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Recent advancements of Industrial IoT (IIoT) have revolutionized modern urbanization and smart cities. While IIoT data contains rich events and objects of interest, processing a massive amount of IIoT data and making predictions in real-time is challenging. Recent advancements in AI allow processing such a massive amount of IIoT data and generating insights for further decision-making processes. In this paper, we propose several key aspects of AI-enabled IIoT data for smart city monitoring. Firstly, we have combined a human-intelligence-enabled crowdsourcing application with that of an AI-enabled IIoT framework to capture events and objects from IIoT data in real-time. Secondly, we have combined multiple AI algorithms that can run on distributed edge and cloud nodes to automatically categorize the captured events and objects and generate analytics, reports, and alerts from the IIoT data in real-time. The results can be utilized in two scenarios. In the first scenario, the smart city authority can authenticate the AI-processed events and assign these events to the appropriate authority for managing the events. In the second scenario, the AI algorithms are allowed to interact with humans or IIoT for further processes. Finally, we will present the implementation details of the scenarios mentioned above and the test results. The test results show that the framework has the potential to be deployed within a smart city.

Event monitoring is the main purpose of monitoring applications based on wireless sensor networks. To realize the autonomous representation of tunnel disasters in wireless sensor networks, this work proposes a Data Agent (DA) inspired by interpersonal interaction behaviors, which bridges the gap between data and humans. The DA divides event monitoring into four states: active perception, understanding, thinking and representation, to realize a kind of human-like event monitoring. First, a data agent behavior model based on a finite state machine is proposed, which can adaptively switch among these four states. Second, four methods including active perception based on a dual neuron perception structure, understanding for forming the information granularity, thinking for forming the disaster knowledge graph, and representation using grayscale and knowledge graphs are proposed to realize the four states of the DA. The experimental results show that the DA inspired by interpersonal interaction can make wireless sensor networks not only achieve efficient and accurate disaster self-monitoring but also represent the disaster situation intuitively and quickly through grayscale and knowledge graphs.

The hybrid cloud inherits the best aspects of both the public and private clouds. One such benefit is maintaining control of data processing in a private cloud whilst having nearly elastic resource availability in the public cloud. However, the public and private cloud combination introduces complexities such as incompatible security and control mechanisms, among others. The result is a reduced consistency of data processing and control policies in the different cloud deployment models. Cloud load-balancing is one control mechanism for routing applications to appropriate processing servers in compliance with the policies of the adopting organization. This paper presents a process mining-influenced load-balancer for routing applications and data according to dynamically defined business rules. We use a high-level Colored Petri Net (CPN) to derive a model for the process mining-influenced load-balancer and validate the model employing live data from a selected hospital.

In this paper, we reviewed the various advanced technologies and methods that could help patients for measuring adherence of patients. There exist intelligent technologies that are available for measuring medication adherence, including medication event monitoring system (MEMS®), smart blister packs, radio frequency identification (RFID) embedded smart drawers, and wisely aware RFID dosage (WARD) system. Utilization of these advanced technologies and systems have aided in enhancing the adherence to a greater extent. For example, MEMS® refers to the electronic cap that counts the number of bottles opened, but it can be employed only with bottles. Smart blisters are pharmaceutical packagings that possess the capability of monitoring when a pill or tablet is taken out of its packing. All those intelligent technologies can help in active monitoring of patients regarding adherence and capable of eradicating various medication errors due to which adherence is affected.

As a key technology, clustering has been an effective way in large-scale wireless sensor networks (WSNs) to extend the lifetime. However, the static cluster structure in most of the traditional method is formed without considering the development of the event. In this paper, we propose an Energy-Efficient Dynamic Clustering (EEDC) protocol for event monitoring applications in large scale WSN. In EEDC, a dynamic clustering method using Rough Fuzzy C-Means and Genetic algorithm (RFCM-GA) is designed. Firstly, the idea of fuzzy set and rough set in RFCM are used to form the overlapping cluster, which can guarantee the quality of coverage of the developing event. Secondly, we use GA to perform a parallel search in each cluster to find the optimal set of candidate cluster heads (CCHs). RFCM-GA can use its powerful global search capabilities and fast convergence speed to obtain the best clustering results. Simulation results demonstrate that EEDC has higher energy efficiency and prolongs the network lifetime compared to the existing approaches.

In this work Gammatone (GT) filter bank energy features are used with a deep neural network (GT-DNN) to model robust acoustic event detection (AED) in the smart home environment for monitoring human activities. The Gammatone filter bank is modelled for the human auditory system which decomposes the environmental sound events into multiple frequency bands energy features. These features are learned during the training phase of DNN which is similar to the AED task by a human brain. Gammatone filter bank energy features are found superior over popular Mel-scale filter bank features and Gammatone filter bank output provides smooth spectrogram patterns that help to identify the dominant characteristic features of the target events. Moreover, the auditory feature-based Gammatone filter bank approach showed its robustness against the noise compared to Mel-scale filter bank features. In this work, the proposed GT-DNN model is tested on a single board computer (SBC) prototype developed on a popular Raspberry Pi 4 Model B. Experimental F-score results show impressive real-time AED performance. Furthermore, different parameters of the model are optimised and it is used to classify the various acoustic events from Freiburg-106 event dataset. NOISEX-92 dataset is combined with the clean event dataset which is used to train the model. Comparison of AED performance in terms of F-score at different signal to noise ratios (SNRs) is carried out between GT-DNN and baseline Mel-scale filter bank energy features, and improved results are obtained with the GT-DNN method. Moreover, the confidence scores for 10 different classes of events are evaluated in presence of worst category babble noise at 0 dB SNR and excellent classification results are obtained using the proposed method. Detailed analysis and results are given in support of each claim.

Online search engine has been widely regarded as the most convenient approach for information acquisition. Indeed, the intensive information-seeking behaviors of search engine users make it possible to exploit search engine queries as effective “crowd sensors” for event monitoring. While some researchers have investigated the feasibility of using search engine queries for coarse-grained event analysis, the capability of search engine queries for real-time event detection has been largely neglected. To this end, in this article, we introduce a large-scale and systematic study on exploiting real-time search engine queries for outbreak event detection, with a focus on earthquake rapid reporting. In particular, we propose a realistic system of real-time earthquake detection through monitoring millions of queries related to earthquakes from a dominant online search engine in China. Specifically, we first investigate a large set of queries for selecting the representative queries that are highly correlated with the outbreak of earthquakes. Then, based on the real-time streams of selected queries, we design a novel machine learning–enhanced two-stage burst detection approach for detecting earthquake events. Meanwhile, the location of an earthquake epicenter can be accurately estimated based on the spatial-temporal distribution of search engine queries. Finally, through the extensive comparison with earthquake catalogs from China Earthquake Networks Center, 2015, the detection precision of our system can achieve 87.9%, and the accuracy of location estimation (province level) is 95.7%. In particular, 50% of successfully detected results can be found within 62 s after earthquake, and 50% of successful locations can be found within 25.5 km of seismic epicenter. Our system also found more than 23.3% extra earthquakes that were felt by people but not publicly released, 12.1% earthquake-like special outbreaks, and meanwhile, revealed many interesting findings, such as the typical query patterns of earthquake rumor and regular memorial events. Based on these results, our system can timely feed back information to the search engine users according to various cases and accelerate the information release of felt earthquakes.

Automating fault detection in communication networks and distributed systems is a challenging process that usually requires the involvement of supporting tools and the expertise of system operators. Automated event monitoring and correlating systems produce event data that is forwarded to system operators for analyzing error events and creating fault reports. Machine learning methods help not only analyzing event data more precisely but also forecasting possible error events by learning from existing faults. This study introduces an automated fault detection system that assists system operators in detecting and forecasting faults. This system is characterized by the capability of exploiting bug knowledge resources at various online repositories, log events and status parameters from the monitored system; and applying bug analysis and event filtering methods for evaluating events and forecasting faults. The system contains a fault data model to collect bug reports, a feature and semantic filtering method to correlate log events, and machine learning methods to evaluate the severity, priority and relation of log events and forecast the forthcoming critical faults of the monitored system. We have evaluated the prototyping implementation of the proposed system on a high performance computing cluster system and provided analysis with lessons learned.

This paper introduces a wireless smart sensor network to meet the requirements for measuring low-amplitude ambient and sudden event monitoring of civil structures. The developed sensor platform con...

The monitoring of pre-failure movements and their evolution over time is necessary to assess the predisposing and triggering factors. This research focusses on assessing terrain displacements in th...

In recent years, frequent meteorological disasters have caused great concern to people. It is particularly important to timely detect the meteorological events and release early warning information. Most traditional meteorological event detection methods rely on physical sensors, but such practice is usually costly and inflexible. As a new form of lightweight social sensor, social networks make up for the shortcomings of traditional physical sensors. In this paper, we propose a sentence-level feature-based meteorological event detection model to detect 14 types of meteorological events defined by the China Meteorological Administration (CMA) in Sina Weibo. Our joint model consists of two modules: a fine-tuned BERT as the language model and a wide-grained capsule network as the event detection network. The design of our model considers the correlation among meteorological events and achieves the best results on all metrics compared with other baseline models. Moreover, as a practical application, our model has been applied to the meteorological event monitoring platform in the CMA Public Meteorological Service Center to provide online services.

Background Poor adherence to tuberculosis (TB) treatment can result in community transmission and drug resistance. Digital health technologies have been used to enhance adherence to TB medication for proper management, but the cost-effectiveness of this approach remains unclear.Methods We used the real data from the study conducted from April 2014 to December 2020 in Morocco to enhance the adherence to drug-susceptible TB treatment using a smart pillbox with a web-based medication monitoring system, called Medication Event Monitoring Systems (MEMS). We applied a Markov model adding Multi-drug resistant (MDR) TB to evaluate the costs and cost-effectiveness of MEMS, compared to the standard of care (modified directly observed treatment and intervention) from the health perspective. The primary outcome was the incremental cost-effectiveness ratio (ICER) per disability adjusted life-year (DALY) averted. We also performed two-way sensitive analysis between treatment success rate of MEMS and standard of care. Results The average total per-patient health system costs for treating a new TB patient under MEMS versus standard of care were $398.7 and $155.7, respectively. The MEMS strategy would reduce the number of drug-susceptible TB cases by 0.17 and MDR-TB cases by 0.01 per patient over five years. The ICER of MEMS was $434/DALY averted relative to standard of care, and was most susceptible to the TB treatment success rate of both strategies followed by the managing cost of MEMS.Conclusion MEMS is considered cost-effective for managing infectious active TB in Morocco.

The authors propose a Web-Mashup Application Service Framework for Multivariate Time Series Analytics (MTSA) that supports the services of model definitions, querying, parameter learning, model evaluations, data monitoring, decision recommendations, and web portals. This framework maintains the advantage of combining the strengths of both the domain-knowledge-based and the formal-learning-based approaches and is designed for a more general class of problems over multivariate time series. More specifically, the authors identify a general-hybrid-based model, MTSA – Parameter Estimation, to solve this class of problems in which the objective function is maximized or minimized from the optimal decision parameters regardless of particular time points. This model also allows domain experts to include multiple types of constraints, e.g., global constraints and monitoring constraints. The authors further extend the MTSA data model and query language to support this class of problems for the services of learning, monitoring, and recommendation. At the end, the authors conduct an experimental case study for a university campus microgrid as a practical example to demonstrate our proposed framework, models, and language.

Microblog platforms have been extremely popular in the big data era due to its real-time diffusion of information. It's important to know what anomalous events are trending on the social network and be able to monitor their evolution and find related anomalies. In this paper we demonstrate Ring , a r eal-t i me emerging a n omaly monitorin g system over microblog text streams. Ring integrates our efforts on both emerging anomaly monitoring research and system research. From the anomaly monitoring perspective, Ring proposes a graph analytic approach such that (1) Ring is able to detect emerging anomalies at an earlier stage compared to the existing methods, (2) Ring is among the first to discover emerging anomalies correlations in a streaming fashion, (3) Ring is able to monitor anomaly evolutions in real-time at different time scales from minutes to months. From the system research perspective, Ring (1) optimizes time-ranged keyword query performance of a full-text search engine to improve the efficiency of monitoring anomaly evolution, (2) improves the dynamic graph processing performance of Spark and implements our graph stream model on it, As a result, Ring is able to process big data to the entire Weibo or Twitter text stream with linear horizontal scalability. The system clearly presents its advantages over existing systems and methods from both the event monitoring perspective and the system perspective for the emerging event monitoring task.

Cooperative vehicular cyber physical systems build a group of entities that can accomplish a cooperative task using the distributed control approaches. To this end, such a cooperative task can be coordinated and managed by a distributed control plane that will be able to encapsulate all the required functionality in a layered architecture providing the required interoperability. Here, the authors propose such a distributed control plane that consists of the cooperative awareness layer, thecommunication layer and the distributed control layer. Wireless communications play an important role for the mobility provision, taking into account different constraints in order to provide high reliability and low latency. A simulation environment is considered with a leader–follower control format, where the reliability is evaluated. Further, a distributed safety monitoring approach is devised, given a control diagram and mapping of the events to the different components. The event monitoring relies on the self-triggered approach, where a use case is evaluated to highlight the impact of the input and output delays to the model predictive control component of the overall distributed control diagram including the calculation of the number of triggered events.

Recently, an emerging application field through Twitter messages and algorithmic computation to detect real-time world events has become a new paradigm in the field of data science applications. During a high-impact event, people may want to know the latest information about the development of the event because they want to better understand the situation and possible trends of the event for making decisions. However, often in emergencies, the government or enterprises are usually unable to notify people in time for early warning and avoiding risks. A sensible solution is to integrate real-time event monitoring and intelligence gathering functions into their decision support system. Such a system can provide real-time event summaries, which are updated whenever important new events are detected. Therefore, in this work, we combine a developed Twitter-based real-time event detection algorithm with pre-trained language models for summarizing emergent events. We used an online text-stream clustering algorithm and self-adaptive method developed to gather the Twitter data for detection of emerging events. Subsequently we used the Xsum data set with a pre-trained language model, namely T5 model, to train the summarization model. The Rouge metrics were used to compare the summary performance of various models. Subsequently, we started to use the trained model to summarize the incoming Twitter data set for experimentation. In particular, in this work, we provide a real-world case study, namely the COVID-19 pandemic event, to verify the applicability of the proposed method. Finally, we conducted a survey on the example resulting summaries with human judges for quality assessment of generated summaries. From the case study and experimental results, we have demonstrated that our summarization method provides users with a feasible method to quickly understand the updates in the specific event intelligence based on the real-time summary of the event story.

One of the main drivers behind blockchain adoption is a lack of trust among entities serving a common goal, but with different interests. Following the success of Bitcoin, several blockchain platforms have emerged, such as Ethereum and Hyperledger Fabric, to enable conducting distrusted processes in a non-repudiable manner. However, it is not safe to assume the applicability of conventional software design strategies to Blockchain-based solutions. In this paper, we assume an untrusted SLA (service level agreement) relationship between an IoT service provider and its consumer. We adopt Hyperledger Fabric for the purpose of implementing SLA compliance assessment. We design a smart contract that takes blockchain unique features into consideration. The design particularly accounts for the MVCC (multiversion concurrency control) mechanism, which while effective for resolving the double spending problem, causes read-write conflicts when high transmission rates are experienced between the IoT application and the blockchain. Using a fire station event monitoring scenario, we describe our smart contract design and solution for conflicting transactions. We experimentally evaluate our solution and demonstrate clear performance improvements in terms of throughput and latency.

Risk assessment constitutes the first part within the risk management framework and involves evaluating the importance of a risk, either quantitatively or qualitatively. Risk assessment consists of three steps, namely risk identification, risk estimation and risk evaluation. Nevertheless, the risk management framework also includes a fourth step, i.e., the need for feedback on all the risk assessment undertakings. However, there is a lack of such feedback, which constitutes a serious deficiency in the reduction of environmental hazards at the present time. Risk identification of local or regional hazards involves hazard quantification, event monitoring including early warning systems and statistical inference. Risk identification also involves the development of a database where historical hazard information and hazard effects are included. Similarly, risk estimation involves magnitude–frequency relationships and hazard economic costs. Furthermore, risk evaluation consists of the social consequences of the derived risk and involves cost-benefit analysis and community policy. The objective of this review paper is twofold. On the one hand, it is to address meteorological hazards and extremes within the risk management framework. Analysis results and case studies over Mediterranean ecosystems with emphasis on the wider area of Greece, in the eastern Mediterranean, are presented for each of the three steps of risk assessment for several environmental hazards. The results indicate that the risk management framework constitutes an integrated approach for environmental planning and decision-making. On the other hand, it sheds light on advances and current trends in the considered meteorological and environmental hazards and extreme events, such as tornadoes, waterspouts, hailstorms, heat waves, droughts, floods, heavy convective precipitation, landslides and wildfires, using recorded datasets, model simulations and innovative methodologies.

Finding lost persons, collecting information in disturbed communities, efficiently traversing urban areas after a blast or similar catastrophic events have motivated researchers to develop intelligent sensor frameworks to aid law enforcement, first responders, and military personnel with situational awareness. This dissertation consists of a two-part framework for providing situational awareness using both acoustic ground sensors and aerial sensing modalities. Ground sensors in the field of data-driven detection and classification approaches typically rely on computationally expensive inputs such as image or video-based methods [6, 91]. However, the information given by an acoustic signal offers several advantages, such as low computational needs and possible classification of occluded events including gunshots or explosions. Once an event is identified, responding to real-time events in urban areas is difficult using an Unmanned Aerial Vehicle (UAV) especially when GPS is unreliable due to coverage blackouts and/or GPS degradation [10]. Furthermore, if it is possible to deploy multiple in-situ static intelligent acoustic autonomous sensors that can identify anomalous sounds given context, then the sensors can communicate with an autonomous UAV that can navigate in a GPS-denied urban environment for investigation of the event; this could offer several advantages for time-critical and precise, localized response information necessary for life-saving decision-making. Thus, in order to implement a complete intelligent sensor framework, the need for both an intelligent static ground acoustic autonomous unattended sensors (AAUS) and improvements to GPS-degraded localization has become apparent for applications such as anomaly detection, public safety, as well as intelligence surveillance and reconnaissance (ISR) operations. Distributed AAUS networks could provide end-users with near real-time actionable information for large urban environments with limited resources. Complete ISR mission profiles require a UAV to fly in GPS challenging or denied environments such as natural or urban canyons, at least in a part of a mission. This dissertation addresses, 1) the development of intelligent sensor framework through the development of a static ground AAUS capable of machine learning for audio feature classification and 2) GPS impaired localization through a formal framework for trajectory-based flight navigation for unmanned aircraft systems (UAS) operating BVLOS in low-altitude urban airspace. Our AAUS sensor method utilizes monophonic sound event detection in which the sensor detects, records, and classifies each event utilizing supervised machine learning techniques [90]. We propose a simulated framework to enhance the performance of localization in GPS-denied environments. We do this by using a new representation of 3D geospatial data using planar features that efficiently capture the amount of information required for sensor-based GPS navigation in obstacle-rich environments. The results from this dissertation would impact both military and civilian areas of research with the ability to react to events and navigate in an urban environment. Doctor of Philosophy Emergency scenarios such as missing persons or catastrophic events in urban areas require first responders to gain situational awareness motivating researchers to investigate intelligent sensor frameworks that utilize drones for observation prompting questions such as: How can responders detect and classify acoustic anomalies using unattended sensors? and How do they remotely navigate in GPS-denied urban environments using drones to potentially investigate such an event? This dissertation addresses the first question through the development of intelligent WSN systems that can provide time-critical and precise, localized environmental information necessary for decision-making. At Virginia Tech, we have developed a static ground Acoustic Autonomous Unattended Sensor (AAUS) capable of machine learning for audio feature classification. The prior arts of intelligent AAUS and network architectures do not account for network failure, jamming capabilities, or remote scenarios in which cellular data wifi coverage are unavailable [78, 90]. Lacking a framework for such scenarios illuminates vulnerability in operational integrity for proposed solutions in homeland security applications. We address this through data ferrying, a communication method in which a mobile node, such as a drone, physically carries data as it moves through the environment to communicate with other sensor nodes on the ground. When examining the second question of navigation/investigation, concerns of safety arise in urban areas regarding drones due to GPS signal loss which is one of the first problems that can occur when a drone flies into a city (such as New York City). If this happens, potential crashes, injury and damage to property are imminent because the drone does not know where it is in space. In these GPS-denied situations traditional methods use point clouds (a set of data points in space (X,Y,Z) representing a 3D object [107]) constructed from laser radar scanners (often seen in a Microsoft Xbox Kinect sensor) to find itself. The main drawback from using methods such as these is the accumulation of error and computational complexity of large data-sets such as New York City. An advantage of cities is that they are largely flat; thus, if you can represent a building with a plane instead of 10,000 points, you can greatly reduce your data and improve algorithm performance. This dissertation addresses both the needs of an intelligent sensor framework through the development of a static ground AAUS capable of machine learning for audio feature classification as well as GPS-impaired localization through a formal framework for trajectory-based flight navigation for UAS operating BVLOS in low altitude urban and suburban environments.

This article focuses on the existing sensitive information early warning mechanism and auxiliary decision-making is not sound, the emergency command and control is not agile, the application system business is solidified, and the existing monitoring and early warning service mode deals with multi-source, heterogeneous terrorism-related data, data utilization inefficiency and limited data processing service functions, the inability to achieve intelligent control of the process, and the inability to achieve precise monitoring, as well as the multi-dimensional and in-depth intelligent analysis of sensitive data. Research on distributed real-time transmission and distribution of big data based on multi-source and multi-channel adaptation, multi-source, heterogeneous big data distributed online real-time processing based on S4/Strom and distributed memory computing, position-based social security event monitoring, and semi-supervised system self-evolution and other key technologies, proposed to build a big data-driven social security monitoring and early warning platform system.

Complex event monitoring is an important problem in data streams that has drawn much attention. Most previous work assumes that the user knows and provides a complex event pattern for the system to continuously monitor. However, we observe that in many real applications, such as healthcare, security, and businesses, there are heterogeneous substreams and a diverse set of attributes. Often there is no simple uniform pattern prior to a critical event; nor is there clean simple language to describe the pattern leading to the critical event. People often only know it after the fact -- e.g., when something undesirable happens. We propose a novel approach based on relational machine learning and representation learning. We propose and learn probabilistic state machine patterns, which are used to monitor and predict the imminence of critical events. Our experiments demonstrate the efficiency and effectiveness of our approach, as well as its clear superiority over the closest previous approaches such as IL-Miner and LSTM based early prediction.

Compared with open experimental equipment, closed experimental equipment that uses computer experimental software with a private protocol to complete the experimental process has higher security and is widely used in the fields such as space environment experiment simulation. This paper takes the private protocol computer experiment software of the closed experimental equipment as the research object, while proposes a cooperative monitoring strategy for the experiment process. By combining the image structure similarity comparison algorithm with dynamic event monitoring, this method realizes the monitoring of the experimental process of a closed experimental equipment with a proprietary protocol computer experimental software. The test results of this method show that the experimental process monitoring method proposed in this paper has better performance and higher accuracy and reliability.

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.