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Recently, wireless sensor networks (WSNs) have attracted a lot of attention from the network research community, especially in the aspect of event-driven wireless sensor networks (EWSNs), such as adversary locating, fire detection and so on.As the nature of importance of event monitoring, security has to be assured in both of communication and processing. However, due to the inherent resource constraints, security inEWSNs is faceddifferent issues and challenges than traditional WSNs.In this paper, we attempt to give an outline on the security issuesin the EWSNs. And then we propose a new secure architecture for it, which covers potential security issues. In this architecture, we analyze the possible threats and give out the correspondingcountermeasures.

This article presents a single CUSUM scheme (called the GCUSUM chart ) for simultaneously monitoring the time interval T and magnitude X of an event. For example, a traffic accident may be considered as an event, and the total loss in dollars in each case is the event magnitude. Since the GCUSUM chart is developed based on a synthetic statistic G which is a function of both T and X , this new chart is able to make use of the information about the event frequency, as well as the information about the event magnitude. Moreover, the detection power of the GCUSUM chart can be allocated in an optimal manner between those against T shifts and against X shifts, and between those against small shifts and against large shifts. The performance studies show that the GCUSUM chart is more effective than all other charts in the current literature for detecting the out-of-control status of an event. Furthermore, the GCUSUM chart performs more uniformly for detecting process shifts of different types and sizes. This chart is also easier to be designed and implemented than other CUSUM charts for monitoring both T and X . The GCUSUM chart has the potential to be applied to many different areas, especially to the non-manufacturing and service sectors, such as supply chain management, homeland security, office administration and the health care industry.

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 S em , a Secure Event Monitoring protocol against arbitrary malicious attacks by Byzantine adversary nodes. S em 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. S em 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 S em 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 S em using TOSSIM simulations.

The authors use system event monitoring to distinguish between the behavioral characteristics of normal and anomalous computer system users. Identifying anomalous behavior at the system event level diminishes privacy concerns and supports the identification of cross-application behavioral patterns.

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.

For providers of composite services, preventing cases of SLA violations is crucial. Previous work has established runtime adaptation of compositions as a promising tool to achieve SLA conformance. However, to get a realistic and complete view of the decision process of service providers, the costs of adaptation need to be taken into account. In this paper, we formalize the problem of finding the optimal set of adaptations, which minimizes the total costs arising from SLA violations and the adaptations to prevent them. We present possible algorithms to solve this complex optimization problem, and detail an end-to-end system based on our earlier work on the PREvent (prediction and prevention based on event monitoring) framework, which clearly indicates the usefulness of our model. We discuss experimental results that show how the application of our approach leads to reduced costs for the service provider, and explain the circumstances in which different algorithms lead to more or less satisfactory results.

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.

Today, organizations depend much more on IT than they did in the past. Services such as internal portals, email communication, and financial and HR systems rely on computers to move businesses forward. These systems are under pressure to be securer than ever to protect organizations' operational environment. One aspect to consider in this situation is IT security event management. This article presents the design and implementation of two security event monitoring approaches in a distributed systems environment.

A broadcasting delay is an important problem for the application of the critical event monitoring in wireless sensor networks. To prolong the network lifetime some of the sleep scheduling methods are always employed in WSNs it results in a significant broadcasting delay. A novel sleep scheduling method to be proposed it is based on the level-by-level offset schedule to achieve a low broadcasting delay in wireless sensor networks (WSNs). There are two phases to set the alarm broadcasting first one is, if a node detects a critical event, it create an alarm message and quickly transmits it to a center node along a pre-determined path with a node-by-node offset way. Then the center node broadcasts the alarm message to the other nodes along another predetermined path without collision. To eliminate the collision in broadcasting, a colored connected dominant set (CCDS) in the WSN via the IMC algorithm is established. An on demand distance vector routing protocol is established in one of the traffic direction for alarm transmission. The proposed system is used in military and forest fire application. Keywords - Wireless Sensor Network (WSN), critical event monitoring, sleep scheduling, broadcasting delay.

In the operation of power systems, the knowledge of the system state is required by several fundamental functions, such as security assessment, voltage control and stability analysis. By making reference to the static state of the system represented by the voltage phasors at all the network buses, it is possible to infer the system operating conditions. Until the late 1970s, conventional load flow calculations provided the system state by directly using the raw measurements of voltage magnitudes and power injections. The loss of one measurement made the calculation impossible and the presence of measurement errors affected dramatically the computed state.To overcome these limitations, load flowtheory has been combined with statistical estimation constituting the so-called state estimation (SE). The latter consists in the solution of an optimization problem that processes the measurements together with the network model to determine the optimal estimate of the system state. The outputs of load flow and SE are composed of the same quantities, typically the voltage magnitude and phase at all the network buses, but SE uses all the types of measurements (e.g., voltage and current magnitudes, nodal power injections and flows, synchrophasors) and evaluates their consistency using the network model. The measurement redundancy is key to tolerate measurement losses, identify measurement and network parameter errors, and filter out the measurement noise. The foregoing properties of SE allow the system operator to obtain an accurate and reliable estimate of the system state that consequently improves the performance of the functions relying on it. Traditionally, SE has been performed at a relatively low refresh rate of a few minutes, dictated by the time requirements of the related functions together with the low measurement acquisition rate of remote terminal units (RTUs). Nowadays, the emerging availability of phasor measurement units (PMUs) allows to acquire accurate and time-aligned phasors, called synchrophasors, with typical streaming rates in the order of some tens of measurements per second. This technology is experiencing a fast evolution, which is triggered by an increasing number of power system applications that can benefit from the use of synchrophasors. SE processes can exploit the availability of synchrophasor measurements to achieve better accuracy performance and higher refresh rate (sub-second). PMUs already compose the backbone of wide area monitoring systems in the context of transmission networks to which several real-time functionalities are connected, such as inter-area oscillations, relaying, fault location and real-time SE. However, PMUs might represent fundamental monitoring tools even in the context of distribution networks for applications such as: SE [5, 6], loss of main [7], fault event monitoring, synchronous islanded operation [9] and power quality monitoring. The recent literature has discussed the use of PMUs for SE in distribution networks both from the methodological point of view and also via dedicated real-scale experimental setups. Since the pioneering works of Schweppe on power system SE in 1970, most of the research on the subject has investigated static SE methods based on weighted least squares (WLS). Static SE computes the system state performing a “best fit” of the measurements belonging only to the current time-step. Another category of state estimators are the recursive methods, such as the Kalman filter (KF). In addition to the use of the measurements and their statistical properties, they also predict the system state by modelling its time evolution. In general, recursive estimators are characterized by higher complexity and the prediction introduces an additional source of uncertainty that, if not properly quantified, might worsen the accuracy of the estimated state. Besides, their ability to filter out measurement noise could not be exploited due to the low SE refresh rate: even in quasi-steady state conditions, the measurement noise was smaller than the state variations between two consecutive time-steps. However, the effectiveness of power system SE based on KF has been recently reconsidered thanks to the possibility to largely increase the SE refresh rate by using synchrophasor measurements. The chapter starts by providing the measurement and process model of WLS and KF SE algorithms and continues with the analytical formulation of the two families of state estimators, including their linear and non-linear versions as a function of the type of available measurements. Finally, two case studies targeting IEEE transmission and distribution reference networks are given.

As data centers grow larger and strive to provide tight performance and availability SLAs, their monitoring infrastructure must move from passive systems that provide aggregated inputs to human operators, to active systems that enable programmed control. In this paper, we propose Trumpet, an event monitoring system that leverages CPU resources and end-host programmability, to monitor every packet and report events at millisecond timescales. Trumpet users can express many *network-wide events*, and the system efficiently detects these events using *triggers* at end-hosts. Using careful design, Trumpet can evaluate triggers by inspecting every packet at full line rate even on future generations of NICs, scale to thousands of triggers per end-host while bounding packet processing delay to a few microseconds, and report events to a controller within 10 milliseconds, even in the presence of attacks. We demonstrate these properties using an implementation of Trumpet, and also show that it allows operators to describe new network events such as detecting correlated bursts and loss, identifying the root cause of transient congestion, and detecting short-term anomalies at the scale of a data center tenant.

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.

The massive sensor data streams analysis in the monitoring application of internet of things is very important, especially in the environments where supporting such kind of real time streaming data storage and management. In order to support the classification of the massive sensor data streams, in this paper, a massive sensor data streams analysis strategy is proposed based on Hoeffding tree with concept drift for event monitoring application on Hadoop system. The proposed strategy is sufficient for sensor data streams classification tasks using map-reduce platform of Hadoop system. Finally, the possibilities of the strategy are demonstrated on spatial sensing data streams processing operations in comparison with existing solutions in the cloud computing environment. The simulation results show that the strategy achieves more energy savings and also ensures few amounts of sensor data retained in memory.

With the prevalence of intelligent devices, social networks have been playing an increasingly important role in our daily life. Various social networks (e.g., Twitter, Facebook) provide convenient platforms for users to explore the world. In this thesis, we study the problem of multi-perspective analysis of social events detected from social networks. In particular, we aim to make sense of the social events from the following three perspectives: 1) what are these social events about; 2) how do these events evolve along timeline; 3) who are involved in the discussions on these events. We mainly work on two categories of social data: the user-generated contents such as tweets and Facebook posts, and the users' interactions such as the follow and reply behaviours among users. On one hand, the posts reveal valuable information that describes the evolutions of miscellaneous social events, which is crucial for people to understand the world. On the other hand, users' interactions demonstrate users' relationships among each other and thus provide opportunities for analysing the underlying communities behind the social events. However, it is not practical to manually detect social events, monitor event evolutions or profile the underlying communities from the massive amount of social data generated everyday. Hence, how to efficiently and effectively extract, manage and analyse the useful information from the social data for multi-perspective social events understanding is of great importance. The social data is dynamic source of information which enables people to stay informed of what is happening now and who are the active and influential users discussing these social events. For one thing, social data is generated by people worldwide at all time, which may make fast identification of events even before the mainstream media. Moreover, the continuous stream of social data reflects the event evolutions and characterizes the events with changing opinions at different stages. This provides an opportunity to people for timely responses to urgent events. For another, users are often not isolated in social networks. The interactions between users can be utilized to discover the communities who discuss each social event. Underlying community profiling provides answers to the questions like who are interested in these events, and which group of people are the most influential users in spreading certain event topics. These answers deepen our understanding of the social events by considering not only the events themselves but also the users behind these events. The first research task in this thesis is to monitor and index the evolving events from social textual contents. The social data cover a wide variety of events which typically evolve over time. Although event detection has been actively studied, most existing approaches do not track the evolution of events, nor do they address the issue of efficient monitoring in the presence of a large number of events. In this task, we detect events based on the user-generated textual contents and design four event operations to capture the dynamics of events. Moreover, we propose a novel event indexing structure, called Multi-layer Inverted List, to manage dynamic event databases for the acceleration of large-scale event search and update. The second research task is to explore multiple features for social events tracking and visualization. In addition to textual contents utilized in the first task, social data contains various features, such as images and timestamps. The benefits of incorporating different features into event detection are twofold. First, these features provide supplemental information that facilitates the event detection model. Second, different features describe the detected events from different aspects, which enables users to have a better understanding with more vivid visualizations. To improve the event detection performance, we propose a novel generative probabilistic model which jointly models five different features. The event evolution tracking is achieved by applying the maximum-weighted bipartite graph matching on the events discovered in consecutive periods. Events are then visualized by the representative images selected based on our three defined criteria. The third research task is to detect and profile the underlying social communities in social events. The social data not only contains user-generated contents which describe the events evolutions, but also comprises various information on the users who discuss these events, such as user attributes, user behaviours, and so on. Comprehensively utilizing this user information can help to group similar users into communities, and enrich the social event analysis from the community perspective. Motivated by the rich semantics about user behaviours hidden in social data, we extend the community definition as a group of users who are not only densely connected, but also having similar behaviours. Moreover, in addition to detecting the communities, we further profile each of the detected communities for social events analysis. A novel community profiling model is designed to detect and characterize a community by both content profile (what a community is about) and diffusion profile (how it interacts with others).

Suppose you were monitoring a group of people in order to determine if anyone of them did something suspicious (e.g., putting down a bag) or if any two interacted in a suspicious manner (e.g., trading bags). How large a group could you monitor successfully? This paper reports on six experiments in which observers monitor a group of entities, watching for an event. Whether the event was performed by a single entity or was an interaction between a pair, the capacity for event monitoring was two to three items. This was lower than the multiple object tracking capacity for the same stimuli (approximately six items). Capacity was essentially the same whether entities were identical circles or unique cartoon animals; nor was capacity changed by an added requirement to identify the entities involved in an event. Event monitoring appears to be related to, but not identical to, multiple object tracking.

Flood event monitoring plays an important role for emergency management. With the fast growth of social media, a large number of images and videos are uploaded and searched on the internet during disasters, which can be used as “sensors” for improving efficiency of emergency management. This work proposes a novel framework in which the rich information available from social media is incorporated with image analysis to enhance image retrieval for disaster management. The text associated with images of flooding events was used to extract prominent words associated with flooding. The image features are represented by a histogram of visual words obtained using the Bag-of-Words (BoW) model. The text and image analysis are integrated at the feature level, in which the text features are conjoined directly with image features. The proposed approach was evaluated based on two flood event corpuses obtained from the US Federal Emergency Management Agency media library and public Facebook pages and groups related to flood and flood aid (in German). The experimental results demonstrate the improved performance of image recognition after incorporating the text features, which suggests the potential to enhance the efficiency of emergency management.

In recent years Wireless Sensor Networks have provided a effective solution for sensing and gathering spatial data by ZigBee protocol or other wireless network protocols. So the massive sensor data streams processing has reached many areas of monitoring application in internet of things. The sensor data streams constantly flow in and flow out of the monitoring system, cloud computing can provide a scalable storage and the massive data processing power to perform both online and offline analysis and mining of the heterogeneous sensor data streams. In order to support the classification of the sensor data streams, in this paper, a sensor data streams processing strategy is proposed based on Hoeffding tree algorithm for event monitoring application in cloud computing. The proposed strategy is sufficient for sensor data streams classification tasks using map-reduce platform of cloud computing. Finally, the possibilities of the strategy are demonstrated on spatial sensing data streams processing operations in comparison with existing solutions in the MapReduce environment. The simulation results show that the strategy achieves more energy savings and also ensures few amounts of sensor data retained in memory.

Social media platforms are emerging digital communication channels that provide an easy way for common people to share their health and medication experiences online. With more people discussing their health information online publicly, social media platforms present a rich source of information for exploring adverse drug reactions (ADRs). ADRs are major public health problems that result in deaths and hospitalizations of millions of people. Unfortunately, not all ADRs are identified before a drug is made available in the market. In this study, an ADR event monitoring system is developed which can recognize ADR mentions from a tweet and classify its assertion. We explored several entity recognition features, feature conjunctions, and feature selection and analyzed their characteristics and impacts on the recognition of ADRs, which have never been studied previously. The results demonstrate that the entity recognition performance for ADR can achieve an F-score of 0.562 on the PSB Social Media Mining shared task dataset, which outperforms the partial-matching-based method by 0.122. After feature selection, the F-score can be further improved by 0.026. This novel technique of text mining utilizing shared online social media data will open an array of opportunities for researchers to explore various health related issues.

Effective Big Data analytics pose several difficult challenges for modern data management architectures. One key such challenge arises from the naturally streaming nature of big data, which mandates efficient algorithms for querying and analyzing massive, continuous data streams (that is, data that is seen only once and in a fixed order) with limited memory and CPU-time resources. Such streams arise naturally in emerging large-scale event monitoring applications; for instance, network-operations monitoring in large ISPs, where usage information from numerous sites needs to be continuously collected and analyzed for interesting trends. In addition to memory- and time-efficiency concerns, the inherently distributed nature of such applications also raises important communication-efficiency issues, making it critical to carefully optimize the use of the underlying network infrastructure. In this chapter, we provide a brief introduction to the distributed data streaming model and the Geometric Method (GM), a generic technique for effectively tracking complex queries over massive distributed streams. We also discuss several recently-proposed extensions to the basic GM framework, such as the combination with stream-sketching tools and local prediction models, as well as more recent developments leading to a more general theory of Safe Zones and interesting connections to convex Euclidean geometry.

Underwater acoustic sensor networks (UWASNs), composed of acoustic sensor nodes, have become a hot field in ocean techniques. They were widely applied in marine data collection, event monitoring, resource exploration, etc. Coverage of zone is the fundamental address to the UWASNs deployment. A good deployment strategy should make effective area as much as possible, according to resource allocation. A lot of research are focusing on sensor networks deployment optimization on the land. Underwater deployment Strategies has not achieved good effect because of the complexity of ocean environment. This paper provides a deployment optimization strategy for mobile nodes using Particle Swarm Optimization Algorithm for UWASNs. This algorithm is based on simulation of birds swarm intelligence characteristics and aimed to solve the continuous variable optimization problem. Simulation Result shows that this method can obviously improve the coverage of deployment. If iterative times is enough, the coverage percent can reach 99% in specific situations. This algorithm can provide reference for mobile node deployment.

Monitoring of sensitive areas with sensors deployment in large area network is the objective of many research groups now days. There are many applications such as health monitoring, environmental monitoring, agriculture, industrial use, wildlife protection etc, that requires wireless sensor network for continuous monitoring. Various research groups are working on real time implementation of WSN instead of simulation. Real time implementation of WSN faces various issues, and results are also not as good as simulated results. This paper is focused on highlighting the issues in deployment of sensors for real time event monitoring system. It is based on the observation of sensor deployment in the forest (Panna Tiger Reserve, Panna, M.P, India) for the project “Forest and Wild life protection using Large area Wireless Sensor Network” at Indian Institute of Information Technology-Allahabad, India. This paper also covers those issues which are faced at the deployment of both levels of individual sensor node as well as group of sensor nodes.

This paper measures the infrasonic signal of a Satellite Launch event and analyzes the signal characteristics. Using four infrasonic sensors to finish the satellite launch event monitoring tests, the experimental results show that difference between the infrasonic signal of satellite launch event and the infrasonic signal of background is very clear, the signal amplitude is dozens times of the background in the range of tens of kilometers; the closer, the frequency distribution of the wider, almost throughout the infrasonic frequency range in the range of several kilometers; obvious frequency concentration by increasing distance, the frequency focus in 7-12Hz at 50 kilometers, and as the distance increases, the frequency down .

Personal users on Twitter frequently post observations about their immediate environment as part of the 500 million tweets posted everyday. These observations and their implicitly associated time and location data are a valuable source of information for monitoring objects and events, such as earthquake, hailstorm, and shooting incidents. However, given the informal and uncertain expressions used in personal Twitter messages, and the various type of accounts existing on Twitter, capturing personal observations of objects and events is challenging. In contrast to the existing supervised approaches, which require significant efforts for annotating examples, in this paper, we propose an unsupervised approach for filtering personal observations. Our approach employs lexical analysis, user profiling and classification components to significantly improve filtering precision. To identify personal accounts, we define and compute a mean user profile for a dataset and employ distance metrics to evaluate the similarity of the user profiles under analysis to the mean. Our extensive experiments with real Twitter data show that our approach consistently improves filtering precision of personal observations by around 22i¾?%.

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.

The most important goal in event-driven wireless sensor networks is to transmit the event information to users as soon as possible. One of the most important factors that would make it possible to reach this goal is the design of efficient detecting protocols. In this paper, we present an efficient event detecting protocol (EEDP) specified for event monitoring applications. In the event occuring area, each node broadcasts its primary detection result to make a decision corporately. And then the decision-made node will choose the next hop using the underlying routing protocol to forward a single alarm packet. To improve the reliable transmission of the single alarm packet, we use a dynamic multicopy scheme similar as conventional multipath routing for continuous flow. The simulation results validate that EEDP is a practical and efficient protocol for event detection applications where the constraints of end-to-end delay and reliability are stringent.

We consider a high density of sensors randomly placed in a geographical area for event monitoring. The monitoring regions of the sensors may have significant overlap, and a subset of the sensors can be turned off to conserve energy, thereby increasing the lifetime of the monitoring network. Prior work in this area does not consider the event dynamics. In this paper, we show that knowledge about the event dynamics can be exploited for significant energy savings, by putting the sensors on a periodic on/off schedule. We discuss energy-aware optimization of the periodic schedule for the cases of an synchronous and a asynchronous network. To reduce the overhead of global synchronization, we further consider a spectrum of regionally synchronous networks where the size of the synchronization region is specifiable. Under the periodic scheduling, coordinated sleep by the sensors can be applied orthogonally to minimize the redundancy of coverage and further improve the energy efficiency. We consider the interactions between the periodic scheduling and coordinated sleep. We show that the asynchronous network exceeds any regionally synchronous network in the coverage intensity, thereby increasing the effectiveness of the event capture, though the opportunities for coordinated sleep decreases as the synchronization region gets smaller. When the sensor density is high, the asynchronous network with coordinated sleep can achieve extremely good event capture performance while being highly energy efficient.

The abrupt event monitoring is a challenging and critical issue in water environment systems. There are two main different abrupt events in the monitoring system, namely, the emergency water pollution accident and the abrupt sensor fault. The two different abrupt events have similar data characteristics, and few methods can be used to recognize the events. In this paper, a novel abrupt event monitoring approach based on kernel principal component analysis (KPCA) and support vector machines is proposed, which is combined with the physical redundancy method. The trust mechanism is introduced into the proposed approach to reduce the interference of external noise and improve the performance of quick response for the abrupt events. A spare data area is set up to store the data for the KPCA modeling. The data in the spare data area are updated continuously, and the KPCA model is updated subsequently to improve the adaptivity of the KPCA model for the abrupt event monitoring. The experimental results show that the proposed approach is capable of detecting and recognizing the two different abrupt events efficiently.

In duty-cycled wireless sensor networks (WSNs) for stochastic event monitoring, existing efforts are mainly concentrated on energy-efficient scheduling of sensor nodes to guarantee the coverage performance, ignoring another crucial issue of connectivity. The connectivity problem is extremely challenging in the duty-cycled WSNs due to the fact that the link connections between nodes are transient thus unstable. In this paper, we propose a new kind of network, partitioned synchronous network, to jointly address the coverage and connectivity problem. We analyze the coverage and connectivity performances of partitioned synchronous network and compare them with those of existing asynchronous network. We perform extensive simulations to demonstrate that the proposed partitioned synchronous network has a better connectivity performance than that of asynchronous network, while coverage performances of two types of networks are close.

Network Functions Virtualization (NFV) Point of Presence (PoP) Data Centers are often constrained by compute and storage capacity and the cost of energy required to operate the data centers. High energy cost is a general concern for NFV operators, and in particular for specific-purpose NFV PoP DCs such as those in mobile core networks. In this context, optimized resource management and workload distribution based on a domain-agnostic policy engine for driving energy efficiency in data centers is proposed. An open stack based solution is proposed to enable policy-based monitoring and energy management. The specified policies are used to enforce soft and hard constraints in the system with periodic event monitoring and dynamic resource management to minimize energy consumption.

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.

Wireless sensor networks (WSNs) are being deployed for structural health monitoring (SHM) applications at an increasing rate. A WSN is often organized into groups or clusters for distributed monitoring purposes. However, we discover that the dependability (in terms of the monitoring ability/quality and low false alarm rate) is greatly affected by such grouping schemes, as they do not satisfy application-specific monitoring aspects. We present an SHM application oriented network architecture (SHMnet) and analyze health event monitoring performance with it. We propose a substructure-oriented sensor organization (SOSO), considering the formation of engineering structures and finding that a large physical structure consists of a number of substructures. We enable deployed sensors to be organized into groups (unlike dynamic clusters/trees) in such a way that each group of sensors can monitor a substructure independently. We evaluate SHMnet via simulations using real data traces. The evaluation results, compared to existing work, show that SHMnet achieves at least five times the energy saving (including the energy for communication) in WSNs and dependability in terms of high ability of monitoring and low false alarm rate.

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.

This work presents a distributed implementation for processing Maximizing Range Sum (MaxRS) query in Wireless Sensor Networks (WSN). MaxRS query is useful in many spatially-distributed event monitoring and target tracking applications. Given the location and current readings of the nodes, and a rectangle R, MaxRS finds a location of R that maximizes the sum of the readings of all the nodes covered by R. Our system performs MaxRS query in a user-specified time-interval γ and using the result obtained, attempts to maintain a certain degree of energy conservation in the WSN, based on a user-defined threshold δ. Since centralized processing of the raw readings and subsequently determining the MaxRS may incur significant communication overheads, we developed a distributed algorithm to compute MaxRS. We implemented our system in a heterogeneous WSN consisting of TelosB and SunSPOT motes, and illustrate the end-user tools: GUI for specifying required parameters, and real-time visualization of MaxRS solutions and estimated network energy consumption.

In order to quickly establish a data gathering system in a flexible manufacturing environment, this paper presents a ZigBee-based wireless sensor network combined with a data processing system. The proposed system includes a system simulator, instrument definition module and network monitor module. System functions include network parameter setting, real-time system status sensing and data collecting, intelligent event monitoring and warning, and ad hoc response mechanisms. The proposed system enables users to quickly plug-and-play any digital instrument in their production systems and automatically capturing production data. Production controller can adopt these data to make decisions in real-time.

One critical problem in the operations of chemical processes is the occurrence of abnormal events. Therefore, an effective process monitoring methodology that can help detect, diagnose and predict abnormal events becomes potentially very useful. For the purpose of knowledge representation of chemical abnormality, a specified type of timed fuzzy Petri net (tFPN) approach is explicitly introduced in this paper. The dominant feature of tFPN metrics can be recognized from the fact that a timing factor is assigned to each transition, as well as a degree of reliability is associated with each place, which allows accurately representing the dynamic nature of fuzzy knowledge pertaining to abnormal events. Following a procedure towards abnormal event monitoring, two efficient algorithms in terms of abnormality prognostication and diagnosis are exploited by means of reachability analysis of tFPN. The benefits of derived techniques and solutions are illustrated through a case study consisting in a polypropylene reactor.

In this paper we present algorithms for building and maintaining efficient collection trees that provide the conduit to disseminate data required for processing monitoring queries in a wireless sensor network. While prior techniques base their operation on the assumption that the sensor nodes that collect data relevant to a specified query need to include their measurements in the query result at every query epoch, in many event monitoring applications such an assumption is not valid. We introduce and formalize the notion of event monitoring queries and demonstrate that they can capture a large class of monitoring applications. We then show techniques which, using a small set of intuitive statistics, can compute collection trees that minimize important resources such as the number of messages exchanged among the nodes or the overall energy consumption. Our experiments demonstrate that our techniques can organize the data collection process while utilizing significantly lower resources than prior approaches.

Fusion of cooperative intelligent sensor nodes in a distributed environment can provide a high performance event monitoring system mainly for security issues. Partitioning the hardware design space into entities called agents, which are autonomous units of execution that have the capability of interacting with the environment and each other has been made much more attractive by the recent advances in the capabilities of reconfigurable hardware. In a reconfigurable embedded processing environment one possible benefit is use of multi-agent approach in a common design methodology for both the hardware and software components of the system to do parallel processing with high speed and flexibility. In this paper we will explore the arguments for applying multi-agent techniques to highlight how such techniques can be applied using current-generation hardware description languages in reconfigurable hardware to be suitable for physical environmental security.

Одним из основных институтов инфраструктурной поддержки малого предпринимательства является бизнес-инкубатор. В его задачи входит не только оказание имущественной поддержки в виде сдачи в аренду помещений, оборудования и т. д., но также содействие развитию малых предприятий путем предоставления информационно-аналитических услуг. В качестве основных информационно-аналитических инструментов инфраструктурной поддержки малых предприятий авторы выделяют консалтинг, обучение, проектную экспертизу и событийный мониторинг. Для успешной реализации информационно-аналитической поддержки малого предпринимательства необходим постоянный мониторинг результатов интеграции информационно-аналитических инструментов инфраструктурной поддержки малых предприятий в процесс бизнес-инкубирования. В настоящее время не существует единой методики, позволяющей оценить результативность информационно-аналитической поддержки малого предпринимательства. Авторами предложена универсальная методика оценки результативности интеграции информационно-аналитических инструментов инфраструктурной поддержки малых предприятий в процесс бизнес-инкубирования, основанная на методах анализа иерархий и экспертных оценок. Предлагаемая методика включает показатели результативности каждого из пяти выделенных уровней оценки результативности интеграции информационно-аналитических инструментов в процесс бизнес-инкубирования. Показатели вышестоящего уровня рассчитываются на основе показателей нижестоящего уровня, каждому из которых в результате применения метода анализа иерархий и метода экспертных оценок присваивается свой нормализованный вектор приоритетов - коэффициент важности данного показателя. Для каждого из показателей авторами были установлены нормальные значения и выведены формулы для расчета. Практическое использование предложенной методики будет способствовать решению следующих проблем: во-первых, сопоставления действующих бизнес-инкубаторов; во-вторых, выбора малыми предприятиями лучшего на их взгляд бизнес-инкубатора в качестве института поддержки; в-третьих, установления плановых показателей развития информационно-аналитической поддержки.

Zusammenfassung Trackingsysteme stellen Bewegungsinformationen über Objekte zur Verfügung. Diese Bewegungsinformationen können mit zusätzlichen Daten kombiniert werden, um Higher-Level-Fusionssysteme zu konstruieren, die zur Erkennung von Verhaltens- und Bedrohungsmustern dienen und somit zur Situation Awareness beitragen. Muster, die interessante Situationen charakterisieren, können sich von Zeit zu Zeit ändern und hängen von der spezifischen Fragestellung ab. In diesem Artikel stellen wir eine Methode vor, Datenbanksysteme als zentrale Komponente in einem Higher-Level-Fusionssystem zur Entdeckung spezieller Situationen zu verwenden. Es wird eine Systemarchitektur präsentiert, die kommerzielle relationale Datenbankmanagementsysteme nutzt. Schließlich wird die Möglichkeit diskutiert, Muster zur Anomalieerkennung in Trackingszenarien mittels Relationaler Algebra auszudrücken.

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.

Combining a range of assessment parameters into one usable entity has been identified as an important goal in providing a practical, objective and robust assessment of welfare, particularly in laboratory animals. This paper refines and extends one such previously published method. The proposed Extended Welfare Assessment Grid provides for the incorporation of changes in the state of an animal over time, allowing for predictive, retrospective, scheduled, or event monitoring. It enables the numeric, as well as visual, representation of the animal's welfare, placing this in the context of the careful and realistic justification for experimental use of the animal. This assessment method represents a valuable tool for those tasked with ensuring ethical oversight, as well as for those planning the use, or monitoring, of animals in research. It is particularly applicable to animals used in long-term studies, especially non-human primates. It is believed that this system will draw attention to the temporal component of suffering that is often overlooked in the planning of research schedules and allow an assessment of cumulative suffering imposed by the events that occur.

Experimental and numerical analyses have been carried out to demonstrate the feasibility and evaluate the limitation of using fiber Bragg gratings for impact source location on a cold-rolled aluminum plate. Within the area enveloped by a four-FBG-array, source location is reasonably accurate. Beyond that area, prediction error may not be acceptable. Numerical simulations show that such error may be attributed to the limitation of equipment resolution on the one hand, and angular insensitivity of the FBG on the other hand. Based on the results, it is reasoned that prediction accuracy can be improved by employing stronger light source, steeper filter response, and orthogonal rosettes instead of single axis FBGs.

In a prospective descriptive laboratory study, 25 Helping Hand(™) (HH) (10 without and 15 with reminder system) and 50 Medication Event Monitoring Systems (MEMS) (25 with 18-month and 25 with 2-year battery life) were manipulated twice daily following a predefined protocol during 3 consecutive weeks. Accuracy was determined using the fixed manipulation scheme as the reference. Perfect functioning (i.e., total absence of missing registrations and/or overregistrations) was observed in 70% of the HH without, 87% of the HH with reminder, 20% MEMS with 18 months, and 100% with 2-year battery life respectively. ispartof: Sensors vol:10 issue:3 pages:1652-1660 ispartof: location:Switzerland status: published

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.