up to 5/spl times/ reduction in energy over existing approaches.

In wireless sensor networks, energy efficiency is crucial to achieving satisfactory network lifetime. To reduce the energy consumption significantly, a node should turn off its radio most of the time, except when it has to participate in data forwarding. We propose a new technique, called sparse topology and energy management (STEM), which efficiently wakes up nodes from a deep sleep state without the need for an ultra low-power radio. The designer can trade the energy efficiency of this sleep state for the latency associated with waking up the node. In addition, we integrate STEM with approaches that also leverage excess network density. We show that our hybrid wakeup scheme results in energy savings of over two orders of magnitude compared to sensor networks without topology management. Furthermore, the network designer is offered full flexibility in exploiting the energy-latency-density design space by selecting the appropriate parameter settings of our protocol.

Sensor networks consist of autonomous wireless sensor nodes that are networked together in an ad hoc fashion. The tiny nodes are equipped with substantial processing capabilities, enabling them to combine and compress their sensor data. The aim is to limit the amount of network traffic, and as such conserve the nodes' limited battery energy. However, due to the small packet payload, the MAC header is a significant, and energy-costly, overhead. To remedy this, we propose a novel scheme for a MAC address assignment. The two key features which make our approach unique are the exploitation of spatial address reuse and an encoded representation of the addresses in data packets. To assign the addresses, we develop a purely distributed algorithm that relies solely on local message exchanges. Other salient features of our approach are the ability to handle unidirectional links and the excellent scalability of both the assignment algorithm and address representation. In typical scenarios, the MAC overhead is reduced by a factor of three compared to existing approaches.

Pervasive micro-sensing and actuation may revolutionize the way in which we understand and manage complex physical systems: from airplane wings to complex ecosystems. The capabilities for detailed physical monitoring and manipulation offer enormous opportunities for almost every scientific discipline, and it will alter the feasible granularity of engineering. We identify opportunities and challenges for distributed signal processing in networks of these sensing elements and investigate some of the architectural challenges posed by systems that are massively distributed, physically-coupled, wirelessly networked, and energy limited.

Wireless ad-hoc sensor networks have recently emerged as a premier research topic. They have great long-term economic potential, ability to transform our lives, and pose many new system-building challenges. Sensor networks also pose a number of new conceptual and optimization problems. Some, such as location, deployment, and tracking, are fundamental issues, in that many applications rely on them for needed information. We address one of the fundamental problems, namely coverage. Coverage in general, answers the questions about quality of service (surveillance) that can be provided by a particular sensor network. We first define the coverage problem from several points of view including deterministic, statistical, worst and best case, and present examples in each domain. By combining the computational geometry and graph theoretic techniques, specifically the Voronoi diagram and graph search algorithms, we establish the main highlight of the paper-optimal polynomial time worst and average case algorithm for coverage calculation. We also present comprehensive experimental results and discuss future research directions related to coverage in sensor networks.

In this paper, we look at different battery capacity models that have been introduced in the literatures. These models describe the battery capacity utilization based on how the battery is discharged by the circuits that consume power. In an attempt to validate these models, we characterize a commercially available lithium coin cell battery through careful measurements of the current and the voltage output of the battery under different load profile applied by a micro sensor node. In the result, we show how the capacity of the battery is affected by the different load profile and provide analysis on whether the conventional battery models are applicable in the real world. One of the most significant finding of our work will show that DC/DC converter plays a significant role in determining the battery capacity, and that the true capacity of the battery may only be found by careful measurements.

In systems that require low energy consumption, voltage scaling is an invaluable circuit technique. It also offers energy awareness, trading off energy and performance. In wireless handheld devices, the communication portion of the system is a major power hog. We introduce a new technique, called modulation scaling, which exhibits benefits similar to those of voltage scaling. It allows us to trade off energy against transmission delay and as such introduces the notion of energy awareness in communications. Throughout our discussion, we emphasize the analogy with voltage scaling. As an example application, we present an energy aware wireless packet scheduling system.

Article Share on Architecture strategies for energy-efficient packet forwarding in wireless sensor networks Authors: Vlasios Tsiatsis Networked & Embedded Systems Laboratory, E.E. Dept, UCLA, Los Angeles, CA Networked & Embedded Systems Laboratory, E.E. Dept, UCLA, Los Angeles, CAView Profile , Scott Zimbeck Networked & Embedded Systems Laboratory, E.E. Dept, UCLA, Los Angeles, CA Networked & Embedded Systems Laboratory, E.E. Dept, UCLA, Los Angeles, CAView Profile , Mani Srivastava Networked & Embedded Systems Laboratory, E.E. Dept, UCLA, Los Angeles, CA Networked & Embedded Systems Laboratory, E.E. Dept, UCLA, Los Angeles, CAView Profile Authors Info & Claims ISLPED '01: Proceedings of the 2001 international symposium on Low power electronics and designAugust 2001 Pages 92–95https://doi.org/10.1145/383082.383102Online:06 August 2001Publication History 16citation650DownloadsMetricsTotal Citations16Total Downloads650Last 12 Months6Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The recent advances in radio and em beddedsystem technologies have enabled the proliferation of wireless microsensor networks. Such wirelessly connected sensors are released in many diverse environments to perform various monitoring tasks. In many such tasks, location awareness is inherently one of the most essential system parameters. It is not only needed to report the origins of events, but also to assist group querying of sensors, routing, and to answer questions on the network coverage. In this paper we present a novel approach to the localization of sensors in an ad-hoc network. We describe a system called AHLoS (Ad-Hoc Localization System) that enables sensor nodes to discover their locations using a set distributed iterative algorithms. The operation of AHLoS is demonstrated with an accuracy of a few centimeters using our prototype testbed while scalability and performance are studied through simulation.

Despite enormous progress in networking and computing technologies, their application has remained restricted to conventional person-to-person and person-to-computer communication. However, continual reduction in cost and form factor is now making it possible to imbed networking - even wireless networking - and computing capabilities not just in our PCs and laptops but also other objects. Further, a marriage of these ever tinier and cheaper processors and wireless network interfaces with emerging micro-sensors based on MEMS technology is allowing cheap sensing, processing, and communication capabilities to be unobtrusively embedded in familiar physical objects. The result is an emerging paradigm shift where the primary role of information technology would be to enhance or assist in "person to physical world communication via familiar physical objects with embedded (a) micro-sensors to react to external stimuli, and (b) wireless networking and computing engines for tetherless communication with compute servers and other networked embedded objects. In this paper we present the application of sensor-based wireless networks to a "Smart Kindergarten that we are developing to target developmental problem-solving environments for early childhood education. This is a natural application as young children learn by exploring and interacting with objects such as toys in their environment. Our envisioned system would enhance the education process by providing a childhood learning environment that is individualized to each child, adapts to the context, coordinates activities of multiple children, and allows continual unobtrusive evaluation of the learning process by the teacher. This would be done by wirelessly-networked, sensor-enhanced toys and other classroom objects with back-end middleware services and database techniques. We explore wireless networking, middleware, and data management technologies for realizing this application, and describe challenges arising from ad hoc distributed structure, unreliable sensing, large scale/density, and novel sensor data types that are characteristic of such deeply instrumented environments with inter-networked physical objects.

In wireless sensor networks, the vast majority of wide-scale traffic consists of only a few bytes, including all network and application layer IDs. Therefore, MAC addresses, which are vital in a shared medium, present major overhead, particularly because they are traditionally chosen network-wide unique. To tackle this overhead, we propose a dynamic MAC addressing scheme based on a distributed algorithm. The assigned addresses are reused spatially and represented by variable length codewords. Our scheme scales very well with the network size, rendering it well suited for sensor networks with thousands or millions of nodes

Energy aware system operation, and not just low power hardware, is an important requirement for wireless embedded systems. These systems, such as wireless multimedia terminals or wireless sensor nodes, combine (soft) real-time constraints on computation and communication with requirements of long battery lifetime. In this paper, we present an OS-directed dynamic power management technique for such systems that goes beyond conventional techniques to provide an adaptive power vs. fidelity trade-off. The ability of wireless systems to adapt to changing fidelity in the form of data losses and errors is used to tradeoff against energy consumption. We also exploit system workload variation to proactively manage energy resources by predicting processing requirements. The supply voltage, and clock frequency are set according to predicted computation requirements of a specific task instance, and an adaptive feedback control machanism is used to keep system fidelity (deadline misses) within specifications. We present the theoretical framework underlying our approach in the context of both a static priority-based preemptive task scheduler as well as a dynamic priority based one, and present simulation-based performance analysis that shows that our technique provides large energy savings (up to 76%) with little loss in fidelity (<4%). Further, we describe the implementation of our technique in the eCos real-time operating system (RTOS) running on a StrongARM processor to illustrate the issues involved in enhancing RTOSs for energy awareness.

Providing multiple modes to support dynamically changing environments, standards, and new services is prevalent in embedded systems, especially in mobile radio systems. Because such a system frequently contains time-constrained tasks, it is important to analyze the temporal requirements as well as the functional correctness. This paper presents a method to analyze temporal requirements imposed on an embedded real-time system supporting multiple modes. While most performance analysis methods focus only on testing the feasibility of a task or a system, our method goes further by addressing the problem of locating hot spots of a system thereby helping the designer to choose among alternative designs or architectures. We formally define the analysis problem and show that it is very unlikely to be solved efficiently. We present a heuristic algorithm, which is accurate and fast enough to be used in iterative processes in system-level analysis and design. The analysis problem is extended to accommodate probabilistic behavior exhibited by soft real-time tasks.

Limiting the power consumption of real time embedded systems is an important aspect, especially in portable systems (laptops, cellular phones) with tight power constraints. In this paper, we present a power-saving prediction strategy that exploits the fixed priority scheduling of the real-time tasks running on these embedded systems. Power reduction is achieved by developing an efficient low power scheme with prediction of the expected execution time of real time tasks and making use of the idle time of system for scheduling these tasks in low power modes. In the process there may be few tasks missing their deadlines. This results in a tradeoff between power saved and deadlines missed. Our simulation results for different applications show that the proposed prediction mechanism achieves a high degree of power conservation with a very small penalty of missed deadlines. Our mechanism is simple and can be implemented in most of the real time operating systems.

The advent of wireless micro sensors promises many yet unrealized benefits. A network of such sensors or “sensor network” introduces a new set of challenges. Besides being able to communicate effectively, sensor networks have demanding sensing tasks. First, they must be aware of their environment and oftentimes are required to adapt to their surroundings. Second, they must coordinate among them to perform a greater group-sensing task. In this context, the study of sensor networks has numerous other aspects besides communication. To create a better understanding of sensor networks and to facilitate the development of new protocols and applications, detailed simulation and performance evaluation techniques need to be developed. In this paper, we introduce our ongoing efforts in the development of SensorSim, a simulation framework that introduces new models and techniques for the design and analysis of sensor networks. SensorSim inherits the core features of traditional event driven network simulators, and builds up new features that include ability to model power usage in sensor nodes, hybrid simulation that allows the interaction of real and simulated nodes, new communication protocols and real time user interaction with graphical data display. After discussing the details of SensorSim, we provide our current results, that demonstrate various capabilities of SensorSim.

With advances in packet routing technology, and resource reservation protocols, the Internet is expected to provide ubiquitous integrated transport of speech, audio, video, and other real-time multimedia data in addition to the current best effort data traffic. Such integrated transport will also need to be supported for the increasing number of mobile users who access the Internet over wireless access networks, using Mobile-IP to retain continual IP connectivity. We present a simple quality of service (QoS) signaling protocol for mobile users in an integrated service Internet. The protocol works by combining pre-provisioned RSVP tunnels with Mobile IP. Our protocol, even-though simple, captures the essence of QoS provisioning for wireless and mobile networks. The wireless medium provides a completely different medium than wires, and therefore one's expectations from it should be different. Service quality is inherently mobility dependent, and intermittent disconnections are bound to happen. It is not the signaling protocol's task to completely overcome or conceal transient conditions from applications, but rather applications should try to adapt. Our approach can be easily implemented today with minimal changes to other components of the Internet architecture. We also evaluate the application level performance impact of the QoS provisioning delays associated with our protocol on a prototypical packet speech application with various playout buffering strategies, and compare against the performance of the ordinary RSVP protocol suite with Mobile IP.

The quality of wireless links suffers from temporally and spatially varying channel degradations such as noise, interference, and multipath fading. A proper addressing of these impairments is crucial to sustaining quality of service (QoS) in wireless integrated service packet networks such as wireless ATM. Newer radios that allow parameters such as processing gain, symbol rate, and transmit power to be varied on a per packet basis offer interesting opportunities. This paper explores a highly adaptive approach to wireless link control where the various physical and link layer parameters are continually adapted in response to channel and traffic variations. Furthermore, we also explore adaptivity at the upper layer of the protocol stack, since applications typically can not cope with residual distortions in the packet flow. It is illustrated that lower and upper layer adaptation strategies are tightly intertwined, such that they should be managed jointly. Node and basestation architectures to efficiently support such adaptivity are also described.

The quality of wireless links suffers from time-varying channel degradations such as interference, flat-fading, and frequency-selective fading. Current radios are limited in their ability to adapt to these channel variations because they are designed with fixed values for most system parameters such as frame length, error control, and processing gain. The values for these parameters are usually a compromise between the requirements for worst-case channel conditions and the need for low implementation cost. Therefore, in benign channel conditions these commercial radios can consume more battery energy than needed to maintain a desired link quality, while in a severely degraded channel they can consume energy without providing any quality-of-service (QoS). While techniques for adapting radio parameters to channel variations have been studied to improve link performance, in this paper they are applied to minimize battery energy. Specifically, an adaptive radio is being designed that adapts the frame length, error control, processing gain, and equalization to different channel conditions, while minimizing battery energy consumption. Experimental measurements and simulation results are presented to illustrate the adaptive radio's energy savings.

The growing class of portable systems, such as personal computing and communication devices, has resulted in a new set of system design requirements, mainly characterized by dominant importance of power minimization and design reuse. The energy efficiency of systems-on-a-chip (SOC) could be much improved if one were to vary the supply voltage dynamically at run time. We developed the design methodology for the low-power core-based real-time SOC based on dynamically variable voltage hardware. The key challenge is to develop effective scheduling techniques that treat voltage as a variable to be determined, in addition to the conventional task scheduling and allocation. Our synthesis technique also addresses the selection of the processor core and the determination of the instruction and data cache size and configuration so as to fully exploit dynamically variable voltage hardware, which results in significantly lower power consumption for a set of target applications than existing techniques. The highlight of the proposed approach is the nonpreemptive scheduling heuristic, which results in solutions very close to optimal ones for many test cases. The effectiveness of the approach is demonstrated on a variety of modern industrial strength multimedia and communication applications.

The growing class of portable systems, such as personal computing and communication devices, has resulted in a new set of system design requirements, mainly characterized by dominant importance of power minimization and design reuse. We develop the design methodology for the low power core-based real-time system-on-chip based on dynamically variable voltage hardware. The key challenge is to develop effective scheduling techniques that treat voltage as a variable to be determined, in addition to the conventional task scheduling and allocation. Our synthesis technique also addresses the selection of the processor core and the determination of the instruction and data cache size and configuration so as to fully exploit dynamically variable voltage hardware, which result in significantly lower power consumption for a set of target applications than existing techniques. The highlight of the proposed approach is the non-preemptive scheduling heuristic which results in solutions very close to optimal ones for many test cases. The effectiveness of the approach is demonstrated on a variety of modern industrial-strength multimedia and communication applications.

We consider the problem of schtifing the mixed worMoad of both sporadic (on-fine) and periodic (off-fine) tasks on variable voltage processor to optimize power consumption while ensuring that ~ periodic tasks meet their destines and to accept as many sporadic tasks, which can be guaranteed to meet their destines, as possible.The proposed efficient dgonthms restit in the scheduling solutions, which are very close to the minimum bound achievable with the dynamictiy variable voltage approach.The effectiventis of the proposed dgonthrns is shown on extensive experiments with rd-fife design examples.

A key problem in transporting multimedia traffic across wireless networks is a controlled sharing of the wireless link by different packet streams. So far this problem has been treated as that of providing support for quality of service in time division multiplexing based medium access control protocols (MAC). Adopting a different perspective to the problem, this paper describes an approach based on extending the class-based queueing (CBQ) based controlled hierarchical link sharing model proposed for the Internet. Our scheme enhances CBQ, which works well in wired links such as point-to-point wires of fixed bandwidth, to also work well with wireless links based on radio channels that are (i) inherently shared on-demand among multiple radios, and (ii) are subject to highly dynamic bandwidth variations due to spatially and temporally varying fading with accompanying burst errors. The proposed scheme is based on combining a modified version of CBQ with channel-state dependent packet scheduling.

Wireless network links are characterized by rapidly time varying channel conditions and battery energy limitations at the wireless mobile user nodes. Therefore static link control techniques that make sense in comparatively well behaved wired links do not necessarily apply to wireless links. New adaptive link layer control techniques are needed to provide robust and energy efficient operation even in the presence of orders of magnitude variations in bit error rates and other radio channel conditions. For example, research has advocated adaptive link layer techniques such as adaptive error control, channel state dependent protocols, and variable spreading gain. We explore dynamic sizing of the MAC layer frame, the atomic unit that is sent through the radio channel. A trade-off exists between the desire to reduce the header and physical layer overhead by making frames large, and the need to reduce frame error rates in the noisy channel by using small frame lengths. Clearly the optimum depends on the channel conditions. Through analysis supported by physical measurements with Lucent's WaveLAN radio we show that adaptive sizing of the MAC layer frame in the presence of varying channel noise indeed has a large impact on the user seen throughput (goodput). In addition, we show how that adaptive frame length control can be exploited to improve the energy efficiency for a desired level of goodput, and to extend the usable radio range with graceful throughput degradation. We describe the implementation of the adaptive MAC frame length control mechanism in combination with adaptive hybrid FEC/ARQ error control in a reconfigurable wireless link layer packet processing architecture for a low-power adaptive wireless multimedia node.

The energy efficiency of systems-on-a-chip can be much improved if one were to vary the supply voltage dynamically at run time. We describe the synthesis of systems-on-a-chip based on core processors, while treating voltage (and correspondingly the clock frequency) as a variable to be scheduled along with the computation tasks during the static scheduling step. In addition to describing the complete synthesis design flow for these variable voltage systems, we focus on the problem of doing the voltage scheduling while taking into account the inherent limitation on the rates at which the voltage and clock frequency can be changed by the power supply controllers and clock generators. Taking these limits on rate of change into account is crucial, since changing the voltage by even a volt may take time equivalent to 100 s to 10000 s of instructions on modern processors. We present both an exact but impractical formulation of this scheduling problem as a set of nonlinear equations, as well as a heuristic approach based on reduction to an optimally solvable restricted ordered scheduling problem. Using various task mixes drawn from a set of nine real life applications, our results show that we are able to reduce power consumption to within 7% of the lower bound obtained by imposing no limit at the rate of change of voltage and clock frequencies.

The increasing deployment of wireless access technology, along with the emergence of high speed integrated service networks, such as ATM, promises to provide mobile users with ubiquitous access to multimedia information in the near future. One of the key problems in building connection-oriented ATM networks that support host mobility is designing mechanisms for rerouting virtual circuits to maintain data flow to and from mobile hosts. Ideally, VC rerouting must be done fast enough so as to cause minimal disruption to applications while minimizing the signaling overhead. In this paper, we evaluate the impact of several virtual circuit rerouting strategies on application performance. We initially identify the primitive operations required by any rerouting policy and use this to analytically quantify the cost of each rerouting policy in terms of wireless link disruption as a function of various network parameters. We then evaluate the effect of the VC rerouting policy on application-level performance using simulations. Our results show that the effect of rerouting policies are strongly dependent on the transport protocol policies and application QoS requirements, in addition to the network topology.

We introduce a transformation, named rephasing, that manipulates the timing parameters in control-data-flow graphs (CDFG's) during the high-level synthesis of data-path-intensive applications. Timing parameters in such CDFG's include the sample period, the latencies between input-output pairs, the relative times at which corresponding samples become available on different inputs, and the relative times at which the corresponding samples become available at the delay nodes. While some of the timing parameters may be constrained by performance requirements, or by the interface to the external world, others remain free to be chosen during the process of high-level synthesis. Traditionally high-level synthesis systems for data-path-intensive applications either have assumed that all the relative times, called phases, when corresponding samples are available at input and delay nodes are zero (i.e., all input and delay node samples enter at the initial cycle of the schedule) or have automatically assigned values to these phases as part of the data-path allocation/scheduling step in the case of newer schedulers that use techniques like overlapped scheduling to generate complex time shapes. Rephasing, however, manipulates the values of these phases as an algorithm transformation before the scheduling/allocation stage. The advantage of this approach is that phase values can be chosen to transform and optimize the algorithm for explicit metrics such as area, throughput, latency, and power. Moreover, the rephasing transformation can be combined with other transformations such as algebraic transformations. We have developed techniques for using rephasing to optimize a variety of design metrics, and our results show significant improvements in several design metrics. We have also investigated the relationship and interaction of rephasing with other high-level synthesis tasks.

Kaleido is a system for flexible concurrent processing of multimedia flows where applications are decomposed into building blocks called "active buffers" whose computation and I/O requirements are characterized so as to allow reservation of CPU and bandwidth resources. The input and output ports of active buffers in a Kaleido application are connected by channels. The active buffers in an application may themselves be mapped either to a single compute node, or to multiple compute nodes connected by a network. The Kaleido runtime system software transparently handles the resulting local and remote channels by providing the abstraction of a distributed "active backplane". This "active backplane" also allows dynamic applications whose functional partitioning among compute nodes can be adapted to network, server, and terminal resources. The paper also describes I/O-centric hardware extensions that we have developed for efficient handling of concurrent multimedia streams in Kaleido.

Energy efficiency, which directly affects battery life and portability, is perhaps the single most important design metric in hand-held computing devices capable of mobile networking over wireless radio links.By virtue of their being relatively thin clients, a high fraction of the power consumption in portable wireless computing devices is accounted for by the transport of packet data over the wireless link [Stemm96].In particular, the error con-.trol strategy (e.g.convolutional and block channel coding for forward error correction (FBC), ARQ protocols, hybrids) used for wireless link data transport has a direct impact on battery power consumption.Error control has traditionally been studied by channel coding researchers from the perspective of selecting an error control scheme to achieve a desired level of radio channel performance.We instead study the problem of error control from a perspective more relevant to battery operated devices: the amount of battery energy consumed to transmit bits across a wireless link.This includes both the physical transmission of useful and redundancy data, as well as the computation of the error control redundancy.We first describe a novel error control where the most battery energy efficient hybrid combination of an appropriate FBC code and ABQ protocol is chosen, and adapted over time, for each stream (ATM virtual circuit or IP/RSVP flow).Next, we present analysis and simulation results to guide the selection and adaptation of the most energy efficient error control scheme as a function of quality of service, packet size, and channel state.

Article Power optimization in programmable processors and ASIC implementations of linear systems: transformation-based approach Share on Authors: Mani Srivastava AT&T Bell Laboratories, 600 Mountain Avenue, Murray Hill, NJ AT&T Bell Laboratories, 600 Mountain Avenue, Murray Hill, NJView Profile , Miodrag Potkonjak Computer Science Department, University of California, Los Angeles, CA Computer Science Department, University of California, Los Angeles, CAView Profile Authors Info & Claims DAC '96: Proceedings of the 33rd annual Design Automation ConferenceJune 1996 Pages 343–348https://doi.org/10.1145/240518.240583Online:01 June 1996Publication History 18citation318DownloadsMetricsTotal Citations18Total Downloads318Last 12 Months4Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Transformations have been widely used in VLSI design, high level synthesis and DSP. We propose a two-step approach for transformation ordering which combines the use of optimization-intensive CAD techniques with knowledge-based user-driven search strategy. The first step is the development of basic building blocks which target small sets of transformations which are well suited for optimization intensive CAD treatment. Next, transformation orderings are developed using knowledge about mathematical laws, an application domain, and the relationship among transformations. Transformation ordering scripts combine several building blocks to form effective approaches for optimization of several design metrics in many common computational structures. As the highlight of the approach, we developed a method which efficiently simultaneously optimizes the throughput, latency, power, and area of linear computations.

There is an emergent interest in providing mobile users with ubiquitous wireless access to multimedia information. In this paper we present a network architecture and protocols to achieve this goal and describe their implementation in a prototype network called SWAN. Our network model assumes end to end ATM connectivity. Thus, the key question we address is how best to enhance ATM to support host mobility and wireless access.

Many applications in DSP, telecommunications, graphics, and control have computations that either involve a large number of multiplications of one variable with several constants, or can easily be transformed to that form. A proper optimization of this part of the computation, which we call the multiple constant multiplication (MCM) problem, often results in a significant improvement in several key design metrics, such as throughput, area, and power. However, until now little attention has been paid to the MCM problem. After defining the MCM problem, we introduce an effective problem formulation for solving it where first the minimum number of shifts that are needed is computed, and then the number of additions is minimized using common subexpression elimination. The algorithm for common subexpression elimination is based on an iterative pairwise matching heuristic. The power of the MCM approach is augmented by preprocessing the computation structure with a new scaling transformation that reduces the number of shifts and additions. An efficient branch and bound algorithm for applying the scaling transformation has also been developed. The flexibility of the MCM problem formulation enables the application of the iterative pairwise matching algorithm to several other important and common high level synthesis tasks, such as the minimization of the number of operations in constant matrix-vector multiplications, linear transforms, and single and multiple polynomial evaluations. All applications are illustrated by a number of benchmarks.

With the popularity of portable devices such as personal digital assistants and personal communicators, as well as with increasing awareness of the economic and environmental costs of power consumption by desktop computers, energy efficiency has emerged as an important issue in the design of electronic systems. While power efficient ASIC's with dedicated architectures have addressed the energy efficiency issue for niche applications such as DSP, much of the computation continues to be implemented as software running on programmable processors such as microprocessors, microcontrollers, and programmable DSP's. Not only is this true for general purpose computation on personal computers and workstations, but also for portable devices, application-specific systems etc. In fact, firmware and embedded software executing on RISC and DSP processor cores that are embedded in ASIC's has emerged as a leading implementation methodology for speech coding, modem functionality, video compression, communication protocol processing etc. This paper describes architectural techniques for energy efficient implementation of programmable computation, particularly focussing on the computation needed in portable devices where event-driven user interfaces, communication protocols, and signal processing play a dominant role. Two key approaches described here are predictive system shutdown and extended voltage scaling. Results indicate that a large reduction in power consumption can be achieved over current day solutions with little or no loss in system performance.

We introduce a transformation, named rephasing, that manipulates the timing parameters in control-dataflow graphs.Traditionally high-level synthesis systems for DSP have either assumed that all the relative times, called phases, when corresponding samples are available at input and delay nodes are zero or have automatically assigned values to as part of the scheduling step when software pipelining is simultaneously applied.Rephasing, however, manipulates the values of these phases as a transformation before the scheduling.The advantage of this approach is that phases can be chosen to optimize the algorithm for metrics such as area and power.Moreover, rephasing can be combined with other transformations.We have developed techniques for using rephasing to optimize several design metrics.The experimental results show significant improvements.

Modern electronic systems contain a mix of software running on general-purpose programmable processors, algorithms hardwired into dedicated hardware such as custom boards and chips, electromechanical components, and mechanical interconnect and packaging. Far more time Is spent in designing the boards, writing the software to drive, and integrate the hardware, and other such system level issues, than is spent in designing any application-specific ICs that may be needed. Therefore a systems perspective of the design process is essential, as opposed to the conventional "chip-focused" approach. A design framework, called SIERA, for application-specific systems is described in which higher level aspects of system design, including software, multichip design issues present at the board level, and hardware-software integration are addressed, in addition to the design of individual custom chips. A high-level description of the system as a network of processes is mapped to a system architecture template consisting of multiple boards using dedicated hardware modules and ASIC's as well as software processes running on programmable hardware modules. Application of SIERA's design methodology to a multisensory robot control system is also presented.>

In complex modern day electronic systems, far more time is spent in designing the boards, writing the software to drive and integrate the hardware, and other such system level issues, than is spent in designing any application-specific ICs that may be needed. Unfortunately, most of the research in computer-aided design has been focussed on the more glamorous ASIC design problem, as a result of which the design methodologies and tools at the system level are much more primitive than at the chip level. We have developed a design framework for application-specific systems, called SIERA, that addresses the higher level aspects of system design, including multichip design issues at the board-level, and hardware-software codesign and integration, in addition to the design of individual ASICs. SIERA allows rapid-prototyping of multiboard systems where the functionality is implemented using a mix of dedicated hardware modules and ASICs, as well as software running on programmable hardware modules. A key step in the design methodology provided by SIERA is that of generating the physical implementation of the system hardware from a description of the system architecture. The analogue of this problem at the chip level is referred to as silicon assembly or silicon compilation. In this paper we address this problem at the system level, and describe how the generation and interfacing of board-level modules, board-level physical design, simulation of custom boards, and the overall management of board design are handled in SIERA. While some of the problems could be solved by adapting or extending techniques from the existing ASIC design tools, others required new approaches. Case-studies of several real-life applications are also presented to demonstrate the effectiveness of the board-level physical design methodology embodied in SIERA compared to the traditional PCB design systems.>

Although throughput alone can be arbitrarily improved for several classes of systems using previously published techniques, none of those approaches are effective when latency constraints, which are increasingly important in embedded DSP systems, are considered. After formally establishing the relationship between latency and throughput in general computation, we explore the effect of pipelining on latency, and establish necessary and sufficient conditions under which pipelining does not alter latency. Many systems are either linear, or have subsystems that are linear. For such cases we have used a state-space based approach that treats various transformations in an integrated fashion, and answers analytically whether it is possible to simultaneously meet any given combination of constraints on latency and throughput, The analytic approach is constructive in nature, and produces a complete implementation when feasibility conditions are fulfilled. We also present a suboptimal but hardware efficient heuristic approach for the special case of initially-relaxed single-input single-output linear time-invariant computations. A novel software platform consisting of a high-level synthesis system coupled to a symbolic algebra system was used to implement the proposed algorithm transformations. Instead of optimizing to improve throughput and latency, our transformations can also be used to increase the implementation efficiency while achieving the same latency and throughput as the original design.>

Throughput has been widely traditionally recognized as the most popular performance metric for implementation of application specific computations. However, increasingly applications such as embedded controllers impose constraints on both throughput and latency as important metrics of speed. Although throughput alone can be arbitrarily improved for several classes of systems using previously published techniques, none of those approaches are effective when latency constraints are considered. DSP, communications, and control systems are often either linear, or have subsystems that are linear. Recently an optimal technique for simultaneous optimization of throughput and latency of linear computations was introduced by M.B. Srivastava and M. Potkonjak (1994). However, in many cases this technique introduces significant area overhead. We apply certain key aspects of that technique (on-arrival-processing and maximally fast implementation of linear computations) with exploration of state-space based transformations to develop four synthesis techniques which generate high throughput, low latency, low area, and low power application specific processors for the special case of single input linear computations. The new transformation techniques can also be used to increase the implementation efficiency while achieving the same latency and throughput as the original design-we obtained large improvements in area and power on many benchmarks when using the proposed transformations in this alternate role.>

Many numerically intensive applications have computations that involve a large number of multiplications of one variable with several constants.A proper optimization of this part of the computation, which we call the multiple constant multiplication (MCM) problem, often results in a significant improvement in several key design metrics.After defining the MCM problem, we formulate it as a special case of common subexpression elimination.The algorithm for common subexpression elimination is based on an iterative pairwise matching heuristic.The flexibility of the MCM problem formulation enables the application of the iterative pairwise matching algorithm to several other important high level synthesis tasks.All applications are illustrated by a number of benchmarks.

This paper introduces heuristic transformation techniques to simultaneously optimize throughput and latency of linear time-invariant systems. The technique is based on a properly coordinated manipulation of an arbitrary initial specification by both high level synthesis and symbolic algebraic manipulation tools. The technique produces implementations that not only have high throughput and low latency, but also have lower area and power requirements compared to the initial specifications. The effectiveness of the technique is demonstrated on a number of high level synthesis DSP benchmarks.>

The application of a unified framework for the rapid prototyping of hardware and software for application-specific systems to the development of a real-time multisensor robot control system is described. The key features of the computer-aided system design methodology offered by this framework are exemplified through this system. The system controls, in real-time, a six-degree-of-freedom articulated robot arm using position, force and proximity sensing. Another key aspect of the robot control system is the extensive use of special-purpose dedicated hardware, which provides better performance than systems that are largely based on general-purpose computers.>

The authors present a CAD (computer-aided design) framework for design of application-specific systems that use a mix of dedicated hardware modules and software processes running on programmable hardware modules. Many application-specific systems are actually being designed using this framework. Some are using the entire top-down mixed hardware-software architecture template based methodology, whereas others are fruitfully employing just the board-level module generators and libraries for specific custom boards. The main contribution of this work is the handling of board-level module generation, system software generation, and hardware-software integration in a unified framework. The application area addressed is that of systems that interact with their environment in real-time; a robot control system and a speech recognition system are examples of such systems.>

Dedicated systems with hardware and software tailored for the application provide tremendous performance improvements over systems based on general-purpose hardware. The authors describe SIERA, a system being developed for rapid-prototyping of the hardware and software components of such dedicated real-time systems starting from a high-level description. Based on their experience of automated generation at the chip level which they developed with the LAGER system, they identify two distinct phases in the design process. The first is the process of mapping the high-level system specification to a set of interacting hardware and software modules. The second is the generation of these software and hardware modules. A mix of mapping, synthesis and library based techniques is being utilized to accomplish these tasks.>

LAGER is an integrated computer-aided design system for algorithm-specific integrated circuit design, targeted at applications such as speech processing, image processing, telecommunications, and robot control. LAGER provides user interfaces at behavioral, structural, and physical levels and allows easy integration of novel CAD tools. LAGER consists of a behavioral mapper and a silicon assembler. The behavioral mapper maps the behavior onto a parameterized structure to produce microcode and parameter values. The silicon assembler then translates the filled-out structural description into a physical layout, and, with the aid of simulation tools, the user can fine tune the data path by iterating this process. The silicon assembler can also be used without the behavioral mapper for high-sample-rate applications. A number of algorithm-specific ICs designed with LAGER have been fabricated and tested, and as examples, a robot arm controller chip and a real-time image segmentation chip are described.>