Top PDF Big Data Collection: Analysis and Processing of Efficient IoT Based Sensor

Big Data Collection: Analysis and Processing of Efficient IoT Based Sensor

Big Data Collection: Analysis and Processing of Efficient IoT Based Sensor

configure it or take measurement for it. The second window is the „„Simulation Control‟‟ window from where we can „„start‟‟, „„pause‟‟, make a „„s tep‟‟ forward, and „„reload‟‟ the simulation. The window on the top-right corner is where we can take notes, and that is why named „„Notes‟‟ window. The window in the middle called „„Mote output‟‟ is where are printed for each node all outputs of serial po rts. The last window observed when we create a New Simulation, is the „„Timeline‟‟ window. Since we have built our IPv6 over Low-power Personal Area Network (6LoWPAN) shown in Fig. 4 above, we can use more tools such as the Radio Messages tool from the menu Tools.In the “Radio messages” window, we choose the “6LoWPAN Analyzer with PCAP‟‟from the menu Analyzer. With that choice we made and after we start the simulation, the network traffic (data packets) is saved in a PCAP file for future analysis. Another useful tool is the Power Tracker which can be found in the menu Tools with the name “Mote radio duty cycle”. With this tool we can calculate the percentage of power used by every node in the network separately, and the average power used by all nodes.
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EFFICIENT BIG DATA ANALYSIS USING HADOOP FRAMEWORK FOR SENSOR NETWORK DATA

EFFICIENT BIG DATA ANALYSIS USING HADOOP FRAMEWORK FOR SENSOR NETWORK DATA

Locally storing: The data is stored in sensor nodes locally, having lower query efficiency and lower communication overhead. It implements the grid processing technique. Grid processing produces the virtual supercomputers by using extra processing sources geographically sent out with web, and also processing sources are separate processing groups which might be not really in a single domain. This processing haves the internet calculation and even the storage devices. This products and services architectural mastery is designed in order to define the latest common and also regular architectural mastery pertaining to grid-based program. Various pursuits around the globe understand the interconnection associated with sensor nodes together with g infrastructure of grid processing. Sensor grid is actually a real cross architectural mastery which in turn integrates instant sensor cpa networks together with the infrastructures of grid to have real-time physical information variety and the also the sharing associated with computing and also storage devices sources pertaining to physical information control and also managing.
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Cluster based priority traverse processing for efficient data collection 
		in WSN

Cluster based priority traverse processing for efficient data collection in WSN

in the network. The detected values consequently are treated as events indicating change of phenomenon that are of interest. On the other hand, the common characteristic of outlier detection and event detection is that they employ spatiotemporal correlations among sensor data of neighboring nodes to distinguish between events and noises. This is based on the fact that noisy measurements and sensor faults are defined to be unrelated, while event measurements are likely to be spatially correlated. Since sensor nodes are uniformly and independently deployed in a rectangular sensor field within a transmission range of sensor nodes is r. That is, any two sensors whose Euclidean distance is within r can communicate with each other. So the sensor nodes are spatially correlated and there is no chance of occurrence of erroneous data in the network. The data with drastic change with predefined past data are confidently event- driven data.
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WiFi Based GSM Based Efficient Data Collection from Sensor Network

WiFi Based GSM Based Efficient Data Collection from Sensor Network

Smart sensing nodes with embedded CPUs, low power radios and sensors which are used to monitor environmental conditions such as temperature, pressure, humidity, vibration and energy consumption. The system consists of hardware, networks, services, storage, and interfaces to provide the better computing service. It is also possible to upload the data obtained from the wireless sensor nodes to the Web services by using protocols like Simple Object Access Protocol and Representational State Transfer, using messaging mechanisms such as emails and SMS or social networks and blogs. Using IoT web service application, wireless communication has developed in the field like e-health care services, smart homes, or even vehicular area networks (VAN). By connecting, evaluating and linking the sensor, data conclusions can be made in real time, trends can be predicted and hazardous situations can be avoided.
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IOT BASED INFORMATION SYSTEM FOR ENVIRONMENT MONITORING AND MANAGEMENT USING K MEDOID ALGORITHM

IOT BASED INFORMATION SYSTEM FOR ENVIRONMENT MONITORING AND MANAGEMENT USING K MEDOID ALGORITHM

Environmental problems such as climate change and natural disasters have received much interest in recent years. Environmental monitoring and management provides us different methods to gain deeper knowledge about climate changes, natural disasters and the spread of infectious diseases. The study on environmental changes and conditions helps to improve the habitat and economic conditions. Research in climate changes helps to derive different solutions for better agriculture, habitat and efficient lifestyle. The Internet of Things (IoT) is a notion in which Internet will be integrated into everyday objects that we use in our house, by tagging chips or sensors thus creating an intelligent network of the physical objects. IoT based information system for environmental monitoring and management provides an efficient mechanism for monitoring the temperature and humidity changes and management of the same. This new concept is the combination of IoT and data mining. Internet of things offers large amount of data and data mining activities offers information extraction from the unstructured big data. The proposed system uses K medoid for clustering the weather based data. The application of IoT based information system for environmental monitoring and management includes fire detection, in the field of agriculture etc.
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Efficient Data Collection using Mobile Sink Scheme in IoT

Efficient Data Collection using Mobile Sink Scheme in IoT

So, the current work aims to investigate a plan of incorporating the efficient mobile sink movement with delay requirements. The sink periodically collects data from the head nodes in the network. In the proposed model four-phase data gathering approach is used for data collection. Identification of subnetworks is done using Spectral Graph Partitioning (SGP) technique and to minimize the energy consumption for data transmission enhanced EM clustering algorithm is applied in which the responsibility factor value parameter is used to form clusters. Thereafter, we propose two distance sensitive trajectory scheduling approaches to solve the route planning problem of mobile sink. The sink moves towards the subnetworks for the collection of sensed data and the trajectory of the sink is based on two proposed trajectory schemes. The first is the Cluster Head Distance Sensitive based Trajectory Scheduling Scheme (CHDS-TSS) that formed routes by considering the distance between the cluster head nodes. If this distance is less than the double of communication range, then the sink moves towards a particular point and collects data simultaneously. It helps in the reduction of sink movement. Further to reduce the sink movement, the second scheme is proposed i.e. Group Head Distance Sensitive based Trajectory Scheduling Scheme (GHDS-TSS). There is fixed initial point for the movable sink and movement goes along in a well-planned trajectory that covers the whole network, collects data from network head nodes containing sensors for the task and finally reverts to the initial point. We compare the performance of CHDS-TSS and GHDS-TSS with generic EEM mobility approach of data collection where the sink moves towards every cluster head for data collection. The simulation results reveal that our schemes improve the energy consumption, delay, packet delivery ratio, and network lifetime issues.
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Efficient Data Aggregation and Collection in Tree Based Wireless Sensor Networks

Efficient Data Aggregation and Collection in Tree Based Wireless Sensor Networks

In paper[2],Data Aggregation and Principal Component Analysis In WSN[2017] explains that the data aggregation techniques helps to reduce the energy consumption of sensor nodes and boosts the scalability of the network.In this paper, uses Principal Component Analysis (PCA) techniques which aggregate all the data received at the leaf node into one packet instead of relaying all the incoming packet.so that it reduces the number of transmission bytes in the intermediate nodes and increase the lifetime of intermediate node.It reduces transmissions among the sink and one or more data aggregation nodes in the network.
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Classification and Processing of Big Data in Sensor Network Based on Suffix Tree Clustering

Classification and Processing of Big Data in Sensor Network Based on Suffix Tree Clustering

Abstract—Aiming at the perception data acquired by the widely used, fast- developing but still not perfect wireless sensor network system, a relatively complete and universal system for the collection, transmission, storage and cluster analysis of perception data is designed. Perception data is spliced and compressed at the node and reconstructed at the base station, the problem of the acquisition of perception data and energy consumption of transmission is optimized, the distributed storage system is established, and the data reading mechanism and data storage architecture are designed accordingly. The data acquisition protocol and the traditional protocol, the storage system itself and the Oracle database system, and Standard Deviation and Eigensystem Realization Algorithm are respectively adopted for comparison test. Based on Standard Deviation algorithm, the operation of suffix tree clustering is carried out, and the general steps of suffix tree clustering are studied and the structure of perception data and the characteristics of storage are adapted, and the data classification operation based on suffix tree clustering is completed. The results show that proposed Standard Deviationalgorithm algorithm not only inherits the efficiency of the classical algorithm for processing big data, but also has obvious effect on large-scale discrete data processing, and the efficiency is obviously improved compared with the traditional method.
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A traffic data collection and analysis method based on wireless sensor network

A traffic data collection and analysis method based on wireless sensor network

Chaos algorithm is widely used in traffic flow data processing, and chaotic identification is the premise of chaotic analysis. However, because of the complexity of chaos, its intrinsic mechanism has not been fully re- vealed, so the academic community has not yet proposed a unified definition of chaos. Aiming at the chaotic iden- tification, scholars have proposed many criterions, such as Poincare section [4], bifurcation diagram [5], power spectrum [6], Kolmogorov entropy [7], and topological entropy [8]. The most commonly used criteria are the largest Lyapunov exponent [9, 10] and the fractal dimen- sion [11, 12], but these two parameters are based on phase space reconstruction [13, 14]. Only in real phase space or near-real phase space that the two parameters can accurately analyze and identify the signal. The time delay method based on the Takens embedding theorem [15] is a main way for phase space reconstruction. How- ever, this method has been influenced by many causes in practice, so the real phase space model of the object is often difficult to get, which leads to the unreliability of the identification results.
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The Data Analysis and Encrypting Data using Efficient Algorithm in Big Sensor Network Data

The Data Analysis and Encrypting Data using Efficient Algorithm in Big Sensor Network Data

MapReduce [5] for better change. Circulated processing gives a faultless stage to inducing of big data, stockpiling and unwinding with its colossal estimation control [6]. It is unavoidable to experience the issue of managing big data in different veritable applications. These days remarkable sort of work has been capable for arranging big data with cloud. A normal cloud based appropriated structure for big data dealing with is Amazon EC2 base as an association. A scattered stockpiling is upheld by Amazon S3. MapReduce [7] is clutched as a programming model for big data dealing with over conveyed processing. The issue of dealing with incremental big data is researched at different focuses from different points of view. b) WSN handling in connection with cloud At the moment that data from liberal sensor systems is should have been gathered and observed remotely sensor-Cloud is beneficial for a couple of uses. For ecological checking, social insurance, business exchanges, transportation, WSN connects with innovative blueprints. Remote sensor system structures have created assorted courses of action in various fields, for example, cataclysm watching, catastrophe warming, natural studying, and business change strategy and data gathering. Sensor cloud masterminds has been conveyed to set up the remote sensor data gathered by WSN. Plan of sensor cloud is helpful in different applications for the most part when the data is found remotely. Big data is hard to get ready utilizing close to database association devices since volume of big data is developing quickly with accumulation in data sets [8]. c) Error detection in systems Data error is unavoidable in different certifiable complex structure structures. To discover and find errors in big data sets winds up being incredibly taking a stab at undertaking with typical computational forces of standard structures as there is energetic improvement of big data conveyed from complex system structures, for example, interpersonal affiliations and huge scale sensor structures. Wang et al. give an essential gathering to errors on interpersonal relationship in context of error conditions examination which traces the lead of error conditions. This bunching combines 6 sorts of standard errors with missing data or errordata. Nature of four focus level system measures is looked this grouping structure [9]. Mukhopadhyay [10] proposed a model based error change method for Wireless sensor system. Shrewd sensor systems are utilized as a bit of this rectification technique. This structure depends on upon the change with data configuration evaluate. To locate the essential driver of errors is as fundamental as perceiving and curing error. To separate concealed driver of error, an instrument a sensor system looking at is utilized. Notwithstanding, the things which should be enhanced are client interface, adaptability and time execution.
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Energy Efficient Data Collection in Wireless Sensor Networks

Energy Efficient Data Collection in Wireless Sensor Networks

Wireless Sensor Networks (WSN) consists of several sensor enable nodes which are distributed in an envir- onment and use batteries as energy resource. These tiny sensor nodes, which consist of sensing, data processing, and communicating components, result in the idea of sensor networks based on collaborative effort of a large number of nodes. Such sensor nodes could be deployed in home, military, science, and industry applications such as transportation, health care, disaster recovery, warfare, security, industrial and building automation, and even space exploration. Among a large variety of applications, phenomena monitoring is one of the key areas in wireless sensor networks and in such networks, you can query the physical quantities of the environment [1-3].
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Analysis of Iot and Big Data -Challenges

Analysis of Iot and Big Data -Challenges

Internet of things is growing a site for merging the society IOT may be carried out anywhere where it brings advantages to people. Mobile phones now a day are so beneficial and are linking people to gadgets increasingly with the development of generation. This evolution of "Internet of things "will carry next massive possibilities, it'll also attain to factor to attach the prevailing systems and then elevate that by way of connecting more matters viable due to the wireless sensor networks (WSN) and different technologies. Data will then carry or shift from one vicinity to the alternative and with this cloud will come into picture and could mechanically shipping the information. The Internet of things is refer diverse variety electrical gadgets, software, sensors, and one-of-a-kind community connectivity to accumulate and for buying and selling data, with the help of a few network connectivity [1]. With the assist of net of factors it will become viable to experience and control remote objects within stay community structure, growing probabilities for objects and pc-based structures, and proving in improve performance, accuracy and monetary benefit. A few not unusual programs of IoT can occur in smart towns like smart parking i.e., take a look at of the parking area availability within the town .Structural health and diverse applications in detection of clever smart phone with the assist of wi- fi or bluetooth tool. Smart lightening and a few various programs in smart surroundings are woodland hearth detection, snow level monitoring, Earthquake early detection, transportable water monitoring, pollution stage inside the sea , river floods . Different areas wherein IoT can help making matters easy and transportable are perimeter get right of entry to manipulate, other areas are air pollution, Earthquake early detection, river floods, smart Grid, radiations degree, intelligent shopping packages, Fleet monitoring, ozone presence offspring care, energy and water use and those many ways there are many upcoming regions where Internet of things have established to be godsend. however with this there are numerous other challenges to face with IoT and big statistics.
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An Efficient Monitoring Station and Data Collection Network Based on Wireless Sensor Network

An Efficient Monitoring Station and Data Collection Network Based on Wireless Sensor Network

ABSTRACT:In the context of ubiquitous wireless sensor network,this paper presents a framework for wireless sensor networks(WSNs) designed to observe impacts of climate change in cropfields. It transmits the information of the sensor modules to the intermediates station with gsm interface, like the focus of, gas and temperture (MQ2 and LM35) sensor. And most of the collected information will be passed back again to the server (IOT) through WSN (GSM). Therefore, the ideal aspects of indoor atmosphere might be modified and controlled nicely with proper air quality, like temperture, etc. It will regulate a far more comfortable surroundings of a particular location and then made a more efficient energy saving system. With this particular safety-critical area monitoring program we are going to have much more practical significance as well as application worth in enhancing the greater living environment. Modern protection crucial places monitoring system must offer real-time monitoring of setting for individuals to enhance safety and lifetime.
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Big IoT Data Analysis: A Generic Overview

Big IoT Data Analysis: A Generic Overview

in the fields of IT and business. Although, the development of big data is already lagging, these technologies are inter-dependent and should be jointly developed. In general, the deployment of IoT increases the amount of data in quantity and category; hence, offering the opportunity for the application and development of big data analytics. Moreover, the application of big data technologies in IoT accelerates the research advances and business models of IoT. The relationship between IoT and big data, which is shown in the figure above, can be divided into three steps to enable the management of IoT data. The first step comprises managing IoT data sources, where connected sensors devices use applications to interact with one another. For example, the interaction of devices such as CCTV cameras, smart traffic lights, and smart home devices, generates large amounts of data sources with different formats. This data can be stored in low cost commodity storage on the cloud. In the second step, the generated data are called ―big data, which are based on their volume, velocity, and variety. These huge amounts of data are stored in big data files in shared distributed fault tolerant databases. The last step applies analytics tools such as MapReduce, Spark, Splunk, and Skytree that can analyze the stored big IoT data sets. The four levels of analytics start from training data, then move on to analytics tools, queries, and reports [1].
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Delay and energy efficient data collection scheme based matrix filling theory for dynamic traffic IoT

Delay and energy efficient data collection scheme based matrix filling theory for dynamic traffic IoT

Data collection is the basic functions of the Internet of Things (IoT), in which the sensed data are concentrations from sensor nodes to the sink, with a timely style, so the smart response can be done for emergency. The goal of multi-modal sensor data fusion is to obtain simple and accurate data to enhance system reliability and fault tolerance. Energy efficiency and small delay are the most important indicators which govern the performance of IoT. Convergecast is a low-latency data collection strategy based on effective time division multiple access (TDMA), in which each sensor node generates a packet, and m packets can aggregate to a packet. However, in most practical networks, sensor nodes do not necessarily generate packets during each data collection cycle, but instead generate packets from time to time. In the previous convergecast strategy, each node was fixedly allocated a slot, which increased the delay and wasted energy. A delay and energy-efficient data collection (DEEDC) scheme-based matrix filling theory is proposed to collect data in a randomly generated WSNs with minimum delay and energy consumption. The DEEDC scheme uses a clustering approach. For each cluster, the number of slots required for transmission is calculated by matrix filling theory, not the number of nodes that actually generate data. This ensures that data can be collected in a network with randomly generated data (number of slots ≤ number of nodes), thereby avoiding the allocation of slots for each node and the acquisition of redundant data to lead to the wastage of time and energy. Based on the above, a mixed slot scheduling strategy is proposed to construct energy and delay-efficient, collision-free schedule scheme. After extensive theoretical analysis, by using the DEEDC scheme, the delay is reduced by about 50~80%, and the energy consumed is reduced by about 40~57%.
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Study on embedded optical sensor data collection and signal processing

Study on embedded optical sensor data collection and signal processing

In determining a good two main processor of the system, is to analyze the peripheral circuit design based on the processor to realize the function of the system, mainly consists of power module, optical module, data acquisition module and main control module. The light source, the design of the main function modules in the photoelectric principle and characteristics of various optoelectronic devices are the analysis, research, according to the electric signal photoelectric sensor output basic variety, complete pre circuit signal processing. The data acquisition module is mainly used for the analog signal acquisition circuit came for subsequent processing, analog signals into digital signal through the DSP's built-in A/D converter. The main control unit module comprises a power supply circuit chip the power supply, storage module and storage of experimental data and run the operating system. The overall design of the hardware circuit system was shown in Figure 6.
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Research on financial network big data processing technology based on fireworks algorithm

Research on financial network big data processing technology based on fireworks algorithm

procedure is more efficient. The related tests show that the performance of the Spark platform can exceed 10~100 times of the Hadoop platform. In addition, Spark platform can realize machine learning and inter- active analysis of online data. Each thread task can dir- ectly fetch the required data from memory, thus realizing the high sharing of data, improving the effi- ciency of data processing and the speed of algorithm op- eration. While Hadoop MapReduce needs to write multiple parallel or serial MR tasks when performing data tasks, the data between tasks cannot be shared, resulting in low performance, long analysis time, large memory occupancy, and other issues, and it is only suit- able for offline large data analysis. With the rapid devel- opment of modern intelligent monitoring equipment, it is very important to mine and apply large data online in real time and efficiently. Therefore, the characteristics of Spark large data platform make it have more advantages in iterative algorithm and interactive data mining algorithm.
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Efficient Energy and Data Collection in Wireless Sensor Networks Using Leach Based Clustering Technique

Efficient Energy and Data Collection in Wireless Sensor Networks Using Leach Based Clustering Technique

The regular method in WSNs to manage diagnosis of fault is very problem specific: (i) The health maintenance procedures in hand crafts [10-11] relying on expert knowledge and limited application scope, (ii) sending (a subset of) crude wellbeing information to the basic information back-end and utilizing standard system checking apparatuses and observing principles for issue location [12], (iii) using special handheld devices for enabling interactive on-site diagnoses by experts [13], (iv) sending partially diagnosed data to the central analysis host for final diagnosis [14], (v) a probabilistic approach for intermittent fault diagnosis [8]. The common methods to detect faulty nodes are (i) mutual tests between nodes, and (ii) the exchange of heartbeat messages between them. Since tests are complex to acquire in practice, the heartbeat based mechanism is the mainly extensively employed technique for practical applications [16-17]. Past investigations have shown that disappointments because of discontinuous shortcomings are more typical than those because of changeless blame in some common WSNs [2], [6]. As it is difficult to identify these mistakes with off-line testing, the online diagnosis [17-18] strategies are most preferable. The invalidation or comparison models consider more general type of faults. They are more appropriate for diagnosing soft or value faults, where faulty nodes continue to communicate and to function with corrupted behavior.
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Power Efficient Data Collection in Wireless Sensor Networks

Power Efficient Data Collection in Wireless Sensor Networks

Wireless sensors era is exponentially growing area now days; it received attention in the past years due to its popularity with effective cost in all environments, there are many applications area wireless sensor network is very useful like; military applications, application for checking environmental conditions (temperature, humidity, weather conditions etc.), health applications, industrial or commercial applications and home applications. Wireless sensor nodes are very cost effective and they can be easily deployed in all areas, harsh environment, and even blow the surface level in water but they are having limited power and battery lifetime. If power is down and exhausted, the sensors become dead and un-useful and no more for use.
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WSN based Industrial Parameters Monitoring and Controlling System

WSN based Industrial Parameters Monitoring and Controlling System

On a Raspberry Pi2(Single-Board Computer) board of ARM11 architecture will be ported with an Embedded Linuxoperating system and using Ethernet protocol for IOTapplications, we will acquire the data from the WirelessSensor Network (WSN), post the data over the web such thatit can be viewed over internet on any browser as well also inadvancement will operate the appliance from the web.Using ARM controller we can connect all types of sensorsand we can connect 8 bit microcontroller based sensornetwork to ARM controller using different wired or wirelesstechnology. Many open source libraries and tools areavailable for ARM-linux wireless sensor networkdevelopment and controlling. We can monitor and controlthe wireless sensor network remotely using internet and webserver. The system describes the development of a wirelessindustrial environment measuring temperature, humidity,atmospheric pressure, soil moisture, water level and lightdetection. Where the wireless connection is implemented toacquire data from the various sensors, in addition to allowset up difficulty to be as reduced. By using Wi-Fitechnology we send the sensors data to authorized person.
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