Snoring and breath-stopping are key symptoms of sleep apnea. Most existing studies primarily focus on wearable devices or smartphone-based systems. Wearable devices can be uncomfortable, while smartphone-based systems often require specific angles, distances, or positions, making them sensitive to environmental changes. This paper proposes a contactless and engagement-free system for snoring and breath-stopping detection using a seismic sensor. Distinguishing between snoring, breath-stopping, and normal breathing from raw data alone is challenging. Snoring features typically reside in a higher frequency range than breath-stopping and normal breathing, with breath-stopping features appearing in a lower frequency range. Calculating the differential and integral of raw data can enhance features in low and high frequencies, respectively. We introduce AFFCL, an attention feature fusion and contrastive learning framework to leverage information from differential and integral signals. AFFCL generates both shared and exclusive features from the differential and integral signals and employs an attention mechanism for feature fusion. Additionally, cluster-level supervised contrastive learning in AFFCL further enhances system performance. Our system has been performed 5-fold cross-validation on 44 people, which achieves an average accuracy of 93.40% and an F1 score of 92.42%. The accuracy for detecting breath-stopping, snoring, and normal breathing are 89.54%, 94.60%, and 96.06%, respectively. Evaluation results demonstrate that our system effectively identifies breath-stopping and snoring.

Multi-agent systems in non-stationary environments face challenges due to rapidly changing dynamics, leading to quick obsolescence of experiences in the replay buffer. To address this, we propose the Adaptive Experience Replay with Attention-Based Sequence Embedding (AERAS) framework, which integrates sequence embedding with an attention mechanism to prioritize experiences based on their relevance. By assigning adaptive weights, AERAS emphasizes relevant experiences while diminishing the impact of outdated ones, enhancing efficiency and learning performance in multi-agent reinforcement learning. Evaluations on the StarCraft II Multi-Agent Challenge and Google Research Football environments show that AERAS consistently outperforms state-of-the-art methods, achieving faster convergence and higher win rates. Ablation studies confirm the essential roles of sequence embedding and attention mechanisms in boosting AERAS's robustness and adaptability, underscoring its effectiveness in managing non-stationary environments within multi-agent systems.

Plant roots typically exhibit a highly complex and dense architecture, incorporating numerous slender lateral roots and branches, which significantly hinders the precise capture and modeling of the entire root system. Additionally, roots often lack sufficient texture and color information, making it difficult to identify and track root traits using visual methods. Previous research on roots has been largely confined to 2D studies; however, exploring the 3D architecture of roots is crucial in botany. Since roots grow in real 3D space, 3D phenotypic information is more critical for studying genetic traits and their impact on root development. We have introduced a 3D root skeleton extraction method that efficiently derives the 3D architecture of plant roots from a few images. This method includes the detection and matching of lateral roots, triangulation to extract the skeletal structure of lateral roots, and the integration of lateral and primary roots. We developed a highly complex root dataset and tested our method on it. The extracted 3D root skeletons showed considerable similarity to the ground truth, validating the effectiveness of the model. This method can play a significant role in automated breeding robots. Through precise 3D root structure analysis, breeding robots can better identify plant phenotypic traits, especially root structure and growth patterns, helping practitioners select seeds with superior root systems. This automated approach not only improves breeding efficiency but also reduces manual intervention, making the breeding process more intelligent and efficient, thus advancing modern agriculture.

Monitoring vital signs, such as heart rate (HR) and respiratory rate (RR) is critical for veterinary medicine. The existing contact based systems are difficult to use on animals for longer periods as they may cause movement restriction. Contactless solutions have recently gained more popularity due to their ease-of-use. However, there are very few validated systems for animals. In this study, we propose a contactless vital signs monitoring system, CageDot for animals. Our system provides continuous real-time monitoring of HR and RR during hospitalization. The CageDot is placed under the animal cage and detects the heart vibrations using a geophone sensor. The CageDot also has a signal quality control algorithm to address the problem of obtaining high-quality cardiac data in real-life noisy environments, such as hospitals. Compared to the existing works that use controlled environments, this is especially significant. The algorithm includes several steps that include background noise/movement removal, subject movement detection, heartbeat extraction, and vital signs estimation. The experimental results on 16 hospitalized dogs and cats show that this system can achieve high accuracy for vital signs monitoring with a mean absolute error (MAE) of 4.71 for HR (3.8% error rate) and 1.28 (8% error rate) for RR.

The emergence of networked electrical machines has increased susceptibility to anomalies, including cyber-attack and physical faults, potentially leading to significant operational disruptions. In this article, we propose an online adaptive anomaly detection algorithm, adaptive enveloped singular spectrum transformation (AdaESST), which aims to identify hard-to-detect anomalies effectively. AdaESST first extracts informative components of signals by embedding the waveform data into subspaces using singular value decomposition, and then calculates anomalous score based on the subspace distance between two subsequence time series. AdaESST outperforms traditional detection methods by its capacity to adjust to new operational scenarios, thereby offering persistent protection in dynamic industrial environments. Throughout all numerical experiments simulating real-world industrial conditions, AdaESST exhibits high detection accuracy in monitoring motor and point of common coupling (PCC) currents, demonstrating its capability to safeguard against sophisticated anomalies. The detection accuracy for PCC currents is on par with that for motor currents. In essence, AdaESST has the potential to reduce the requirements for sensors, thereby lowering maintenance costs while maintaining high data integrity and security. The work contributes to enhancing the security of networked electrical machines, presenting a resilient and cost-efficient strategy in the face of emerging anomalies.

The evolution of technology has paved way for smart and improved healthcare. Invasive and contact-based methods, that have been the gold-standard for healthcare monitoring, are being replaced by contact-less methods to enable engagement-free and continuous remote patient monitoring. These methods are getting more and more popular, not just for humans, but for animals too. In our study, we develop an innovative contact-less monitoring system, CageDot, that uses geophone sensors for tracking animal activities and following infectious disease progress. The activity levels of the animals can act as a surrogate for animal health and help to document illness and recovery with treatment. Our proposed system was extensively tested by performing three multi-week experiments over a period of 1.5 years. The results show that the CageDot system can accurately and continuously track the animal activities and follow infectious disease progress in a non-invasive and contact-free way. This has a significant application and impact in the future drug and vaccine development, because the existing approach relies on daily health check by humans, which is time consuming and not continuous that may potentially skip important events.

In this study, we introduce BedDot, the first contact-free and bed-mounted continuous blood pressure monitoring sensor. Equipped with a seismic sensor, BedDot eliminates the need for external wearable devices and physical contact, while avoiding privacy or radiation concerns associated with other technologies such as cameras or radars. Using advanced preprocessing techniques and innovative AI algorithms, we extract time-series features from the collected bedseismogram signals and accurately estimate blood pressure with remarkable stability and robustness. Our user-friendly prototype has been tested with over 75 participants, demonstrating exceptional performance that meets all three major industry standards, which are Association for the Advancement of Medical Instrumentation (AAMI), Food and Drug Administration (FDA) and the British and Irish Hypertension Society (BHS), and outperforms current state-of-the-art deep learning models for time series analysis. As a non-invasive solution for monitoring blood pressure during sleep and assessing cardiovascular health, BedDot holds immense potential for revolutionizing the field.

Health monitoring is essential for both humans and animals in daily life. While numerous health monitoring systems have been developed, the majority are designed exclusively for either humans or animals, and most require direct physical contact. We have developed BedDot, a contactless health monitoring system for both humans and animals using a seismic sensor. BedDot can be deployed in various environments, such as bed and seat settings for humans, as well as in cages for animals, to monitor occupancy, heart rate (HR), respiratory rate (RR), and blood pressure (BP). Our system demonstrates high accuracy in the clinical experiments of 150 patients and 16 dogs and cats.

Recent studies demonstrate the efficacy of machine learning algorithms for learning strategies to solve combinatorial optimization problems. This study presents a novel solution to address the Electric Vehicle Routing Problem with Time Windows (EVRPTW), leveraging deep reinforcement learning (DRL) techniques. Existing DRL approaches frequently encounter challenges when addressing the EVRPTW problem: RNN-based decoders struggle with capturing long-term dependencies, while DDQN models exhibit limited generalization across various problem sizes. To overcome these limitations, we introduce a transformer-based model with a heterogeneous attention mechanism. Transformers excel at capturing long-term dependencies and demonstrate superior generalization across diverse problem instances. We validate the efficacy of our proposed approach through comparative analysis against two state-of-the-art solutions for EVRPTW. The results demonstrated the efficacy of the proposed model in minimizing the distance traveled and robust generalization across varying problem sizes.

This paper presents the design and evaluation of an engagement-free and contactless vital signs and occupancy monitoring system called BedDot. While many existing works demonstrated contactless vital signs estimation, they do not address the practical challenge of environment noises, online bed occupancy detection and data quality assessment in the realworld environment. This work presents a robust signal quality assessment algorithm consisting of three parts: bed occupancy detection, movement detection, and heartbeat detection, to identify high-quality data. It also presents a series of innovative vital signs estimation algorithms that leverage the advanced signal processing and Bayesian theorem for contactless heart rate (HR), respiration rate (RR), and inter-beat interval (IBI) estimation. The experimental results demonstrate that BedDot achieves over 99% accuracy for bed occupancy detection, and MAE of 1.38 BPM, 1.54 BPM, and 24.84 ms for HR, RR, and IBI estimation, respectively, compared with an FDA-approved device. The BedDot system has been extensively tested with data collected from 75 subjects for more than 80 hours under different conditions, demonstrating its generalizability across different people and environments.

In this article, we provide vulnerability assessments for a multistage rolling mill system under various sensor integrity attacks in response to the increasing cyber-attack threats in manufacturing systems. We first present detailed modeling of the whole system. Then, five typical integrity attacks are designed to simulate the possible cyber threats to the thickness sensor, speed sensor, and looper angle sensor. To comprehensively evaluate the impact, we propose a vulnerability assessment framework that includes 1) five device-level evaluation metrics to assess the manufacturing quality, operation safety and milling productivity, and 2) system-level indices to reflect the comprehensive impact on the multistage system. To verify the effectiveness of the proposed evaluation methods, simulations of different sensor attack scenarios in a five-stage rolling mill system are conducted in MATLAB/Simulink. The proposed metrics successfully assess the impact caused by different attack cases and show the possibility of applying the proposed indices for attack detection and mitigation in the future.

Cyber Physical Systems (CPS) are networked systems of cyber (computation and communication) and physical (sensors and actuators) components that interact in a feedback loop with the possible help of human intervention, interaction and utilization. These ...

Collecting Wi-Fi fingerprints is essential for Wi-Fi-based indoor localization techniques. However, this process can be time-consuming and labor-intensive due to the spatial and tempo-ral variations of Wi-Fi signals caused by environmental factors, interference, and fading. Moreover, the variability of signals emit-ted by different access points can hinder localization accuracy, especially in complex indoor environments. To overcome these challenges, we propose the Attention Mechanism-based Transfer Learning Indoor Localization (AMTL-Loc) framework, which transfers a pre-trained model from a source space to a target space and adapts it using minimal data by extracting redundant information from Wi-Fi fingerprints. Our experimental evalu-ations show that the AMTL-Loc framework can significantly reduce the fingerprint collection workload in diverse indoor environments while maintaining high localization accuracy compared to existing state-of-the-art indoor localization methods. Therefore, our framework offers a promising solution to enhance the efficiency and accuracy of Wi-Fi-based indoor localization techniques.

WiFi connections are vulnerable to simulated attacks from rogue access points (APs) or devices whose SSID and/or MAC/IP address are the same as legitimate devices. This kind of attack is difficult to counter with traditional network security mechanisms. In this article, we propose a new security mechanism that uses environment-independent features extracted from channel state information (CSI) to detect and identify rogue WiFi devices or APs, and reject their connections. We find that due to the$I/Q$imbalance and imperfect oscillator of each WiFi network card (NIC), the nonlinear phase error of different subcarriers will vary with the NIC. Through our experimental verification, this cross-subcarrier phase feature is invariant to the location and the environment. We deploy systems on two platforms that can extract constant phase errors from the constantly changing CSI in less than 1 s, which is at least$8 \times $faster than that of the state-of-the-art solution. Extensive experiments on commercial routers and end devices in different scenarios show that based on the Industrial Platform Computer (IPC) platform, where only nonencrypted rogue connections can be detected, the detection accuracy rate reaches 96%, and the false alarm rate is less than 2%. Based on the ASUS router platform (a commercial WiFi router), WiFi channels and smart device types are not restricted, which greatly improved universality, and the accuracy of device connection detection can even reach more than 99%. We improve a device-type identification method based on the communication traffic features of the device when connected to WiFi. Experiments show that even if there are multiple similar devices from the same manufacturer, the accuracy of device-type detection exceeds 99%.

As cyber-physical systems are becoming more wide spread, it is imperative to secure these systems. In the real world these systems produce large amounts of data. However, it is generally impractical to test security techniques on operational cyber-physical systems. Thus, there exists a need to have realistic systems and data for testing security of cyber-physical systems [1]. This is often done in testbeds and cyber ranges. Most cyber ranges and testbeds focus on traditional network systems and few incorporate cyber-physical components. When they do, the cyber-physical components are often simulated. In the systems that incorporate cyber-physical components, generally only the network data is analyzed for attack detection and diagnosis. While there is some study in using physical signals to detect and diagnosis attacks, this data is not incorporated into current testbeds and cyber ranges. This study surveys currents testbeds and cyber ranges and demonstrates a prototype testbed that includes cyber-physical components and sensor data in addition to traditional cyber data monitoring.

COVID-19 has devastated the entire world for the past couple of years. Timely and efficient detection and identification of a virus are crucial in preventing the wider virus spread. By using intelligent sensors based on Surface-Enhanced Raman Scattering (SERS), it is possible to detect and identify virus automatically. In this study, we successfully applied the XGBoost Algorithm (Supervised Machine Learning) to classify the type of the virus using the SERS sensor data. The supervised approach has a limitation when a new type of virus arises, whose shape is different from the previously known samples. To tackle this problem, we investigated the unsupervised learning approaches that can cluster the virus data into different groups without labeled data. The unsupervised approach presented in this paper is called k-Shape Clustering. This technique compares the cross-correlation between different samples and then clusters them into similar or different groups. If a subvariant of a virus emerges, it would be clustered into the existing virus groups; if a new type of virus is found, it would be clustered into a new group. Both of the approaches have shown very promising results based on extensive evaluations.

Rapid and accurate detection and localization of electronic disturbances simultaneously are important for preventing its potential damages and determining potential remedies. The existing anomaly detection methods are severely limited by the low accuracy, expensive computational cost, and the need for highly trained personnel. There is an urgent need for a scalable online algorithm for the in-field analysis of large-scale power electronics networks. In this article, we propose a fast and accurate algorithm for anomaly detection and localization of power electronics networks: the stratified colored-node graph (CONGO). This algorithm hierarchically models the change of correlated waveforms and then correlated sensors using the CONGO. By aggregating the change of each sensor with its neighbors’ inputs, we can spontaneously identify and localize the anomaly that cannot be detected by data collected from a single sensor. As our proposed method only focuses on the changes within a short time frame, it is highly computational efficient and only needs small data storage. Thus, our method is ideal for online and reliable anomaly detection and localization of large-scale power electronic networks. Compared to the existing anomaly detection methods, our method is entirely data driven without training data, highly accurate and reliable for wide-spectrum anomalies detection, and more importantly, capable of both detection and localization. Thus, it is ideal for the in-field deployment for large-scale power electronic networks. As illustrated by a distributed energy resources (DERs) power grid with 37-node, our method can effectively detect and localize various cyber and physical attacks.

In this article, a systematic assessment of cyber-physical security on the energy management system for connected and automated electric vehicles is proposed, which, to our knowledge, has not been attempted before. The generalized methodology of impact analysis of cyber attacks is developed, including novel evaluation metrics from the perspectives of steady state and transient performance of the energy management system and innovative index-based resilience and security criteria. Specifically, we propose a security criterion in terms of dynamic performance, comfortability, and energy, which are the most critical metrics to evaluate the performance of an electronic control unit (ECU). If an attack does not impact these metrics, it perhaps can be negligible. Based on the statistical results and the proposed evaluation metrics, the impact of cyber attacks on ECU is analyzed comprehensively. The conclusions can serve as guidelines for attack detection, diagnosis, and countermeasures.

Temporal data are continuously collected in a wide range of domains. The increasing availability of such data has led to significant developments of time series analysis. Time series classification, as an essential task in time series analysis, aims to assign a set of temporal sequences to different categories. Among various approaches for time series classification, the distance metric learning based ones, such as the virtual sequence metric learning (VSML), have attracted increased attention due to their remarkable performance. In VSML, virtual sequences attract samples from different classes to facilitate time series classification. However, the existing VSML methods simply employ fixed virtual sequences, which might not be optimal for the subsequent classification tasks. To address this issue, in this paper, we propose a novel time series classification method named Discriminative Virtual Sequence Learning (DVSL). Following the unified framework of sequence metric learning, our DVSL method jointly learns a set of discriminative virtual sequences that help separate time series samples in a feature space, and optimizes the temporal alignment by dynamic time warping. Extensive experiments on 15 UCR time series datasets demonstrate the efficiency of DVSL, compared with several representative baselines.

In this paper, we introduce a novel real-time and contact-free sensor system, Helena, that can be mounted on a bed frame to continuously monitor sleep activities (entry/exit of bed, movement, and posture changes), vital signs (heart rate and respiration rate), and falls from bed in a real-time and pervasive computing manner. The smart sensor senses bed vibrations generated by body movements to characterize sleep activities and vital signs based on advanced signal processing and machine learning methods. The device can provide information about sleep patterns, generate real-time results, and support continuous sleep assessment and health tracking. The novel method for detecting falls from bed has not been attempted before and represents a life-changing for high-risk communities, such as seniors. Comprehensive tests and validations were conducted to evaluate system performances using FDA approved and wearable devices. Our system has an accuracy of 99.5% detecting on-bed (entries), 99.73% detecting off-bed (exits), 97.92% detecting movements on the bed, 92.08% detecting posture changes, and 97% detecting falls from bed. The system estimation of heart rate (HR) ranged ±2.41 beats-per-minute compared to Apple Watch Series 4, while the respiration rate (RR) ranged ±0.89 respiration-per-minute compared to an FDA oximeter and a metronome.

Internet of Things (IoT) enables extensive connections between cyber and physical "things". Nevertheless, the streaming data among IoT sensors bring "big data" issues, for example, large data volumes, data redundancy, lack of scalability and so on. Under "big data" circumstances, IoT system monitoring becomes a challenge. Furthermore, cyberattacks which threaten IoT security are hard to be detected. In this paper, we propose an online distributed IoT security monitoring algorithm (ODIS). An advanced influential point selection operation extracts important information from multidimensional time series data across distributed sensor nodes based on the spatial and temporal data dependence structure. Then, an accurate data structure model is constructed to capture the IoT system behaviors. Next, hypothesis testing is carried out to quantify the uncertainty of the monitoring tasks. Besides, the distributed system architecture solves the scalability issue. Using a real sensor network testbed, we commit cyberattacks to an IoT system with different patterns and strengths. The proposed ODIS algorithm demonstrates promising detection and monitoring performances.

With the dramatic growth of seismic data volume, efficient and accurate seismic source location has become a significant challenge to seismologists. Recently, time reversal imaging (TRI) has been widely applied in automatic seismic source location for its robustness and accuracy, but its wave-equation-based implementation is usually computationally expensive. To achieve an efficient in situ and real-time source location, the emerging sensor network is a good option. In this article, we propose a simplified Gaussian beam TRI (SGTRI) method to implement the seismic source location in a distributed sensor network. Gaussian beam (GB) is a high-frequency asymptotic solution of the wave equation, which can help reduce the computation costs of the wavefield extrapolation in conventional TRI. Traditionally, the GB construction for reflection seismic imaging covers the entire subsurface space. However, for certain source localization, only limited areas contribute. Thus, we propose a beamforming-technique-based simplified GB construction to further boost efficiency. Then, we propose an imaging condition for the SGTRI to construct the final source location map. Using synthetic experiments, we demonstrate the accuracy, robustness, and efficiency of the proposed method compared with conventional TRI. In the end, a field application also shows promising results.

In this article, a systematic and generalized methodology is originally proposed to assess the vulnerability of electric drive systems due to sensor data integrity attacks. Novel evaluation metrics from the perspectives of steady-state and transient performance of electric drive systems are established to evaluate the system condition under different attacks. By using these metrics, innovative index-based resilience and security criteria, together with the stability theorem, are proposed specifically for electric drive systems, which can then be used for cyber-attack detection and diagnosis in a more systematic manner. Then, based on the simulation results under 15 attack cases (five typical types), the qualitative attack impacts on the dynamic characteristics and the statistical damage of different cyber-attacks to the defined metrics are analyzed, which can serve as useful guidelines for attack detection, diagnosis, and countermeasures.

This paper presents a novel real-time smart system performing fall detection, location, and notification based on floor vibration data produced by fall downs. Only using floor vibration as the recognition source, the system incorporates a person identification through vibration produced by footsteps to inform who is the fallen person. Our approach operates in a real-time style, which means the system recognizes a fall immediately and can identify a person with only one or two footsteps. A collaborative in-network location method is used in which sensors collaborate with each other to recognize the person walking, and more importantly, detect if the person falls down at any moment. We also introduce a voting system among sensor nodes to improve person identification accuracy. Our system is robust to identify fall downs from other possible similar events, such as jumps, door close, and objects fall down. Such a smart system can also be connected to smart commercial devices (such as Google Home or Amazon Alexa) for emergency notifications. Our approach represents an advance in smart technology for elder people who live alone. Evaluation of the system shows that it is able to detect fall downs with an acceptance rate of 95.14% (distinguishing from other possible events), and it identifies people with one or two steps in a 97.22% (higher accuracy than other methods that use more footsteps). The fall down location error is smaller than 0.27 m, which is acceptable compared with the height of a person.

Instantaneous Heart Rate (IHR) detection is important but challenging. As a kind of biomedical signal, photoplethysmography (PPG) is a good source to extract the intrinsic IHR information for further diagnostic interpretation. However, because of the non-stationary characteristics, the traditional IHR estimation results can be unreliable, and may be contaminated with artifacts and noises. Even state-of-art time-frequency analysis techniques can not fully handle the subharmonics interferences to obtain a reliable and robust IHR estimation. In this paper, we propose a hybrid IHR estimation approach based on the non-harmonic analysis (NHA) and an advanced time-periodic transform (TPT). We propose to use a NHA model to adaptively extract intrinsic oscillatory modes from PPG data. The previously unsuppressed sub-harmonic components can be removed thanks to the extracted mono-frequency components. Then the IHR can be estimated from the left ridge on TPT. Experiment results demonstrate the advantages of the proposed approach, which is promising for PPG signal processing and analysis.

WiFi has become a pervasive communication medium in connecting various devices of WLAN and IoT. However, WiFi connections are vulnerable to the impersonation attack from rogue access points (AP) or devices, whose SSID and/or MAC/IP address are identical to the legitimate devices. This kind of attack is difficult to countermeasure with traditional network security mechanisms. In this paper, we present a novel security mechanism to detect and identify rogue WiFi devices or AP using environment-independent characteristics extracted from channel state information (CSI), and refuse their connections. We find that nonlinear phase errors of different subcarriers change with WiFi network interface cards (NIC), due to the I/Q imbalance and imperfect oscillator of each WiFi NIC. Validated by our experiments, this phase feature across subcarriers is consistent and invariant to location and external environment, and can be extracted to build an essential signature of the NIC itself. Such signature of the transmitter can be calculated in real-time by the receiver and cannot be forged by rogue devices. Extensive experiments with dozens of WiFi devices demonstrate that the proposed mechanism can reliably detect the rogue WiFi connections and prevent impersonation in various scenarios. The speed of identification is 8× faster than that of the state-of-the-art solution. Moreover, the accuracy of rogue connection detection is up to 96% and false alarm rate is shown below 2%.

Monitoring sleep quality and status is important to learn health condition for improvement and prevent sleep apnea. A bed-mounted seismometer system is proposed to monitor the heart and respiratory rates, and body movement and posture, during the sleep. To effectively monitor sleep status, an innovative local maxima statistics based approach and an instantaneous property based method are developed to estimate heart and respiratory rates, respectively. These methods are more robust and stable compared to previous works. Besides, algorithms for body movement and posture identification are also investigated based on instantaneous properties. We incorporated these algorithms to create a novel contactless sleep monitoring system that can keep track of heart rate, respiration rate, movement patterns and posture changes on events near to the bed. Our technology includes a small, powerful and low-cost smart seismometer, that can be easily installed to a bed frame under the mattress or boxes, and a user-friendly graphic interface, that can be paired with smartphone devices and display results through the APPs notifications. A prototype system is demonstrated, showing great potentials in monitoring a person's sleep status under different conditions.

This paper presents a novel in-network person identification and fall detection system that uses floor seismic data produced by footsteps and fall downs as an only source for recognition. Compared with other existing methods, our approach is done in real-time, which means the system is able to identify a person almost immediately with only one or two footsteps. An adapted in-network localization method is proposed in which sensors collaborate among them to recognize the person walking, and most importantly, detect if the person falls down at any moment. We also introduce a voting system among sensor nodes to improve accuracy in person identification. Our system is innovative since it can be robust to identify fall downs from other possible events, like jumps, door close, objects fall down, etc. Such a smart system can also be connected to smart commercial devices (like Google Home or Amazon Alexa) for emergency notifications. Our approach represents an advance in smart technology for elder people who live alone. Evaluation of the system shows it is able to identify people with one or two steps in an average of 93.75% (higher accuracy than other methods that use more footsteps), and it detects fall downs with an acceptance rate of 95.14% (distinguishing from other possible events). The fall down localization error is smaller than 0.28 meters, which it is acceptable compared to the height of a person.

Monitoring and tracking the underground moving target have many important applications, such as homeland security and defense applications. A wireless seismic network is designed to sense the seismic wave generated by the moving target and locate the target based on Reverse Time Migration (RTM) method. A new Gaussian Beam Migration (GBM) algorithm is used to improve the traditional RTM algorithm. The GBM-RTM method is a time-frequency domain algorithm, where the complex time domain calculation is transformed to a simple frequency domain calculation. By using GBM-RTM method, we accelerate the computation and reduce the communication cost. Through the extensive simulations and experiments, we demonstrate the trajectory tracking of the moving underground targets. The stability and accuracy of our proposed localization algorithms are also evaluated.

Cyber attacks and malfunctions challenge the wide applications of Internet of Things (IoT). Since they are generally designed as embedded systems, typical auto-sustainable IoT devices usually have a limited capacity and a low processing power. Because of the limited computation resources, it is difficult to apply the traditional techniques designed for personal computers or super computers, like traffic analyzers and antivirus software. In this paper, we propose to leverage statistical learning methods to characterize the device behavior and flag deviations as anomalies. Because the system statistics, such as CPU usage cycles, disk usage, etc., can be obtained by IoT application program interfaces, the proposed framework is platform and deviceindependent. Considering IoT applications, we train multiple machine learning models to evaluate their feasibility and suitability. For the target auto-sustainable IoT devices, which operate well-planned processes, the normal system performances can be modeled accurately. Based on time series analysis methods, such as local outlier factor, cumulative sum, and the proposed adaptive online thresholding, the anomalous behaviors can be effectively detected. Comparing their performances on detecting anomalies as well as the computation sources required, we conclude that relatively simple machine learning models are more suitable for IoT security, and a data-driven anomaly detection method is preferred.

Internet of Things (IoT) are vulnerable to both cyber and physical attacks. Therefore, a cyber-physical security system against different kinds of attacks is in high demand. Traditionally, attacks are detected via monitoring system logs. However, the system logs, such as network statistics and file access records, can be forged. Furthermore, existing solutions mainly target cyber attacks. This paper proposes the first energy auditing and analytics-based IoT monitoring mechanism. To our best knowledge, this is the first attempt to detect and identify IoT cyber and physical attacks based on energy auditing. Using the energy meter readings, we develop a dual deep learning (DL) model system, which adaptively learns the system behaviors in a normal condition. Unlike the previous single DL models for energy disaggregation, we propose a disaggregation-aggregation architecture. The innovative design makes it possible to detect both cyber and physical attacks. The disaggregation model analyzes the energy consumptions of system subcomponents, e.g., CPU, network, disk, etc., to identify cyber attacks, while the aggregation model detects the physical attacks by characterizing the difference between the measured power consumption and prediction results. Using energy consumption data only, the proposed system identifies both cyber and physical attacks. The system and algorithm designs are described in detail. In the hardware simulation experiments, the proposed system exhibits promising performances.

The effect of home energy management system (HEMS) is even more pronounced at the edge of smart grid infrastructure. However, the isolated scheduling horizons and the uncertainty about scheduling inputs are the major challenges for HEMS. In this paper, a novel demand-side management system, namely, a real-time electricity scheduling (RTES) for residential home energy management, is presented to operate the smart home. The proposed management system attempts to achieve minimizing the cost payment by optimally scheduling smart appliances and improving the utilization of renewable energy. Most importantly, it considers the uncertainty in the renewable generation and the subjectivity in electricity consumption. Our RTES adopts a 24-h rolling horizon, and the optimization problem be solved by an effective genetic algorithm at regular intervals. Moreover, to reduce the impact caused by the discrepancy between the predictive information and the actual information, we design an effective real-time prediction method for the renewable generation, and update the inputs of scheduling system before each optimization calculation. Simulation results confirm that the proposed approach can improve the performance of the home electricity scheduling, reduce the impact of uncertainty on the system, and reduce the total energy costs.

Seismic reflectivity inversion is widely applied to improve the seismic resolution to obtain detailed underground understandings. Based on the convolution model, seismic inversion removes the wavelet effect by solving an optimization problem. Taking advantage of the sparsity property, the ℓ1-norm is commonly adopted in the regularization terms to overcome the noise/interference vulnerability observed in the lp-losses minimization. However, no one has provided a deterministic conclusion that ℓ1-norm regularization is the best choice for seismic reflectivity inversion. Instead of using an unproved fixed regularization norm, we propose an optimal seismic reflectivity inversion approach. Our method adaptively adopts an ℓp-loss-ℓq-regularization (i.e., ℓp,q-regularization) for p = 2, 0q-norm regularization. Then, the majorization-minimization and CV algorithms are briefly described. The performance of the proposed seismic inversion approach is evaluated through synthetic examples and a field example from the Bohai Bay Basin, China.

The ability to use networked seismic instruments to image subsurface civil infrastructure and activities in data-limited extreme environments is crucial for many civil and security applications. This paper presents a non-invasive smart seismic network for imaging subsurface structures using ambient noise only. An innovative in-network spatial auto-autocorrelation method is designed to image different layers of underground infrastructures at different frequency bands and finally result in a 3D image. The proposed approach is general and can characterize the near-surface sediment and the infrastructure simultaneously. The experiments demonstrate that underground utility lines affecting sediment can be imaged, and the potential of using the method for abnormal activities like leakages. An exhaustive evaluation regarding bandwidth utilization, communication cost, and system resilience were conducted to highlight the benefits of the proposed approach.

Ambient Noise Seismic Imaging (ANSI) is a recently developed geophysical methodology to image the shallow subsurface structures of earth using ambient/environment noise as the source. Integrating ANSI computing within distributed sensor networks will enable real-time continuous monitoring of subsurface dynamics for sustainability studies. However, the research challenges associated with this innovative approach are significant. Traditional data collection using sensor networks imposes practical difficulty for real-time applications, because of the sheer amount of data and large-dense sensor arrays versus limited network bandwidth. This paper is the first to investigate how to utilize the computing capabilities of sensor nodes to perform the computation of ANSI under resource constraints. We explored two distributed approaches (aggregation and consensus) for computing ambient noise eikonal tomography and obtaining phase velocity maps. We performed experiments using CORE emulator to obtain phase velocity maps on real data from USArray Transportable Array. Results demonstrate that our approaches can illuminate phase velocities under network constraints. We also show that the proposed aggregation and consensus algorithms not only balance the computation load but also achieve low communication cost and high data loss tolerance.

Edge or Fog computing is emerging as a new computing paradigm where the data processing, networking, storage and analytics are performed closer to the devices (IoT) and applications. The edge of a network plays an important role in the IoT system. It is an optimal site for off-loading bandwidth hungry IoT data. In order to generate business value out of the large volume of data on the edge, we need decentralized machine learning (ML) algorithms. These algorithms must enable edge devices to communicate autonomously and deliver information seamlessly to the decision makers. In this paper, we present EdgeSGD a decentralized stochastic gradient descent method to solve a large linear regression problem on the edge network. The solution is applied to the seismic imaging use-case and evaluated using an edge computing testbed. The proposed algorithm avoids sending raw data to the cloud, and offers faster and balanced computation. We compare the algorithm with the existing methods such as MapReduce, DGD and EXTRA. The results show that the EdgeSGD converges faster to the optimal value and is robust towards node/link failure. Finally, we test the algorithm using real-world seismic data trace from Parkfield, CA and show that our proposed algorithm can illuminate the underlying fault region of San-Andreas.

In this paper, we propose a novel adaptive beamforming algorithm for coprime array by compressive sensing the virtual uniform linear array signal. Based on the idea of coprime sampling, a much longer virtual uniform linear array can be generated from a coprime array. With a compressive sensing matrix, a connection can be built between the coprime array with fewer physical sensors and the virtual uniform linear array with much more virtual sensors. Hence, the proposed adaptive beamforming algorithm takes full advantage of the longer virtual array. The performance increment provided by the virtual array is much larger than the performance loss due to the introduced compressive sensing. Hence, the beam-former using the virtual array is expected to obtain much better performance than those using the coprime array directly. Simulation results demonstrate the effectiveness of the proposed adaptive beamforming algorithm.

In this paper, we propose a novel robust adaptive beamforming algorithm with direction-of-arrival (DOA) support for the coprime array. Specifically, by using the property of coprime number, we may estimate the DOAs of sources by matching two super-resolution spatial spectra of the pair of decomposed coprime subarrays. After that, the power of each source can be estimated via a covariance matrix joint estimation problem corresponding to the pair of decomposed coprime sub-arrays. Taking the estimated DOAs and their corresponding power as the support information, the interference-plus-noise covariance matrix for the coprime array can be reconstructed, from which the minimum variance distortionless response beamformer weight vector can be calculated. Simulation results show that the proposed adaptive beamforming algorithm is more robust to signal look direction mismatch than the existing algorithms.

In both industry and academia, the seismic exploration does not yet have the capability of illuminating the physical dynamics with high resolution and in real-time. The major bottleneck in real-time monitoring today is to transfer large volume of raw data for post processing. Although computation capacity and sampling rate of sensors have increased exponentially, we still have challenges in terms of communication and battery life. To monitor physical dynamics in real-time and to avoid costly data transfer, we need to perform in-situ computation and analytics. In this paper, we present a decentralized least-squares solver that can perform in-network computation and generate tomography image in real time. The proposed solution is evaluated using both synthetic and real data set. Preliminary evaluation shows that the decentralized method can recreate the image close to the one obtained from the centralized computation. We envision the system can be applied to a wide range of seismic exploration topics such as hydrothermal, oil exploration, mining safety, mining resource monitoring. The scientific and social impact is broad and significant.

We consider the decentralized consensus optimization problem arising from in-situ seismic tomography in large-scale sensor networks. Unlike traditional seismic imaging performed in a centralized location, each node in this setting privately holds an objective function and partial data. The goal of each node is to obtain the optimal solution of the whole seismic image, while communicating only with its immediate neighbors. We present a fast decentralized gradient descent method and prove that this new method can reach optimal convergence rate of O(1/k2) where k is the number of communication/iteration rounds. Extensive numerical experiments on synthetic and real-world sensor network seismic data demonstrate that the proposed algorithms significantly outperform existing methods.

Many real-world wireless sensor network applications such as environmental monitoring, structural health monitoring, and smart grid can be formulated as a least-squares problem. In distributed Cyber-Physical System (CPS), each sensor node observes partial phenomena due to spatial and temporal restriction and is able to form only partial rows of least-squares. Traditionally, these partial measurements were gathered at a centralized location. However, with the increase in sensors and their measurements, aggregation is becoming challenging and infeasible. In this paper, we propose distributed randomized kaczmarz that performs in-network computation to solve least-squares over the network by avoiding costly communication. As a case study, we present a volcano monitoring application on a distributed CORE emulator and use real data from Mt. St. Helens to evaluate our proposed method.

Smart Grid is a complex cyber-physical system that modernizes the traditional electric power infrastructure by sensing, control, computation and communication. Validating the functionality, security and reliability of Smart Grid applications within such a system requires the modeling and emulation of both power networks and communication networks, as well as the interactions between them. In this paper, we present the design, implementation and evaluation of an integrated scalable cyber-physical experiment environment for Smart Grid, called ScorePlus. Compared with previous related works, ScorePlus fills the gap by: 1) Creating and integrating both software emulator and hardware testbed, such that they all follow the same architecture and interface, and the same Smart Grid application program can be tested on either of them without any modification; 2) Providing remote access to the hardware testbed such that users can configure physical devices of the hardware testbed through Internet; 3) Supporting scalable distributed experiments such that multiple software emulators and hardware testbeds running at different locations are able to connect and form a larger Smart Grid system.

Network diagnosis is a vital aspect in ensuring an efficient and robust functioning of any kind of mesh network. In this paper we present a network diagnosis method which determines the delay map of a mesh network using only end-to-end delay measurements without having the knowledge of the path taken. We model the problem of network diagnosis as an inverse problem and using a concept of ray tracing, solve for the delay in the network. With the help of simulations we show that our algorithm is able to detect nodes in the network based on their delays with reasonable accuracy using only O(n) probes for obtaining measurements. We further demonstrate a real world application of our algorithm in the domain of internet backbone networks by using data pertaining to a major US based network provider.

Future smart grid (SG) has been considered a complex and advanced power system, where energy consumers are connected not only to the traditional energy retailers (e.g., the utility companies), but also to some local energy networks for bidirectional energy trading opportunities. This paper aims to investigate a hybrid energy trading market that is comprised of an external utility company and a local trading market managed by a local trading center (LTC). The existence of local energy market provides new opportunities for the energy consumers and the distributed energy sellers to perform the local energy trading in a cooperative manner such that they all can benefit. This paper first quantifies the respective benefits of the energy consumers and the sellers from the local trading and then investigates how they can optimize their benefits by controlling their energy scheduling in response to the LTC's pricing. Two different types of the LTC are considered: 1) the nonprofit-oriented LTC, which solely aims at benefiting the energy consumers and the sellers; and 2) the profit-oriented LTC, which aims at maximizing its own profit while guaranteeing the required benefit for each consumer and seller. For each type of the LTC, the optimal trading problem is formulated and the associated algorithm is further proposed to efficiently find the LTC's optimal price, as well as the optimal energy scheduling for each consumer and seller. Numerical results are provided to validate the benefits of the hybrid energy trading market and the performance of the proposed algorithms.

Due to the low cost and ease of deployment, wireless sensor networks (WSNs) are emerging as sensing paradigms that the structural engineering field has begun to consider as substitutes for traditional tethered structural health monitoring (SHM) systems. Different from other applications of WSNs such as environmental monitoring, SHM applications are much more data intensive and it is not feasible to stream the raw data back to the server due to the severe bandwidth and energy limitations of low-power sensor networks. In-network processing is a promising approach to address this problem but designing distributed versions for the sophisticated SHM algorithms is much more challenging because SHM algorithms are computationally intensive, and involve data-level collaboration of multiple sensors. In this paper, we select a classical SHM algorithm: the eigen-system realization algorithm (ERA), and propose a few distributed ERAs suitable for WSNs. In particular, we first design a method to incrementally calculate the ERA and then propose three schemes upon which the incremental ERA can be carried out along an Hamiltonian path, along a path in the minimum connected dominating set (MCDS) and along the shortest path tree (SPT). The efficacy of these schemes are demonstrated and compared through both simulation experiment. We believe the proposed schemes can also serve as a guideline when applying WSNs for other applications like SHM which are also data-intensive and involve sophisticated signal processing of collected information.

A novel class of malicious data attacks, LPAttack, are presented against the state estimation process in Smart Grid. Here LP represents leverage points, which are the outliers in the factor space of the regression model for Smart Grid state estimation. The attacker strategically manipulates the parameter data in Smart Grid to mislead the control center with incorrect system parameter information, such that leverage points are created within the factor space of the state estimation regression model. As a result, the attacker can freely inject arbitrary errors into the meter measurements corresponded with the leverage points, while bypassing the existing bad data detection mechanism. We first introduce the fundamental principles and strategies of launching LPAttack in Smart Grid. Then the potential countermeasure based on robust Schweppe-Huber Generalized-M estimator is proposed. Finally, we evaluate the LPAttack principles and its countermeasure through simulations in IEEE test system, and examine in particular the effect of the attacks on Locational Marginal Prices in real-time pricing power market.

This paper presents a new multi-objective microgrid reconfiguration problem formulation. Unlike existing distribution system or microgrid reconfiguration algorithms, we consider the effect of uncertainty arising from the renewable energy generation and investigate the tradeoff between the invented index measuring the reliability of reconfiguration and the total load served. The resulting optimization problem is computationally prohibitive due to the binary circuit breaker variables and the probability constraint accounting for the uncertainty of renewable generation. Nevertheless, a semidefinite programming (SDP) reformulation is developed based on convex relaxation techniques and the scenario-based approximation. Furthermore, weighted-sum method is applied in the reformulation and we eventually obtain the Pareto solution points of the microgrid reconfiguration. Numerical tests validate the intrinsic tradeoff between the two objectives and demonstrate the effectiveness of the proposed solution methodology.

Sensor networks have particularly important applications in challenging environments. However, those challenging environments also pose significant challenges to network sustainability and reliability. In such environments, the network often becomes disruptive and even unavailable during downtime. This results in undesired loss of valuable spatial-temporal sensor data. Data persistence can be achieved by using in-situ encoding and caching of data through distributed mechanisms. However, the existing methods in the literature are mainly based on network random walks, which not only incur significant communication overhead, but also are prone to network or node failures. In this article, we presentECPC, a distributedErasureCoding with randomizedPowerControl protocol for preserving data in disruptive sensor networks.ECPConly requires each sensor node to perform several rounds of broadcast in its neighborhood at some randomly chosen radio transmission power levels, and thus it incurs low communication overhead. We proved thatECPCachieves the expected code degree distribution and pseudo-global randomness of erasure coding principles. We have also evaluated the performance ofECPCby comparing it with several key related approaches in the literature (such as EDFC and RCDS). The performance comparisons validate that our proposedECPCprotocol can reach higher data reliability under varying node failure probabilities. In addition,ECPCprotocol is also shown to be scalable with different network sizes.

In this paper, we investigate the cooperative resource sharing and pricing for the licensed Primary User (PU) and Cognitive Radio Networks (CRNs), where the PU jointly determines how to share its under-utilized radio resource with Secondary Users (SUs) and how to charge the SUs accordingly. Meanwhile, the SUs jointly determine how to utilize the shared radio resource from the PU and their preferred payments. Since both the PU and SUs expect to benefit from cooperation, we model their interactions as a Nash bargaining problem. Viewing the nonconvexity of bargaining problem, we first propose a two-step procedure to solve it efficiently. The two-step procedure explores the connection between the bargaining problem and its associated social optimization problem, and thus turns the original nonconvex bargaining problem into two consecutive convex optimization problems. We then propose two efficient algorithms, each with guaranteed convergence, to solve these two problems, respectively. Numerical results show that our proposed two-step procedure achieves the optimality of the bargaining problem with significantly reduced computational complexity. Also, our joint resource sharing and pricing scheme guarantees that each SU and PU can positively benefit from the cooperative bargaining, and the benefit is fairly allocated among them.

Existing volcano instrumentation and monitoring system use centralized approach for data collection and image reconstruction and they lack the capability of obtaining real time information. A new distributed method is required which can obtain a high resolution seismic tomography in real time. In this paper, we present a component-average distributed multiresolution evolving tomography algorithm for processing data and inverting volcano tomography in the network, while avoiding centralized computation and costly data collection. The new algorithm distributes the computational burden to sensor nodes and performs real time tomographic inversion under constraints of network resources. We implemented and evaluated the algorithm in a customized simulator using synthetic data. The experiment results validate that our proposed algorithm not only balances the computation load but also achieves high data loss tolerance.

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

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

In challenging environments, sensor networks may become disruptive even unavailable (downtime), resulting in data losses. In-situ encoding and caching data in a distributed fashion can preserve data persistence during downtime. However, the existing approaches are mainly based on random walks, which incurs significant communication overhead and may itself fail due to network disruptions. In this paper, we present a distributed Erasure Coding with randomized Power Control (ECPC) mechanism to preserve downtime data persistence in disruptive sensor networks. ECPC only requires each node to perform a single broadcast at each of its several randomly selected power levels. Thus it incurs low communication overhead. Moreover the storage space requirement is lower and uniform across the network. It applies randomized power control in localized data broadcast to achieve expected code degree distribution and ensure pseudo-global randomness for erasure coding. Performance comparisons between ECPC and other existing approaches show that ECPC mechanism can reach higher data reliability under varying node failure probabilities. In addition, our ECPC approach is scalable with network sizes.

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

Opportunistic network coding has been developed and applied in disruptive networks to provide optimal data delivery. Though network coding system utilizes coding opportunities among multiple paths, its application in data collection suffers from a disconnected sink node and the limited storage space available for data cache. The state-of-the-art approach has studied preserving data persistence as an optimization problem under storage and energy constraints, without considering disruptive network dynamics during data redistribution. In this paper, we propose Ravine Streams (RS) to maximize data preservation under the constraints of limited storage and probabilistic node failure throughout data redistribution. Our RS approach leverages adaptive power control to achieve ensured storage of each redistribution data. Meanwhile, in the course of data redistribution, distributed coding-based rebroadcast strategy not only reduces the data duplication, but also improves the statistical property of symbol randomness. We show that the performance of preserving data persistence of proposed RS is approximately bounded by the optimal solutions. The experimental evaluations demonstrate that RS increases data delivery ratio, consumes even less communication energy with only comparable storage cost, when compared with existing data preserving algorithms.

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

Disruptive network communication entails transient network connectivity, asymmetric links, and unstable nodes, which pose severe challenges to data collection in sensor networks. Erasure coding can be applied to mitigate the dependency of feedback in such a disruptive network condition, improving data collection. However, the collaborative data collection through an in-network erasure coding approach has been underexplored. In this paper, we present an Opportunistic Network Erasure Coding protocol (ONEC) to collaboratively collect data in dynamic disruptive networks. ONEC derives the probability distribution of coding degree in each node and enables opportunistic in-network recoding, and guarantees that the recovery of original sensor data can be achieved with high probability upon receiving any sufficient amount of encoded packets. First, it develops a recursive decomposition structure to conduct probability distribution deconvolution, supporting heterogeneous data rates. Second, every node conducts selective in-network recoding of its own sensing data and received packets, including those opportunistic overheard packets. Last, ONEC can efficiently recover raw data from received encoded packets, taking advantages of low decoding complexity of erasure codes. We evaluate and show that our ONEC can achieve efficient data collection in various disruptive network settings. Moreover, ONEC outperforms other epidemic network coding approaches in terms of network goodput, communication cost, and energy consumption.

Data collection, redistribution and retrieval are essential components of wireless sensor networks (WSNs). In dense WSN deployments, the sensor data are usually sent to a sink that can be reached through one or multiple hops. In the case where communications with the sink are disrupted due to various reasons, the data must be stored in the network for later retrieval. When considering in-network storage, we must redistribute the data among an energy-constrained network with sensors that have a low storage capacity. In previous works, the data redistribution problem has been studied, but the focus was only on the redistribution costs while the data retrieval costs (which have been analyzed in other works as an independent problem) were ignored. We recognize that these two problems should be studied in concert and therefore, in this paper, we combine both data redistribution and retrieval into a single problem. We propose a graph transformation, formulate the problem as a minimum cost flow optimization problem and use linear programming to find the optimal solution. Moreover, we introduce an algorithm named EDR2: energy-efficient data redistribution and retrieval. EDR2is a distributed energy-efficient algorithm for in-network storage and later retrieval in WSNs. To evaluate our solution on a large scale, we modeled different scenarios in a 400-node network, used the GNU Linear Programming Kit (GLPK) to obtain the optimal solutions, and ran simulations to find the solutions using our algorithm. Finally, we implemented EDR2using real sensors to demonstrate the feasibility of our algorithm. We compared EDR2with two heuristic algorithm and show that our approach is an energy-efficient solution for node selection when redistributing data in a WSN for eventual retrieval.

In this paper we have proposed and designed FindingHuMo (Finding Human Motion), a real-time user tracking system for Smart Environments. FindingHuMo can perform device-free tracking of multiple (unknown and variable number of) users in the Hallway Environments, just from non-invasive and anonymous (not user specific) binary motion sensor data stream. The significance of our designed system are as follows: (a) fast tracking of individual targets from binary motion data stream from a static wireless sensor network in the infrastructure. This needs to resolve unreliable node sequences, system noise and path ambiguity, (b) Scaling for multi-user tracking where user motion trajectories may crossover with each other in all possible ways. This needs to resolve path ambiguity to isolate overlapping trajectories, FindingHumo applies the following techniques on the collected motion data stream: (i) a proposed motion data driven adaptive order Hidden Markov Model with Viterbi decoding (called Adaptive-HMM), and then (ii) an innovative path disambiguation algorithm (called CPDA). Using this methodology the system accurately detects and isolates motion trajectories of individual users. The system performance is illustrated with results from real-time system deployment experience in a Smart Environment.

This paper presents a distributed Demand and Response algorithm for smart grid with the objective of optimizing social-welfare. Assuming the power demand range is known or predictable ahead of time, our proposed distributed algorithm will calculate demand and response of all participating energy demanders and suppliers, as well as energy flow routes, in a fully distributed fashion, such that the social-welfare is optimized. During the computation, each node (e.g., demander or supplier) only needs to exchange limited rounds of messages with its neighboring nodes. It provides a potential scheme for energy trade among participants in the smart grids. Our theoretical analysis proves that the algorithm converges even if there is some random noise induced in the process of our distributed Lagrange-Newton based solution. The simulation also shows that the result is close to that of centralized solution.

Cloud computing is an increasingly important solution for providing services deployed in dynamically scalable cloud networks. Services in the cloud computing networks may be virtualized with specific servers which host abstracted details. Some of the servers are active and available, while others are busy or heavy loaded, and the remaining are offline for various reasons. Users would expect the right and available servers to complete their application requirements. Therefore, in order to provide an effective control scheme with parameter guidance for cloud resource services, failure detection is essential to meet users' service expectations. It can resolve possible performance bottlenecks in providing the virtual service for the cloud computing networks. Most existing Failure Detector (FD) schemes do not automatically adjust their detection service parameters for the dynamic network conditions, thus they couldn't be used for actual application. This paper explores FD properties with relation to the actual and automatic fault-tolerant cloud computing networks, and find a general non-manual analysis method to self-tune the corresponding parameters to satisfy user requirements. Based on this general automatic method, we propose specific and dynamic Self-tuning Failure Detector, called SFD, as a major breakthrough in the existing schemes. We carry out actual and extensive experiments to compare the quality of service performance between the SFD and several other existing FDs. Our experimental results demonstrate that our scheme can automatically adjust SFD control parameters to obtain corresponding services and satisfy user requirements, while maintaining good performance. Such an SFD can be extensively applied to industrial and commercial usage, and it can also significantly benefit the cloud computing networks.

In recent years, using wireless sensor networks (WSNs) for structural health monitoring (SHM) has attracted increasing attention. Traditional centralized SHM algorithms developed by civil engineers can achieve the highest damage detection quality since they have the raw data from all the sensor nodes. However, directly implementing these algorithms in a typical WSN is impractical considering the large amount of data transmissions and extensive computations required. Correspondingly, many SHM algorithms have been tailored for WSNs to become distributed and less complicated. However, the modified algorithms usually cannot achieve the same damage detection quality of the original centralized counterparts. In this paper, we select a classical SHM algorithm: the eigen-system realization algorithm (ERA), and propose a distributed version for WSNs. In this approach, the required computations in the ERA are updated incrementally along a path constructed from the deployed sensor nodes. This distributed version is able to achieve the same quality of the original ERA using much smaller wireless transmissions and computations. The efficacy of the proposed approach is demonstrated through both simulation and experiment.

A sensor network, unlike a traditional communication network, is deeply embedded in physical environments and its operation is mainly driven by the event activities in the environment. In long-term operations, the event activities usually show certain patterns that can be learned and exploited to optimize the network design. However, this has been underexplored in the literature. One work related to this is using Activity Transition Probability Graph (ATPG) for radio duty cycling [Tang et al. (2011) ActSee: Activity-Aware Radio Duty-Cycling for Sensor Networks in Smart Environments. Proc. IEEE INSS 2011, Penghu, Taiwan, June 12–15. IEEE Press]. In this paper, we present a novel Energy and Activity-aware Routing (EAR) protocol for sensor networks. As a case study, we have evaluated EAR with the data trace of real Smart Environments. In EAR an ATPG is learned and built from the event activity patterns. EAR is an online routing protocol, that chooses the next-hop relay node by utilizing: activity pattern information in the ATPG graph and a novel index of energy balance in the network. EAR extends the network lifetime by maintaining an energy balance across the nodes in the network, while meeting the application performance with desired throughput and low data delivery latency. We theoretically prove that: (i) the network throughput with EAR achieves a competitive ratio (i.e. the ratio of the performance of any offline algorithm that has knowledge of all past and future packet arrivals to the performance of our online algorithm) that is asymptotically optimal, and (ii) EAR achieves a lower bound in the network lifetime. Extensive experimental results from: (i) a 82 node Motelab sensor network testbed [Werner-Allen et al. (2005) MoteLab: A Wireless Sensor Network Testbed. Proc. ACM IPSN 2005, Los Angeles, CA, USA, April 25–27, pp. 483–488. IEEE Press, NJ, USA] and (ii) a varying size network (20–100) in sensor network simulator TOSSIM, validate that EAR outperforms the existing methods both in terms of network performance (network lifetime, network energy consumption) and application performance (low latency, desired throughput) for an energy-constrained sensor network.

This paper presents the design, deployment, and evaluation of a real-world sensor network system in an active volcano - Mount St. Helens. In volcano monitoring, the maintenance is extremely hard and system robustness is one of the biggest concerns. However, most system research to date has focused more on performance improvement and less on system robustness. In our system design, to address this challenge, automatic fault detection and recovery mechanisms were designed to autonomously roll the system back to the initial state if exceptions occur. To enable remote management, we designed a configurable sensing and flexible remote command and control mechanism with the support of a reliable dissemination protocol. To maximize data quality, we designed event detection algorithms to identify volcanic events and prioritize the data, and then deliver higher priority data with higher delivery ratio with an adaptive data transmission protocol. Also, a light-weight adaptive linear predictive compression algorithm and localized TDMA MAC protocol were designed to improve network throughput. With these techniques and other improvements on intelligence and robustness based on a previous trial deployment, we air-dropped 13 stations into the crater and around the flanks of Mount St. Helens in July 2009. During the deployment, the nodes autonomously discovered each other even in-the-sky and formed a smart mesh network for data delivery immediately. We conducted rigorous system evaluations and discovered many interesting findings on data quality, radio connectivity, network performance, as well as the influence of environmental factors.

Sensor network has found critical applications in extreme environments. However, in the extreme environments, a predictable and stable path may never exist, since the transient network connectivity, asymmetric links and unstable nodes are prevalent. Thus, the extreme environments severely challenge its basic function of data collection. Particularly, those disruptive conditions make traditional data collection protocols inefficient. In this paper, we design Opportunistic Network Erasure Coding (ONEC) protocol for collaborative data collection in disruptive sensor networks. The idea behind ONEC protocol is to study how each node determines code degree distribution and receding strategies in a distributed fashion, so that all data of network can be recovered with high probability upon receiving any sufficient amount of encoded packets. First, each node derives code degree distribution through recursive discrete deconvolution. Second, every node conducts selective receding of its own sensing data and opportunistically received data. Last, the ONEC ensures decoder can recover all data from any sufficient amount of encoded packets with high probability. The performance evaluations through extensive simulation validate that ONEC can truly achieve efficient data collection with high reliability in disruptive sensor networks and outperform other existing approaches in terms of network goodput, message complexity and buffer space.

In a data collection sensor network, how to maximize the network lifetime through topology control remains an open research problem. Previous work has studied this problem by aiming to build a max-lifetime data collection tree, however, tree-based data collection does not necessarily yield maximum network lifetime. In this paper, we consider collaborative multipath data delivery and formulate the lifetime maximization problem as a max-fair-flow problem, then study how to collaboratively adjust the transmission power of sensor nodes to achieve the maxfair-flow, thus maximizing the network lifetime. We give both theoretical proofs and simulations to validate its correctness and performance.

A real-time monitor and rescue system must be able to both quickly and reliably detect the event happening in its monitoring region. Furthermore, it is required to fulfill certain rescue mission, e.g., navigate victims to exit through safe path in case of emergency. Current monitor and rescue approaches generally rely on either teleoperated robots, or teams of wireless robots. Typically the robots used in these systems tend to have high cost which make them unpractical in large scale deployment and applications. In this work, we present a realtime monitor and rescue system, TelosW-Bot Net, utilizing integrated networks. The integrated network is an integration of stationary sensor networks and robots: static sensor networks comprised of large numbers of small, simple, and inexpensive wireless sensors, and the robots which can communicate and controlled by sensor nodes. We demonstrate the efficacy of our system in real test bed composed of 46 sensors, which is one of the largest robotic sensor network to our knowledge, providing empirical results.

Nodes in a wireless sensor network (WSN) are usually powered by batteries. Hence, it is important to efficiently expend the battery energy of each node in the WSN so that both runtime of the nodes and the lifetime of the WSN are prolonged. An event-driven energy-conserving scheme, called adaptive power-saving scheme (APS), is proposed. APS is able to adapt the sleep duration of a node to traffic variations. We have implemented APS based on TelosW motes, TinyOS, and NesC language. Experimental results show that APS outperforms the fixed time scheme (FTS) in terms of energy consumption and packet loss ratio.

A number of energy-aware routing protocols were proposed to seek the energy efficiency of routes in multihop wireless networks. Among them, several geographical localized routing protocols were proposed to help making smarter routing decision using only local information and reduce the routing overhead. However, all proposed localized routing methods cannot guarantee the energy efficiency of their routes. In this paper, we first give a simple localized routing algorithm, called Localized Energy-Aware Restricted Neighborhood routing (LEARN), which can guarantee the energy efficiency of its route if it can find the route successfully. We then theoretically study its critical transmission radius in random networks which can guarantee that LEARN routing finds a route for any source and destination pairs asymptotically almost surely. We also extend the proposed routing into three-dimensional (3D) networks and derive its critical transmission radius in 3D random networks. Simulation results confirm our theoretical analysis of LEARN routing and demonstrate its energy efficiency in large scale random networks.

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

We present a lightweight lossless compression algorithm for realtime sensor networks. Our proposed adaptive linear filtering compression (ALFC) algorithm performs predictive compression using adaptive linear filtering to predict sample values followed by entropy coding of prediction residuals, encoding a variable number of samples into fixed-length packets. Adaptive prediction eliminates the need to determine prediction coefficients a priori and, more importantly, allows compression to dynamically adjust to a changing source. The algorithm requires only integer arithmetic operations and thus is compatible with sensor platforms that do not support floating-point operations. Significant robustness to packets losses is provided by including small but sufficient overhead data to allow each packet to be independently decoded. Real-world evaluations on seismic data from a wireless sensor network testbed show that ALFC provides more effective compression and uses less resources than an alternative recent work of lossless compression, S-LZW. Experiments in a multi-hop sensor network also show that ALFC can significantly improve raw data throughput and energy efficiency. We also implement the algorithm in our real sensor network, and show that our linear prediction based compression algorithm significantly improves data reliability and network efficiency.

This paper presents the design and deployment experience of an air-dropped wireless sensor network for volcano hazard monitoring. The deployment of five self-contained stations into the rugged crater of Mount St. Helens only took one hour with a helicopter. The stations communicate with each other through an amplified 802.15.4 radio and establish a self-forming and self-healing multihop wireless network. The transmit distance between stations was up to 8 km with favorable topography. Each sensor station collects and delivers real-time continuous seismic, infrasonic, lightning, GPS raw data to a gateway. The main contribution of this paper is the design of a robust sensor network optimized for rapid deployment during periods of volcanic unrest and provide real-time long-term volcano monitoring. The system supports UTC-time-synchronized data acquisition with 1 ms accuracy, and is remotely configurable. It has been tested in the lab environment, the outdoor campus, and the volcano crater. Despite the heavy rain, snow, and ice as well as gusts exceeding 160 km per hour, the sensor network has achieved a remarkable packet delivery ratio above 99 percent with an overall system uptime of about 93.8 percent over the 1.5 months evaluation period after deployment. Our initial deployment experiences with the system demonstrated to discipline scientists that a low-cost sensor network system can support real-time monitoring in extremely harsh environments.

This paper presents the design and deployment experience of an air-dropped wireless sensor network for volcano hazard monitoring. The deployment of five stations into the rugged crater of Mount St. Helens only took one hour with a helicopter. The stations communicate with each other through an amplified 802.15.4 radio and establish a self-forming and self-healing multi-hop wireless network. The distance between stations is up to 2 km. Each sensor station collects and delivers real-time continuous seismic, infrasonic, lightning, GPS raw data to a gateway. The main contribution of this paper is the design and evaluation of a robust sensor network to replace data loggers and provide real-time long-term volcano monitoring. The system supports UTCtime synchronized data acquisition with 1ms accuracy, and is online configurable. It has been tested in the lab environment, the outdoor campus and the volcano crater. Despite the heavy rain, snow, and ice as well as gusts exceeding 120 miles per hour, the sensor network has achieved a remarkable packet delivery ratio above 99 % with an overall system uptime of about 93.8 % over the 1.5 months evaluation period after deployment. Our initial deployment experiences with the system have alleviated the doubts of domain scientists and prove to them that a low-cost sensor network system can support real-time monitoring in extremely harsh environments.

Opportunistic routing [1, 2] was shown to improve the network throughput greatly. The core idea is to allow any node in the forwarder list, which overhears the transmission and is closer to the destination to participate in forwarding the packet. The nodes in forwarder list are prioritized and the lower priority forwarder will discard the packet if it has been forwarded by a higher priority forwarder. One open problem is how to select and prioritize forwarder list efficiently. In the paper, we investigate how to select and prioritize forwarder list to minimize energy consumptions. We study the case where the transmission power of each node is fixed (known as non-adjustable transmission model) as well as the case where each node is able to adjust its transmission power for each transmission (known as adjustable transmission model). Energy optimum algorithms to select and prioritize forwarder list in both cases are presented and analyzed. Worth to mention that, our methods do not assume any geometrical properties or energy models, they apply to practical and dynamic wireless networks. In addition, we conducted extensive simulations in TOSSIM to study the performance of the proposed routing protocol by comparing it with ExOR [1].

We present a lightweight lossless compression algorithm for realtime sensor networks. Our proposed adaptive linear filtering compression (ALFC) algorithm performs predictive compression, using adaptive linear filtering to predict sample values followed by entropy coding of prediction residuals, encoding a variable number of samples into fixed-length packets. Adaptive prediction eliminates the need to determine prediction coefficients a priori and, more importantly, allows compression to dynamically adjust to a changing source. The algorithm requires only integer arithmetic operations and thus is compatible with sensor platforms that do not support floating-point operations. Significant robustness to packets losses is provided by including small but sufficient overhead data to allow samples in each packet to be independently decoded. Real-world evaluations on seismic data from a wireless sensor network testbed show that ALFC provides more effective compression and uses less resources than some other lossless compression approaches such as S-LZW. Experiments in a multi-hop sensor network also show that ALFC can significantly improve raw data throughput and energy efficiency.

Earlier sensor network MAC protocols focus on energy conservation in low-duty cycle applications, while some recent applications involve real-time high-data-rate signals. This motivates us to design an innovative localized TDMA MAC protocol to achieve high throughput and low congestion in data collection sensor networks, besides energy conservation. TreeMAC divides a time cycle into frames and frame into slots. Parent determines children's frame assignment based on their relative bandwidth demand, and each node calculates its own slot assignment based on its hop-count to the sink. This innovative 2-dimensional frame-slot assignment algorithm has the following nice theory properties. Firstly, given any node, at any time slot, there is at most one active sender in its neighborhood (including itself). Secondly, the packet scheduling with TreeMAC is bufferless, which therefore minimizes the probability of network congestion. Thirdly, the data throughput to gateway is at least 1/3 of the optimum assuming reliable links. Our experiments on a 24 node test bed demonstrate that TreeMAC protocol signi ficantly improves network throughput and energy efficiency, by comparing to the TinyOS's default CSMA MAC protocol and a recent TDMA MAC protocol Funneling-MAC.

This paper present a wake-on sensor network formed with the wake-on motes, TelosW. Our wake-on hardware and software design enable lower power operations and longer network lifetime.

Abstract In this paper, we study the two‐tiered wireless sensor network (WSN) architecture and propose the optimal cluster association algorithm for it to maximize the overall network lifetime. A two‐tiered WSN is formed by number of small sensor nodes (SNs), powerful application nodes (ANs), and base‐stations (BSs, or gateways). SNs capture, encode, and transmit relevant information to ANs, which then send the combined information to BSs. Assuming the locations of the SNs, ANs, and BSs are fixed, we consider how to associate the SNs to ANs such that the network lifetime is maximized while every node meets its bandwidth requirement. When the SNs are homogeneous (e.g., same bandwidth requirement), we give optimal algorithms to maximize the lifetime of the WSNs; when the SNs are heterogeneous, we give a 2‐approximation algorithm that produces a network whose lifetime is within 1/2 of the optimum. We also present algorithms to dynamically update the cluster association when the network topology changes. Numerical results are given to demonstrate the efficiency and optimality of the proposed approaches. In simulation study, comparing network lifetime, our algorithm outperforms other heuristics almost twice. Copyright © 2007 John Wiley & Sons, Ltd.

In this paper, we propose a lightweight and transparent management framework for TinyOS sensor networks, called L-SNMS, which minimizes the overhead of management functions, including memory usage overhead, network traffic overhead, and integration overhead. We accomplish this by making L-SNMS virtually transparent to other applications hence requiring minimal integration. The proposed L-SNMS framework has been successfully tested on various sensor node platforms, including TelosB, MICAz and IMote2.

We study efficient interference-aware joint routing and TDMA link scheduling for a multihop wireless network to maximize its throughput. Efficient link scheduling can greatly reduce the interference effect of close-by transmissions. Unlike the previous studies that often assume a unit disk graph model, we assume that different terminals could have different transmission ranges and interference ranges. In our model, a communication link may not exist due to barriers or is not used by a predetermined routing protocol. Using a mathematical formulation, we develop interference aware joint routing and TDMA link schedulings that optimize the networking throughput subject to various constraints. Our linear programming formulation will find a flow routing whose achieved throughput (or fairness) is at least a constant fraction of the optimum. Then, by assuming known link capacities and link traffic loads, we study link scheduling under the RTS/CTS interference model and the protocol interference model with fixed transmission power. For both models, we present both efficient centralized and distributed algorithms that use time slots within a constant factor of the optimum. We also present efficient distributed algorithms whose performances are still comparable with optimum, but with much less communications. Our theoretical results are corroborated by extensive simulation studies.

The paper proposes a strategy for each Bluetooth device selecting proper communication neighbors and assigning proper label, hence all nodes together form a hierarchical self-routing Bluetooth networks. Both the scatternet formation and routing protocols do not require any geometric information, and the final network topology has the following attractive properties: (1) the diameter of the scatternet is O(log(n)) and the backbone is a hop spanner; (2) the degree of each master node is bounded by a constant 7; (3) the number of piconets is close to optimal; (4) each cluster has self-routing property. Moreover, the network topology can be maintained dynamically and locally with low communication cost, and the message delivery is guaranteed even during structure updating in clusters. The network supports efficient IP-based routing through Distributed Hash Tables(DHTs). The actual routing performance on the structure is evaluated through extensive simulations, the result shows the average communication hops are indeed around log(n).

This paper studies the WSN application scenario with periodical traffic from all sensors to a sink. We present a time-optimum and energy-efficient packet scheduling algorithm and its distributed implementation. We first give a general many-to-one packet scheduling algorithm for wireless networks, and then prove that it is time-optimum and costs max (2N(u1)-1, N(u0)-1) time slots, assuming each node reports one unit of data in each round. Here N(u0) is the total number of sensors, while N(u1) denotes the number of sensors in a sink's largest branch subtree. With a few adjustments, we then show that our algorithm also achieves time-optimum scheduling in heterogeneous scenarios, where each sensor reports a heterogeneous amount of data in each round. Then we give a distributed implementation to let each node calculate its duty-cycle locally and maximize efficiency globally. In this packet scheduling algorithm, each node goes to sleep whenever it is not transceiving, so that the energy waste of idle listening is also eliminated. Finally, simulations are conducted to evaluate network performance using the Qualnet simulator. Among other contributions, our study also identifies the maximum reporting frequency that a deployed sensor network can handle

We study efficient link scheduling for a multihop wireless network to maximize its throughput. Efficient link scheduling can greatly reduce the interference effect of close-by transmissions. Unlike the previous studies that often assume a unit disk graph model, we assume that different terminals could have different transmission ranges and different interference ranges. In our model, it is also possible that a communication link may not exist due to barriers or is not used by a predetermined routing protocol, while the transmission of a node always result interference to all non-intended receivers within its interference range. Using a mathematical formulation, we develop synchronized TDMA link schedulings that optimize the networking throughput. Specifically, by assuming known link capacities and link traffic loads, we study link scheduling under the RTS/CTS interference model and the protocol interference model with fixed transmission power. For both models, we present both efficient centralized and distributed algorithms that use time slots within a constant factor of the optimum. We also present efficient distributed algorithms whose performances are still comparable with optimum, but with much less communications. Our theoretical results are corroborated by extensive simulation studies.

In this paper, we address the problem of energy efficient localized routing in wireless ad hoc networks. Numerous energy aware routing protocols were proposed to seek the power efficiency of routes. Among them, several geographical localized routing protocols were proposed to help making smarter routing decision using only local information and reduce the routing overhead. However, most of the proposed localized routing methods cannot theoretically guarantee the power efficiency of their routes. In this paper, we give the first localized routing algorithm, called localized energy aware restricted neighborhood routing (LEARN), which can guarantee the power efficiency of its route asymptotically almost sure. Given destination node t, an intermediate node v will only select a certain neighbor v such thatvutles alpha for a parameter alphan= radicbetalnl/pin for some beta > pi/alpha, our LEARN routing protocol will find the route for any pair of nodes asymptotically almost sure. When the transmission range rn= radicbetalnl/pin for some beta < pi/alpha, the LEARN routing protocol will not be able to find the route for any pair of nodes asymptotically almost sure. We also conducted simulations to study the performance of LEARN and compare it with a typical localized routing protocol (GPSR) and a global ad hoc routing protocol (DSR)

This article studies topology control in heterogeneous wireless sensor networks, where different wireless sensors may have different maximum transmission ranges and two nodes can communicate directly with each other if and only if they are within the maximum transmission range of each other. We present several localized topology control strategies in which every wireless sensor maintains logical communication links to only a selected small subset of its physical neighbors using information of sensors within its local neighborhood in a heterogeneous network environment. We prove that the global logical network topologies formed by these locally selected links are sparse and/or power efficient and our methods are communication efficient. Here a structure is power efficient if the total power consumption of the least cost path connecting any two nodes in it is no more than a small constant factor of that in the original heterogeneous communication network. By utilizing the wireless broadcast channel capability, and assuming that a message sent by a sensor node will be received by all sensors within its transmission region with at most a constant number of transmissions, we prove that all our methods use at most O(n) total messages, where each message has O (log n ) bits. We also conduct extensive simulations to study the practical performance of our methods.

We propose a novel localized topology-control algorithm for each wireless node to locally select communication neighbors and adjust its transmission power accordingly such that all nodes together self-form a topology that is energy efficient simultaneously for both unicast and broadcast communications. We theoretically prove that the proposed topology is planar, which meets the requirement of certain localized routing methods to guarantee packet delivery; it is power-efficient for unicast - the energy needed to connect any pair of nodes is within a small constant factor of the minimum; it is also asymptotically optimum for broadcast - the energy consumption for broadcasting data on top of it is asymptotically the best among all structures constructed using only local information; it has a constant bounded logical degree, which will potentially save the cost of updating routing tables if used. We further prove that the expected average physical degree of all nodes is a small constant. To the best of our knowledge, this is the first localized topology-control strategy for all nodes to maintain a structure with all these desirable properties. Previously, only a centralized algorithm was reported. Moreover, by assuming that the node ID and its position can be represented in O(log n) bits for a wireless network of n nodes, the total number of messages by our methods is in the range of theoretical results are corroborated in the simulations.

We propose a novel communication efficient topology control algorithm for each wireless node to select communication neighbors and adjust its transmission power, such that all nodes together self-form a topology that is energy efficient simultaneously for both unicast and broadcast communications. We prove that the proposed topology is planar, which guarantees packet delivery if a certain localized routing method is used; it is power efficient for unicast-- the energy needed to connect any pair of nodes is within a small constant factor of the minimum under a common power attenuation model; it is efficient for broadcast: the energy consumption for broadcasting data on top of it is asymptotically the best compared with structures constructed locally; it has a constant bounded logical degree, which will potentially reduce interference and signal contention. We further prove that the average physical degree of all nodes is bounded by a small constant. To the best of our knowledge, this is the first communication-efficient distributed algorithm to achieve all these properties. Previously, only a centralized algorithm was reported in [3]. Moreover, by assuming that the ID and position of every node can be represented in O(log n) bits for a wireless network of n nodes, our method uses at most 13n messages, where each message is of O(log n) bits. We also show that this structure can be efficiently updated for dynamical network environment. Our theoretical results are corroborated in the simulations.

Abstract — Topology control has been well studied in wireless ad hoc networks. However, only a few topology control methods (e.g. [1]) take into account the low interference as a goal of the methods. Some researchers tried to indirectly reduce the interference by reducing the transmission power or by devising low degree topologies, but none of those protocols can guarantee low interference. In this paper we present several algorithms to construct network topologies such that the maximum (or average) link (or nodal) interference of the topology is either minimized or approximately minimized. The algorithms and definitions introduced in this paper are not based on any geometry information about the nodes and they work for any graph models of wireless communication. The theoretical results are corroborated by simulation studies. I.

Peer-to-peer (P2P) networking came from a family of technologies and techniques for organizing distributed applications that takes advantage of the resources available at the Internet edges. We propose an innovative P2P system architecture, called CBRBrain, to implement the content based routing (CBR) service over the backbone routers instead of at the terminal hosts. Hence CBRBrain avoids some drawbacks in previous P2P systems and significantly improves the efficiency and security. Data locating process is easily implemented on CBRBrain by associating a hashed key with each data item and storing the (key, address) pair in routers. The cost for topology update is neglectful since the routers is almost static in the Internet and is not affected by the frequent joining and leaving of hosts at all. The traffic generated by CBRBrain system over the Internet is also expected to be significantly smaller as compared with the other P2P systems. The logical topology of CBRBrain backbone can be any self-routing structure to enable the content based routing service. In the paper, we recommend to construct the underlying logical topology based on the pseudo-balanced de Bruijn graph, since it has many nice properties such as bounded degrees, low diameters and fault tolerance. As an illustration, we describe the mechanism of P2P file sharing application under CBRBrain architecture. Our work is a first step in building an intelligent backbone of the next generation Internet and facilitates the emergence of various intelligent applications over the Internet besides P2P file sharing.

We study topology control in heterogeneous wireless ad hoc networks, where mobile hosts may have different maximum transmission powers and two nodes are connected if they are within the maximum transmission range of each other. We present several strategies so that all wireless nodes self-maintain sparse and power efficient topologies in heterogeneous network environments with low communication cost. The first structure is sparse and can be used for broadcasting. The second structure keeps the minimum power consumption path, and the third structure is a length and power spanner with a bounded degree. Both the second and third structures are power efficient and can be used for unicast. Here a structure is power efficient if the total power consumption of the least cost path connecting any two nodes in it is no more than a small constant factor of that in the original heterogeneous communication graph. All our methods use at most O(n) total messages, where each message has O(logn) bits.

Abstract. Topology control in wireless ad hoc networks is to select a subgraph of the communication graph (when all nodes use their maximum transmission range) with some properties for energy conservation. In this paper, we propose two novel localized topology control methods for homogeneous wireless ad hoc networks. Our first method constructs a structure with the following attractive properties: power efficient, bounded node degree, and planar. Its power stretch factor is at most ρ = 11−(2 sin π k)β, and each node only has to maintain at most k + 5 neighbors where the integer k> 6 is an adjustable parameter, and β is a real constant between 2 and 5 depending on the wireless transmission environment. It can be constructed and maintained locally and dynamically. Moreover, by assuming that the node ID and its position can be represented in O(log n) bits each for a wireless network of n nodes, we show that the structure can be constructed using at most 24n messages, where each message is O(log n) bits. Our second method improves the degree bound to k, relaxes the theoretical power spanning ratio to ρ =

We propose a family of structures, namely, k-localized minimum spanning tree (LMST/sub k/) for topology control and broadcasting in wireless ad hoc networks. We give an efficient localized method to construct LMST/sub k/ using only O(n) messages under the local-broadcast communication model, i.e., the signal sent by each node would be received by all nodes within the node's transmission range. We also analytically prove that the node degree of the structure LMST/sub k/ is at most 6, LMST/sub k/ is connected and planar and, more importantly, the total edge length of the LMST/sub k/ is within a constant factor of that of the minimum spanning tree when k/spl ges/2 (called low weighted hereafter). We then propose another low weighted structure, called Incident MST and RNG Graph (IMRG), that can be locally constructed using at most 13n messages under the local broadcast communication model. Test results are corroborated in the simulation study. We study the performance of our structures in terms of the total power consumption for broadcasting, the maximum node power needed to maintain the network connectivity. We theoretically prove that our structures are asymptotically the best possible for broadcasting among all locally constructed structures. Our simulations show that our new structures outperform previous locally constructed structures in terms of broadcasting and power assignment for connectivity.