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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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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

A conventional wireless sensor network node consists of a number of components: microprocessor, memory, sensor, and radio. Advances in nanotechnology have enabled the miniaturization of these components, thus enabling wireless nanoscale sensor networks (WNSN). Due to their small size, WNSN nodes are expected to be powered by harvesting energy from the environment. Unfortunately, there is a mismatch in the energy that can be harvested and the energy required to power all the aforementioned components in a WNSN node. In this article, we propose a simplified sensor node architecture for event detection. We call our architecture Sensorless Event MONitoring in self-powered WNSNs (SEMON). A SEMON node consists of only an energy harvester and a radio with minimal processing capacity. We assume that each event to be monitored will generate a different amount of energy, and we can therefore use this amount of energy as the signature of an event. When an event occurs, a SEMON node harvests the energy released by the event and turns it into a radio pulse with an amplitude proportional to the harvested energy. A remote station is used to decode the amplitude of the pulse to recognize the event that has occurred. We propose two methods for the remote station to decode the events that have occurred. The first method is based on thresholds. The second method makes use of an event model that gives the probability that a sequence of events will occur. This enables us to formulate the decoding problem using Hidden Markov Models. We study the decoding performance of both methods. Finally, we provide a case study on using the SEMON architecture to monitor the chemical reactions inside a reactor.

Wireless sensor networks (WSNs) are being suggested at an increasing rate for structural health monitoring (SHM). The objective is to monitor complex events (e.g., damage) in structures (e.g., an industrial machine and a high-rise building) that are usually carried out with wired-based SHM systems. However, monitoring events with a WSN deployed over large structures is challenging due to WSN constraints (high-resolution data transmission and energy) and the quality of monitoring. In this paper, we attempt to design a cyber-physical system (CPS) of structural event monitoring with WSNs and propose a novel model-based in-network decision making in the CPS named MODEM. We think of the idea of generic event detection (like target/object) schemes, and enable each sensor to sense and make a simplified local decision (0/1) on the complex events. We then think of the formation of engineering structures and find that a large physical structure consists of a number of substructures. We enable deployed sensors to be organized into groups in such a way that a groupwise final decision (e.g., 0/1) can be provided for each substructure independently so that the existence of an event (if there is any) in a specific substructure can be identified by WSNs. MODEM is fully distributed in nature, promises to have the monitoring quality similar to the original wired-based schemes, and consumes much less energy for transmissions and computations than existing schemes do. The effectiveness of MODEM is shown via both simulations and real experiments.

The Internet of Things (IoT) are being adopted to improve the efficiency and reliability of diverse monitoring applications, including critical events (such as fire, structural damage, crack) in the smart city's public safety application. In this paper, we present BigReduce, an IoT-enabled scheme to reduce a big quantity of data through decision-making when acquiring data and transferring the acquired data throughout the IoT network, while providing event sensitivity in industrial environments. At first, BigReduce executes decision-making briefly on the ongoing collected data in a discrete-time based on the event sensitivity so that data acquisition regarding the event-insensitivity is reduced. Then, BigReduce executes decisionmaking locally at the IoT devices to decide whether or not transmitting only a part of the gathered data that have a high event-sensitivity so that data with event-insensitivity is reduced. At an upstream IoT device (say cluster head), BigReduce runs a distributed decision-making to verify whether or not the collected data have to be transferred. Outcomes achieved through a proof-of-concept implementation shows that BigReduce is capable of reducing data quantity by 87% and energy cost by at least 82% compared to related schemes.

Most of all the devices including Information Technology (IT) enterprise devices consists of data or documents. All those devices require security or protection from any kind of threats. Threats can be from external or internal. The internal threats that are generated from the current system or device on which routine works are carried out are discussed. Internal threats like disk capacity, storage capacity warning, resource exhaustion, message tampering, unsuccessful login, max tries etc. These threats are unpredictable and at any time they may occur so device should be under monitoring. Hence the server is developed employing User Datagram Protocol (UDP), works like a Daemon which monitors and stores the security alerts indicating different events that arise from the system itself into the database. Here security event logger test application is used to generate the alerts and for three different events alerts are generated and tested. Analyzing the alerts that are stored in the form of syslog message format, measures are taken to safeguard the device and data, depending on the severity of the alerts.

The monitoring of the indoor air quality against the presence of contaminant sources is of paramount importance since it directly influences the occupants’ comfort, productivity, health and safety. Various approaches exist in the literature for monitoring and controlling the building environment. However, there is an identified lack of automated tools for evaluating, testing and comparing such approaches. This paper presents COMOB (COntaminat MOnitoring in Buildings), a developed MATLAB toolbox for sensor placement and contaminant monitoring in buildings. Specifically, the COMOB toolbox realizes an offline sensor placement procedure for positioning the contaminant sensors inside the building, such that the harmful effects due to a possible airborne contaminant event are minimized. In addition, an online contaminant monitoring approach is also implemented for detecting and isolating contaminant events in the building interior. Besides the aforementioned algorithms, the modularity of the COMOB toolbox facilitates the implementationof various other algorithms related to air-quality monitoring in buildings. In particular, the COMOB toolbox is able to generate multiple airflow and contaminant concentration data-sets by varying the building parameters; thus enabling the comprehensive and systematic evaluation and testing of the developed algorithms. Finally, the functionalities of the COMOB toolbox are demonstrated through a case-study corresponding to a real building model. COMOB is released under an open-source license, and is available at https://github.com/KIOS-Research/COMOB-Toolbox.

Trust management of the Internet of Connected Vehicles has been a hot topic in recent years with the rapid development of UGV technologies. However, existing resolutions based on trustworthiness verification among vehicles make the traffic event transmission quite inefficient. In this article, we assume that the deployed roadside units (RSUs) can provide efficient communication between any pair of RSU and vehicle and propose vehicle cash (Vcash), a reputation framework for identifying denial of traffic service, to resolve the trustworthiness problem in the application level of the Internet of Connected Vehicles. In our reputation framework, every vehicle communicates with the RSU directly for traffic event verification, and spreads verified traffic event notification. We borrow the idea of market trading, and set up trading rules to restrict the malicious vehicle’s spread of false message, and to encourage vehicles to contribute to the traffic event monitoring and verification. To evaluate the effectiveness of our reputation framework, we conduct a simulation experimental. Our experiment results indicate that our proposal manages to avoid bogus event spread, and a vehicle in our framework has to contribute to the traffic event detection to normally employ the traffic service.

Wireless smart sensors (WSS) have been proposed as an effective means to reduce the high cost of wired structural health monitoring systems. However, many damage scenarios for civil infrastructure involve sudden events, such as strong earthquakes, which can result in damage or even failure in a matter of seconds. Wireless monitoring systems typically employ duty cycling to reduce power consumption, hence, they will miss such events if they are in power-saving sleep mode when the events occur. This paper develops a demand-based WSS to meet the requirements of sudden event monitoring with minimal power budget and low response latency, without sacrificing high-fidelity measurements or risking a loss of critical information. In the proposed WSS, a programmable event-based switch is implemented utilizing a low-power trigger accelerometer, the switch is integrated in a high-fidelity sensor platform. Particularly, the approach can rapidly turn on the WSS upon the occurrence of a sudden event and seamlessly transition from low-power acceleration measurement to high-fidelity data acquisition. The capabilities of the proposed WSS are validated through laboratory and field experiments. The results show that the proposed approach is able to capture the occurrence of sudden events and provide high-fidelity data for structural condition assessment in an efficient manner.

Tweet streams provide a variety of real-life and real-time information on social events that dynamically change over time. Although social event detection has been actively studied, how to efficiently monitor evolving events from continuous tweet streams remains open and challenging. One common approach for event detection from text streams is to use single-pass incremental clustering. However, this approach does not track the evolution of events, nor does it address the issue of efficient monitoring in the presence of a large number of events. In this paper, we capture the dynamics of events using four event operations (create, absorb, split, and merge), which can be effectively used to monitor evolving events. Moreover, we propose a novel event indexing structure, called Multi-layer Inverted List (MIL), to manage dynamic event databases for the acceleration of large-scale event search and update. We thoroughly study the problem of nearest neighbour search using MIL based on upper bound pruning, along with incremental index maintenance. Extensive experiments have been conducted on a large-scale real-life tweet dataset. The results demonstrate the promising performance of our event indexing and monitoring methods on both efficiency and effectiveness.

The number of single-person households (SPHs) has been consistently increasing owing to various social issues, such as separation by death, declining marriage rate, and increasing divorce rate. Unfortunately, this demographical change is creating a new social problem, namely, lonely death. In response to this problem, many researchers have attempted to develop wearable sensor-based and computer vision-based systems that monitor occupant behaviors and detect possible emergency events in indoor environments. However, existing approaches face challenges in monitoring SPHs owing to their technical disadvantages; for instance, if the occupant is not wearing the electronic sensor, or if the signal is occluded by other objects, it is not possible to monitor SPHs. Moreover, as existing studies focus only on classifying the occupant's daily activities, such as eating, sitting, and talking, the emergency events that are significant for SPH monitoring are still unclear. To address these challenges, this study investigates emergency events that have a critical impact on the occupant's health and proposes a deep learning-based sound recognition model to monitor occupant behaviors and detect possible emergency events in SPH environments. Experiments are conducted using audio data collected from actual SPH home environments and online data-sharing websites. The average precision and recall rates of the developed model are 78.0% and 90.8%, respectively. The results demonstrate that the developed model could successfully distinguish emergency sound events from the sounds of regular human activities. The findings can not only secure and rescue SPHs in danger but also provide new research directions for indoor occupant and event monitoring.

Blockchain technology is currently emerging as the next promising revolutionary technology. Many different applications have started to venture into this technology. The decentralization and distribution nature of blockchain allows dynamic applications to be build on it, including Internet-of-Things (IoT) applications. IoT devices have been commonly linked to smart systems with its applications mainly surrounding on event detection and monitoring. Coupling this IoT capabilities with blockchain network offers great potential for scalable event detection and monitoring for large-scale network. Nevertheless, detection mechanism should be able to scale up with such requirements. In this paper, we propose a conceptual framework on implementation of distributed pattern recognition for event monitoring within IoT-blockchain network. This conceptual framework is supported by a preliminary study on IoT-blockchain implementation and distributed pattern recognition implementation using a Graph Neuron (GN) approach.

Contemporary social media networks can be viewed as a break to the early two-step flow model in which influential individuals act as intermediaries between the media and the public for information diffusion. Today's social media platforms enable users to both generate and consume online contents. Users continuously engage and disengage in discussions with varying degrees of interaction leading to formation of distinct online communities. Such communities are often formed at high-level either based on metadata, such as hashtags on Twitter, or popular content triggered by few influential users. These online communities often do not reflect true connectivity and lack the cohesiveness of traditional communities. In this study, we investigate real-time formation of temporal communities on Twitter. We aim at defining both high and low levels connections and to reveal the magnitude of clustering cohesion on temporal basis. Inspired by a real-life event center sitting arrangement scenario, the proposed method aims to cluster users into distinct and cohesive online temporal communities. Membership to a community relies on intrinsic tweet properties to define similarity as the basis for interaction networks. The proposed method can be useful for local event monitoring and clique-based marketing among other applications., 5 pages, 7 figures

Medication administration is one pathway by which Adverse Drug Events (ADE) can occur. While Electronic Medical Administration Record (eMAR) systems help reduce the number of ADEs, current eMAR implementations suffer from workarounds that defeat safety and verification mechanisms meant to limit the number of potential ADEs that occur during medication administration. In this paper, we introduce Multi-Element ChipLess (MECL) RFID tags which enable real-time event notifications through event signatures. Event signatures correspond to the physical configuration of different RFID elements in a chipless RFID tag. Augmenting physical objects, such as a pill container, with MECL-RFID can allow caregivers to detect the moment a particular pill container is opened or closed. We present the fundamentals behind real-time event detection using MECL-RFID and propose a cyber-physical intervention system that can be used to reduce ADEs through realtime event monitoring and notifications sent to clinicians administering medication. We also present a prototype MECL-RFID to demonstrate potential future improvements to eMAR systems that minimize ADEs., Comment: Proceedings of the Wireless Health 2014 on National Institutes of Health

Vehicular nodes are equipped with more and more sensing units, and a large amount of sensing data is generated. Recently, more and more research considers cooperative urban sensing as the heart of intelligent and green city traffic management. The key components of the platform will be a combination of a pervasive vehicular sensing system, as well as a central control and analysis system, where data-gathering is a fundamental component. However, the data-gathering and monitoring are also challenging issues in vehicular sensor networks because of the large amount of data and the dynamic nature of the network. In this paper, we propose an efficient continuous event-monitoring and data-gathering framework based on fog nodes in vehicular sensor networks. A fog-based two-level threshold strategy is adopted to suppress unnecessary data upload and transmissions. In the monitoring phase, nodes sense the environment in low cost sensing mode and generate sensed data. When the probability of the event is high and exceeds some threshold, nodes transfer to the event-checking phase, and some nodes would be selected to transfer to the deep sensing mode to generate more accurate data of the environment. Furthermore, it adaptively adjusts the threshold to upload a suitable amount of data for decision making, while at the same time suppressing unnecessary message transmissions. Simulation results showed that the proposed scheme could reduce more than 84 percent of the data transmissions compared with other existing algorithms, while it detects the events and gathers the event data.

Monitoring household electricity consumption is important to help reduce energy usage. To improve the detecting accuracy of switch on and off events, this paper proposed a new improved event monitoring method based on CUSUM, which aims for better detection performance particularly in terms of low power appliances. Furthermore, introducing confidence level as the criteria and the bootstrapping algorithm effectively reduce detection errors and missed detections. The experiment results show that the new approach achieves higher accuracy compared to the traditional method of non-intrusive load monitoring (NILM).

In this paper, we consider monitoring multiple events in a sensing field using a large-scale wireless sensor network (WSN). The goal is to develop communication-efficient algorithms that are scalable to the network size. Exploiting the sparse nature of the events, we formulate the event monitoring task as an $\ell _1$ regularized nonnegative least squares problem where the optimization variable is a sparse vector representing the locations and magnitudes of events. Traditionally the problem can be reformulated by letting each sensor hold a local copy of the event vector and imposing consensus constraints on the local copies, and solved by decentralizedalgorithms such as the alternating direction method of multipliers (ADMM). This technique requires each sensor to exchange theirestimates of the entire sparse vector and hence leads to high communication cost. Motivated by the observation that an event usually has limited influence range, we develop two communication-efficient decentralized algorithms, one is the partial consensus algorithm and the other is the Jacobi approach. In the partial consensus algorithm that is based on the ADMM, each sensor is responsible for recovering those events relevant to itself, and hence only consent with neighboring nodes on a part of the sparse vector. This strategy greatly reduces the amount of information exchanged among sensors. The Jacobi approach addresses the case that each sensor cares about the event occurring at its own position. Jacobi-like iterates are shown to be much faster than other algorithms, and incur minimal communication cost per iteration. Simulation results validate the effectiveness of the proposed algorithms and demonstrate the importance of proper modelling in designing communication-efficient decentralized algorithms.

In a sparse wireless sensor network (WSN), event parameters such as event location and affected area cannot be directly estimated from the sensor observations, as the average distortion of sensor observations, because of the sparsity of network, is greater than in the dense ones. Moreover, observations of all the nodes which detect an event do not have the same significance in the estimation of event parameters. The authors propose a sensor collaboration scheme, named probabilistic event monitoring in sparse network (PEMS), which assists, in a distributed manner, the sensor nodes with significant observations to participate in the event monitoring process. The proposed collaboration scheme, PEMS, is based on the principle of Monte Carlo methods. Further, the event monitoring nodes restructure the network topology with the help of facility location theory for efficient energy management. Extensive simulations show that the event parameters with nominal distortion can be estimated from the observations transmitted by the PEMS-assisted event-monitoring nodes and those nodes can enhance their energy efficiency at the time of event monitoring.

Users of the internet often wish to follow certain news events, and the interests of these users often overlap. General search engines (GSEs) cannot be used to achieve this task due to incomplete coverage and lack of freshness. Instead, a broker is used to regularly query the built-in search engines (BSEs) of news and social media sites. Each user defines an event profile consisting of a set of query rules called event rules (ERs). To ensure that queries match the semantics of BSEs, ERs are transformed into a disjunctive normal form, and separated into conjunctive clauses (atomic event rules, AERs). It is slow to process all AERs on BSEs, and can violate query submission rate limits. Accordingly, the set of AERs is reduced to eliminate AERs that are duplicates, or logically contained by other AERs. Five types of event are selected for experimental comparison and analysis, including natural disasters, accident disasters, public health events, social security events, and negative events of public servants. Using 12 BSEs, 85 ERs for five types of events are defined by five users. Experimental comparison is conducted on three aspects: event rule reduction ratio, number of collected events, and that of related events. Experimental results in this paper show that event rule reduction effectively enhances the efficiency of crawling.

This paper introduces a social sensing-based dynamic duty cycle management scheme, namely probabilistic duty cycle in sensor medium access control (PDC-SMAC) protocol, for wireless sensor networks (WSNs). The objective of PDC-SMAC is to minimise energy consumption in monitoring `rare events' by reducing the period of `ineffective sensing'. Ineffective sensing is defined as event monitoring or sensing, even if there is no event to monitor or sense. Existing schemes suffer from unnecessary energy consumption because of ineffective sensing. For example, the period and frequency of occurrence of an event in military applications is unpredictable, and sometimes the delay between two consecutive occurrences is very large. We exploit the social sensing behaviour of social networks for avoiding ineffective sensing by managing the duty cycle of a node in a sensor network. PDC-SMAC minimises the energy consumption of a WSN as the event monitoring overhead is accounted for by the social network. Simulation results show that PDC-SMAC consumes 37% less energy in an ideal environment and 15% less energy on overall performances as compared with the benchmark considered for comparison. It minimises message delivery latency by 27 - 60% for different packet generation intervals and by 47% for different event occurrence rates, compared with the benchmark.

9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2017 : Sydney; Australia), The work described in this paper illustrates how social media is a valuable source of data which may be processed for informative knowledge discovery which may help in better decision making. We concentrate on Twitter as the source for the data to be processed. In particular, we extracted and captured tweets written in Turkish. We analyzed tweets online and real-time to determine most recent trending events, their location and time. The outcome may help predicting next hot events to be broadcasted in the news. It may also raise alert and warn people related to upcoming or ongoing disaster or an event which should be avoided, e.g., traffic jam, terror attacks, earthquake, flood, storm, fire, etc. To achieve this, a tweet may be labeled with more than one event. Named entity recognition combined with multinomial naive Bayes and stochastic gradient descent have been integrated in the process. The reported 95% success rate demonstrate the applicability and effectiveness of the proposed approach. © 2017 Association for Computing Machinery., ACM SIGMOD,Gemalto,IEEE Computer Society,IEEE TCDE,Springer Nature

Given a collection of event-related documents, event ranking generates a list of ranked events based on the input query. Ranking news events, which takes event related news documents for the generation of ranked events, is both an essential research issue and important component for many security oriented applications, such as public event monitoring, retrieval, detection and mining. Previous related work solely relies on queries of event relevant aspects, and user relevant aspects of queries that are critical for security applications are totally ignored. In this paper, we deal with the problem of news ranking by incorporating user relevant information into the input query, from the cluster of relevant new documents and comments. Given an input query, which contains event related objective aspects(e.g. actors, locations, date) and user related subjective aspects(e.g. public attention and opinion polarity), we develop a Learning-to-Rank framework to integrate aspect-level correlation between query and event. Experiments on a crawled large news corpus show the effectiveness of our proposed approach compared to several baseline models.

This paper describes the implementation of a service to identify and geo-locate real world events that may be present as social activity signals in two different social networks. Specifically, we focus on content shared by users on Twitter and Instagram in order to design a system capable of fusing data across multiple networks. Past work has demonstrated that it is indeed possible to detect physical events using various social network platforms. However, many of these signals need corroboration in order to handle events that lack proper support within a single network. We leverage this insight to design an unsupervised approach that can correlate event signals across multiple social networks. Our algorithm can detect events and identify the location of the event occurrence. We evaluate our algorithm using both simulations and real world datasets collected using Twitter and Instagram. The results indicate that our algorithm significantly improves false positive elimination and attains high precision compared to baseline methods on real world datasets.

Modern distributed systems are often considered to be black boxes that greatly limit the potential to understand behaviors at the level of detail necessary to diagnose some of the most important types of performance problems. Recently researchers have found abnormal response time delays, one to two orders of magnitude longer than the average response time, that exist in short periods and cause economic loss for service providers. These very short bottlenecks are hard to detect due to their short life spans and their variety of possible reasons. In this paper, we propose milliScope (mScope), the first millisecond-granularity software-based resource and event monitoring for distributed systems that achieves both performance, low overhead at high frequency, and high accuracy matched with other firmware monitoring tool. More specifically, milliScope is a fine-grained monitoring framework to collaborate multiple mScopeMonitors for event and resource monitoring to reconstruct the flow of each client request and profile execution performance in a distributed system. We utilize the resource mScopeMonitors for system resource monitoring, and we develop our own event mScopeMonitors to identify the execution boundary in a lightweight, precise and systematic methodology. The semantic and syntactic of these monitoring logs with arbitrary formats are enriched by our multistage data transformation tool, mScopeDataTransformer, which unifies the diverse monitoring logs into a dynamic data warehouse, mScopeDB, for advanced analysis. We conduct several illustrative scenarios in which milliScope successfully diagnoses the response time anomalies caused by very short bottlenecks using a representative web application benchmark (RUBBoS).

This paper introduces an indirect train traffic monitoring method to detect and infer real-time train events based on the vibration response of a nearby building. Monitoring and characterizing traffic events are important for cities to improve the efficiency of transportation systems (e.g., train passing, heavy trucks, and traffic). Most prior work falls into two categories: (1) methods that require intensive labor to manually record events or (2) systems that require deployment of dedicated sensors. These approaches are difficult and costly to execute and maintain. In addition, most prior work uses dedicated sensors designed for a single purpose, resulting in deployment of multiple sensor systems. This further increases costs. Meanwhile, with the increasing demands of structural health monitoring, many vibration sensors are being deployed in commercial buildings. Traffic events create ground vibration that propagates to nearby building structures inducing noisy vibration responses. We present an information-theoretic method for train event monitoring using commonly existing vibration sensors deployed for building health monitoring. The key idea is to represent the wave propagation in a building induced by train traffic as information conveyed in noisy measurement signals. Our technique first uses wavelet analysis to detect train events. Then, by analyzing information exchange patterns of building vibration signals, we infer the category of the events (i.e., southbound or northbound train). Our algorithm is evaluated with an 11-story building where trains pass by frequently. The results show that the method can robustly achieve a train event detection accuracy of up to a 93% true positive rate and an 80% true negative rate. For direction categorization, compared with the traditional signal processing method, our information-theoretic approach reduces categorization error from 32.1 to 12.1%, which is a 2.5× improvement.

This paper presents a novel energy-efficient and fault-tolerant pulse switching protocol for ultra-light-weight wireless cellular network applications. The key idea of pulse switching is to abstract a single pulse, as opposed to multi-bit packets, as the information exchange mechanism. In this paper it is shown to be sufficient for event and target tracking applications with binary sensing in terms of cellular localization. Event monitoring and target tracking with conventional packet transport can be prohibitively energy-inefficient due to the communication, processing, and buffering overheads of the large number of bits within a packet's data, header, and preambles. Additionally, both of them can be unreliable without protection from errors and faults occurrence. The paper presents a joint MAC and Routing architecture for pulse switching with a novel cellular event localization in the presence of errors and faults. Through analytical modeling and simulation experiments, it is shown that cellular pulse switching can be an effective means for event based networking, which can potentially replace the packet transport when the information is binary in nature.

The dispersion of contaminants from sources (events) inside a building can compromise the indoor air quality and influence the occupants' comfort, health, productivity and safety. Such events could be the result of an accident, faulty equipment or a planned attack. Under these safety-critical conditions, immediate event detection should be guaranteed and the proper actions should be taken to ensure the safety of the people. In this paper, we consider an event as a fault in the process that disturbs the normal system operation. This places the problem of contaminant event monitoring in the fault diagnosis framework of detection and isolation. A main contribution of this work is the development of the state-space method, based on multi-zone building models, that enables the use of advanced fault diagnosis tools for contaminant event monitoring. Specifically, in this paper, we develop estimator schemes with adaptive thresholds for the detection and isolation of a single contaminant source under conditions of noise and modeling uncertainty. We demonstrate our proposed formulation using a 2-zone illustration example and a more realistic 14-zone building setting.

Objectives: To introduce the Canadian Network for Public Health Intelligence’s new Knowledge Integration using Web-based Intelligence (KIWI) technology, and to perform preliminary evaluation of the KIWI technology using a case study. The purpose of this new technology is to support surveillance activities by monitoring unstructured data sources for the early detection and awareness of potential public health threats. Methods: A prototype of the KIWI technology, adapted for zoonotic and emerging diseases, was piloted by end-users with expertise in the field of public health and zoonotic/emerging disease surveillance. The technology was assessed using variables such as geographic coverage, user participation, and others; categorized by high-level attributes from evaluation guidelines for internet based surveillance systems. Special attention was given to the evaluation of the system’s automated sense-making algorithm, which used variables such as sensitivity, specificity, and predictive values. Event-based surveillance evaluation was not applied to its full capacity as such an evaluation is beyond the scope of this paper. Results: KIWI was piloted with user participation = 85.0% and geographic coverage within monitored sources = 83.9% of countries. The pilots, which focused on zoonotic and emerging diseases, lasted a combined total of 65 days and resulted in the collection of 3243 individual information pieces (IIP) and 2 community reported events (CRE) for processing. Ten sources were monitored during the second phase of the pilot, which resulted in 545 anticipatory intelligence signals (AIS). KIWI’s automated sense-making algorithm (SMA) had sensitivity = 63.9% (95% CI: 60.2-67.5%), specificity = 88.6% (95% CI: 87.3-89.8%), positive predictive value = 59.8% (95% CI: 56.1-63.4%), and negative predictive value = 90.3% (95% CI: 89.0-91.4%). Discussion: Literature suggests the need for internet based monitoring and surveillance systems that are customizable, integrated into collaborative networks of public health professionals, and incorporated into national surveillance activities. Results show that the KIWI technology is well posied to address some of the suggested challenges. A limitation of this study is that sample size for pilot participation was small for capturing overall readiness of integrating KIWI into regular surveillance activities. Conclusions: KIWI is a customizable technology developed within an already thriving collaborative platform used by public health professionals, and performs well as a tool for discipline-specific event monitoring and early warning signal detection.

Monitoring events in communication and computing systems becomes more and more challenging due to the increasing complexity and diversity of these systems. Several supporting tools have been created to assist system administrators in monitoring an enormous number of events daily. The main function of these tools is to filter as many as possible events and present highly suspected events to the administrators for fault analysis, detection and report. While these suspected events appear regularly on large and complex systems, such as cloud computing systems, analyzing them consumes much time and effort. In this study, we propose an approach for evaluating the severity level of events using a classification decision tree. The approach exploits existing fault datasets and features, such as bug reports and log events to construct a decision tree that can be used to classify the severity level of other events. The administrators refer to the result of classification to determine proper actions for the suspected events with a high severity level. We have implemented and experimented the approach for various bug report and log event datasets. The experimental results reveal that the accuracy of classifying severity levels by using the decision trees is above 80%, and some detailed analyses are also provided.

With the wide use of EDGEs (electronic data gathering equipments) such as sensors and RFID (radio frequency identification) devices, unprecedented volumes of event streams have been generated. Mining frequent episodes within the latest time windows over event streams plays a significant role in event monitoring. It helps to generate episode rules, which can reflect the latest change, and predict future events effectively. The paper proposes how to mine MinEpi (minimal occurrence based frequent episode) within the latest time windows. The existing MinEpi mining methods are all Apriori-like, which need to scan data time after time and generate quantities of candidate episodes. This results in high time and space cost. Moreover, Apriori-like methods cannot be applied to event streams. For these problems, the paper proposes the episode matrix and frequent episode tree based mining method (EM&FET), which can generate frequent 2-episodes by constructing an episode matrix and generate higher-level frequent episodes directly by extending lower-level ones gradually, only scanning data once without candidate generation. Moreover, the paper further improves EM&FET, which enhances efficiency and saves space greatly. The experiments on different types of real data sets show the effectiveness and high efficiency of EM&FET and its improvement.

Seamless information exchange in smart substations is vital to ensure the successful operation of functions in a smart grid domain. IEC 61850 is an international standard that enables interoperability between devices in smart substations through standardized data model and communication services. One of the communication services defined in IEC 61850 is the generic object oriented substation event (GOOSE). The communication using IEC 61850 GOOSE replaces point to point copper wiring between intelligent electronic devices (IED) in conventional substations with high speed communication over Ethernet technology. However, the implementation of GOOSE has posed new challenges, mainly in monitoring and managing signals that carry specific substation functions. This is because with GOOSE, these signals are transmitted using single Ethernet connection which carries significant amount of information in a form of network packets at any point of time. Therefore, there is a need for power utilities who implement IEC 61850 standard in substations, such as Tenaga Nasional Berhad (TNB) to develop tools which are able capable in administrating and maintaining the features of IEC 61850 in substations. This paper will discuss the development of a prototype IEC 61850 GOOSE monitoring application which is a part of TNB IEC 61850 Substation Intelligent and Management System (61850 SIMS). This paper will also elaborate on the application development process, which is according to the Software Development Life Cycle (SDLC). The application is designed to represent IEC 61850 GOOSE information in terms and expressions that can be appreciated by conventional protection and maintenance engineers. The prototype application was successfully developed and tested at TNB IEC 61850 System Verification and Simulation (SVS) laboratory. This paper will also discuss on the application capabilities and new features that are planned to be included for future improvements.

To solve the problem of using information to control behavior for unmanned underwater vehicle (UUV), this paper presents a situation awareness method on the basis of ontology. During the period of UUV operation, situation perceived information is more important for the safety of UUV and therefore, real-time and accurate perception of the situation has an important impact on UUV autonomous decision making. The according to the characteristics of information processing of situation awareness of UUV, architecture of situation awareness and the core ontology of UUV are set up in this paper, event monitoring module, event recognition module, and all of above base on the basis constructed ontology model of situation awareness. The ontology method expands the situation awareness information, and improves the efficiency by applying the rules reasoning and Bayesian network. Finally, the random obstacles event and the deviation event are simulated to test the validation of UUV situation awareness and its ability of decision making. The results show that UUV can quickly trigger re-planning threads according to situation awareness information, which realize autonomous controlling aiming to uncertain events. The simulating results verify the effectiveness of situation awareness method in the marine environment.

Discusses insight based on event counters built into Knights Landing, and using those counters with the Intel® VTune Amplifier. Also discusses timing, a critical element in evaluating performance.

Social media feeds are rapidly emerging as a novel avenue for the contribution and dissemination of information that is often geographic. Their content often includes references to events occurring at, or affecting specific locations. Within this article we analyze the spatial and temporal characteristics of the twitter feed activity responding to a 5.8 magnitude earthquake which occurred on the East Coast of the United States (US) on August 23, 2011. We argue that these feeds represent a hybrid form of a sensor system that allows for the identification and localization of the impact area of the event. By contrasting this with comparable content collected through the dedicated crowdsourcing ‘Did You Feel It?’ (DYFI) website of the U.S. Geological Survey we assess the potential of the use of harvested social media content for event monitoring. The experiments support the notion that people act as sensors to give us comparable results in a timely manner, and can complement other sources of data to enhance our situational awareness and improve our understanding and response to such events.

Software defined network separates data and control planes that facilitate network management functions, especially enabling programmable network control functions. Event monitoring is a fault management function involved in collecting and filtering event notification messages from network devices. This study presents an approach of distributed event monitoring for software defined network. Monitoring events usually deals with a large amount of event log data, log collecting and filtering processes thus require a high degree of automation and efficiency. This approach takes advantage of the OpenFlow and syslog protocols to collect and store log events obtained from network devices on a syslog server. It also uses the adaptive semantic filtering method to filter and present non-trivial events for system administrators to take further actions. We have evaluated this approach on a network simulation platform and provided some log collection and filtering results with analysis.

Online event detection techniques are usually used in single data source. This paper analyzes event detection in the perspective of multiple data sources, combining news reports and microblogs. Detect events from news, combining microblogs to do event monitoring and early warning. Also improve feature selection methods for multiple data sources event detection. Finally, the methods are applied to the detection of food safety events and the results of the research show that event detection with multiple data sources is meaningful and valuable.

Wireless sensor networks can be used for event warning applications. Till date, in most of the proposed schemes, the raw or aggregated sensed data are periodically sent to a data consuming center. However, with those schemes, the occurrence of an emergency event such as a fire is hardly reported timely, which is a strict requirement for event warning applications. In wireless sensor networks, it is also highly desired to conserve energy so that network lifetime can be maximized. Furthermore, to ensure the quality of surveillance, some applications require that if an event occurs, it needs to be detected by at least k sensors, where k is a user-defined parameter. In this work, we examine the Timely Energy-efficient k-Watching Event Monitoring (TEKWEM) problem and propose a scheme, which involves an event detection model and a warning delivery model, for monitoring composite events and delivering warnings to users. Theoretical analysis and simulation results are shown to validate the proposed scheme.

The user experience for networked applications is becoming a key benchmark for customers and network providers. Perceived user experience is largely determined by the frequency, duration and severity of network events that impact a service. While today's networks implement sophisticated infrastructure that issues alarms for most failures, there remains a class of silent outages (e.g., caused by configuration errors) that are not detected. Further, existing alarms provide little information to help operators understand the impact of network events on services. Attempts to address this through infrastructure that monitors end-to-end performance for customers have been hampered by the cost of deployment and by the volume of data generated by these solutions. We present an alternative approach that pushes monitoring to applications on end systems and uses their collective view to detect network events and their impact on services - an approach we call Crowdsourcing Event Monitoring (CEM). This paper presents a general framework for CEM systems and demonstrates its effectiveness for a P2P application using a large dataset gathered from BitTorrent users and confirmed network events from two ISPs. We discuss how we designed and deployed a prototype CEM implementation as an extension to BitTorrent. This system performs online service-level network event detection through passive monitoring and correlation of performance in end-users' applications.

The IMMUNE project (Intelligent Monitoring and Managing of UNexpected Events) focuses on basic research regarding event monitoring and event tolerant control. Event detection methods and control law reconfiguration approaches have been investigated for their applicability in the context of developing advanced aircraft flight control systems. For promising methods, prototype software tools have been implemented in a desktop simulation environment to serve as basis for evaluation studies.