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This paper investigates a risk-averse firm's investment strategy in real-time event-monitoring technologies coupled with dynamic disruption response decisions. The firm does not know how long the disruption will last, and the event-monitoring solutions provide the firm with a quicker update regarding the length of the disruption. When the firm receives an update, it has the flexibility to revise its initial response action. We model a two-stage stochastic program to study this problem where risk aversion is modeled in the form of a Service-at-Risk constraint. Our paper makes four important contributions. First, it characterizes the optimal recourse strategies given an update and identifies the conditions when the firm benefits from the recourse. Second, we characterize the optimal investment and initial response strategies where we find that the investment behavior is non-monotone. Third, our paper shows an interesting impact of risk aversion on the investment decision such that the firm may benefit from investing in event monitoring at moderate degrees of risk aversion, but not necessarily at low or high degrees of risk aversion. Fourth, we find that a firm's benefit from event monitoring is more profound when the capacity of contingency response is moderately limited. [ABSTRACT FROM AUTHOR]

Purposes A load feature extraction method based on a combination of event monitoring and Gaussian mixture model clustering is proposed to explore the potential of energy saving and emission reduction at the customer side, to finely analyze and manage the customers’ electricity consumption behavior, and to improve the efficiency of electricity utilization. Methods First, the active power fluctuation of each appliance during a single operation is extracted by the event monitoring algorithm based on sliding window, and the start-up time, number of times, and operation duration of the appliance can be obtained by the event monitoring algorithm. Second, to address the problem that the same appliance often has similar power but inconsistent operation status, the Gaussian mixture model clustering algorithm with the advantages of “soft classification” and flexible class clusters is adopted to finely classify the load operating status and form a load status feature library that is consistent with the actual operation of power-using equipment. Finally, with the public data set AMPds2 as the research object, the method proposed in this paper is applied to study the energy consumption habits of residential customers, and the validation analysis is carried out. Findings The results show that the proposed method can extract load features better than other models.

This article proposes an end-to-end event monitoring method for a distributed sensor network with non-concentrated label noises based on a collaborative soft-label network (CSLN). The proposed CSLN framework is composed of a fundamental learner (FL) and a collaborative label modification. FL is designed based on a bidirectional gated recurrent unit (BiGRU) with attention to tackle the indistinct boundary problem by modeling multiscale dependencies. BiGRU can learn latent representations through capturing local dependencies in a bidirectional time flow. The attention mechanism is capable of modeling long-range dependencies through assigning learnable weights to the latent representations. Then, the collaborative label modification is established to reduce label noises by combining a truncated loss function and a dual-space smoothing technique. The truncated loss function can prevent FL from overfitting to noisy labels by isolating them in optimization. The dual-space smoothing technique can generate soft labels based on the local similarities. Furthermore, the proposed CSLN method is optimized by a bi-loop recursive strategy to reduce label noises gradually through alternatively training FL and generating soft labels. The feasibility and effectiveness of the proposed method are validated through real-field experiments of perimeter security applications based on distributed optical fiber sensors (DOFSs). [ABSTRACT FROM AUTHOR]

This article discusses applications for monitoring and visualizing events from a cloud or fog environment. Applications where images are processed by machine learning algorithms for intelligent video surveillance systems are considered. A review of analogues of programs that implement the concept of the Internet of Things has been made. Examples of the real use of ready-made systems in various fields are considered. The problems of using systems with different architectures are discussed. A proposed solution is in the modification of the generalized architecture of the applications for monitoring and visualizing events. It allows improve events monitoring based on the ability to run applications on devices with different technical characteristics and under different operating systems. The solution is shown in the example of ensuring the safety of escalator equipment. Three applications have been developed for a smartphone, a desktop and a website for the visualization of events from a cloud or fog environment. The developed applications were tested on a large amount of data (video information). Specific examples are given.

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. [ABSTRACT FROM AUTHOR]

Sensors in Cyber-Physical Systems (CPS) are typically used to collect various aspects of the region of interest and transmit the data towards upstream nodes for further processing. However, data collection in CPS is often unreliable due to severe resource constraints (e.g., bandwidth and energy), environmental impacts (e.g., equipment faults and noises), and security concerns. Besides, detecting an event through the aggregation in CPS can be intricate and untrustworthy if the sensor's data is not validated during data acquisition, before transmission, and before aggregation. This paper introduces In-network Generalized Trustworthy Data Collection (IGTDC) framework for event detection in CPS. This framework facilitates reliable data for aggregation at the edge of CPS. The main idea of IGTDC is to enable a sensor's module to examine locally whether the event's acquired data is trustworthy before transmitting towards the upstream nodes. It further validates whether the received data can be trusted or not before data aggregation at the sink node. Additionally, IGTDC helps to identify faulty sensors. For reliable event detection, we use collaborative IoT tactics, gate-level modeling with Verilog User Defined Primitive (UDP), and Programmable Logic Device (PLD) to ensure that the event's acquired data is reliable before transmitting towards the upstream nodes. We employ Gray code in gate-level modeling. It helps to ensure that the received data is reliable. Gray code also helps to distinguish a faulty sensor. Through simulation and extensive performance analysis, we demonstrate that the collected data in the IGTDC framework is reliable and can be used in the majority of CPS applications.

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.

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Hysteresis occurs in ecological systems when there are internal dynamics that produce variable responses to an external stimuli or stressors. Understanding and quantifying hysteresis is important to inform understanding of system behaviour, support modelling, and provide comparisons within and between systems. We introduce a new tool, HARP (Hysteresis Area, Residual and Peaks) to describe and quantify hysteresis. HARP quantifies the key features of stream discharge–constituent concentration relationships, including the magnitude of the hysteresis (deviation from linearity), phase shifts in the system (end-state deviation from the pre-event state), and the timing of the turning points in the relationship of discharge and concentration. HARP is suitable to use on complex discharge–concentration data, including figure-eight loops, and supports statistical analysis to quantify the characteristics of independent events. The HARP metrics are introduced using discharge–water quality constituent concentration data, but are equally applicable to any system with properties of hysteresis. HARP provides a tool to develop quantitative observations of stream hydrological processes into quantitative enumeration statistics that facilitate comparisons of events and streams. [Display omitted] • We developed Hysteresis, Area, Residual and Peak (HARP) metrics. • HARP metrics describe and quantify hysteresis in flow–concentration relationships. • HARP metrics facilitate quantification and comparison of behaviour within and between streams. • HARP metrics provide a valuable way to capture the hysteresis. [ABSTRACT FROM AUTHOR]

Sampling rate adaptation is a critical issue in many resource-constrained networked systems, including Wireless Sensor Networks (WSNs). Existing algorithms are primarily employed to detect events such as objects or physical changes at a high, low, or fixed frequency sampling usually adapted by a central unit or a sink, therefore requiring additional resource usage. Additionally, this algorithm potentially makes a network unable to capture a dynamic change or event of interest, which therefore affects monitoring quality. This article studies the problem of a fully autonomous adaptive sampling regarding the presence of a change or event. We propose a novel scheme, termed "event-sensitive adaptive sampling and low-cost monitoring (e-Sampling)" by addressing the problem in two stages, which leads to reduced resource usage (e.g., energy, radio bandwidth). First, e-Sampling provides the embedded algorithm to adaptive sampling that automatically switches between high- and low-frequency intervals to reduce the resource usage, while minimizing false negative detections. Second, by analyzing the frequency content, e-Sampling presents an event identification algorithm suitable for decentralized computing in resource-constrained networks. In the absence of an event, the "uninteresting" data is not transmitted to the sink. Thus, the energy cost is further reduced. e-Sampling can be useful in a broad range of applications. We apply e-Sampling to Structural Health Monitoring (SHM) and Fire Event Monitoring (FEM), which are typical applications of high-frequency events. Evaluation via both simulations and experiments validates the advantages of e-Sampling in low-cost event monitoring, and in effectively expanding the capacity of WSNs for high data rate applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In most wireless sensor network applications a large amount of data has been continuously collected for future data query and analysis. Data storage management becomes an important challenge in wireless sensor networks. A popular application of sensor networks is event monitoring. In such applications, the observers may not be interested in the sensors or the raw data from the sensors, but more interested in the events. To address the issues of data storage for event monitoring, an optimal data storage scheme is proposed specifically for Fire detection event. In this work, mainly storage node position problem is considered. Hybrid particle swarm optimization is integrated with FCM clustering to attain the suitable positions for k storage nodes in WSN based on the energy cost of data transmission which in turn assists in the detection of the fire event. To reduce data access energy consumption, FCM clustering based on data storage (CBDS) algorithm has been proposed. CBDS has appropriately adapted to adjust the location of data storage through the process of data storage and query access cost calculations respectively. This paper is focused on monitoring the fire event detection in WSN based on the hybrid PSO with FCM clustering. [ABSTRACT FROM AUTHOR]

Using mobile chargers in wireless rechargeable sensor networks is an option for charging all the nodes in the network. In order to achieve some performance requirement such as coverage, it is necessary to effectively schedule the mobile chargers to serve every node of the network. We notice that nearly all previous works on mobile charger scheduling assume that mobile chargers move along predetermined paths which are computed based on perfect priori information. In this paper, we consider the problem of scheduling mobile chargers in an on-demand way to maximize the covering utility. On receiving re-charging requests from the nodes, the mobile charger decides how to move itself. The covering utility is defined to quantify the effectiveness of event monitoring. We formulate the scheduling problem as an optimization one. We propose three heuristics for this problem after proving its NP-Completeness. We further generalize our solutions to accommodate the multiple mobile chargers case. Finally we evaluate our solutions through extensive simulations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Analysis of people's movements represented by continuous sequences of spatio-temporal data tuples have received lots of attention in recent years. The focus of those studies was mostly GPS data recorded on a constant sample rate. However, the creation of intelligent location-aware models and environments also requires reliable localization in indoor environments as well as in mixed indoor/outdoor scenarios. In these cases, signal loss makes usage of GPS infeasible; therefore other recording technologies evolved. Our approach is analysis of episodic movement data. This data contains some uncertainties among time (continuity), space (accuracy), and the number of recorded objects (coverage). Prominent examples of episodic movement data are spatio-temporal activity logs, cell-based tracking data, and billing records. To give one detailed example, Bluetooth tracking monitors the presence of mobile phones and intercoms within a sensor's footprints. Usage of multiple sensors provides flows among the sensors. Most existing data mining algorithms use interpolation and therefore are infeasible for this kind of data. For example, speed and movement direction cannot be derived directly from episodic data; trajectories may not be depicted as a continuous line; and densities cannot be computed. Still, the data hold much information on group movement. Our approach is to aggregate movement in order to overcome the uncertainties. Deriving a number of objects for the spatio-temporal compartments and transitions among them gives interesting insights on the spatio-temporal behavior of moving objects. As a next step to support analysts, we propose clustering the spatio-temporal presence and flow situations. This work focuses as well on creation of a descriptive probability model for the movement based on Spatial Bayesian Networks. We present our methods on a real world data set collected during a football game in Nîmes, France in June 2011. [ABSTRACT FROM AUTHOR]

The remarkable success of online social media sites marks a shift in the way people connect and share information. Much of this information now contains some form of geographical content because of the proliferation of location-aware devices, thus fostering the emergence ofgeosocialmedia – a new type of user-generated geospatial information. Through geosocial media we are able, for the first time, to observe human activities in scales and resolutions that were so far unavailable. Furthermore, the wide spectrum of social media data and service types provides a multitude of perspectives on real-world activities and happenings, thus opening new frontiers in geosocial knowledge discovery. However, gleaning knowledge from geosocial media is a challenging task, as they tend to be unstructured and thematically diverse. To address these challenges, this article presents a system prototype for harvesting, processing, modeling, and integrating heterogeneous social media feeds towards the generation of geosocial knowledge. Our article addresses primarily two key components of this system prototype: a novel data model for heterogeneous social media feeds and a corresponding general system architecture. We present these key components and demonstrate their implementation in our system prototype, GeoSocial Gauge. [ABSTRACT FROM AUTHOR]

Abstract: Event monitoring is a common application in wireless sensor networks. For event monitoring, a number of sensor nodes are deployed to monitor certain phenomenon. When an event is detected, the sensor nodes report it to a base station (BS), where a network operator can take appropriate action based on the event report. In this paper, we are interested in scenarios where the event must be reported within a time bound to the BS possibly over multiple hops. However, such event reports can be hampered by compromised nodes in the middle that drop, modify, or delay the event report. To defend against such an attack, we propose Sem, a Secure Event Monitoring protocol against arbitrary malicious attacks by Byzantine adversary nodes. Sem provides the following provable security guarantees. As long as the compromised nodes want to stay undetected, a legitimate sensor node can report an event to the BS within a bounded time. If the compromised nodes prevent the event from being reported to the BS within the bounded time, the BS can identify a pair of nodes that is guaranteSchool of Electrical and Computer Engineeringed to contain at least one compromised node. To the best of our knowledge, no prior work in the literature can provide such guarantees. Sem is designed to use the minimum level of asymmetric cryptography during normal operation when there is no attack, and use cryptographic primitives more liberally when an attack is detected. This design has the advantage that the overall Sem protocol is lightweight in terms of the computational resources and the network traffic required by the cryptographic operations. We also show an operational example of Sem using TOSSIM simulations. [Copyright &y& Elsevier]

This paper presents a novel pulse switching protocol framework for ultra light-weight wireless network applications. The key idea is to abstract a single Ultra Wide Band (UWB) pulse as the information switching granularity. Pulse switching is shown to be sufficient for on-off style event monitoring applications for which a monitored parameter can be modeled using a binary variable. Monitoring such events 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 for synchronization. The paper presents a joint MAC-routing protocol architecture for pulse switching with a novel hop-angular event localization strategy. Through analytical modeling and simulation-based experiments it is shown that pulse switching can be an effective means for event networking, which can potentially replace the traditional packet transport when the information to be transported is binary in nature. [ABSTRACT FROM PUBLISHER]

We develop, in this paper, a representation of time and events that supports a range of reasoning tasks such as monitoring and detection of event patterns which may facilitate the explanation of root cause(s) of faults. We shall compare two approaches to event definition: the active database approach in which events are defined in terms of the conditions for their detection at an instant, and the knowledge representation approach in which events are defined in terms of the conditions for their occurrence over an interval. We shall show the shortcomings of the former definition and employ a three-valued temporal first order nonmonotonic logic, extended with events, in order to integrate both definitions. [ABSTRACT FROM AUTHOR]

Introduction: Following pulmonary vein isolation (PVI) for atrial fibrillation (AF), early recurrences are frequent, benign and classified as a part of a “blanking period.” This study characterizes early recurrences and determines implications of early AF following PVI. Methods and Results: Seventy-two consecutive patients (59.8 ± 10.7 years, 69% male) were studied following PVI for paroxysmal or persistent AF. Subjects were fitted with an external loop recorder for automatic, continuous detection of AF recurrence for 3 months. AF prevalence was highest 2 weeks after PVI (54%) and declined to an eventual low of 22%. A significant number (488, 34%) of recurrences were asymptomatic; however, all patients with ≥1 AF event had ≥1 symptomatic event. No clear predictor of early recurrence was identified. Forty-seven (65%) patients had at least 1 AF episode, predominantly (39 of 47 patients, 83%) within 2 weeks of PVI. Of the 33 patients who did not experience AF within the first 2 weeks, 85% (28/33) were complete responders (P = 0.03) at 12 months. Recurrence at any time within 3 months was not associated with procedural success or failure. Conclusions: Early AF recurrence peaks within the first few weeks after PVI, but continues at a lower level until the completion of monitoring. A blanking period of 3 months is justified to identify patients with AF recurrences that do not portend procedure failure. Freedom from AF in the first 2 weeks following ablation significantly predicts long-term AF freedom. [ABSTRACT FROM AUTHOR]

Abstract: Subsequent to an initial wet season flood event in the Brisbane River, Australia, both fast (naked disk) and slow (membrane-covered) variants of SDB–RPS Empore disk passive sampling devices were deployed with an automated grab sampling program. A trend increase in the aquatic dissolved concentrations of diuron and simazine was observed over a 10-day period. Kinetic and equilibrium parameters for each sampler were calculated based on the dynamic concentration. Absolute percent difference for duplicate passive samples was <10% in the fast and <25% in the slow samplers. For kinetic sampling, significantly shortened integrative periods are available with the fast compared with the slow variant, with higher sampling rates offering improved detection limits. The study demonstrates a method for determining kinetic parameters of passive samplers in a variable concentration field deployment, and illustrates the differences in quality between active and passive data, in terms of capturing changes in concentration associated with rainfall events. [Copyright &y& Elsevier]

Event monitoring and active behaviors are important aspects in many software systems and application domains, not only in database management systems. In this article, we propose an event-condition-action (ECA) approach that spans from application data to application components and behaviors. Starting from an exception manager we previously developed in the context of a workflow management system, we derived an autonomous active component capable of handling a variety of events and enacting actions in response to detected events. The ECA server runs as an autonomous engine and can be seamlessly integrated with existing systems, thus enhancing the systems' functionalities and maintainability by separating active and nonactive design concerns. [ABSTRACT FROM AUTHOR]

External electrocardiographic loop recording permits extended cardiac rhythm monitoring and ECG storage before and after device activation. The purpose of the study was to assess the diagnostic yield of external loop recorders in patients with more than two syncopal events within the last 6 months and a negative tilt table test. Twenty-four consecutive patients (51 ± 14 years, male n = 9) were enrolled with 3 ± 4 recurrent syncopal events within the last 6 months and no overt structural heart disease. The loop recorder CardioCall continuously records a two-channel ECG via skin electrodes. When activated, up to 4.5 minutes of the ECG preceding activation is stored. The time between tilt table testing and monitoring was 5 ± 4 weeks. The average monitoring period covered 50 ± 22 days. Recording was either unsuccessful or terminated prematurely in 5 patients. The reasons were skin irritation secondary to the electrodes in two patients, cable damage in one patient, and two patients inadvertently erased the stored data when replacing the batteries. Fourteen patients activated the loop recorder at least once. Symptoms leading to device activation were syncope (n = 1), dizziness (n = 11), feeling unwell (n = 7), palpitations (n = 5), headaches (n = 1), and chest pain (n = 1). The loop recorder documented sinus tachycardia during the syncopal event. Sinus tachycardia was also observed in 7 other patients, and atrial flutter in two patients. Eight patients had recurrent syncope with two patients experiencing syncope prior to the monitoring period, one syncope occurred in a patient who inadvertently erased the stored data, one event coincided with sinus tachycardia, and 4 (17%) patients had syncope during 15 ± 10 months after termination of loop monitoring. The external loop recorder was not very useful for arrhythmia detection in patients with syncopal events, no overt heart disease, and a negative tilt table test because the cardiac rhythm was stored in only 1 of 8 (13%) patients with recurrent syncope. Reasons for the low diagnostic yield of external loop recorders were infrequent syncopal events after the baseline evaluation, with rare events during the limited monitoring period in particular, and premature termination or unsuccessful recording in 21% of patients. (PACE 2003; 26:1837–1840) [ABSTRACT FROM AUTHOR]