Fault tolerant multiple event detection in a wireless sensor network

With an increasing acceptance of Wireless Sensor Networks (WSNs), the health of individual sensor is becoming critical in identifying important events in the region of interest. One of the key challenges in detecting event in a WSN is how to detect it accurately transmitting minimum information providing sufficient details about the event. At the same time, it is also important to devise a strategy to handle multiple events occurring simultaneously. In this paper, we propose a Polynomial-based scheme that addresses these problems of Event Region Detection (PERD) by having a aggregation tree of sensor nodes. We employ a data aggregation scheme, TREG (proposed in our earlier work) to perform function approximation of the event using a multivariate polynomial regression. Only coefficients of the polynomial (P$P$) are passed instead of aggregated data. PERD includes two components: event recognition and event report with boundary detection. This can be performed for multiple simultaneously occurring events. We also identify faulty sensor(s) using the aggregation tree. Performing further mathematical operations on the calculated P$P$ can identify the maximum (max) and minimum (min) values of the sensed attribute and their locations. Therefore, if any sensor reports a data value outside the [min, max] range, it can be identified as a faulty sensor. Since PERD is implemented over a polynomial tree on a WSN in a distributed manner, it is easily scalable and computation overhead is marginal. Results reveal that event(s) can be detected by PERD with error in detection remaining almost constant achieving a percentage error within a threshold of 10%$10\%$ with increase in communication range. Results also show that a faulty sensor can be detected with an average accuracy of 94%$94\%$ and it increases with increase in node density.     

A framework for a video analysis tool for suspicious event detection

This paper proposes a framework to aid video analysts in detecting suspicious activity within the tremendous amounts of video data that exists in today’s world of omnipresent surveillance video. Ideas and techniques for closing the semantic gap between low-level machine readable features of video data and high-level events seen by a human observer are discussed. An evaluation of the event classification and detection technique is presented and a future experiment to refine this technique is proposed. These experiments are used as a lead to a discussion on the most optimal machine learning algorithm to learn the event representation scheme proposed in this paper.

A resource-efficient event algebra

Events play many roles in computer systems, ranging from hardware interrupts, over event-based software architecture, to monitoring and managing of complex systems. In many applications, however, individual event occurrences are not the main point of concern, but rather the occurrences of certain event patterns. Such event patterns can be defined by means of an event algebra, i.e., expressions representing the patterns of interest are built from simple events and operators such as disjunction, sequence, etc.We propose a novel event algebra with intuitive operators (a claim which is supported by a number of algebraic properties). We also present an efficient detection algorithm that correctly detects any expression with bounded memory, which makes this algebra particularly suitable for resource-constrained applications such as embedded systems.     

Measurement and simultaneous compression of multidimensional -ray histograms using address randomizing transforms

In the paper we present efficient algorithm for measurement and simultaneous compression of multidimensional symmetrical γ-ray histograms from event data streams. The compression of data volume is achieved due to both the symmetry of the γ-ray spectra and compression capabilities of the employed randomizing transform. The algorithm of compression is very fast. Acquired compressed data can be later processed in an interactive way.     

机场供油系统单顾客流仿真模型

机场供油系统是一种典型的排队服务系统。本文通过对单顾客流输入下五种情况的研究,分别建立了系统的仿真模型,提出了适合解决该问题的事件调度法与活动扫描法等两种仿真策略并给出了伪码算法。仿真实践表明,准确而全面的模型分析,对于后期开发仿真程序,优化系统性能是非常重要的。     

Smith预估控制算法在事件通道中的应用

采用Smith预估反馈控制方法，通过离散事件仿真，对实时CORBA事件通道中任务的QoS级别进行控制，从而控制事件通道中的任务流量。试验结果证明，用该控制方法对事件通道进行性能控制，能够获得低延迟、高可靠的实时通信。     

基于事件的发布-订阅系统模型

Internet的广泛应用已经改变了分布式系统的规模，使得传统的基于请求/应答的点对．点的同步通信已不能很好地满足大规模的动态分布式应用环境。为了加强大规模的分布式环境中实体之间的通信协作，系统要求更加灵活的通信模型，以反映应用的动态和非耦合特性。基于事件通信的中间件是建立大规模分布式系统的有效方式，发布者/订阅者（Publisher/subscriber）是目前广泛使用的基于事件的通信模型，支持发布者和订阅者之间在时间、空间和同步方面的非耦合以及多对多的通信模式，提供大规模系统所要求的交互间的松散耦合。本文详细阐述了事件系统的组成以及基于事件系统的PUB／SUB模型，提出了基于PUB／SUB的事件系统中间件的核心及实现的关键，这些中间件为大规模的分布式应用提供更多和更有力的保障。     

CORBA/SNMP网关中事件管理的实现

提出了CORBA/SNMP网关事件管理部分的实现框架，针对CORBA公共事件服务的局限性，给出了相应的解决方案，包括分组、过滤、插入序列和再同步等，建立了数学模型进行分析，同时通过试验验证了算法的可行性。