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Real-time Vehicular Pollution Detection Model using IoT and Distributed Streaming Techniques
This paper analyzes and monitors an important environmental factor, namely air. This paper checks the air quality with the help of DSM501A sensors in the Jiu Valley. The device used in the paper uses the "DSM501A" dust sensor, which is one of the cheapest sensors on the market and is capable of detecting PM2.5 and PM10 particles.
With the recent developments in Internet of Things (IoT), the number of sensors that generate raw data with high velocity, variety, and volume is tremendously increased. By employing complex event processing (CEP) systems, valuable information can be extracted from raw data and used for further applications. CEP is a stream processing technology that matches atomic events to complex events via predefined rules mostly created by experts. However, especially in heterogeneous IoT environments, it may become extremely hard to define these rules manually due to complex and dynamic nature of data. Defining rules requires accurate knowledge of relevant events by utilizing temporal dependencies and relations among attributes of events. Therefore, there is a need for an automatic rule extraction system which is capable of generating rules automatically from unlabeled IoT data. This paper proposes a generalized framework for automatic CEP rule extraction with the help of deep learning (DL) methods. The proposed framework contains two main phases which are data labeling and automatic rule extraction phases. We compare several DL methods with each other and regression-based methods to evaluate the proposed framework. In addition, several rule mining methods are employed in the automatic rule extraction phase in a comparative manner. The reconstruction error and prediction success rate of our framework are evaluated using an air quality dataset collected from a smart city application. The results indicate that the proposed framework is able to generate meaningful and accurate rules for unlabeled IoT data.
With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic models for stream annotation are scarce, as generally, semantics are heavy to process and not ideal for Internet of Things (IoT) environments, where the data are frequently updated. We present a light model to semantically annotate streams, IoT-Stream. It takes advantage of common knowledge sharing of the semantics, but keeping the inferences and queries simple. Furthermore, we present a system architecture to demonstrate the adoption the semantic model, and provide examples of instantiation of the system for different use cases. The system architecture is based on commonly used architectures in the field of IoT, such as web services, microservices and middleware. Our system approach includes the semantic annotations that take place in the pipeline of IoT services and sensory data analytics. It includes modules needed to annotate, consume, and query data annotated with IoT-Stream. In addition to this, we present tools that could be used in conjunction to the IoT-Stream model and facilitate the use of semantics in IoT.
Apache Flink 1 is an open-source system for processing streaming and batch data. Flink is built on the philosophy that many classes of data processing applications, including real-time analytics, continuous data pipelines, historic data processing (batch), and iterative algorithms (machine learning, graph analysis) can be expressed and executed as pipelined fault-tolerant dataflows. In this paper, we present Flink's architecture and expand on how a (seemingly diverse) set of use cases can be unified under a single execution model.
The intention of this paper is to provide an overview on the subject of Human-Computer Interaction. The overview includes the basic definitions and terminology, a survey of existing technologies and recent advances in the field, common architectures used in the design of HCI systems which includes unimodal and multimodal configurations, and finally the applications of HCI. This paper also offers a comprehensive number of references for each concept, method, and application in the HCI.
One of the buzzwords in the Information Technology is Internet of Things (IoT). The future is In-ternet of Things, which will transform the real world objects into intelligent virtual objects. The IoT aims to unify everything in our world under a common infrastructure, giving us not only control of things around us, but also keeping us informed of the state of the things. In Light of this, present study addresses IoT concepts through systematic review of scholarly research papers, corporate white papers, professional discussions with experts and online databases. Moreover this research article focuses on definitions, geneses, basic requirements, characteristics and aliases of Internet of Things. The main objective of this paper is to provide an overview of Internet of Things, architectures, and vital technologies and their usages in our daily life. However, this manuscript will give good comprehension for the new researchers, who want to do research in this field of Internet of Things (Technological GOD) and facilitate knowledge accumulation in efficiently .
The trends and variability of PM10, PM2.5 and
PMcoarse concentrations at seven urban and rural background
stations in five European countries for the period between 1998 and 2010
were investigated. Collocated or nearby PM measurements and
meteorological observations were used in order to construct Generalized
Additive Models, which model the effect of each meteorological variable
on PM concentrations. In agreement with previous findings, the most
important meteorological variables affecting PM concentrations were wind
speed, wind direction, boundary layer depth, precipitation, temperature
and number of consecutive days with synoptic weather patterns that favor
high PM concentrations. Temperature has a negative relationship to
PM2.5 concentrations for low temperatures and a positive
relationship for high temperatures. The stationary point of this
relationship varies between 5 and 15 °C depending on the station.
PMcoarse concentrations increase for increasing temperatures
almost throughout the temperature range. Wind speed has a monotonic
relationship to PM2.5 except for one station, which exhibits
a stationary point. Considering PMcoarse, concentrations tend
to increase or stabilize for large wind speeds at most stations. It was
also observed that at all stations except one, higher PM2.5
concentrations occurred for east wind direction, compared to west wind
direction. Meteorologically adjusted PM time series were produced by
removing most of the PM variability due to meteorology. It was found
that PM10 and PM2.5 concentrations decrease at
most stations. The average trends of the raw and meteorologically
adjusted data are -0.4 μg m-3 yr-1 for
PM10 and PM2.5 size fractions. PMcoarse
have much smaller trends and after averaging over all stations, no
significant trend was detected at the 95% level of confidence. It is
suggested that decreasing PMcoarse in addition to
PM2.5 can result in a faster decrease of PM10 in
the future. The trends of the 90th quantile of PM10 and
PM2.5 concentrations were examined by quantile regression in
order to detect long term changes in the occurrence of very large PM
concentrations. The meteorologically adjusted trends of the 90th
quantile were significantly larger (as an absolute value) on average
over all stations (-0.6 μg m-3 yr-1).
A large number of distributed applications requires continuous and timely processing of information as it flows from the periphery to the center of the system. Examples include intrusion detection systems which analyze network traffic in real-time to identify possible attacks; environmental monitoring applications which process raw data coming from sensor networks to identify critical situations; or applications performing online analysis of stock prices to identify trends and forecast future values.
Traditional DBMSs, which need to store and index data before processing it, can hardly fulfill the requirements of timeliness coming from such domains. Accordingly, during the last decade, different research communities developed a number of tools, which we collectively call Information flow processing (IFP) systems, to support these scenarios. They differ in their system architecture, data model, rule model, and rule language. In this article, we survey these systems to help researchers, who often come from different backgrounds, in understanding how the various approaches they adopt may complement each other.
In particular, we propose a general, unifying model to capture the different aspects of an IFP system and use it to provide a complete and precise classification of the systems and mechanisms proposed so far.
Radio frequency identification systems with passive tags are powerful tools for object identification. However, if multiple
tags are to be identified simultaneously, messages from the tags can collide and cancel each other out. Therefore, multiple
read cycles have to be performed in order to achieve a high recognition rate. For a typical stochastic anti-collision scheme,
we show how to determine the optimal number of read cycles to perform under a given assurance level determining the acceptable
rate of missed tags. This yields an efficient procedure for object identification. We also present results on the performance
of an implementation.
Particulate air pollution at commonly occurring concentrations is associated with daily deaths. Recent attention has focused on the shape of the concentration-response curve, particularly at low doses. Several recent articles have reported that particulate matter with aerodynamic diameter < or = 10 microm (PM(10)) was associated with daily deaths with no evidence of a threshold. These reports have used smoothing or spline methods in individual cities and pooled the results across multiple cities to obtain estimates that are more robust. To date, fine particulate matter (aerodynamic diameter Less than or equal to 2.5 microm; PM(2.5)), a component of PM(10), has not been examined in this regard. We examined this association in a hierarchical model in six U.S. cities. In the first stage, we fit log-linear models including smooth functions of PM(2.5) in each city, controlling for season, weather, and day of the week. These smooth functions allowed for nonlinearities in the city-specific associations. We combined the estimated curves across cities using a hierarchical model that allows for heterogeneity. We found an essentially linear relationship down to 2 microg/m(3). The same approach was applied to examine the concentration response to traffic particles, controlling for particles from other sources. Once again, the association showed no sign of a threshold. The magnitude of the association suggests that controlling fine particle pollution would result in thousands of fewer early deaths per year.
Today’s world is the most air-polluted world and air pollution has become a major concern nowadays. With the increased population, the number of vehicles also increased at an alarming rate, especially in metropolitan and big cities. This has led to the rise in environmental temperatures. It has also increased various health issues. This is due to the poor quality air caused by the harmful gases emitted by these vehicles. This paper proposes an idea for monitoring and controlling the vehicular pollution using latest Internet of Things (IoT) technology and sensors.
Recently, Internet of Things (IoT) has been attracting attention due to its economical impact and high expectations for drastically changing our modern societies. Worldwide by 2022, over 50 billion IoT devices including sensors and actuators are predicted to be installed in machines, humans, vehicles, buildings, and environments. Demand is also huge for the real-time utilization of IoT data streams instead of the current off-line analysis/utilization of stored big data. The real-time utilization of massive IoT data streams suggests a paradigm shift to new horizontal and distributed architecture because existing cloud-based centralized architecture will cause large delays for providing service and waste many resources on the cloud and on networks. Content curation, which is the intelligent compilation of valuable content from IoT data streams, is another key to fully utilize and penetrate IoT technologies. In this paper, we survey the emerging technologies toward the real-time utilization of IoT data streams in terms of networking, processing, and content curation and clarify the open issues. Then we propose a new framework for IoT data streams called the Information Flow of Things (IFoT) that processes, analyzes, and curates massive IoT streams in real-time based on distributed processing among IoT devices.
Degradation of air quality in cities is the result of a complex interaction between natural and anthropogenic environmental conditions. With the increase in urbanization and industrialization and due to poor control on emissions and little use of catalytic converters, a great amount of particulate and toxic gases are produced [1]. The objective of this paper is to monitor air pollution on roads and track vehicles which cause pollution over a specified limit. Increasing number of automobiles is a serious problem that has been around for a very long time. This paper proposes use of Internet of Things(IoT)[2] to address this problem. Here, combination of Wireless Sensor Network and Electrochemical Toxic Gas Sensors and the use of a Radio Frequency Identification (RFID) tagging system to monitor car pollution records anytime anywhere.
Natural gas is one of the most promising alternative fuels to meet the upcoming stringent Euro 6 emissions regulations in the European Union, as well as the planned reductions in CO2 emissions. For spark-ignition engines, bi-fuel fuelling equipment is widely available and engine conversion technology for European automobiles is well established, thereby facilitating usage of natural gas in its compressed form (CNG). In light of the promising characteristics and increasing usage of natural gas as a vehicular fuel, this study investigates emissions from a passenger car featuring a spark-ignition engine capable of running on both CNG and standard gasoline. Results from emissions testing of the vehicle on a chassis dynamometer are presented and discussed in the context of the Euro 6 emissions requirements. The test vehicle featured a multipoint gas injection system and was an unmodified, commercially available European vehicle meeting the Euro 5 standard. The results indicated that when fueled with CNG, such a vehicle can comfortably meet Euro 6 emissions limits, with certain differences observed in the emissions according to the fuel type used. Furthermore, when running on CNG the vehicle was observed to emit considerably less carbon dioxide than when fueled with gasoline, with the reduction closely agreeing with the results of other studies.
The nitrogen dioxide (NO2) pollution in urban areas of Europe can be partially attributed to the increasing market penetration of diesel cars that show higher distance-specific nitrogen oxides (NOx) emissions than gasoline cars. The on-road NOx emissions of diesel cars, furthermore, appear to exceed substantially applicable emissions standards. This observation raises concerns that the introduction of more stringent Euro 6 emissions standards in 2014 may not adequately reduce the distance-specific on-road NOx emissions of new diesel cars. We address the existing concerns by analyzing the gaseous emissions of one novel Euro 6 diesel car and six Euro 4–5 diesel cars with Portable Emissions Measurement Systems (PEMS). We find that the average on-road NOx emissions of the Euro 6 car (0.21 ± 0.09 g per kilometer [g km−1]) are considerably lower than those of the Euro 4 cars (0.76 ± 0.12 g km−1) and the Euro 5 cars (0.71 ± 0.30 g km−1). The selective catalytic reduction (SCR) system of the Euro 6 diesel car is suitable to limit NOx emissions during real-world on-road driving. Still, all tested cars, including the Euro 6 diesel car, exceed their NOx emissions standards on the road by 260 ± 130%. This finding suggests that the current type-approval procedure does not adequately capture the on-road NOx emissions of diesel cars. By introducing a complementary emissions test procedure that covers a wide range of normal operating conditions, the European legislative authorities can address this problem and ensure that Euro 6 will indeed deliver an adequate reduction in the NOx emissions of new diesel cars.
Designed to provide a comprehensive and broadly based introduction to the study of human communication, this book presents the concept of communication as interrelated constituent processes that operate at varying levels of complexity and acquire significance only in the context of larger intrapersonal, interpersonal, or socio-cultural systems of behavior. The first part presents foundations--the frame of reference used in this book and different models of communication. Other parts focus on the intrapersonal system (human information processing and psychological orientation), interpersonal system (verbal interaction, nonverbal interaction, and interpersonal contact), and the sociocultural system (situational geography, communication networks, and communication and social influence). (SH)
Model management is a generic approach to solving problems of data programmability where precisely engineered mappings are required. Applications include data warehousing, e-commerce, object-to-relational wrappers, enterprise information integration, ...
Internet of things and nodemcu
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A Comparative Study of Arduino, Raspberry Pi and ESP8266 as IoT Development Board
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A deep learning-based CEP rule extraction framework for IoT data
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Internet of Things (IoT): A literature review
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