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Advancements in nanotechnology promise new capabilities for the Internet of Things (IoT) to monitor extremely fine-grained events with sensors as small as a hundred nanometers. Researchers predict that such tiny sensors can be connected to the Internet using graphene-based nano-antenna radiating in the terahertz band, giving rise to the so-called Internet of Nano-Things (IoNT). Powering such wireless communications with nanoscale energy supply, however, is a major challenge to overcome. Since in many application domains, different types of events discharge different amounts of energy to the environment, we propose an energy-neutral event monitoring framework, called eNEUTRAL IoNT, that allows the sensors to transmit event information using only the amount of energy harvested from the events. We design and analyze two implementation options for this framework. The first option uses a single pulse containing the entire energy harvested from the event but manipulates its time duration to create a unique pulse amplitude for a given combination of event type and its location. In the second option, the harvested event energy is divided into two pulses so that the energy of the first pulse uniquely defines a location and the second pulse uses the remaining energy to identify event types. To minimize classification error at the receiver, we optimize pulse durations in the single-pulse option and pulse energies in the dual-pulse option. Feasibility of eNEUTRAL IoNT is demonstrated using extensive numerical experiments involving terahertz channels. We find that the dual-pulse approach significantly outperforms the single-pulse approach achieving 99% accuracy for detecting both location and event type in 10-node network monitoring two different event types for a radius of 28 mm.

Flood is one of the most frequent and widespread natural hazards globally, which can cause tremendous economic damage and human casualties. As such, flood event monitoring is essential, for which S...

Summary What is known Currently, medication bottles with an electronic cap are frequently used to measure medication adherence. This system is termed medication event monitoring system (MEMS). To our knowledge, the optimal method to summarize data from MEMS has not yet been determined. Objective Look for best practices on how to quantify adherence data from MEMS. Methods Review of PubMed, Embase and Cochrane databases for the articles on medication adherence with MEMS. Results Of 1493 identified articles, 207 were included in this review. The MEMS cap was used for a median of 3 months (IQR: 4; range: 1 week to 24 months) in various health conditions. Many different outcome measures were used. Most studies computed an adherence score, expressed as the percentage of days on which the correct dose of medication was taken. The threshold to mark people as adherent was most frequently, arbitrarily, set at 80% (range: 67%‐95%). We found no data to support a specific threshold. Discussion Although the commonly used definition of adherence has face validity, we found no validation studies, and not all studies used the same cut‐off for adherence. Ideally, a cut‐off should be defined and validated in the context of the specific drug and its pharmacokinetic and dynamic characteristics, and perhaps other contextual factors, rather than generically. In addition, there was large heterogeneity in the definition of what “correct intake” of medication is. What is new and conclusion Outcome measures for MEMS data lacked standardization, and no demonstrable effort to validate any definition against a relevant clinical outcome is available. Consensus on the definition of adherence is urgently needed., Outcome measures for Medication Event Monitoring System (MEMS) data lacked standardization and no demonstrable effort to validate any definition against a relevant clinical outcome is available. A consensus effort on the definition of adherence is urgently needed.

Sensor nodes in wireless sensor networks (WSNs) are usually battery-operated and resource-constrained. The sensor nodes are often deployed in remote areas where the batteries cannot easily be recharged or replaced. Consequently, power becomes a limited resource in WSNs. Thus, energy consumption of the sensor nodes is among parameters of paramount importance. Subsequently, source location privacy (SLP) routing schemes must be energy-efficient and overall cost-effective. Angle-based routing schemes can cost-effectively protect the SLP. The goal of this study is to propose a new path node offset angle routing algorithm to improve the packet transmission cost of two existing SLP routing schemes. The proposed algorithm considers path node offset angles, arbitrary factors, and contrived regions to compute relatively short but greatly randomized routing paths. The routes offer a reduced number of packet forwarding events in the near-sink region and eventually diminish the packet transmission cost. Performance analysis results verify that the proposed path node offset angle routing algorithm effectively improves the packet transmission cost of the schemes and guarantees strong SLP protection throughout the WSN domain. Furthermore, the routing algorithm is capable of alleviating the energy-hole problem.

The complexity of cloud datacenter scenarios poses new challenges in infrastructure management processes such as the impracticality of collecting specific service level events from inside the datacenter infrastructure, and the scalability issues that can appear during event monitoring when thousands of virtual machines have to be polled at a granularity of seconds. Therefore, it would be desirable to provide mechanisms for obtaining these types of events without incurring in the previously described problems. To this end, we propose a generic and scalable method based on the application of deep neural network architectures for predicting service level events using only a reduced number of generic datacenter infrastructure statistics that can be monitored in a scalable way. We demonstrate in a controlled scenario of a real datacenter and using only three variables from a physical machine that it is possible to predict events in real-time and with decent accuracy, without needing to deploy any meter in the end-user equipment. Specifically, we demonstrate this over two service-level events: i) the so-called Noisy Neighbors effect, a harmful situation that appears in physical machines due to the interferences created by the interaction of virtual machines running on them; and ii) the jitter values of a multimedia call running in a virtual machine. We set up a testbed in a real datacenter deploying physical and virtual machines, running a large amount of different experiments for 1000 hours and collecting samples at a 10 seconds granularity in a dataset of 260,000 records. Two different scenarios, in which training and testing data sets contain significant statistical differences, are deployed to demonstrate a better generalization ability of deep models in changing scenarios when compared with traditional Machine Learning techniques. A set of different deep architectures are proposed for both use cases and approximately 4,000 deep models were trained and tested. In both use cases, the best deep models show a good performance when predicting service level events, even if the inputs do not exactly follow the statistical patterns of the data used during training.

With the advancement in the field of Internet-of-Things (IoT), event monitoring applications have rapidly evolved from a simple event data acquisition towards predictive event analytics involving multi-sensory data aggregation in a distributed environment. Existing event monitoring schemes are mainly relying on ine_cient centralized processing mechanism, which may lead to the common single-point of failure for the entire system. In addition, there is no proper method for verifying the event data generated by the monitoring system. In this paper, we present a distributed event monitoring scheme using a Hierarchical Graph Neuron (HGN) distributed pattern recognition algorithm. HGN is a single-cycle learning graph-based recognition scheme that is modelled for in-network deployment. In this work, event data retrieved from multi-sensory IoT devices within a distributed event monitoring network is converted into pattern. To address the event data verification problem, we integrate our proposed scheme with blockchain technology. Combining this IoT event monitoring capabilities with blockchain-based data storage and verification could leads towards a scalable event detection and monitoring model for large-scale network. The results obtained from our simulation shows that the proposed scheme o_ers high event detection accuracy and capable of minimizing the event storage requirements on blockchain network.

Current IoT systems are highly distributed systems that integrate cloud , edge , and fog computing approaches depending on where intelligence and processing capabilities are allocated. This distribution and heterogeneity make development and deployment pipelines very complex and fragmented with multiple delivery endpoints above hardware. This fact prevents rapid development and makes the operation and monitoring of production systems a difficult and tedious task, including cybersecurity event monitoring. The DevSecOps can be defined as a cultural approach to improve and accelerate the delivery of business value by making dev/sec/ops teams’ collaboration effective. This paper focuses on self-service cybersecurity monitoring as an enabler to introduce security practices in a DevOps environment. To that end, we have defined and formalized an activity that supports ‘ Fast and Continuous Feedback from Ops to Dev ’ by providing a flexible monitoring infrastructure so that teams can configure their monitoring and alerting services according to their criteria ( you build, you run, and now you monitor ) to obtain fast and continuous feedback from the operation and thus, better anticipate problems when a production deployment is performed. This activity has been formalized using the Software & Systems Process Engineering Metamodel by OMG and its instantiation is described through a case study that shows the versioned and repeatable configuration of a cybersecurity monitoring infrastructure ( Monitoring as Code ) through virtualization and containerization technology. This self-service monitoring/alerting allows breaking silos between dev, ops, and sec teams by opening access to key security metrics, which enables a sharing culture and continuous improvement.

This paper proposes a distributed energy-efficient algorithm for environmental event detection and monitoring using a mobile wireless sensor network. The mobile sensors could be sensors mounted on vehicles that move in the monitored environment and the mounted sensors collect environmental data. The proposed technique collects the data from the mobile sensors using a mobile sink (e.g. drone). The mobile sink processes the collected to detect and monitor environmental events such as air pollution. The path of the mobile sink affects the accuracy and speed of detecting environmental events. To efficiently detect events, the mobile sink path is shortened and yet all sensed data by the mobile sensors are collected by mobile sink. Therefore, mobile sensors group themselves and then each group selects a group head (GH). The mobile sensors send the sensed data to the their GH, which aggregates the data. To shorten the path of the mobile sink, the proposed method creates a path that passes through the GHs’ locations only. The proposed method uses a space-filling curve to determine the order of GHs along the path of the mobile sink. The mobile sink collects the group’s aggregated data from these GHs. Then, the mobile sink process the collected data to detect the environmental events.

WSN consists of a number of nodes and base stations and is used for event monitoring in various fields such as war situations, forest fires, and home networks. WSN sensor nodes are placed in fields that are difficult for users to manage. It is therefore vulnerable to attackers, and attackers can use false nodes or MAC injection attacks through the hijacked nodes to reduce the lifetime of the network or trigger false alarms. In order to prevent such attacks, several security protocols have been proposed, and all of them have been subjected to MAC-dependent validation, making it impossible to defend against false report attacks in extreme attack circumstances. As attacks have recently become more diverse and more intelligent, WSNs require intelligent methods of security. Based on the report information gathered from the base station, the proposed method provides a technique to prevent attacks that may occur where all MAC information is damaged by carrying out verification of a false report attack through the machine learning based prediction model and the evaluation function.

Background/Aims: Electronic medical records are now frequently used for capturing patient-level data in clinical trials. Within the Veterans Affairs health care system, electronic medical record data have been widely used in clinical trials to assess eligibility, facilitate referrals for recruitment, and conduct follow-up and safety monitoring. Despite the potential for increased efficiency in using electronic medical records to capture safety data via a centralized algorithm, it is important to evaluate the integrity and accuracy of electronic medical record–captured data. To this end, this investigation assesses data collection, both for general and study-specific safety endpoints, by comparing electronic medical record–based safety monitoring versus safety data collected during the course of the Veterans Affairs Nephropathy in Diabetes (VA NEPHRON-D) clinical trial. Methods: The VA NEPHRON-D study was a multicenter, double-blind, randomized clinical trial designed to compare the effect of combination therapy (losartan plus lisinopril) versus monotherapy (losartan) on the progression of kidney disease in individuals with diabetes and proteinuria. The trial’s safety outcomes included serious adverse events, hyperkalemia, and acute kidney injury. A subset of the participants (~62%, n = 895) enrolled in the trial’s long-term follow-up sub-study and consented to electronic medical record data collection. We applied an automated algorithm to search and capture safety data using the VA Corporate Data Warehouse which houses electronic medical record data. Using study safety data reported during the trial as the gold standard, we evaluated the sensitivity and precision of electronic medical record–based safety data and related treatment effects. Results: The sensitivity of the electronic medical record–based safety for hospitalizations was 65.3% without non-VA hospitalization events and 92.3% with the non-VA hospitalization events included. The sensitivity was only 54.3% for acute kidney injury and 87.3% for hyperkalemia. The precision of electronic medical record–based safety data was 89.4%, 38%, and 63.2% for hospitalization, acute kidney injury, and hyperkalemia, respectively. Relative treatment differences under the study and electronic medical record settings were 15% and 3% for hospitalization, 123% and 29% for acute kidney injury, and 238% and 140% for hyperkalemia, respectively. Conclusion: The accuracy of using automated electronic medical record safety data depends on the events of interest. Identification of all-cause hospitalizations would be reliable if search methods could, in addition to VA hospitalizations, also capture non-VA hospitalizations. However, hospitalization is different from a cause-specific serious adverse event that could be more sensitive to treatment effects. In addition, some study-specific safety events were not easily identified using the electronic medical records. This limits the effectiveness of the automated central database search for purposes of safety monitoring. Hence, this data captured approach should be carefully considered when implementing endpoint data collection in future pragmatic trials.

Extreme events have long been underestimated in the extent to which they shape the surface of our planet, our environment, its ecological integrity, and the sustainability of human society. Extreme events are by definition rarely observed, of significant impact and, as a result of their spatiotemporal range, not always easily predicted. Extremes may be short-term catastrophic events such as tsunamis, or long-term evolving events such as those linked to climate change; both modify the environment, producing irreversible changes or regime shifts. Whatever the driver that triggers the extreme event, the damages are often due to a combination of several processes and their impacts can affect large areas with secondary events (domino effect), whose effects in turn may persist well beyond the duration of the trigger event itself. Early studies of extreme events were limited to opportunistic approaches: observations were made within the context of naturally occurring events with high societal impact. Given that climate change is now moving us out of a relatively static climate regime during the development of human civilization, extreme events are now a function of underlying climate shifts overlain by catastrophic processes. Their impacts are often due to synergistic factors, all relevant in understanding process dynamics; therefore, an integrated methodology has become essential to enhance the reliability of new assessments and to develop strategies to mitigate societal impacts. Here we summarize the current state of extreme event monitoring in the marine system, highlighting the advantages of a multidisciplinary approach using Research Infrastructures for providing the temporal and spatial resolution required to monitor Earth processes and enhance assessment of associated impacts.

Mobile Edge Computing (MEC) reforms the cloud paradigm by bringing unprecedented computing capacity to the vicinity of end users at the edge of core networks. This provides users with powerful computing and storage capacities, energy efficiency, and mobility—and context-aware supporting. Multicasting in MEC is a fundamental functionality of many network applications of mobile users, including online conferencing, event monitoring, video streaming, and so on. To guarantee the security and privacy of each multicast traffic session, a service chain that consists of security network functions usually is associated with each multicast request to process its traffic. In this chapter, we study NFV-enabled multicasting that is a fundamental routing problem in an MEC network. We first devise approximation algorithms for the cost minimization problem of admitting a single NFV-enabled multicast request, by assuming that the virtualized network functions may or may not be consolidated into a single location. We then devise an online algorithm with a provable competitive ratio for the online throughput maximization problem when NFV-enabled multicast requests arrive one by one without the knowledge of future request arrivals. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising.

Nowadays, the internet of things (IoT) enabled event monitoring, controlling and managing systems employ different kinds of short range, medium-range and long-range wireless technologies. Wireless communication modules of most of the commercial IoT enabled automation systems operates in industrial, scientific and medical (ISM) frequency band. Therefore, identification of active wireless modules has become most essential to timely detect and track unauthorized users within the restricted zones. Further, it is also useful for finding density of wireless modules operating in the same and/or different frequencies with different and/or same communication protocols. In this paper, we attempt to present an automated wireless module identification (WMI) system based on the communication protocols of the three wireless modules, such as ZigBee, Bluetooth and Wi-Fi which are operated in the ISM frequency band. In this study, three WMI systems are developed based on deep learning networks (DLN), such as two-dimensional convolutional neural network (2D-CNN), long short-term memory (LSTM), and convolutional long short-term deep neural network (CLDNN). We evaluated three DLN based WMI methods on real-time RF signals recorded by using Blade RF, a software defined radio (SDR).

Ensuring optimal medication adherence is a complex problem and require involvement of multiple stakeholders in a healthcare system. Poor medication adherence leads to severe health consequences as well a serious increase in healthcare costs. To address the problem of medication nonadherence, technological solutions which use Medication Event Monitoring Systems (MEMS) combined with in-person interventions have been recommended by researchers. In this paper, we elaborate the architecture of an IoT and blockchain based medication adherence system that uses a newer generation MEMS device developed by Baswen Medication. The system consists of the functionalities for stakeholder registration, device configuration, medication adherence data management and reminder/notification support to increase degree of medication adherence. We justify the advantages of using blockchain technology and report on the progress of system development.

Recent advances in emerging technologies gave birth to new parading of networks calledInternet of Things (IoT). The emergence of such communicating objects has led practitioners to envision networking a large number of sensors for event monitoring, data collection and filtering. Due to the use of wireless technologies, a secure communication is strongly needed to protect valuable information. Secure group communication is one of the most significant requirements for IoT applications. This employs a group rekeying mechanism for a secure and efficient delivery of data. In this paper, we present a new efficient and scalable rekeying protocol for wireless communicating things. This protocol uses $O(log_2 \sqrt[3]n )$ memory complexity, improving significantly the literature works, where n is the network size. Simulation results show that our protocol has better performances than existing works in several criteria.

The development of large-scale systems in modern conditions is not possible without the application of information technology. But then there is the problem of ensuring the security of the functioning of applied information systems and resources circulating, stored and processed in them. Because of the operation in large-scale system, the information security system is also a large and complex system. Therefore, the management of the information security system should be added to the management of the system itself. The most important stage of management is decision-making. Decision support systems are widely used in the field of management and are just beginning to find their application in the field of information security. There are different approaches to decision support. But all of them involve the generation of solutions based on the analysis of big data. This article proposes an approach to decision-making based on information system's security monitoring data. To automate the decision-making process based on the results of event monitoring, software package is proposed. In article the formal model and architecture of software is considered. The result of the software functioning is an integrated assessment of the current security level of information system. The assessment is formed through the analysis of private security indicators obtained on the basis of periodic monitoring data.

Sensor event monitoring and detection facilitate to recognize the activities of the human lives in a smart home daily. The major aim of this technology is to attain optimal performance in activity recognition. The symbol of the daily activity recognition involves the usage of feature inputs which have a noteworthy consequence on the output. However, commonly used representations of features dependent on daily activity have limited performance on the recognition activity. Based on the dynamic nature of the sensor events caused by daily activities, this paper presents a statistical processing approach for time series of sensor events. First time, the statistical values are extracted from sensor events dependent on time series. Subsequently, different categories of statistic formulae are proposed to solve daily activity features. To evaluate the proposed approach, two distinct datasets are adopted for activity recognition based on artificial neural network (ANN), fuzzy logic (FL), and hybrid neuro-fuzzy logic (HNFL). The experimental results reveal that the proposed HNFL approach provides power optimization.

Different from the traditional daytime Remote Sensing (RS) observation data, Nighttime light (NTL) RS images have shown their great potential in earth observation applications from a unique point of view. With the launch of the China’s new generation Luojia1-01 (LJ1-01) NTL satellite, the acquisition of the high spatial resolution and high quality NTL imagery make it possible to identify the disaster event and its temporal change by using the automatic Change Detection (CD) techniques. It is a strong complement to the daytime remote sensing information. In this paper, we proposed a multiple feature fusion CD approach for fire disaster event monitoring in multitemporal high resolution LJ1-01 NTL images. The multiple texture features were fused by taking advantages of the Multivariate Alteration Detection (MAD) and its Iteratively-Reweighted version (IR-MAD) algorithms, in order to improve the CD performance limited by using the original single-band gray-level NTL images. Experimental results obtained on the multitemporal LJ1-01 NTL images demonstrated the effectiveness of the proposed CD technique in implementing an automatic and accurate extraction of fire disaster event of the 2018 California Camp fire. The proposed approach outperformed the ones only relying on the gray-scale original band and single texture features. The conclusion of this study explores the possibility and potential by using high resolution NTL data for CD, in particular, for the effective emergency and rescue in major disaster monitoring applications.

Traffic analysis attacks are common in monitoring wireless sensor networks (WSNs). Therefore, in this study, we explore the features of various adversaries who present threats of traffic analysis attacks against source node location privacy (SLP) routing protocols. Subsequently, we present a series of experiments to analyze various adversary attacks. Analysis results show that an enhanced hotspot-locating adversary (EH-LA) is a big threat against SLP protocols. When compared to patient adversary (PA) and hotspot-locating adversary (H-LA), the EH-LA poses significantly stronger attacks. The results also show that the strength of the PA, H-LA, and EH-LA attacks can improve when factors such as the network operation duration, adversary hearing range, and source node packet rate are varied. On the other hand, factors such as increased source-sink distance, node density, and network size have shown to impede the adversaries attack success.

Background: This study was designed to monitor the short-term (up to 12 weeks) use and safety of quetiapine (Seroquel) extended release (XL) and quetiapine immediate release (IR) prescribed to patients with a clinical diagnosis of schizophrenia, and/or manic episodes associated with bipolar disorder by psychiatrists under normal conditions of use. Methods: A Specialist Cohort Event Monitoring (SCEM) study was conducted in England February 2010–April 2013. This observational cohort study recruited patients prescribed quetiapine XL within the secondary care setting by psychiatrists. A reference cohort of quetiapine IR users was also recruited. Baseline and 12 week observational data were collected from psychiatrists who abstracted information from medical records onto bespoke questionnaires. Data were collected on demographics, indication, past medical history, prescribing information and events of interest. Summary descriptive statistics were calculated. Results: The final cohort consisted of 869 eligible patients; 646 XL users and 223 IR users. The majority of XL and IR users were female (56.2% and 55.6%, respectively), with a median age of 40 (interquartile range [IQR]: 29, 49) and 39 (IQR: 28, 50) years, respectively. The most frequent indication for treatment was Manic episodes associated with Bipolar Affective disorder (53.4% XL and 49.8% IR). Median index dose was 200 mg/day (IQR: 100, 300) for XL users and 50 mg/day (IQR: 50, 100) for IR users, while median final maintenance dose was 400mg/day (IQR: 250, 600) and 300 mg/day (IQR: 100, 400), respectively. The most frequently reported event of interest in both cohorts was sedation ( n = 151, 23.9% XL cohort and n = 49, 23.0% IR cohort). Conclusion: Utilisation of quetiapine XL appeared to be in line with prescribing guidelines in terms of dose, and commonly reported events of interest were in concordance with the known safety profile. Overall, this SCEM study provided important information on the safety and utilisation of quetiapine XL in the secondary care setting in England.

This article is devoted to the development of measures, such as organizational, technical and legal measures, to ensure the reliability of the functioning of information systems in respect to governmental and critically important information systems. The article talks about measures and recommendations to ensure the reliability of Information Systems in relation to Public Information Systems; measures to Ensure the reliability of Information Systems in relation to Critical Information Systems, main criteria for classifying information and communication infrastructure objects as critical information and communication infrastructure objects, critical sectors, their subsectors and critical services, questionnaires for the evaluation of infrastructure providing critical services, criteria for assessing the criticality of services, differences between standard information system (IS) and critical information system; looks at one of the possible options for the process of sustainable operation of critical information system. The application of the proposed methods and approaches will increase the level of reliability of information systems, to ensure the timely identification and elimination of relevant risks. Ensuring the reliability of information systems in relation to public information systems envolves things such as: organizing control over the activity planning aimed at reliable and safe functioning of state information system; implementing processes to ensure the reliability, fault tolerance and security of information systems; formation of regulatory requirements to ensure the reliability of public information system; monitoring the continuous training and certification of employees working with information system; raising awareness of employees and management responsible for the performance of public information systems on the most relevant threats, vulnerabilities, risks and incidents related to the reliability and security of information system; conducting regular audit and assessment of the level of ensuring the reliability of state information system; carrying out preventive measures to eliminate incidents and reduce the risk of information system failures; connecting the telecommunications network to a single gateway to the Internet; connecting the state information system to the information security event monitoring system and transmission of operational information on threats and incidents to the national Information Security coordination center.

Wireless sensor networks (WSNs) have been widely used in various applications for continuous event monitoring and detection. Dual to lack of a protected physical boundary, WSNs are vulnerable to trace-back attacks. The existing secure routing protocols are designed to protect source location privacy by increasing uncertainty of routing direction against statistic analysis on traffic flow. Nevertheless, the security has not been quantitatively measured and shown the direction of secure routing design. In this paper, we propose a theoretical security measurement scheme to define and analyze the quantitative amount of the information leakage from each eavesdropped message. Through the theoretical analysis, we identify vulnerabilities of existing routing algorithms and quantitatively compute the direction information leakage based on various routing strategy. The theoretical analysis results also indicate the direction for maximization of source location privacy.

Multi-pedestrian tracking in aerial imagery has several applications such as large-scale event monitoring, disaster management, search-and-rescue missions, and as input into predictive crowd dynamic models. Due to the challenges such as the large number and the tiny size of the pedestrians (e.g., 4 x 4 pixels) with their similar appearances as well as different scales and atmospheric conditions of the images with their extremely low frame rates (e.g., 2 fps), current state-of-the-art algorithms including the deep learning-based ones are unable to perform well. In this paper, we propose AerialMPTNet, a novel approach for multi-pedestrian tracking in geo-referenced aerial imagery by fusing appearance features from a Siamese Neural Network, movement predictions from a Long Short-Term Memory, and pedestrian interconnections from a GraphCNN. In addition, to address the lack of diverse aerial pedestrian tracking datasets, we introduce the Aerial Multi-Pedestrian Tracking (AerialMPT) dataset consisting of 307 frames and 44,740 pedestrians annotated. We believe that AerialMPT is the largest and most diverse dataset to this date and will be released publicly. We evaluate AerialMPTNet on AerialMPT and KIT AIS, and benchmark with several state-of-the-art tracking methods. Results indicate that AerialMPTNet significantly outperforms other methods on accuracy and time-efficiency., ICPR 2020

Twentieth century has witnessed a tremendous growth in the share of renewable resources in the power grid. Along with the various benefits, this has also raised several technical challenges as well as concerns in system operation, control and protection. These concerns have led to the introduction of advanced metering and protection equipments such as Phasor Measurement Units (PMUs). The highly sampled ‘Big Data’ recorded by PMUs contain significant information about the health of the system. Efficient and timely analysis of this data can address most of the power system concerns such as voltage stability, power system modeling, fault event monitoring, unintentional islanding, state estimation etc. This paper presents a method to detect unintentional islanding using machine learning technique on PMU data. A grid connected PV system is simulated in SIMULINK for data generation. Machine Learning algorithm is adapted to train and further test the data. The results show very good accuracy on test data.

International audience; Poor medication adherence remains frequent in schizophrenia. The present study examined the efficacy of two month-long pilot interventions using the Medication Event Monitoring System (MEMS®). Thirty-three outpatients at high risk for relapse were randomized to receive a smartphone-based intervention, a nurse-based intervention, or treatment as usual. All patients then used the MEMS® to objectively measure medication adherence over six months. No differences were observed in adherence measures or relapse rates across the three groups. When using electronic medication monitoring as an objective measure of adherence, easily-implemented interventions may not significantly improve adherence in patients at high risk for relapse.

Event monitoring is an essential application of Smart City platforms. Real-time monitoring of gully and drainage blockage is an important part of flood monitoring applications. Building viable IoT sensors for detecting blockage is a complex task due to the limitations of deploying such sensors in situ. Image classification with deep learning is a potential alternative solution. However, there are no image datasets of gullies and drainages. We were faced with such challenges as part of developing a flood monitoring application in a European Union-funded project. To address these issues, we propose a novel image classification approach based on deep learning with an IoT-enabled camera to monitor gullies and drainages. This approach utilises deep learning to develop an effective image classification model to classify blockage images into different class labels based on the severity. In order to handle the complexity of video-based images, and subsequent poor classification accuracy of the model, we have carried out experiments with the removal of image edges by applying image cropping. The process of cropping in our proposed experimentation is aimed to concentrate only on the regions of interest within images, hence leaving out some proportion of image edges. An image dataset from crowd-sourced publicly accessible images has been curated to train and test the proposed model. For validation, model accuracies were compared considering model with and without image cropping. The cropping-based image classification showed improvement in the classification accuracy. This paper outlines the lessons from our experimentation that have a wider impact on many similar use cases involving IoT-based cameras as part of smart city event monitoring platforms.

An efficient routing using a virtual backbone (VB) network is one of the most significant improvements in the wireless sensor network (WSN). One promising method for selecting this subset of network nodes is by finding the minimum connected dominating set (MCDS), where the searching space for finding a route is restricted to nodes in this MCDS. Thus, finding MCDS in a WSN provides a flexible low-cost solution for the problem of event monitoring, particularly in places with limited or dangerous access to humans as is the case for most WSN deployments. In this paper, we proposed an adaptive scatter search (ASS-MCDS) algorithm that finds the near-optimal solution to this problem. The proposed method invokes a composite fitness function that aims to maximize the solution coverness and connectivity and minimize its cardinality. Moreover, the ASS-MCDS methods modified the scatter search framework through new local search and solution update procedures that maintain the search objectives. We tested the performance of our proposed algorithm using different benchmark-test-graph sets available in the literature. Experiments results show that our proposed algorithm gave good results in terms of solution quality.

Distributed Stream Processing Systems (DSPSs) are among the currently most emerging topics in data management, with applications ranging from real-time event monitoring to processing complex dataflow programs and big data analytics. The major market players in this domain are clearly represented by Apache Spark and Flink, which provide a variety of frontend APIs for SQL, statistical inference, machine learning, stream processing, and many others. Yet rather few details are reported on the integration of these engines into the underlying High-Performance Computing (HPC) infrastructure and the communication protocols they use. Spark and Flink, for example, are implemented in Java and still rely on a dedicated master node for managing their control flow among the worker nodes in a compute cluster. In this paper, we describe the architecture of our AIR engine, which is designed from scratch in C++ using the Message Passing Interface (MPI), pthreads for multithreading, and is directly deployed on top of a common HPC workload manager such as SLURM. AIR implements a light-weight, dynamic sharding protocol (referred to as "Asynchronous Iterative Routing"), which facilitates a direct and asynchronous communication among all client nodes and thereby completely avoids the overhead induced by the control flow with a master node that may otherwise form a performance bottleneck. Our experiments over a variety of benchmark settings confirm that AIR outperforms Spark and Flink in terms of latency and throughput by a factor of up to 15; moreover, we demonstrate that AIR scales out much better than existing DSPSs to clusters consisting of up to 8 nodes and 224 cores., 16 pages, 6 figures, 15 plots

For target tracking and event monitoring applications, complete coverage is commonly used to measure the coverage performance of a wireless sensor networks. However, complete coverage may not be achievable when sensor nodes are randomly distributed in the area of interest. In this chapter, point coverage is considered as an alternative measure of coverage. Point coverage probabilities are derived for homogeneous and heterogeneous networks. The relationship between the number of nodes and coverage probability is analyzed. Comparisons between complete coverage and point coverage are made by analysis and simulation. Performance optimization and cost control are discussed for heterogeneous sensor networks.

Nowadays, there is an increasing number of workloads, i.e. data serving, analytics, AI, HPC workloads, etc., executed on the Cloud. Although multi-tenancy has gained a lot of attention to optimize resource efficiency, current state-of-the-art resource orchestrators rely on typical metrics, such as CPU or memory utilization, for placing incoming workloads on the available pool of resources, thus, neglecting the interference effects from workload co-location. In this paper, we design an interference-aware cloud orchestrator, based on micro-architectural event monitoring. We integrate our solution with Kubernetes, one of the most widely used and commercially adopted cloud orchestration frameworks nowadays, and we show that we achieve higher performance, up to 32% compared to its default scheduler, for a variety of cloud representative workloads.

In this paper, we present the Wise toolkit for microservice-based system performance experiments. Wise comprises a microservice-based application benchmark with controllable workload generation; milliScope, a set of system resource and event monitoring tools; and WED-Make, a workflow language and code generation tool for the construction and execution of system experiments with automatic provenance collection. We also show a running example reproducing the experimental verification of the millibottleneck theory of performance bugs to illustrate how we have used Wise for the performance study of microservice-based benchmark applications in the cloud.

This paper presents a Cybersecurity Framework for end-to-end IoT Platforms called DRAFT. It is developed in response to rising number of Cyberattacks against IoT applications and services. The framework is composed of a risk assessment framework, a Security Incident and Event Monitoring (SIEM) tool, and a Cyberattack resilient framework. DRAFT framework has been integrated into an end-to-end IoT Platform and has been tested using five known and simulated Cyberattacks using the Fed4FIRE+ federated testbeds. The results obtained show that the proposed Cybersecurity framework is capable of increasing Cyberattack resilience of the IoT Platform under test., © 2020 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Composite event monitoring, which focuses on detecting complicated events from multi-model sensory data, is an important application of heterogeneous sensor networks. Before carrying out such tasks, the deployment of heterogeneous sensors is indispensable, and is vital to the quality of event monitoring. Existing works discussing sensor deployment for composite event coverage are primarily on traditional sensor networks. Recently, a novel network architecture, namely Battery-free Sensor Networks (BF-WSNs) are proposed. Unlike the traditional ones, the BF-WSNs harvest ambient energy and have unlimited lifetime. Considering the difference in characteristics of networks, novel deploying strategies for heterogeneous BF-WSNs are eagerly demanded for better quality in composite event monitoring. In this paper, we address the problem of Sensor Deployment for Composite Event Monitoring in Battery-free Sensor Networks (BF-SDCEM). It is formalized and proved to be NP-hard. Then, the approximate solution is proposed to optimize the quality of coverage and maintain network connectivity. Simulations are carried out to evaluate the performance of the proposed method under different network parameters and to show their effectiveness.

Cyber-physical systems are becoming more important to study and understand within our modern society. Previously., physical and cognitive disturbances were the predominate concern for power systems, but a high penetration of internet connected devices within the power grid requires careful consideration of the implication cyber disturbances may have on physical or cognitive aspects of the system. This work introduces a new benchmark model intended to accommodate interdisciplinary studies that may include all three types of disturbances (physical, cyber and cognitive) and the interrelation between them. The model is designed to run on MATLAB Simulink and OPAL-RT real time simulation systems to enable greater flexibility for hardware in the loop and cyber event monitoring. Potential applications are discussed, as well as a demonstration of a test case for detecting and clearing a fault.

The idea of creating smart homes is not a new one, it has been around since the early 1900s. However, the need to have more automated, proactive, and sustainable smart home, requires a high dependency on the internet, which created a new challenge of information security issues in addition to physical security issues that should be solved recently. In this paper, we propose a prototype of smart home that ensures the physical and cyber security of the systems of the designed smart home. In our prototype, we support the design of open system where the owner can select multi-vendor of the smart home to deploy smart services and functions at home, while ensure the security using System Incident and Event Monitoring systems. Our prototype uses security intelligence to improve the responsive way to both physical and cyber security threats and risks. The requirements of the proposed system have been collected in collaboration with Dubai Electrical and Water Authority (DEWA).

BackgroundThe AliveCor (Kardia) monitor attaches to a smartphone and allows a single-lead ECG to be recorded during symptoms. In 2016, we introduced the use of this smartphone device for investigating palpitations, without syncope, in children. The aim of our study was to review our experience with the smartphone device, comparing it with our previous standard conventional approach to cardiac event monitoring using the Cardiocall monitor, which uses skin electrodes and is given for a finite period.MethodsOver a period of 24 months, 80 smartphone monitors were issued and compared with the most recent 100 conventional event monitors. The number of ECG recordings received, arrhythmias documented, quality of ECG recordings and patient satisfaction were evaluated.ResultsMedian patient age was 11 years in the smartphone monitor group compared with 10 years in the conventional group. Seventy-nine of 80 (98%) patients with a smartphone monitor sent an ECG recorded during symptoms, compared with 62/100 (62%) from the conventional group. A total of 836 ECG recordings were sent from the smartphone monitors compared with 752 from the conventional group. Eight per cent of ECG recordings in each group were of inadequate quality for analysis. Twenty of 80 (25%) patients with a smartphone monitor had documented tachyarrhythmia compared with 6/100 (6%) patients with the conventional monitor (pConclusionsA smartphone-based event monitor allows simple, effective, long-term ECG event monitoring in children that is highly acceptable to the patient and parent.

Critical infrastructure monitoring is one of the most important applications of a smart city. The objective is to monitor the integrity of the structures (e.g., buildings, bridges) and detect and pinpoint the locations of possible events (e.g., damages, cracks). Regarding today's complex structures, collecting data using wireless sensor data over extensive vertical lengths creates enormous challenges. With a direct BS deployment, a big amount of data will accumulate to be relayed to the BS. As a result, traditional models and schemes developed for health monitoring are largely challenged by low-cost, quality-guaranteed, and real-time event monitoring. In this article, we propose BigReduce, a cloud based health monitoring application with an IoT framework that could cover most of the key infrastructures of a smart city under an umbrella and provide event monitoring. To reduce the burden of big data processing at the BS and enhance the quality of event detection, we integrate real-time data processing and intelligent decision making capabilities with BigReduce. Particularly, we provide two innovative schemes for health event monitoring so that an IoT sensor can use them locally; one is a big data reduction scheme, and the other is a decision making scheme. We believe that BigReduce will result in a remarkable performance in terms of data reduction, energy cost reduction, and the quality of monitoring.

We developed a Multinomial Nave Bayes Classifier to categorize the microblog posts into five types according to the text content of posts.Different types of information had significantly different propagation patterns in terms of scale and topological features.Social media users exhibited significantly different interaction patterns for different types of information at different stages. Social media has been playing an increasingly important role in information publishing and event monitoring in emergencies like natural disasters. The propagation of different types of information on social media is critical in understanding the reaction and mobility of social media users during natural disasters. In this research, we analyzed the dynamic social networks formed by the reposting (retweeting) behaviors in Weibo.com (the major microblog service in China) during Yiliang Earthquake. We developed a Multinomial Nave Bayes Classifier to categorize the microblog posts into five types based on the content, and then characterized the information propagation patterns of the five types of information at different stages after the earthquake occurred. We found that the type of information has significant influence on the propagation patterns in terms of scale and topological features. This research revealed the important role of information type in the publicity and propagation of disaster-related information, thus generated data-driven insights for timely and efficient emergency management using the publicly available social media data.

Applications face additional latency when they launch or access a disk to load data into the memory. In this paper, a user-level disk prefetching framework, called iFetcher, is introduced to hide this delay in Linux-based operating systems. This employs an inode-notification ( inotify ) application programming interface (API), which provides an efficient method of tracing operations in a Linux file system, and reports these events to applications in real time. During an initial training run, iFetcher traces the pattern with which data are read by an application. Subsequently, it searches the areas directly ahead of passages where it is requested that a lot of data are read, and locates events that can be used to trigger preloading. When the application is run again and a trigger event is reported (by the inotify API), the corresponding data are read into the page cache prior to actual demand. iFetcher has a low overhead, because disk reads are only need to be monitored during training. Furthermore, it does not require any modifications to the Linux kernel, because it runs at the user level. Five benchmark applications were run on a PC using a solid-state drive, and results demonstrated that iFetcher reduced the launch times by up to 41% and post-launch data loading times by up to 9%.

Monitoring agents are deployed in distributed network environments to collect data and report events to a central coordinator. A widespread event is defined as an event that is observed by all monitoring agents simultaneously. Identifying widespread events can assist in detecting problems, such as cyberattacks, network malfunctions, and abnormal usages. However, detecting widespread events is challenging and a significant communication overhead can be incurred, because every monitoring agent must report any observed event to a central coordinator. In this paper, we study the problem of detecting widespread events accurately. Our proposed schemes can significantly reduce communication overhead and achieve perfect detection accuracy, which was not achieved by any previous studies. The new schemes can also identify generalized widespread events, defined as events observed by more than a predefined number of monitoring agents.

The evolution of Wireless Multimedia Sensor Networks(WMSNs) has been enhanced by the existence of cheap hardware such as microphones and (CMOS) cameras that can ubiquitously hold multimedia content from the entire environment. The spatial proximity of the different cameras (the overlapping Field of Views (FoVs)) causes a transmission of redundant data. Redundancy is the existence of data that is additional to the actual data and permits. A redundancy causes transmission of same data repeatedly. In WMSN, the redundant data elimination is a critical task to reduce the communication cost in terms of unnecessary energy wastage, bandwidth used and CPU processing. In this context, this paper proposed a brief discussion of the following techniques: i) Event monitoring based actuation algorithms which depends on activating the minimum number of cameras sensors while still maintaining the necessary coverage of the event of interest to reduce the possibility of multimedia data redundancy. ii) Cover set construction strategies for enabling efficient scheduling of nodes in mission-critical surveillance applications. iii) Clustering algorithms for WMSNs based on the overlapped field of views (FoVs) coverage areas.

This paper investigates the operation inspection and anomaly diagnosis of transformers in substations, and carries out an application study of artificial intelligence-based sound recognition technology in transformer discharge diagnosis to improve the timeliness and diagnostic capability of intelligent monitoring of substation equipment operation. In this study, a sound parameterization technology in the field of sound recognition is used to implement automatic discharge sound detections. The sound samples are pre-processed and then Mel-frequency cepstrum coefficients (MFCCs) are extracted as features, which are used to train Gaussian mixture models (GMMs). Finally, the trained GMMs are used to detect discharge sounds in the place of transformers in substations. The test results demonstrate that the audio anomaly detection based on MFCCs and GMMs can be used to effectively recognize anomalous discharge in the high scenario of transformers.

Intro: Until recently, ambulatory ECG (AECG) monitoring in pediatric patients was limited to Holter or event monitoring, which pose a unique challenge in this population due to the cumbersome application of wired leads. Patch AECG monitors offer a convenient, though more costly, means of ambulatory rhythm assessment in pediatric patients. The simplicity of application and extended monitoring duration may provide an advantage over traditional wired AECG devices, but there is little data directly comparing their clinical impact within the pediatric population. …