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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.

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.