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Cyber and physical attacks threaten the security of distribution power grids. The emerging renewable energy sources such as photovoltaics (PVs) introduce new potential vulnerabilities. Based on the electric waveform data measured by waveform sensors in the distribution power networks, in this paper, we propose a novel high-dimensional data-driven cyber physical attack detection and identification approach (HCADI). Firstly, we analyze the cyber and physical attack impacts (including cyber attacks on the solar inverter causing unusual harmonics) on electric waveforms in distribution power grids. Then, we construct a high dimensional streaming data feature matrix based on signal analysis of multiple sensors in the network. Next, we propose a novel mechanism including leverage score based attack detection and binary matrix factorization based attack diagnosis. By leveraging the data structure and binary coding, our HCADI approach does not need the training stage for both detection and the root cause diagnosis, which is needed for machine learning/deep learning-based methods. To the best of our knowledge, it is the first attempt to use raw electrical waveform data to detect and identify the power electronics cyber/physical attacks in distribution power grids with PVs.

The massive machine-type communications (mMTC) is one of the three generic services for 5G. With the connection-based random access (CBRA) scheme, each machine-type device (MTD) establishes a connection with the base station (BS) prior to its data transmission. Due to the explosive growth of the number of MTDs, many MTDs would establish connections with the BS, which necessitates the study on how to efficiently manage the massive connections with MTDs. To address this issue, in this article, we propose a unified utility-based analytical framework for the optimal connection management of mMTC in 5G networks, where the signaling overhead for connection establishment, access delay, and the connection resource utilization ratio is included. Specifically, we first derive key performance metrics, i.e., the mean time length of each connection and resource utilization ratio, as functions of traffic input rate and inactivity timer. By further considering the signaling overheads and access delay of each MTD, the network utility is formulated and maximized by optimally choosing the inactivity timer. We then present a detailed discussion on the effect of system parameters on the optimal inactivity timer and the corresponding maximum network utility. Finally, we extend the analytical framework to the scenario in which the CBRA scheme coexists with the packet-based random access scheme, i.e., transmitting packets in the random access channel without connection establishment. The critical threshold in terms of the traffic input rate is characterized, which sheds important light on the access scheme selection issue.

As one of the three main application scenarios of 5 G cellular system, massive Machine-Type Communications (mMTC) has been regarded as the key solution to facilitate the IoT paradigm. One major bottleneck for accommodating mMTC is the severe congestion at the cellular random access channel when plenty of Machine-Type Devices (MTDs) send access requests concurrently while the preamble resources are limited. To remedy this issue, limiting the number of retransmissions and dropping access requests after the limit is reached can be an effective approach. Yet, the effect of the preamble retransmission limit $K$ on the optimal access performance of mMTC in cellular networks remains largely unexploited, which motivates the study in this paper. Specifically, in this paper, we start by characterizing the network steady-state points based on the limiting probability of successful transmission of access requests. We then obtain explicit expressions of the access throughput and the mean access delay of successfully-transmitted access requests as functions of $K$ and the number of preambles $M$ . The maximum access throughput and the corresponding optimal backoff window size are further derived. It is shown that the maximum access throughput is independent of $K$ , while the mean access delay can be significantly reduced with a small $K$ , yet, at the expense of increased request dropping ratio. In addition, to improve both the throughput and delay performance, the analysis shows that more preambles should be allocated but the performance gain becomes marginal when $M$ is large. Therewith, an algorithm is proposed for determining the least number of preambles $M^\ast$ that maximizes the access throughput and the preamble resource utilization ratio. Numerical results show that a smaller preamble retransmission limit $K$ can further reduce $M^\ast$ .

Power electronics systems have become increasingly vulnerable to cyber–physical threats due to their growing penetration in the Internet-of-Things (IoT)-enabled applications, including connected electric vehicles (EVs). In response to this emerging need, a cyber–physical-security initiative was recently launched by the IEEE Power Electronics Society (PELS). With increasing connectivity due to vehicle-to-everything (V2X) and the number of electronic control units, connected EVs are facing greater cyber–physical security challenges. However, existing research extensively focuses on the network security of internal combustion engine vehicles and fails to address the cyber–physical security of EVs specifically. In this article, the challenges and future visions of cyber–physical security are discussed for connected EVs from the perspective of firmware security, vehicle charging safety, and powertrain control security. The vulnerabilities of EVs are investigated under a variety of cyberattacks, ranging from energy-efficiency-motivated attacks to safety-motivated attacks. Simulation results, including hardware-in-the-loop (HIL) results, are provided to further analyze the cyberattack impacts on both converter (device) and vehicle (system) levels. More importantly, an architecture for the next-generation power electronics systems is proposed to address the cyber–physical security challenges of EVs. Finally, potential research opportunities are discussed in detail, including detection and migration for firmware security, model-based, and data-driven detection and mitigation. To the best of our knowledge, this is the first comprehensive study on cyber–physical security of powertrain systems in modern EVs.

In this paper, the challenges and a future vision of the cyber-physical security of photovoltaic (PV) systems are discussed from a firmware, network, PV converter controls, and grid security perspective. The vulnerabilities of PV systems are investigated under a variety of cyber-attacks, ranging from data integrity attacks to software-based attacks. A success rate metric is designed to evaluate the impact and facilitate decision making. Model-based and data-driven methods for threat detection and mitigation are summarized. In addition, the blockchain technology that addresses cyber-attacks in software and cyber networks is described. Simulation and experimental results that show the impact of cyber-attacks at the converter (device) and grid (system) levels are presented. Finally, potential research opportunities are discussed for next-generation, cyber-secure power electronics systems. These opportunities include multi-scale controllability, self-/event-triggering control, artificial intelligence/machine learning, hot patching, and online security. As of today, this study will be one of the few comprehensive studies in this emerging and fast-growing area.

Image-guided therapies have been on the rise in recent years as they can achieve higher accuracy and are less invasive than traditional methods. By combining augmented reality technology with image-guided therapy, more organs, and tissues can be observed by surgeons to improve surgical accuracy. In this review, 233 publications (dated from 2015 to 2020) on the design and application of augmented reality-based systems for image-guided therapy, including both research prototypes and commercial products, were considered for review. Based on their functions and applications. Sixteen studies were selected. The engineering specifications and applications were analyzed and summarized for each study. Finally, future directions and existing challenges in the field were summarized and discussed.

In this paper, a bed-mounted vibration sensor-based system is proposed to monitor vital parameters during sleep, including heartbeat rate (HR) and respiratory rate (RR), body movements and sleep postures. Our system enables smart healthcare that monitors daily sleep in a ubiquitous and non-invasive manner. Besides, the system is contact-free, as no external wearable devices and physical contacts are required. Furthermore, the vibration-based approach also avoids the privacy violation caused by the usage of surveillance cameras. To effectively monitor sleep status, a robust stable signal mode decomposition based HR and RR estimation method is developed for the complicated and noisy vibration signals. Besides, algorithms for body movement and sleep posture identification are also proposed based on vibration signal features. A prototype system is demonstrated with system details, showing great potentials in monitoring sleep status in a real-time user-friendly manner. Experimental results of short term and long term experiments with different participants and beds show that our system achieves satisfying accuracy compared with dedicated commercial devices.

Mangrove species classification is of particular importance for coastal conservation and restoration. However, it is challenging to distinguish species-level differences with limited training data. In this letter, we propose an object-oriented classification method for mangrove forests by using the hyperspectral image (HSI) and the 3-D Siamese residual network. First, superpixel segmentation is utilized to obtain objects with various shapes and scales. Second, 3-D patches of each object are extracted from the original HSI, and those patches containing training samples are adopted to pairwise train the network. The 3-D spatial pyramid pooling (3-D-SPP) is added in the network to extract features in multiple scales. Finally, the abstract features of test samples are learned by the trained network, and the labels are determined by the nearest neighbor classifier within the metric space. Experiments on real mangrove hyperspectral data demonstrate the effectiveness of the proposed method in species classification of mangroves.

Instead of using traditional intrusive surveillance technologies such as cameras or body sensors, we propose a smart home system sensing the footstep-induced vibrations to feature the same functions which can be used for the assisted-living purpose. Based on distributed sensing and processing units, we adopt a distributed computing approach to preprocess the time-series data, recognize footstep signals, and extract vibration features locally. Through communications over the sensor networks, our system is capable to estimate the occupancy via counting the number of occupants. Besides, based on the multicomponent seismometer sensing system, we propose a novel indoor footstep localization method called angle constrained time difference of arrivals relying on both angle and arrival information of the recorded waveforms. According to separated pedestrian locations, different trajectories of multiple people can be tracked. From the location-tracking history, the resident's daily activities and social interactions can be inferred. In our experiments, the proposed system obtains promising results. Specifically, the location error is 0.14 m with a 0.11 m standard deviation. And the multipeople identification accuracy is above 87.83%.

Zhuzhou is an important part of the Changsha-Zhuzhou-Xiangtan city group in southern China. To grasp the economic benefits of urban wetland resources value in Zhuzhou City, strengthen wetland conservation and utilization, and ensure the sustainable development of cities, this paper takes Zhuzhou 2016 wetland remote sensing image interpretation map and multi-source data as the foundation, and build urban wetland ecosystem service evaluation system. Through deduplicate double counting, this paper evaluates the value of urban wetland ecosystem service in Zhuzhou City employing shadow project method, replacement cost method, travel cost method, conditional value method and other economic value evaluation methods. The results indicate: (a) The total value of urban wetland ecosystem services is $1,527,908,900 in 2016. (b) The ultimate value of urban wetland services ranked as follows: water storage regulation value ($539,566,265), climate regulation value ($424,930,361), tourism recreation value ($174,543,328), water supply value ($133,183,901), biological product value ($121,987,952), atmospheric composition regulation value ($92,111,687), soil erosion prevention value ($15,799,608), water purification value ($14,598,298), and aesthetic heritage value ($10,075,346); (c) The value of different types of wetlands ranked as follows: paddy fields ($526,111,672), riverine wetland ($526,111,672), ponds ($329,628,343), reservoirs ($149,275,241), wastewater treatment plant ($207,831/a); (d) The value per unit area of different types of wetlands ranked as follows: reservoirs ($240,919/ha), riverine wetland ($236,627/ha), pond ($145,693/ha), paddy fields ($26,551/ha) and wastewater treatment plant ($17,003/ha). The evaluation results reveal the great contribution of urban wetland system service to the Zhuzhou city with numbers, that not only provides data basis for wetland conservation and management but also provides a reference for the refined evaluation of urban wetland ecosystem service value.

The recent advancement of motion tracking technology offers better treatment tools for conditions, such as movement disorders, as the outcome of the rehabilitation could be quantitatively defined. The accurate and fast angular information output of the inertial measurement unit tracking systems enables the collection of accurate kinematic data for clinical assessment. This article presents a study of a low-cost microelectromechanical system inertial measurement unit-based tracking system in comparison with the conventional optical tracking system. The system consists of seven microelectromechanical system inertial measurement units, which could be mounted on the lower limbs of the subjects. For the feasibility test, 10 human participants were instructed to perform three different motions: walking, running, and fencing lunges when wearing specially designed sleeves. The subjects’ lower body movements were tracked using our inertial measurement unit-based system and compared with the gold standard—the NDI Polaris Vega optical tracking system. The results of the angular comparison between the inertial measurement unit and the NDI Polaris Vega optical tracking system were as follows: the average cross-correlation value was 0.85, the mean difference of joint angles was 2.00°, and the standard deviation of joint angles was ± 2.65°. The developed microelectromechanical system–based tracking system provides an alternative low-cost solution to track joint movement. Moreover, it is able to operate on an Android platform and could potentially be used to assist outdoor or home-based rehabilitation.

Understanding earthquakes and volcano eruptions phenomena require the analysis of large amounts of subsurface data. Currently, data collected from these events need post-processing in an off-line mode due to hardware limitations and strong bandwidth constraints. Furthermore, analytics may take days, even weeks, in being processed after collection. In some cases, underground structures require previous studies and subsurface knowledge from experts to establish certain input parameters. By leveraging current IoT technologies, we introduce an autonomous seismic imaging system (ASIS), a real-time system for monitoring and generating analytics of seismic data based on a sensor network. ASIS processes real-time analytics near where the events are generated (fog computing) to mitigate bandwidth limitations. Calculations for earthquake detection, location, and magnitude are processed in situ . ASIS allows monitoring sensor status, visualizing the data of each sensor, and generating two dimensional/three dimensional (2-D/3-D) subsurface structures. The system is able to learn the underground structure by taking advantage of ambient noise data analysis, which significantly reduces the need of initial parameters. We incorporated a renewable energy source to extend sensor functionality, and we made the system self-healing and fault-tolerant. Indoor and outdoor evaluation of the system showed the error of earthquake detection is 13.7 ms and the spatial location accuracy is 5.1 ft.

Background The main treatment of parapelvic cysts is flexible ureteroscope currently. Considering the intraoperative localization of the cyst may fail with flexible ureteroscope, we tend to use an innovative method by ultrasound-guided for easily locating cystic wall during flexible ureteroscopic surgery Methods We retrospectively reviewed 17 consecutive cases of parapelvic renal cysts treated by ultrasound-guided flexible ureteroscope between March 2017 and May 2020. The differences of simple flexible ureteroscopic technique and ultrasound-guided flexible ureteroscopic technique were compared. The surgical procedures, postoperative complications, results and patients’ follow-ups were evaluated. Results The cysts wall were seen clearly in 10 patients with ureteroscopic vision. Another 7 patients changed to ultrasound-guided flexible ureteroscopic surgery since it was difficult to identify the cyst wall. Mean operative time were 25.9 ± 8.7 minutes and 37.1 ± 10.1 minutes for conventional and modified technique respectively (P =0.004), of which 17.6 ± 5.8 minutes and 26.5 ± 8.4 minutes to search the cysts, respectively (P = 0.002), and the mean time of the incising were 7.1 ± 4.9 minutes and 12.1 ± 5.6 minutes, respectively (P = 0.000). All of the patients were followed-up 12 months, there were no serious complications and recurrence observed. Conclusions We demonstrated that it is feasible and safe to treat parapelvic renal cyst by ultrasound-guided flexible ureteroscopic incision and drainage. The less sample size and further studies were the limitations of our study.

The Internet of Things (IoT) refers to a network of connected devices that collects and exchanges data through the Internet. These things can be artificial or natural and interact as autonomous agents that form a complex system. In turn, business process management (BPM) was established to analyze, discover, design, implement, execute, monitor, and evolve collaborative business processes within and across organizations. While the IoT and BPM have been regarded as separate topics in research and in practice, we strongly believe that, on the one hand, the management of IoT applications will greatly benefit from BPM concepts, methods, and technologies. On the other hand, the IoT poses challenges that will require enhancements and extensions of the current state of the art in the BPM field. In this article, we question the extent to which these two paradigms can be combined, and we discuss emerging challenges and intersections from a research and practitioner's point of view in terms of complex software systems development.

This is a sequel of our previous work [20] on access throughput optimization of Machine-to-Machine (M2M) communications in Long Term Evolution (LTE) networks. By incorporating a finite data transmission rate, this paper aims to characterize the effect of data transmission on the optimal access performance of Machine-Type Devices (MTDs). Specifically, both the maximum access throughput and the corresponding optimal Access Class Barring (ACB) factor are obtained as explicit functions of the data transmission rate, which show that even with the ACB factor optimally tuned, the access throughput may deteriorate as the number of MTDs increases, and even drop to zero if the data transmission rate is too small. To boost the data transmission rate, more resources should be allocated to data transmission, which, however, leads to fewer chances for access. In light of the tradeoff between the data transmission rate and the access frequency, the time slot length is further optimized for maximizing the normalized maximum access throughput. Simulation results corroborate that by properly choosing the time slot length, substantial gains can be achieved over the default setting in various scenarios.

Group paging is a baseline solution proposed by the long-term evolution (LTE) standardization body for supporting machine-to-machine (M2M) communications in the current-generation and the next-generation cellular networks. Yet, in conventional group paging scheme, upon the reception of paging message, all machine-type devices (MTDs) in a group will simultaneously access the base station, leading to severe network congestion and intolerably low access efficiency. To handle this issue, in this article, we propose a dynamic group paging mechanism, where only the MTDs with packets to send will join the contention process, and the collisions in the random access channel are addressed by the contention resolution scheme. Explicit expressions of key performance measures including the mean access delay of each MTD are derived as functions of the length of waiting period (interval between two consecutive paging periods) $T_{W}$ , where a smaller $T_{W}$ indicates a higher frequency of group paging. It is shown that $T_{W}$ is a key system parameter that determines the crucial tradeoff between the signaling overheads of the system during the paging period and the access delay performance of each MTD. To study how to properly tune the waiting period length, a utility-based analytical framework is established by taking the aforementioned tradeoff into consideration. The optimal waiting period length for maximizing the network utility is derived and verified by simulation results. The analysis in this article reveals that the network should increase the group paging frequency as the traffic becomes heavier or the number of preambles decreases. Providing more preambles can indeed improve the delay performance, while the gain becomes marginal if the number of preambles is large.

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.

The transformative integration of sensor networks and geophysical imaging techniques enables the creation of a system to monitor and analyze seismic data in real time as well as image various subsurface structures, properties, and dynamics. Ambient noise seismic imaging is a technique widely used in geophysical exploration for investigating subsurface structures using recorded background raw ambient noise data. The current state-of-the-art of ambient noise monitoring relies on gathering these high volumes of raw data back to a centralized server or base station to pre-process, cross-correlate, analyze frequency-time components, and generate subsurface tomography. However, modern computational sensors (for example, those with $\sim$ 1.2 GHz of processor and $\sim$ 1 GB of memory) can be not only used for recording raw vibration data but also performing in situ processing and cooperative computing to generate subsurface imaging in real time. In this paper, we present a distributed solution to apply ambient noise tomography over large dense networks and perform in-network computing on huge seismic samples while avoiding centralized computation and expensive data collection. Results show that our approach can detect subsurface velocity variations in real time while meeting network bandwidth constraints and reducing communication cost ( $\sim -75\%$ ).

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.

Objectives The aim of our study was to ascertain the underlying role of microRNAs (miRNAs) in human intervertebral disc degeneration (IDD).Design Bioinformatic analysis from multiple databases.Methods Studies of the association of miRNAs and IDD were identified in multiple electronic databases. All potential studies were assessed by the same inclusion and exclusion criteria. We recorded whether miRNA expression was commonly increased or suppressed in the intervertebral disc tissues and cells of IDD subjects. We used String to identify biological process and cellular component pathways of differentially expressed genes.Results We included fifty-seven articles from 1,277 records in this study. This report identified 40 different dysregulated miRNAs in 53 studies, including studies examining cell apoptosis (26 studies, 49.06%), cell proliferation (15 studies, 28.3%), extracellular matrix (ECM) degradation (10 studies, 18.86%), and inflammation (five studies, 9.43%) in IDD patients. Three upregulated miRNAs (miR-19b, miR-32, miR-130b) and three downregulated miRNAs (miR-31, miR-124a, miR-127-5p) were considered common miRNAs in IDD tissues. The top three biological process pathways for upregulated miRNAs were positive regulation of biological process, nervous system development, and negative regulation of biological process, and the top three biological process pathways for downregulated miRNAs were negative regulation of gene expression, intracellular signal transduction, and negative regulation of biological process.Conclusions This study revealed that miRNAs could be novel targets for preventing IDD and treating patients with IDD by regulating their target genes. These results provide valuable information for medical professionals, IDD patients, and health care policy makers.

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.

Human activities and home appliances generate vibration signals that can be considered seismic events. This paper has designed a smart seismic sensor network to collect and analyze floor seismic data and generate a result that can be implemented in smart home design. There are three algorithms in the system: footstep counting, trajectory tracking, and fall detection. The system uses contact-less and privacy-preserving sensors, they are both affordable for a normal family and easy to install. The algorithms base on mathematical morphology and a deep-learning-based classification model. Also, the system-level context analysis contributes to the result. The system contains a cloud computing structure, and all of the algorithms are implemented in real-time. Comprehensive tests and validations were conducted to evaluate system performances. Our system has an accuracy of 98.47% on footstep count, 96.9% on passing events detection, and 85.9% on route recovery.

Distributed sensor networks empower real-time in-situ computing and seismic imaging without transmitting the raw data to the remote data center. This attribute is valuable for planet exploration th...

Many countries have enacted a quick response to the unexpected coronavirus disease 2019 (COVID-19) pandemic by using existing technologies. For example, robotics, artificial intelligence, and digital technology have been deployed in hospitals and public areas for maintaining social distancing, reducing person-to-person contact, enabling rapid diagnosis, tracking virus spread, and providing sanitation. In this study, 163 news articles and scientific reports on COVID-19-related technology adoption were screened, shortlisted, categorized by application scenario, and reviewed for functionality. Technologies related to robots, artificial intelligence, and digital technology were selected from the pool of candidates, yielding a total of 50 applications for review. Each case was analyzed for its engineering characteristics and potential impact on the COVID-19 pandemic. Finally, challenges and future directions regarding the response to this pandemic and future pandemics were summarized and discussed.

The massive Machine Type Communications (mMTC) is one of the three generic services to be supported by 5G wireless systems. To fulfill the ever-increasing network access demand from a large number of Machine Type Devices (MTDs), this paper develops a cluster-based group paging scheme. Specifically, with the proposed scheme, MTDs are divided into clusters and the group paging period is decomposed into two parts: intracluster access period and inter-cluster access period. In the intracluster access period, preamble reuse is adopted for facilitating the access request transmissions from MTDs in each cluster to its cluster head. In the inter-cluster access period, only cluster heads send access requests to the base station.To evaluate and optimize the access efficiency of the proposed scheme, the probability of successful access of each MTD is characterized, based on which the maximum probability of successful access and the corresponding optimal number of clusters and optimal length of the intra-cluster access period are obtained as explicit functions of key system parameters including the number of preambles and the number of MTDs. A comparative study of the access efficiency for group paging with clustering and without clustering is conducted, which reveals the critical threshold in terms of the number of MTDs, above which clustering is beneficial. The analysis is verified via extensive simulations. It is shown that the access performance of the proposed scheme significantly outperforms that of the traditional group paging scheme, especially in massive access scenarios.

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.

The Huhehu Sag is one of the most important oil and gas depressions in the Hailar Basin. However, the origin of Huhehu oil is still controversial. Previous studies on source rocks have mainly focused on the Nantun Formation (K1n); a few studies have investigated the Damoguaihe Formation (K1d). Based on the Rock–Eval pyrolysis parameters, 172 drill cutting samples from the Huhehu Sag were analyzed to evaluate their geochemical characteristics. Based on the Rock–Eval data, the mudstones from the first member of the Damoguaihe Formation (K1d1) and the second member of the Nantun Formation (K1n2) have moderate to high hydrocarbon generation potential, while mudstones from the first member of the Nantun Formation (K1n1) have poor to good hydrocarbon generation potential. Additionally, both the K1n1 and K1n2 coal members have poor to fair hydrocarbon generation potential, but the K1n2 coal member has a better generative potential. Fifteen Huhehu oils were collected for molecular geochemical analyses to classify the oils into genetic families and to identify the source rock for each oil using chemometric methods. The Huhehu oils were classified into three groups with different maturity levels using hierarchical cluster analysis and principal component analysis. Group A oils (high maturity) are characterized by relatively moderate ratios of Pr/Ph, Pr/n-C17, and Ph/n-C18, as well as an abundance of C29 steranes, mainly derived from the K1n2 and K1n1 mudstone members. In comparison, group B oils (moderate maturity) have relatively low Pr/Ph ratios, moderate Pr/n-C17 and Ph/n-C18 ratios, and low concentrations of C29 steranes. Group C oils (low maturity) show relatively high ratios of Pr/Ph, Pr/n-C17, and Ph/n-C18, as well as high concentrations of C29 steranes. Furthermore, group B oils derived from the K1d1 mudstone member and group C oils derived from the K1n2 coal member were also identified by principal component analysis score plots. Correlation studies suggest a major contribution from the K1n mudstone Formation and the K1d1 mudstone member to the oils of the Huhehu Sag. So, the Nantun Formation and relatively shallow strata of the Damoguaihe Formation (e.g., the K1d1 member) represent important targets for future oil-reservoir exploration in the Huhehu Sag.

Spectrum leasing has been widely regarded as one of the most effective ways to improve the utilization of limited spectrum resources. In this paper, we propose a novel traffic-adaptive spectrum leasing (TASL) scheme by allowing secondary users (SUs) to lease part of licensed spectrum channels from primary users (PUs) temporarily for transmitting the dynamically generated secondary packets. As the time length of each leasing period is variable according to the dynamic generation of secondary packets, the proposed TASL can effectively satisfy the quality-of-service requirement of SUs and also benefit PUs with the financial payoff provided by SUs. By establishing a three-dimensional continuous Markov chain for the proposed TASL, we formulate the average utilities of PUs and SUs in terms of the expected buffering time of primary and secondary packets, as well as the expected transmission throughput of PUs and SUs. Moreover, to coordinate the interests of PUs and SUs in a noncooperative manner, we also propose a Stackelberg game model for PUs and SUs to negotiate various spectrum leasing parameters and further apply two specific rules to guarantee the existence of a unique equilibrium solution. Numerical simulation shows that, compared with those existing spectrum leasing schemes that preset a fixed-time length for leasing periods, the proposed TASL can effectively improve the utilization of the leased channels, increase the average utilities of both PUs and SUs, and be more suitable for newly emerging applications that are sensitive to packet transmission delay and buffering overhead.

A key challenge for enabling machine-to-machine (M2M) communications in long-term evolution (LTE) networks is the intolerably low access efficiency in the presence of massive access requests. To address this issue, a new analytical framework is proposed in this paper to optimize the random access performance of the M2M communications in LTE networks. Specifically, a novel double-queue model is established, which can both incorporate the queueing behavior of each machine-type device (MTD) and be scalable in the massive access scenarios. To evaluate the access efficiency, the network throughput is further characterized, and optimized by properly choosing the backoff parameters including the access class barring (ACB) factor and the uniform backoff (UB) window size. The analysis reveals that the maximum network throughput is solely determined by the number of preambles, and can be achieved by either tuning the ACB factor or the UB window size based on statistical information such as the traffic input rate of each MTD. Simulation results corroborate that with the optimal tuning of backoff parameters, the network throughput can remain at the highest level regardless of how many MTDs in the network, and is robust against feedback errors of the traffic input rate and burstiness of data arrivals.

Maintaining thermal comfort within an occupied building requires energy; thus, optimized solution methods for balancing energy use with indoor environmental quality (adequate thermal comfort, lighting, etc.) are needed. Current building temperature control systems do not adequately take in account the adaptive capability of the occupants, but this concept can be used advantageously during implementation of demand response. Demand response programs can affect both the occupants’ thermal comfort from temporary adjustments to space temperature settings. This paper describes ongoing research and field testing of methods to optimize building energy consumption for heating, ventilation and air conditioning applications accounting for human factors such as the thermal comfort by the occupants. Model predictive controllers could serve as powerful tools to optimize the operation of smart buildings and improve human comfort perceptions while helping to better integrate renewable energy systems with increased grid stability. Practical application: This work outlines how the operation of cooling systems can be optimized with respect to reducing peak demand while still maintaining thermal comfort within acceptable ranges.

The real-time electrical market operations in smart grid require reliable and accurate data from state estimation. However, state estimation is vulnerable to data integrity attacks, in which strategically manipulated meter measurements can bypass the conventional bad data detection and introduce errors. As a result, it becomes more likely for the attackers to control real-time electrical market through manipulations of meter measurements. In this paper, we first reveal the intrinsic relations between data integrity attacks and real-time electrical market operations, and explicitly characterize their complex interactions as a process simulator. Then a simulation-based global optimization problem is formulated from which attackers could maximize financial incentives through constructed data integrity attacks. More importantly, a novel systematic online attack construction strategy is proposed, such that attackers can launch the desired attacks only by the real-time data streams of meter measurements and no power network topology or parameter information is needed. A corresponding online defense strategy is also presented to detect and identify the malicious measurements without extra meter hardware investments. Finally, we evaluate the performance of the proposed attacking strategies and countermeasure through numerical simulations in IEEE test systems with both synthetic and real data from the New York Independent System Operator.

In this paper, we introduce a novel health and sleep nursing assistant called “Helena” for real-time, contactless, and non-invasive monitoring 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 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 (Food and Drug Administration) oximeter and a metronome.

A smart grid is a highly complex cyber-physical electrical power system that uses two-way digital communication and intelligent embedded devices to achieve sensing, control, computation, and communication within power network. To validate the functionality, security, and reliability of such a system requires the modeling and emulation of both communication network and power network, as well as the interactions between them. In this paper, we present smart-grid common open research emulator (SCORE), a distributed software emulator for cyber-physical analysis in smart grid. SCORE integrates the emulations of both power network and communication network, and it is highlighted by the following features. First, SCORE is the first software emulation platform for smart grid, which means that the same application program running in SCORE can be directly ported to embedded devices with little or no migration issues. Second, for one smart grid instance, SCORE supports distributed emulation when the instance is in very large scale. Third, for multiple smart grid emulation instances running on different networked computers, SCORE allows them to dynamically connect or disconnect with each other in run time, such that each instance can capture its own interior system dynamics even without a prior knowledge of the entire smart grid topology.

This paper proposes a fast distributed demand response U+0028 DR U+0029 algorithm for future smart grid based on primaldual interior method and Gaussian belief propagation U+0028 GaBP U+0029 solver. At the beginning of each time slot, each end-user U+002F energysupplier exchanges limited rounds of messages that are not private with its neighbors, and computes the amount of energy consumption U+002F generation locally. The proposed demand response algorithm converges rapidly to a consumption U+002F generation decision that yields the optimal social welfare when the demands of endusers are low. When the demands are high, each end-user U+002F energysupplier estimates its energy consumption U+002F generation quickly such that a sub-optimal social welfare is achieved and the power system is ensured to operate within its capacity constraints. The impact of distributed computation errors on the proposed algorithm is analyzed theoretically. The simulation results show a good performance of the proposed algorithm.

In this paper, we study the problem of decentralized learning in sensor networks in which local learners estimate and reach consensus to the quantity of interest inferred globally while communicati...

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 article, 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.

Nanogrids (NGs) are small scale microgrids typically serving few buildings or loads. An islanded NG is an autonomous system that consists of generation units including renewable energy sources and traditional fuel generators, energy storage systems (ESS) and loads. This paper presents the design and validation of a new optimal energy management (EM) algorithm for an islanded NG. To minimize the generator's operating cost and maximize battery availability at each operating cycle, dynamic programming (DP) framework is employed to solve the underlying optimization problem. The goal of the proposed EM algorithm is to ensure both the use of maximum available solar power and optimal battery state of charge. To meet that goal, the management of the ESS is formulated as a stochastic optimal control problem, where nonlinearities in the battery charging and discharging process are considered. A Markov model is built in order to predict the probability distribution of the solar production used in the stochastic DP formulation. Simulation results are given to illustrate the efficacy of the proposed DP-based approach compared to a rule-based algorithm. Finally, a hardware-in-the-loop system is used to evaluate the real-time operation of the proposed EM algorithms.

Supporting Machine-to-Machine (M2M) communications has been an essential requirement for the Long Term Evolution (LTE) networks. To cope with the massive access requests with stringent delay requirements, in this paper, a community-based random access scheme is proposed by exploiting the spatial correlation of Machine-Type Devices (MTDs). Specifically, a community detection algorithm is first proposed to form the community structure of the network, based on which one MTD is selected in each community to establish a connection with the base station upon each emergency event. Simulation results corroborate that by reducing the number of access requests, the proposed scheme can significantly improve the delay performance especially in the massive access scenarios with a limited number of preambles.

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.

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.

A wireless seismic network can be effectively used as a tool for subsurface monitoring and imaging. By recording and analyzing ambient noise, a seismic network can image underground infrastructures and provide velocity variation information of the subsurface that can help to detect anomalies. By studying the variation in the noise cross-correlation function of the noise, it is possible to determine the subsurface seismic velocity and image underground infrastructures. Ambient noise imaging can be done in a decentralized fashion using Distributed Spatial Auto-Correlation (dSPAC). In dSPAC over sensor networks, the cross-correlation is the most intensive communication process since nodes need to communicate their data with neighbor nodes. In this paper, a new communication-reduced method for cross-correlation is presented to meet bandwidth and cost of communication constraints in networks while ambient noise imaging is performed using dSPAC method. By applying the proposed communication-reduced method, we show that energy and computational cost of the nodes is also preserved.

In this paper, we propose an attack detection framework in the Internet of Things (IoT) devices. The framework applies a data-centric method to process the energy consumption data and classify the attack status of the monitored device. We implement the framework in real hardware, and emulate common types of attacks to evaluate the performance of the attack detection framework. Due to the characteristic of the energy data, not only cyber attacks but also physical attacks such as heating are also emulated and tested. To shorten the detection time, a two-stage strategy is also proposed to first apply a short time window for a rough detection, then a long time window to the fine detection of anomalies. The accuracy of short-term detection is 90%, while in the long-term detections the accuracy reaches 99.5%. Due to the nature of information from energy consumption data, the framework is more secure in cases the kernel of the device is already compromised.

Instantaneous Heart Rate (IHR) detection is important but challenging. As a kind of biomedical signal, photoplethys-mography (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$\times$ 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%.

We present ScorePlus, a software-hardware hybrid and federated experiment environment for Smart Grid. ScorePlus incorporates both a software emulator and hardware testbed, such that they all follow the same architecture, and the same Smart Grid application program can be tested on either of them without any modification; ScorePlus provides a federated environment such that multiple software emulators and hardware testbeds at different locations are able to connect and form a unified Smart Grid system; ScorePlus software is encapsulated as a resource plugin in the OpenStack cloud computing platform, such that it supports massive deployments with large-scale test cases in cloud infrastructure.

We consider the decentralised consensus optimization problem in this paper. A fast decentralised gradient-based algorithm is proposed for a general class of problems with convex and differentiable objective functions. The developed method guarantees that all the nodes in the network would reach consensus on the global variable eventually. Based on the proposed decentralised algorithm, a broadcast-based protocol is designed, which is capable of providing solution for real-time in situ seismic imaging. Extensive numerical experiments on both synthetic and real sensor network seismic data sets validate the superior efficiency of the proposed algorithm over the other benchmarks.

Due to the complexity of the multi-attribute group decision-making problem, how to determine the weight of different experts on different attributes is a difficult point in current multi-attribute decision-making problem. The authors believe that weights of experts should be reflected in the decision-making process, rather than subjectively determined before the decision. If an expert’s decision-making results can better express the collective will, this expert should be given greater weight. Based on this idea, this paper proposes a weight determination method based on the similarity of individual and collective wishes of experts. First, the improved traditional projection method is proposed. The weight is determined by the degree of agreement between the experts’ individual evaluation vector and the ideal evaluation vector, so that the experts’ weights are more consistent with their performance in the decision-making process. Secondly, combining the new weight form with the ideal point method, a multi-attribute group decision-making method based on the agreement degree is proposed. Finally, the validity of the proposed multi-attribute group decision-making method based on the new weight form is verified by a case simulation.

13C solid-state NMR and XPS techniques were used to investigate structural changes in kerogen during oil generation process across a range of maturity. Kerogen was isolated from an shale rock collected from Well W161 in the Dongying Depression, Bohai, China. An artificial pyrolysis experiment was carried out in a closed gold tube system using a heating rate of 2 ℃/h during which 11 temperature-point samples were collected between 350 ℃ and 450 ℃ (Easy%Ro = 0.80 to 1.98). The initial kerogen was analysed using Rock-Eval and the samples were analysed using X-ray photoelectron spectroscopy (XPS) and solid-state 13C nuclear magnetic resonance (13C NMR) to study their functional groups and chemical structures. Six averaged molecular structure models of the initial kerogen and its residues (can not be dissolved by organic reagent) were established to study the changes occurring during oil and gas generation. The results showed that with an increase in temperature, the proportion of aliphatic carbon declined while aromatic carbon increased. After reaching peak oil maturity, a condensation reaction occurred in the residue; some aromatic carbons cracked from the kerogen and combined with the residue structure. Average aromatic cluster increment was calculated to quantify the increase in the size of aromatic clusters within the structures. It was established to evaluate the changes occurring in the molecular structure of type Ⅰ kerogen during heating. Based on these results, ConU (maximum condensation degree) and ConL (lowest condensation degree) are proposed to estimate the degree of condensation of samples with different maturities.

The biomedical signal classification accuracy on motor imagery is not always satisfactory, partially because not all the important features have been effectively extracted. This paper proposes an improved dynamic feature extraction approach based on a time-frequency representation and an optimal sequence similarity measurement. Since the wavelet packet decomposition (WPD) generates more detailed signal variation information and the dynamic time warping (DTW) helps optimally measure the sequence similarity, more important features are kept for classification. We apply the extracted features from our proposed method to Electroencephalogram (EEG) based motor imagery through the OpenBCI device and obtain higher classification accuracy. Compared with traditional feature extraction methods, there is a significant classification accuracy improvement from 83.53% to 90.89%. Our work demonstrates the importance of the advanced feature extraction in time series data analysis, e.g. biomedical signal.