Efficient Event Detection Research Articles

An efficient pattern analysis-based event detection and fault localisation model for reliable power transmission system

An efficient pattern analysis-based event detection and fault localisation model for reliable power transmission system

An efficient complex event detection model for high proportion disordered RFID event stream

With the aim of solving the detection problems for current complex event detection models in detecting a related event for a complex event from the high proportion disordered RFID event stream due to its big uncertainty arrival, an efficient complex event detection model based on Extended Nondeterministic Finite Automaton (ENFA) is proposed in this paper. The achievement of the paper rests on the fact that an efficient complex event detection model based on ENFA is presented to successfully realize the detection of a related event for a complex event from the high proportion disordered RFID event stream. Specially, in our model, we successfully use a new ENFA-based complex event detection model instead of an NFA-based complex event detection model to realize the detection of the related events for a complex event from the high proportion disordered RFID event stream by expanding the traditional NFA-based detection model, which can effectively address the problems above. The experimental results show that the proposed model in this paper outperforms some general models in saving detection time, memory consumption, detection latency and improving detection throughput for detecting a related event of a complex event from the high proportion out-of-order RFID event stream.

Voronoi Neighbor Based Energy Efficient Event Detection in Wireless Sensor Networks

In a wireless sensor network (WSN), a dense deployment results in nodes having overlapping communication and sensing ranges. In such an environment, an event is monitored and reported by multiple nodes, generating high traffic and consuming most of their resources simultaneously. High in-network traffic and redundant readings pauperize the accuracy of the event detection and significantly affect the decision making process. In this work, a novel interpolation technique is proposed to minimize the in-network traffic at the leaf level. Pairwise Markov model along with local inference at every intermediate node, is used to determine the shortest path with minimum energy consumption in a network modelled as a Markov Random Field (MRF). Simulation results prove that the proposed scheme is able to achieve significantly accurate event reporting rate as compared to other existing schemes.

Incident Detection Through Twitter

Traffic incident information is disseminated via Twitter from various types of accounts. It is more common to find active or transitive verbs and adverbs in tweets from personal accounts because individuals report personal experiences (e.g., “just saw an accident”). Tweets from an organization or agency [e.g., 511 (a telephone hotline for transportation information widely used in the United States and Canada), departments of transportation] are more structured and commonly include nouns and past participles (e.g., “one lane blocked”). Organization accounts mostly provide incident location, type, severity, and so on, whereas personal tweets do not usually provide such details. However, an agency tweets about an incident usually after the incident management (IM) officials have already been notified. Because of this timing, a personal tweet is more likely to carry useful “new” information for IM purposes. This study investigated the detection of traffic incidents through Twitter feeds by using these differences in structure and information content found in organization and personal social media accounts. Tweets collected via the Twitter public application programming interface were manually coded and treated separately as either personal or organizational, and the “dictionaries” used to perform relevancy classification were derived separately. Combinations of dictionaries (i.e., personal only, organizational only, personal and organizational) were used for “term frequency–inverse document frequency” and naïve Bayesian analysis. It was shown that analysis specific to account types helped achieve better accuracy in classification for targeting relevant tweets. Therefore, account-specific analysis should be considered for more efficient and effective event detection for IM purposes.

Energy-Harvesting Nanosensor Networks: Efficient event detection

Advances in nanotechnology are paving the way for a new generation of sensor networks at the nanoscale, promising radically new applications in medical, biological, and chemical fields. At the nanoscale, it is difficult to use large batteries for prolonged operations, necessitating energy harvesting as the most viable solution for these nanosensor networks. Unfortunately, the rate at which energy can be harvested at the nanoscale may not be sufficient to simultaneously power all of the conventional device components, including the microprocessor, memory, sensor, and the communication radio. We propose a simplified architecture for energy-harvesting nanosensor networks that uses the energy signatures of monitored events for efficient event recognition at the sink. Using the instantaneous harvested energy to transmit a short pulse of proportional amplitude, the proposed architecture obviates the need for several conventional sensor node elements, including the microprocessor, memory, and sensor. In this article, we validate the feasibility of the architecture using a novel application of nanosensor networks for microscopic monitoring of chemical reactors, and we demonstrate that different types of reactions can be detected accurately at a remote sink.

Integration of Markov random field with Markov chain for efficient event detection using wireless sensor network

Event detection is an important task required in various applications of wireless sensor network (WSN). The existing approaches consider the spatial and temporal correlation of sensor data separately or not in a cohesive way. In this paper an event detection scheme with WSN is introduced, which adopts a hierarchical structure to efficiently integrate the spatial and temporal correlation of sensor data. Here a fusion algorithm considering both the weight of the sensors and spatial information is applied to Markov random field to properly fuse the decisions of the neighboring nodes. Markov chain is also adopted to effectively extract the temporal correlation after the spatial correlation is decided. The simulation results demonstrate that the proposed scheme can effectively increase the detection accuracy and reduce communication cost, in comparison with the existing schemes.

Lip event detection using oriented histograms of regional optical flow and low rank affinity pursuit

Lip event detection is of crucial importance to the better understanding of visual speech perceptually between humans and computers. In this paper, we address an efficient lip event detection approach using oriented histograms of regional optical flow (OH-ROF) and low rank affinity pursuit. First, we align the extracted lip region sequences to reduce the impact of irrelevant motion caused by the moving cameras. Then, an optical flow field is calculated from these sequentially stabilized images and an efficient descriptor, namely OH-ROF, is presented to discriminatively code the visual appearance of each motion frame, whereby each lip motion clip can be represented by a sequence of OH-ROF vectors as its signature. Subsequently, we detect the visual silence event based on the small flow magnitude, and further propose a low rank affinity pursuit method to determine the visual speech event that incorporates the lip-dynamic states of mouth opening and closing. As a result, various kind of lip motion events can be appropriately estimated. The proposed approach neither requires any training set on the labeled videos nor learns the lip motion priors of each visual event in an unconstrained video. Experiments show a promising result in comparison with the state-of-the-art counterparts.

Bi-Level Semantic Representation Analysis for Multimedia Event Detection.

Multimedia event detection has been one of the major endeavors in video event analysis. A variety of approaches have been proposed recently to tackle this problem. Among others, using semantic representation has been accredited for its promising performance and desirable ability for human-understandable reasoning. To generate semantic representation, we usually utilize several external image/video archives and apply the concept detectors trained on them to the event videos. Due to the intrinsic difference of these archives, the resulted representation is presumable to have different predicting capabilities for a certain event. Notwithstanding, not much work is available for assessing the efficacy of semantic representation from the source-level. On the other hand, it is plausible to perceive that some concepts are noisy for detecting a specific event. Motivated by these two shortcomings, we propose a bi-level semantic representation analyzing method. Regarding source-level, our method learns weights of semantic representation attained from different multimedia archives. Meanwhile, it restrains the negative influence of noisy or irrelevant concepts in the overall concept-level. In addition, we particularly focus on efficient multimedia event detection with few positive examples, which is highly appreciated in the real-world scenario. We perform extensive experiments on the challenging TRECVID MED 2013 and 2014 datasets with encouraging results that validate the efficacy of our proposed approach.

An Efficient Data Fusion Approach for Event Detection in Heterogeneous Wireless Sensor Networks

This paper concentrates an efficient event detection approa ch exploiting data fusion technology for the heterogeneous wireless sensor networks. In this type of wireless sensor networks, each sensor is equipped with multiple sensing units. Particularly, in this paper, we study on the data fusion approach based on the type of complementary heterogeneous wireless sensor networks, and the fire disaster detection is utilized as an example of event det ection. The proposed event detection model is constructed of data fusion level and information fusion level. In the data fusion level , resource data are collected from the sensors which are both in the sensing field out of the sensing field. In the information fusion level , the event can be detected by computing the data fusion probabilities. For the fire disaster detection process, data collected from temperature sensors and humidity sensors are combined, and then all the measurements are supposed independent on normal random variables. Afterwards, the data fusion process is implemented utilizing genetic algorithm, by which the population is evolved through a predetermined number of consults. Therefore, for each generation of answers, a new set of artificial creatures can be calculated. Furthermore, the answers can be solved by fragments of the most suitable individuals. Finally, experiments are conducted on a series of simulations using the OMNeT++ tool. Compared with other methods, the proposed data fusion based event detection algorithm can effectively find the event through detecting the notify sta te and alert state, and performs better than other two methods both in the fusion quality and fusion efficiency.

Efficient event detection using self-learning threshold for wireless sensor networks

Event detection is an essential issue for wireless sensor networks research. Energy saving and reliable detection are major challenges for the resource constraints on sensor nodes. To give attention to both of them, this paper presents a distributed event detection approach using self-learning threshold to fully exploit the energy-reliability tradeoff in wireless sensor networks. In the proposed approach, a stream of real-valued sensor readings is mapped into symbol sequences in order to reduce data dimensionality and simplify event description. A dynamic conversion granularity is adopted to improve the effectiveness of symbolic representation. Then the anomaly probabilities of symbol sequences are estimated through Markov model, and sensor nodes participating in the event detection make local decisions in a distributed manner based on the learned anomaly detection threshold. A timer-based node sleep scheduling is developed to prolong network lifetime during the detection process. Subsequently, the final detection decision is made by a bitwise voting based on the local decisions. A comprehensive set of simulations demonstrate that the proposed approach achieves considerable energy conservation while maintaining fast and accurate event detection.

Complex event processing with T-REX

Several application domains involve detecting complex situations and reacting to them. This asks for a Complex Event Processing (CEP) middleware specifically designed to timely process large amounts of event notifications as they flow from the peripheral to the center of the system, to identify the composite events relevant for the application. To answer this need we designed T-Rex, a new CEP middleware that combines expressiveness and efficiency. On the one hand, it adopts a language (TESLA) explicitly conceived to easily and naturally describe composite events. On the other hand, it provides an efficient event detection algorithm based on automata to interpret TESLA rules. Our evaluation shows that the T-Rex engine can process a large number of complex rules with a reduced overhead, even in the presence of challenging workloads.

Fast Subset Scan for Spatial Pattern Detection

SummaryWe propose a new ‘fast subset scan’ approach for accurate and computationally efficient event detection in massive data sets. We treat event detection as a search over subsets of data records, finding the subset which maximizes some score function. We prove that many commonly used functions (e.g. Kulldorff’s spatial scan statistic and extensions) satisfy the ‘linear time subset scanning’ property, enabling exact and efficient optimization over subsets. In the spatial setting, we demonstrate that proximity-constrained subset scans substantially improve the timeliness and accuracy of event detection, detecting emerging outbreaks of disease 2 days faster than existing methods.

SCAP: Energy Efficient Event Detection in Large-Scale Wireless Sensor Networks with Multiple Sinks

For large-scale sensor networks, multiple sinks are often deployed in order to reduce source-to-sink distance and thus cost of data delivery. However, having multiple sinks may work against cost reduction, because routes from sources can diverge towards different sinks which reduces the benefit of in-network data aggregation. In this letter we propose a self-clustering data aggregation protocol (SCAP) that can benefit from having multiple sinks as well as joint routes. In SCAP, nodes which detect the event communicate with each other to aggregate data between themselves, before sending the data to the sinks. The self-clustering extends network lifetime by reducing energy consumption of nodes near the sinks, because the number of paths in which the packets are delivered is reduced. A performance comparison with existing protocols L-PEDAP and LEO shows that SCAP can conserve energy and extend network lifetime significantly, in a multi-sink environment.

감시정찰 센서네트워크에서 시공간 연관성를 이용한 효율적인 이벤트 탐지 기법

In this paper, we present a new efficient event detection algorithm for sensor networks with faults. We focus on multi-attributed events, which are sets of data points that correspond to interesting or unusual patterns in the underlying phenomenon that the network monitors. Conventional algorithms cannot detect some events because they treat only their own sensor readings which can be affected easily by environmental or physical problem. Our approach exploits spatio-temporal correlation of sensor readings. Sensor nodes exchange a fault-tolerant code encoded their own readings with neighbors, organize virtual sensor readings which have spatio-temporal correlation, and determine a result for multi-attributed events from them. In the result, our proposed algorithm provides improvement of detecting multi-attributed events and reduces the number of false-negatives due to negative environmental effects.

Robust event detection scheme for complex scenes in video surveillance

Event detection for video surveillance is a difficult task due to many challenges: cluttered background, illumination variations, scale variations, occlusions among people, etc. We propose an effective and efficient event detection scheme in such complex situations. Moving shadows due to illumination are tackled with a segmentation method with shadow detection, and scale variations are taken care of using the CamShift guided particle filter tracking algorithm. For event modeling, hidden Markov models are employed. The proposed scheme also reduces the overall computational cost by combing two human detection algorithms and using tracking information to aid human detection. Experimental results on TRECVid event detection evaluation demonstrate the efficacy of the proposed scheme. It is robust, especially to moving shadows and scale variations. Employing the scheme, we achieved the best run results for two events in the TRECVid benchmarking evaluation.

무선 센서 네트워크 환경에서 게이트웨이 어플리케이션의 개발 환경을 위한 패킷 처리 관점의 네트워크 프레임워크 설계 및 구현

IEEE 802.15.4 기반의 WSN(Wireless Sensor Network)과 TCP/IP기반의 PN(Public Network)의 중계를 담당하는 WSN Gateway는 이 두 개의 네트워크를 기반으로 하는 핵심 기술에 속한다. WSN Gateway는 하드웨어에 대한 성능을 충분히 활용하면서, 다수의 센서 노드로부터 다양한 종류의 패킷을 불특정 시간에 수신해야 하기 때문에 소프트웨어에 대한 복잡성이 높고 구현이 난해하다. 이러한 문제들을 해결하기 위해, 이 논문에서는 패킷을 식별하는 효율적인 이벤트 검출 구조와, '트랜잭션'이라는 프로토콜의 구현 단위를 제안한다. 제안한 네트워크 프레임워크를 적용한 결과, 소프트웨어의 복잡성을 낮출 수 있었다. 또한, 다양한 성능 요구 사항을 반영할 수 있는 유연한 소프트웨어 구현 환경을 제공하였다. WSN gateway which runs transmission between WSN (Wireless Sensor Network) based on IEEE 802.15.4 and PN (Public Network) based on TCP/IP belongs to core technology of application based on two network. Because WSN Gateway receives various kinds of packet from many sensor nodes in an uncertain time as well as uses hardware's performance enough, it has high level of complexity about software and it is hard to be implemented. To solve these problems, this paper suggests both efficient event detection scheme for identify packet and implementation unit of protocol called 'Transaction'. The results of applying the proposed network framework, complexity of software reduces. And we provide software development environments of reflect various performance requirements.

Self-Adapting Event Configuration in Ubiquitous Wireless Sensor Networks

Wireless Sensor Networks are the key-enabler for low cost ubiquitous applications in the area of homeland security, health-care, and environmental monitoring. A necessary prerequisite is reliable and efficient event detection in spite of sudden failures and environmental changes. Due to the fact that the sensors need to be low cost, they have only scarce resources leading to a certain level of failures of sensor nodes or sensing devices attached to the nodes. Available fault tolerant solutions are mainly customized approaches that revealed several shortcomings, particularly in adaptability and energy efficiency. The authors present a complete event detection concept including all necessary steps from formal event definition to autonomous device configuration. It features an event definition language that allows defining complex events as well as enhance the reliability by tailor-made voting schemes and application constraints. Based on that, this paper introduces a novel approach for self-adapting on-node and in-network processing, called Event Decision Tree (EDT). EDT autonomously adapts to available resources and environmental conditions, even though it requires to (re-)organize collaboration between neighboring nodes for evaluation. The authors’ approach achieves fine-grained event-related fault tolerance with configurable adaptation rate while enhancing maintainability and energy efficiency.

Event Detection of Broadcast Baseball Videos

This paper presents an effective and efficient event detection system for broadcast baseball videos. It integrates midlevel cues including scoreboard information and shot transition patterns into event classification rules. First, a simple scoreboard detection and recognition scheme is developed to extract the game status from videos. Then, a shot transition classifier is designed to obtain the shot transition patterns, which contains several novel schemes including adaptive playfield segmentation, pitch shot detection, field shot detection, as well as infield/outfield classification. The extracted midlevel cues are used to develop an event classifier based on a Bayesian Belief Network. The network is with low complexity because the number of these cues used is small, which not only improves the generalization performance of the event classifier but also reduces system complexity as well as training efforts. Using the inference results of the network, we further derive a set of classification rules to identify baseball events. The set of rules is stored in a look-up table such that the classification is only a simple table look-up operation. The proposed approach is very simple and computational efficient. More importantly, the simulation results indicate that it identifies ten significant baseball events with 95% of precision rate and 92% of recall rate, which is very promising.

Access Control in Decentralised Publish/Subscribe Systems

Publish/subscribe has emerged as an attractive communication paradigm for building Internet-wide distributed systems by decoupling message senders from receivers. Large scale publish/subscribe systems are likely to employ components of the event transport network owned by cooperating, but independent organisations. As the number of participants in the network increases, security becomes an increasing concern. So far most of the research on publish/subscribe has focused on efficient event routing, event filtering, and composite event detection. Very little research has been published regarding securing publish/subscribe systems. This paper extends our previous work to present and evaluate a secure multi-domain publish/subscribe infrastructure that supports and enforces fine-grained access control over the individual attributes of event types.

Event detection in real time on a personal camera phone image collection

Grouping photos of the same event together is extremely useful for the management of personal photo collections. However, most methods cannot be applied to the problem of online event detection in embedded devices because they do not consider hardware constraints or a user’s photo-taking behavior. In this paper, we propose an efficient and effective event detection algorithm for managing personal photo collections in camera phones or digital cameras. The proposed algorithm fuses time and location information, which is deemed the most important information for personal photo management, and works in real time in embedded devices. We model event occurrences in a user’s photo-taking behavior as a Poisson process by imposing certain constraints on calculating the elapsed time. Location information is incorporated into event detection when confidence in a decision based on the Poisson process is not high enough. The algorithm is user-centric because it provides the unique capabilities of accepting and adjusting to user feedback. Our experiment results show that the proposed event detection method has the potential to support emerging multimedia applications in embedded devices.