物联网中具有时间持续性特征的乱序事件查询处理技术研究

现实世界中的很多事件都是基于时间段的,具有明显的时间持续性特征。具有这种特征的事件之间的时态关系复杂多变,在发生乱序事件时,查询处理极具挑战性。物联网环境中对于时序事件的有序到达具有非常严苛的要求,但网络延迟和机器故障却导致事件乱序问题频发。基于建立的时态事件语义表示模型,提出了一种用于处理具有时间持续性特征的乱序事件的查询处理模式,并构建了一种混合解决方案,使物联网环境中乱序事件在到达后的一定时间阈值内达到正确有序。最后,通过实验验证了所提方法的有效性。     

The time model for event processing in internet of things

The time management model for event processing in internet of things has a special and important requirement. Many events in real world applications are long-lasting events which have different time granularity with order or out-of-order. The temporal relationships among those events are often complex. An important issue of complex event processing is to extract patterns from event streams to support decision making in real-time. However, current time management model does not consider the unified solution about time granularity, time interval, time disorder, and the difference between workday calendar systems in different organizations. In this work, we analyze the preliminaries of temporal semantics of events. A tree-plan model of out-of-order durable events is proposed. A hybrid solution is correspondingly introduced. A case study is illustrated to explain the time constraints and the time optimization. Extensive experimental studies demonstrate the efficiency of our approach.     

基于事件逻辑的无线Mesh网络认证协议安全性证明

无线Mesh网络是一种结合无线局域网和移动自组织网络的新型多跳网络,无线网络的开放性和资源受限性使得无线网络容易遭受重放、伪装等攻击。事件逻辑是一种描述并发与分布式系统中状态迁移和算法的形式化方法,可用于证明网络协议的安全性。以事件逻辑为基础提出一系列性质,其中包含多组合信息交互、不叠加、事件匹配、去重复、去未来,以降低协议分析过程中的冗余度以及复杂度,提高协议分析效率。对无线Mesh网络客户端双向认证协议进行分析,证明该协议能够抵抗中间人发起的重放攻击,无线Mesh客户端双向认证协议是安全的。此理论适用于类似复杂无线网络协议形式化分析。     

Pattern-based event detection in sensor networks

Many applications of wireless sensor networks monitor the physical world and report events of interest. To facilitate event detection in these applications, in this paper we propose a pattern-based event detection approach and integrate the approach into an in-network sensor query processing framework. Different from existing threshold-based event detection, we abstract events into patterns in sensory data and convert the problem of event detection into a pattern matching problem. We focus on applying single-node temporal patterns, and define the general patterns as well as five types of basic patterns for event specification. Considering the limited storage on sensor nodes, we design an on-node cache manager to maintain the historical data required for pattern matching and develop event-driven processing techniques for queries in our framework. We have conducted experiments using patterns for events that are extracted from real-world datasets. The results demonstrate the effectiveness and efficiency of our approach.     

Aggressive Complex Event Processing with Confidence over Out-of-Order Streams

In recent years,there has been a growing need for complex event processing (CEP),ranging from supply chain management to security monitoring.In many scenarios events are generated in different sources but arrive at the central server out of order,due to the differences of network latencies.Most state-of-the-art techniques process out-of-order events by buffering the events until the total event order within a specified range can be guaranteed.Their main problems are leading to increasing response time and reducing system throughput.This paper aims to build a high performance out-oforder event processing mechanism,which can match events as soon as they arrive instead of buffering them till all arrive.A suffix-automaton-based event matching algorithm is proposed to speed up query processing,and a confidence-based accuracy evaluation is proposed to control the query result quality.The performance of our approach is evaluated through detailed accuracy and response time analysis.As experimental results show,our approach can obviously speed up the query matching time and produce reasonable query results.     

CrimeSPOT: A language and runtime for developing active wireless sensor network applications

Advances in wireless sensing and actuation technology allow embedding significant amounts of application logic inside wireless sensor networks. Such active WSN applications are more autonomous, but are significantly more complex to implement. Event-based middleware lends itself to implementing these applications. It offers developers fine-grained control over how an individual node interacts with the other nodes of the network. However, this control comes at the cost of event handlers which lack composability and violate software engineering principles such as separation of concerns. In this paper, we present CrimeSPOT as a domain-specific language for programming WSN applications on top of event-driven middleware. Its node-centric features enable programming a node’s interactions through declarative rules rather than event handlers. Its network-centric features support reusing code within and among WSN applications. Unique to CrimeSPOT is its support for associating application-specific semantics with events that carry sensor readings. These preclude transposing existing approaches that address the shortcomings of event-based middleware to the domain of wireless sensor networks. We provide a comprehensive overview of the language and the implementation of its accompanying runtime. The latter comprises several extensions to the Rete forward chaining algorithm. We evaluate the expressiveness of the language and the overhead of its runtime using small, but representative active WSN applications.     

Rule-based inference and decomposition for distributed in-network processing in wireless sensor networks

Wireless sensor networks are application specific and necessitate the development of specific network and information processing architectures that can meet the requirements of the applications involved. A common type of application for wireless sensor networks is the event-driven reactive application, which requires reactive actions to be taken in response to events. In such applications, the interest is in the higher-level information described by complex event patterns, not in the raw sensory data of individual nodes. Although the central processing of information produces the most accurate results, it is not an energy-efficient method because it requires a continuous flow of raw sensor readings over the network. As communication operations are the most expensive in terms of energy usage, the distributed processing of information is indispensable for viable deployments of applications in wireless sensor networks. This method not only helps in reducing the total amount of packets transmitted in the network and the total energy consumed by the sensor nodes, but also produces scalable and fault-tolerant networks. For this purpose, we present two schemes that distribute information processing to appropriate nodes in the network. These schemes use reactive rules, which express relations between event patterns and actions, in order to capture reactive behavior. We also share the results of the performance of our algorithms and the simulations based on our approach that show the success of our methods in decreasing network traffic while still realizing the desired functionality.     

面向CPS复杂事件流的不确定性研究

信息物理融合系统CPS获得广泛应用需要解决的一个关键问题是软件中的信息处理部分,而复杂事件处理是CPS中信息处理的核心任务之一。CPS环境下的事件具有异构、分散、海量和不确定性等特征。在CPS实际应用中,因噪声、传感器误差、通讯技术等原因而造成的事件不确定性急需解决。为了解决CPS系统中存在的海量不确定事件流问题,提出一种处理不确定事件流的复杂事件处理方法USCEP,该方法不仅可以实时有效地处理海量不确定事件流,还可以有效计算复杂事件的概率。USCEP对现有RFID复杂事件监测方法 RCEDA进行了改进,提供了历史概率事件查询处理的支持,提出一种事件概率模型进行概率计算,并通过关联查询表来提高效率。实验表明,在处理不确定事件流时,该方法比传统方法具有更好的性能。     

Temporal Reasoning and Bayesian Networks

This work examines important issues in probabilistic temporal representation and reasoning using Bayesian networks (also known as belief networks). The representation proposed here utilizes temporal (or dynamic) probabilities to represent facts, events, and the effects of events. The architecture of a belief network may change with time to indicate a different causal context. Probability variations with time capture temporal properties such as persistence and causation. They also capture event interaction, and when the interaction between events follows known models such as the competing risks model, the additive model, or the dominating event model, the net effect of many interacting events on the temporal probabilities can be calculated efficiently. This representation of reasoning also exploits the notion of temporal degeneration of relevance due to information obsolescence to improve the efficiency.     

Proximity measures for link prediction based on temporal events

Link prediction is a well-known task from the Social Network Analysis field that deals with the occurrence of connections in a network. It consists of using the network structure up to a given time in order to predict the appearance of links in a close future. The majority of previous work in link prediction is focused on the application of proximity measures (e.g., path distance, common neighbors) to non-connected pairs of nodes at present time in order to predict new connections in the future. New links can be predicted for instance by ordering the pairs of nodes according to their proximity scores. A limitation usually observed in previous work is that only the current state of the network is used to compute the proximity scores, without taking any temporal information into account (i.e., a static graph representation is adopted). In this work, we propose a new proximity measure for link prediction based on the concept of temporal events. In our work, we defined a temporal event related to a pair of nodes according to the creation, maintenance or interruption of the relationship between the nodes in consecutive periods of time. We proposed an event-based score which is updated along time by rewarding the temporal events observed between the pair of nodes under analysis and their neighborhood. The assigned rewards depend on the type of temporal event observed (e.g., if a link is conserved along time, a positive reward is assigned). Hence, the dynamics of links as the network evolves is used to update representative scores to pairs of nodes, rewarding pairs which formed or preserved a link and penalizing the ones that are no longer connected. In the performed experiments, we evaluated the proposed event-based measure in different scenarios for link prediction using co-authorship networks. Promising results were observed when the proposed measure was compared to both static proximity measures and a time series approach (a more competitive method) that also deploys temporal information for link prediction.     

Crisis management using MAS-based wireless sensor networks

Wireless communication is increasingly used to manage large-scale crises (e.g., natural disasters or a large-scale city fire). Communication has traditionally been based on cellular networks. However, real-life experience has proven that the base stations of these networks may collapse or become unreachable during a crisis. An incident commander must also know as much information as possible about the occurring events to control them quickly and efficiently. This paper thus proposes a crisis management approach that overcomes the problems encountered by the base stations and insures relevant, rich and real-time information about events. This approach is based on wireless sensor networks, which are distributed in nature with no need for infrastructure and could be deployed in dangerous and inaccessible zones to gather information. Our proposal uses a multi-agent system as a software layer. The multi-agent system aims to improve the wireless sensor network performance by allowing cooperation between sensor nodes, offering better lifetime management and virtualizing the application layer. This virtualization supports several required applications simultaneously, including event monitoring and object tracking. Through successive simulations, we prove the importance of our approach in crisis management using several criteria to estimate the position’s error in object tracking, end-to-end delay and wireless sensor network lifetime management.     

无线传感网络监测质量的统计分析

无线传感网络作为一种新型的网络技术是当今国内外深受关注的热点研究领域。它是一种新型的网络技术,能够实时地采集分布在网络中的数据信息,并将这些信息传输到网关节点,最终完成复杂的网络监测和跟踪目标的工作。为了解决无线传感网络所面临的挑战,对无线传感网络目标覆盖问题,考虑到随机事件参数未知的指数分布,对随机事件的监测质量进行统计分析,在无线传感网络的背景下对其覆盖问题进行优化。首先,对无线传感网络的背景及现状进行介绍,引出本文的研究目的是对无线传感网络的监测质量进行分析。其次,对无线传感网络的覆盖进行优化调度建立相应网络的模型,设计优化的模拟退火算法,结合统计知识分析,利用参数估计研究其监测质量。最后,通过仿真实验验证本文方法的合理性以及有效性,最终达到延长网络寿命的目的。     

Specifying timing constraints and composite events: an application in the design of electronic brokerages

Increasingly, business applications need to capture consumers' complex preferences interactively and monitor those preferences by translating them into event-condition-action (ECA) rules and syntactically correct processing specification. An expressive event model to specify primitive and composite events that may involve timing constraints among events is critical to such applications. Relying on the work done in active databases and real-time systems, this research proposes a new composite event model based on real-time logic (RTL). The proposed event model does not require fixed event consumption policies and allows the users to represent the exact correlation of event instances in defining composite events. It also supports a wide-range of domain-specific temporal events and constraints, such as future events, time-constrained events, and relative events. This event model is validated within an electronic brokerage architecture that unbundles the required functionalities into three separable components - business rule manager, ECA rule manager, and event monitor - with well-defined interfaces. A proof-of-concept prototype was implemented in the Java programming language to demonstrate the expressiveness of the event model and the feasibility of the architecture. The performance of the composite event monitor was evaluated by varying the number of rules, event arrival rates, and type of composite events.     

Knowledge discovery in corporate events by neural network rule extraction

Over the last two decades, artificial neural networks (ANN) have been applied to solve a variety of problems such as pattern classification and function approximation. In many applications, it is desirable to extract knowledge from trained neural networks for the users to gain a better understanding of the network’s solution. In this paper, we use a neural network rule extraction method to extract knowledge from 2222 dividend initiation and resumption events. We find that the positive relation between the short-term price reaction and the ratio of annualized dividend amount to stock price is primarily limited to 96 small firms with high dividend ratios. The results suggest that the degree of short-term stock price underreaction to dividend events may not be as dramatic as previously believed. The results also show that the relations between the stock price response and firm size is different across different types of firms. Thus, drawing the conclusions from the whole dividend event data may leave some important information unexamined. This study shows that neural network rule extraction method can reveal more knowledge from the data.     

Discovering multi-label temporal patterns in sequence databases

Sequential pattern mining is one of the most important data mining techniques. Previous research on mining sequential patterns discovered patterns from point-based event data, interval-based event data, and hybrid event data. In many real life applications, however, an event may involve many statuses; it might not occur only at one certain point in time or over a period of time. In this work, we propose a generalized representation of temporal events. We treat events as multi-label events with many statuses, and introduce an algorithm called MLTPM to discover multi-label temporal patterns from temporal databases. The experimental results show that the efficiency and scalability of the MLTPM algorithm are satisfactory. We also discuss interesting multi-label temporal patterns discovered when MLTPM was applied to historical Nasdaq data.     

Bandwidth Aggregation-Aware Dynamic QoS Negotiation for Real-Time Video Streaming in Next-Generation Wireless Networks

In next generation wireless networks, Internet service providers (ISPs) are expected to offer services through several wireless technologies (e.g., WLAN, 3G, WiFi, and WiMAX). Thus, mobile computers equipped with multiple interfaces will be able to maintain simultaneous connections with different networks and increase their data communication rates by aggregating the bandwidth available at these networks. To guarantee quality-of-service (QoS) for these applications, this paper proposes a dynamic QoS negotiation scheme that allows users to dynamically negotiate the service levels required for their traffic and to reach them through one or more wireless interfaces. Such bandwidth aggregation (BAG) scheme implies transmission of data belonging to a single application via multiple paths with different characteristics, which may result in an out-of-order delivery of data packets to the receiver and introduce additional delays for packets reordering. The proposed QoS negotiation system aims to ensure the continuity of QoS perceived by mobile users while they are on the move between different access points, and also, a fair use of the network resources. The performance of the proposed dynamic QoS negotiation system is investigated and compared against other schemes. The obtained results demonstrate the outstanding performance of the proposed scheme as it enhances the scalability of the system and minimizes the reordering delay and the associated packet loss rate.     

An Efficient Method for Cleaning Dirty-Events over Uncertain Data in WSNs

Event detection in wireless sensor networks (WSNs) has attracted much attention due to its importance in many applications. The erroneous abnormal data generated during event detection are prone to lead to false detection results. Therefore, in order to improve the reliability of event detection, we propose a dirty-event cleaning method based on spatio-temporal correlations among sensor data. Unlike traditional fault-tolerant approaches, our method takes into account the inherent uncertainty of sensor measurements and focuses on the type of directional events. A probabilitybased mapping scheme is introduced, which maps uncertain sensor data into binary data. Moreover, we give formulated definitions of transient dirty-event (TDE) and permanent dirty-event (PDE), which are cleaned by a novel fuzzy method and a collaborative cleaning scheme, respectively. Extensive experimental results show the effectiveness of our dirty-event cleaning method.     

时空数据库索引方法研究

时空数据库作为数据库研究领域中的一个重要分支,经过近十年的发展,在时空数据模型、时空查询优化与索引和时空本体论等方面取得了许多成果。现实世界中的许多实体都具有空间特性和时态特性,需要数据库管理系统提供有效的时空数据管理能力,如地籍管理系统中的地块、交通管理系统中的车辆等。时空数据库用于管理形状和位置随时问变化的对象。为了快速访问其庞大的数据量,必须建立有效的时空索引以提高各类时空查询的效率。提出了一种新的时空索引方法（瓣索引）,它综合了快照和事件这两种时空信息建模方法。不仅能够处理时间片查询和时间段查询,而且能够进行事件查询。SEST索引使用R-tree结构来存储快照,用一种日志数据结构来存储发生在两次相邻快照之间的事件。通过实验对比SEST索引和HR—tree,结果表明：当变化频率在1％到13％之间时,SEST索引比HR—tree需要的存储空间少;当变化频率在1％到7％之间时,在时间段查询方面,SEST索引比HR—tree要好。因为SEST索引是一种面向事件的结构,所以事件查询时效率很高。     

WSN源位置隐私技术研究

随着无线传感器网络的应用深入到日常生活领域,隐私已成为无线传感器网络成功应用的一大障碍。当无线传感器网络用于监控敏感对象,被监控对象的位置隐私成为一个重要问题。首先分析无线传感器网络的安全特点、信源位置隐私性能评价标准、面临的隐私威胁,最后,基于对无线传感器网络信源位置隐私问题的分析和评述,指出了今后该领域的研究方向。     

Enhancing the performance of futurewireless networks with software-defined networking

To provide ubiquitous Internet access under the explosive increase of applications and data traffic, the current network architecture has become highly heterogeneous and complex, making network management a challenging task. To this end, software-defined networking (SDN) has been proposed as a promising solution. In the SDN architecture, the control plane and the data plane are decoupled, and the network infrastructures are abstracted and managed by a centralized controller. With SDN, efficient and flexible network control can be achieved, which potentially enhances network performance. To harvest the benefits of SDN in wireless networks, the software-defined wireless network (SDWN) architecture has been recently considered. In this paper, we first analyze the applications of SDN to different types of wireless networks. We then discuss several important technical aspects of performance enhancement in SDN-based wireless networks. Finally, we present possible future research directions of SDWN.