Complex event processing model for distributed RFID network

The objectives are to explore the advanced complex data analytics of autonomous transportation in smart cities, improve the industrial production efficiency and product quality, and optimize the industrial production processes. Aiming at the deployment problem of software-defined network (SDN) in distributed cloud data center, the study proposes a spectrum-based SDN deployment algorithm. An industrial complex data model is built. In addition, a complex event processing model is implemented based on the emerging stream data flow execution engine and the emerging Flink stream processing framework. The Flink framework not only performs real-time analysis of fault tolerance but also greatly simplifies the data processing process. Tests of multiple sets of industrial data from iron mine, chemical, and metallurgical plants have proved the operational advantages of the complex event model for the analysis of industrial complex data. Combining the advantages of online filtering, this study proposes a Generalized Robust Least Square (GRLS) data coordination algorithm. The results confirm that the GRLS has better robustness in correcting significant errors. The spectrum-based SDN deployment algorithm solves the problem of SDN domain partition and controller deployment. This proves that complex event processing technology can extract more valuable information from massive and complex data, thereby ensuring the stability of autonomous transportation.KeywordsSmart cityIndustrial environmentAdvanced complex dataData analysisLeast squares algorithmSoftware-defined networkIndustry 4.0Urban planningInternet of ThingsBig data

Data processing is a key issue of the design of RFID middleware, and the general processing mode is based on Event. The data processed and output by this solution canpsilat really meet the needs of various application systems; nevertheless, the complex event processing technology can overcome the disadvantage effectively. According to the principles that can make it effective to manage RFID data, this paper proposes a RFID data processing model based on complex event processing (CEP) technique, provides the related definitions, elaborates the function modules involved (the event monitoring module, the buffer module, the event processing module and the event sending/subscribe module) and their solutions.

In recent years, RFID technology is gradually applied to the field of real-time monitoring, positioning, tracking, etc. As the unreliability of RFID data, the development and the application of RFID technology are limited. In certain practical applications, false negative reading can be solved by multi-reading periodically. However, false positive reading (ie, noise) provided the wrong information to high-level RFID applications. For example, readers are installed on the shopping shelf, they send false anti-theft alarms due to false positive reading. False positive reading has seriously affected the promotion and application of RFID technology. Therefore, it is necessary to design an efficient and feasible method to eliminate false positive reading. The paper proposes an efficient cleaning mechanism to solve the false positive problem in RFID data streams. The result of experiment shows that the method is effective and efficient.

To compensate for the inherent unreliability of RFID data streams, most RFID middleware systems employ a filter, a sliding-window aggregate that interpolates for lost readings. In this paper, we propose SMURF, the first declarative, adaptive smoothing filter for RFID data cleaning. SMURF models the unreliability of RFID readings by viewing RFID streams as a statistical sample of tags in the physical world, and exploits techniques grounded in sampling theory to drive its cleaning processes. Through the use of tools such as binomial sampling and π-estimators, SMURF continuously adapts the smoothing window size in a principled manner to provide accurate RFID data to applications.

Radio Frequency Identication (RFID) has been widely used in most applications. The RFID data generated are usually meaningless, real-time, and large-scale. Complex Event Processing (CEP) technology is used to extract meaningful information from the raw RFID data. This paper proposes a CEP model based on Open Service Gateway Initiative (OSGi) platform with the features of recongurable, customized and so on. Moreover, the open-source Esper technique for processing complex event and event stream is also introduced.

To interpolate the missing data existing in RFID data streams, a space-time with weight algorithm is presented in this paper. Combining the ideology of sample interpolating method with mean value interpolating method, the algorithm employs the smoothing window technology and endows sample value and mean value with different weights, respectively. The sum of the two values with weight is adopted to interpolate the missing data. Experiments are carried out. Compared to sample interpolating method and mean value interpolating method, the presented algorithm performs well on data processing rate and interpolating accuracy rate.

Due to the high sensitivity of RFID tag-reader performance to the operating environment, RFID data streams generated are unreliable and contain a significant amount of missed readings. RFID data cleaning is therefore an essential task for successful deployment of RFID systems. One of the common techniques used by RFID middleware systems to compensate for the missed readings is the use of sliding-window filters. However, setting an optimum window size is non-trivial task especially in mobile tag environments. In this paper we present a new adaptive data cleaning scheme called WSTD based on some of the concepts proposed in SMURF but with an improved transition detection mechanism. WSTD uses the comparison of the two window subrange observations or estimated tag counts to detect when transitions occur within a window. In the mobile environment, our experimental results show that the WSTD scheme performs better than SMURF producing an improvement of about 30% less overall errors than that produced by SMURF.

The great advance and affordability of technologies, communications and sensor technology has led to the generation of large amounts of data in the field of the Internet of Things and smart environments, as well as a great demand for smart applications and services adapted to the specific needs of each individual. This has entailed the need for systems capable of receiving, routing and processing large amounts of data to detect situations of interest with low latency, but despite the many existing works in recent years, studying highly scalable and low latency data processing systems is still necessary. In this area, the efficiency of complex event processing (CEP) technology is of particular significance and has been used in a variety of application scenarios. However, in most of these scenarios there is no performance evaluation to show how the system performs under various loads and therefore the developer is challenged to develop such CEP-based systems in new scenarios without knowing how the system will be able to handle different input data rates and address scalability and fault tolerance. This article aims to fill this gap by providing an evaluation of the various versions of one of the most reputable CEP engines—Esper CEP, as well as its integration with two renowned messaging brokers for data ingestion—RabbitMQ and Apache Kafka. For this purpose, we defined a benchmark with a series of event patterns with some of the most representative operators of the Esper CEP engine and we performed a series of tests with an increasing rate of input data to the system. We did this for three alternative software architectures: integrating open-source Esper and RabbitMQ, integrating one instance of Esper enterprise edition with Apache Kafka, and integrating two distributed instances of Esper enterprise edition with Apache Kafka. We measured the usage of CPU, RAM memory, latency and throughput time, looking for the data input rate with which the system overloads for each event pattern and we compared the results of the three proposed architectures. The results have shown a very low CPU consumption for all implementation options and input data rates; a balanced memory usage, quite similar among the three architectures, up to an input rate of 10,000 or 15,000 events per second, depending on the architecture and event pattern, and a quite efficient response time up to 10,000 or 15,000 events per second, depending on the architecture and event pattern. Based on a more exhaustive analysis of results, we have concluded that the different options offered by Esper for CEP provide very efficient solutions for real-time data processing, although each with its limitations in terms of brokers to be used for data integration, scalability, and fault tolerance; a number of suggestions have been drawn out for the developer to take as a basis for choosing which CEP engine and which messaging broker to use for the implementation depending on the of the system in question.

Cyber-Physical System (CPS) is an emerging intelligent systems, which is considered as a combination of computers and physical world. Events are usually as a natural way for interactions and communications, which occur in the physical world and can be responded in the cyber world. Additionally, using complex event processing (CEP) technology, some complex events can be processed that are better suited for complicated and intelligent CPS. At first, a component-model framework is proposed for modeling CPS architecture based on event-driven pattern operational semantics, which provides a tool to understand the transition based on event-driven pattern in CPS. In order to improve the immense workload on centralized CEP, a mixed master-slaves CEP is proposed. Meanwhile, an architecture of CPS with master-slaves CEP is constructed and its execution mechanism is introduced in detail.

The ultimate goal of froth flotation is to increase the concentration of concentrate. The traditional flotation process basically relies on manual experience to adjust the flotation variables. The control of key factors is neither accurate nor timely, and it is difficult to guarantee the production efficiency. Most of the current researches on froth flotation are limited to image processing, and it is difficult to achieve the ideal index of production by relying solely on image detection technology. Faced with this situation, this paper proposes a research method of foam flotation modeling based on complex events for the one-sidedness of image processing. By constructing a complex event processing model of froth flotation, the traditional discrete froth flotation process could be effectively systematic by the event-driven approach, and the separated sensor data and image acquisition data are aggregated into the same dimension. The complex event processing technology is used to process the data associated with the two in parallel, which could optimize the operation of the froth flotation process more comprehensively.

Signal detection and material identification method for loose particles inside aerospace relays based on overlapping signals

With the advancement in intelligent devices, social media, and the Internet of Things, staggering amounts of new data are being generated and the pace is continuously accelerating. Real-time analytics (RTA) has emerged as a distinct branch of big data analytics focusing on the velocity aspect of big data, in which data is prepared, processed and analyzed as it arrives, with the aim of generating insights and creating business value in near real-time. The objective of this paper is to provide an overview of key concepts and architectural approaches for designing RTA solutions, including the relevant infrastructure, processing and analytics platforms, as well as analytics techniques and tools with the most up-to-date machine learning and artificial intelligence considerations, and position these in the context of the most prominent platforms and analytics techniques. The paper develops a logical analytics stack to support the description of key functionality and relationships between relevant components in RTA solutions based on a thorough literature review and industrial practice. This provides practitioners with guidance in selecting the most appropriate solutions for their RTA problems, including the application of emerging AI technologies in this context. The paper discusses the complex event processing technology that has influenced many recent data streaming solutions in the analytics stack, and highlights the integration of machine learning and artificial intelligence into RTA solutions. Some real-life application scenarios in the finance and health domains are presented, including several of the authors’ earlier contributions, to demonstrate the utilization of the techniques and technologies discussed in this paper. Future research directions and remaining challenges are discussed.

In the greenhouse environment, the application of complex event processing (CEP) technology can effectively tackle the problem of recognition of the complex patterns that appeared in greenhouse conditions. In the existing research, few scholars have proposed a scheme to integrate complicated scenes within the greenhouse environment with high efficiency, convenience, and low coupling. Therefore, in order to solve the problem of hard recognition and fusion of complex patterns in the greenhouse environment, based on the characteristics of the greenhouse, this paper proposes a complex event processing method for greenhouse control. Our method has high applicability and high expansibility, including 13 types of event processing agents and 21 types of typical events involved in greenhouse automatic control. This method has the advantages of low information coupling and multi-domain integration, which can be directly used by agricultural experts and related workers and is of great significance to promote the extensive application of CEP technology in the greenhouse field. Our experiment successfully realized a greenhouse intelligent control system based on CEP technology is successfully realized in our experiment. The experimental statistics shows that the structure of the control system was accessible and effective.

A variety of ubiquitous computing devices, such as radio frequency identification (RFID) and wireless sensor network (WSN), are generating huge and significant events that should be rapidly processed for business excellence. In this paper, we describe how complex event processing (CEP) technology can be applied to ubiquitous process management based on context-awareness. To address the issue, we propose a method for context-aware event processing using event processing language (EPL) statement. Specifically, the semantics of a situation drive the transformation of EPL statement templates into executable EPL statements. The proposed method is implemented in the domain of ubiquitous cold chain logistics management. With the proposed method, context-aware event processing can be realized to enhance business performance and excellence in ubiquitous computing environments. key words: Context-awareness, complex event processing, ubiquitous logistics, RFID, wireless sensor network

Speech is a complex bio-signal that has the potential to provide a rich bio-marker for health. It enables the development of non-invasive routes to early diagnosis and monitoring of speech affecting diseases, such as the ones studied in this work: Depression, and Parkinson's Disease. However, the major limitation of current speech based diagnosis and monitoring tools is the lack of large and diverse datasets. Existing datasets are small, and collected under very controlled conditions. As such, there is an upper bound in the complexity of the models that can be trained using these datasets. There is also limited applicability in real life scenarios where the channel and noise conditions, among others, are impossible to control. In this work, we show that datasets collected from in-the-wild sources, such as collections of vlogs, can contribute to improve the performance of diagnosis tools both in controlled and in-the-wild conditions, even though the data are noisier. Moreover, we show that it is possible to successfully move away from hand-crafted features (i.e. features that are computed based on predefined algorithms, that based on human expertise) and adopt end-to-end modeling paradigms, such as CNN-LSTMs, that extract data driven features from the raw spectrograms of the speech signal, and capture temporal information from the speech signals.