Nowadays, the traditional database paradigm does not have enough storage for the data produced by Internet of Things (IoT) devices leads to the need of cloud storage. These data's are analyzed with the help of Big...
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ISBN:
(纸本)9781538603741
Nowadays, the traditional database paradigm does not have enough storage for the data produced by Internet of Things (IoT) devices leads to the need of cloud storage. These data's are analyzed with the help of Big data mining techniques. Cloud based big dataanalytics and the IoT technology performs an important role in the feasibility study of smart agriculture. smart or precision agricultural systems are estimated to play an essential role in improving agriculture activities. Mobile device usage is very common by everyone, including the farmers. In that, in the daily life of farmers the Information and Communication Technologies (ICT) play a vital role to get the agricultural Information. The IoT has various applications in Digital Agriculture domain like monitoring the crop growth, selection of the fertilizer, irrigation decision support system, etc. In this paper, IoT device is used to sense the agricultural data and it is stored into the Cloud database. Cloud based Big data analysis is used to analyze the data viz. fertilizer requirements, analysis the crops, market and stock requirements for the crop. Then the prediction is performed based on data mining technique which information reaches the farmer via mobile app. Our ultimate aim is to increase the crop production and control the agricultural cost of the products using this predicted information.
As the age of the Internet of Things (IoT) continues to flourish, the concept of smart healthcare has taken an unprecedented turn due to interdisciplinary thrusts. To carry the big healthcare data emanating from the p...
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As the age of the Internet of Things (IoT) continues to flourish, the concept of smart healthcare has taken an unprecedented turn due to interdisciplinary thrusts. To carry the big healthcare data emanating from the plethora of bio-sensors and machines in the IoT sensing plane to the central cloud, next generation high-speed delivery networks are essential. On the other hand, once the IoT data are delivered to the cloud, the massive IoT healthcare data are processed and analyzed employing the state-of-the-art analytics tools such as deep machine learning and so forth. However, given the explosion of big data (from various sources in addition to the healthcare data), the delivery network as well the cloud may experience network and computational congestion, respectively. This may impact the realtime analytics of the healthcare data, e.g., critical for in-house patients and senior citizens aging at home. To address this issue, the emerging IoT edge analytics concept can be regarded as a promising solution to process the big healthcare data close to the source. For large-scale IoT deployments, this functionality is critical because of the sheer volumes of data being generated. In this paper, we propose a deep learning based IoT edge analytics approach to support intelligent healthcare for residential users. The performance of the proposal is validated using computer-based simulation for online training of a real dataset. The reported results of our proposal exhibit encouraging performance in terms of low loss rate, high accuracy, and low execution time to support near real-time actionable decision making on the healthcare data.
The data from the pervasive devices/computing can be taken from the servers of the respective service providers or application oriented services. The data can be further processed by analytics using Big data with diff...
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ISBN:
(纸本)9781538619599
The data from the pervasive devices/computing can be taken from the servers of the respective service providers or application oriented services. The data can be further processed by analytics using Big data with different tools for the analytics like Bluemix, Hadoop and Matlab so, that we can get useful information in real time without further processing and delay. The data in pervasive computing is mostly unstructured. As the data generated from sensor networks and data in mobile and smart devices is increasing day by day, dataanalytics in unstructured data like this is difficult with limited processing power. But the tools like Hadoop, Bluemix, and Matlab with good connectivity to cloud can provide us some important data analysis tools. Big data can help in many ways which will ensure better services and responsiveness which is lacking in most of the devices right now. The inference from the captured data can be further processed for various applications like business, decisions making, forecasting etc.
This work presents a distributed system that is designed for road traffic management in smart cities using cloud computing. This distributed design responds to the particular limitations of an emergency situation, spe...
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Internet of Things (IoT) is one of the fastest developing technologies throughout the India. But, most of the population (70%) in India depending on agriculture. This situation is one of the reason, that hindering the...
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ISBN:
(纸本)9781509032433
Internet of Things (IoT) is one of the fastest developing technologies throughout the India. But, most of the population (70%) in India depending on agriculture. This situation is one of the reason, that hindering the development of country. in order to solve this problem only one solution that, smart agriculture by adding new technological methods instead of present traditional agriculture methods. Hence we proposed new IoT technology with cloud computing and Li-Fi. Wi-Fi is great for general wireless coverage within buildings, whereas Li-Fi[10] is wireless data coverage with high density in confined area. Li-Fi provides better bandwidth, efficiency, availability and security than Wi-Fi and has already achieved blisteringly high speed in the lab. First this project includes remote controlled process to perform tasks like spraying, weeding, bird and animal scaring, keeping vigilance, moisture sensing, etc. Secondly it includes smart warehouse management which includes temperature maintenance, humidity maintenance and theft detection in the warehouse. Thirdly, intelligent decision making based on accurate real time field data for smart irrigation with smart control. Controlling of all these operations will be through any remote smart device or computer connected to Internet and the operations will be performed by interfacing cameras, sensors, Li-Fi or ZigBee modules.
New challenges faced by the current Industrial Internet of Things (IIoT) are stringent latency, capacity constraints, uninterrupted devices with intermittent connectivity cannot be solved with centralized cloud comput...
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ISBN:
(纸本)9781538670989;9781538670972
New challenges faced by the current Industrial Internet of Things (IIoT) are stringent latency, capacity constraints, uninterrupted devices with intermittent connectivity cannot be solved with centralized cloud computing architecture [1]. Sending all the data to the cloud will require prohibitively high network bandwith [2]. vf-OS - Virtual Factory Open Operating System - is a project funded by the H2020 Framework Programme of the European Commission under Grant Agreement 723710, with the purpose to provide an Open Operating System for Virtual Factories composed of a kernel, application programming interface, and middleware specifically designed for the factory of the future. This paper presents the Virtual Open Operating System (vf-OS) approach to IoT analytics, and how vf-OS IO (Input/Output) components and vf-OS analytics can be used to capture data from sensors and Program Logic Controllers (PLCs) to generate and run machine learning models to do analytics in the cloud and in the edge. The main contribution of the paper is the proposal of new architectural solutions and providing alternative scenarios for developers developing applications for the manufacturing sector.
As civilization progressed in the digital age, a need for the human establishments to keep up with this progress arose. Keeping up with the technological advancements has become the way forward. With this objective, s...
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ISBN:
(纸本)9781509035199
As civilization progressed in the digital age, a need for the human establishments to keep up with this progress arose. Keeping up with the technological advancements has become the way forward. With this objective, solutions to problems in cities need to be 'smart'. Using advanced ICT provides us with smart solutions. The most recent advancement of Artificial intelligent has revolutionized smart solutions. This advancement in AI has enhanced the way cities combat the crucial threats to safety and security. This paper presents some of those smart solutions already in place in developing smart cities.
Atmospheric parameters like the temperature, humidity, soil moisture and PH of the soil in the crop-fields are very much important to be minutely observed and noted to maintain the appropriate crop growth without whic...
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ISBN:
(纸本)9781538614433;9781538614426
Atmospheric parameters like the temperature, humidity, soil moisture and PH of the soil in the crop-fields are very much important to be minutely observed and noted to maintain the appropriate crop growth without which crop production may hamper in a large scale. With the Advent of Embedded Based Technologies such as IoT, Robotics, Machine Learning etc. in agriculture has created a great impact to make the farming habits smart and intelligent. But, to design an intelligent system with optimization, cost as well as energy efficiency, and more user-friendliness is an idealistic challenge now-a-days. The system that has been proposed is designed with these ideal constraints in mind. A particular season is targeted to survey the pest condition and growth in crop-fields and how the atmospheric conditions are readily involved in this case. If any abrupt changes in the atmosphere have influenced the pest nature, then it has to be studied and tracked properly to form a research database that can be used for precautionary measures in the next season. The sensor network is formed with the atmospheric parameters and interfaced with Raspberry Pi3 which acts as gateway here to store all the data in the cloud. The data can be accessed by any computer or mobile devices from the cloud. By that method, pest identification is easier by accessing this low cost and energy efficient system which is beneficial in a large scale for the agricultural scientists, pest technicians and also farmers if they are trained about the usage and benefits of the system.
It is believed that technology will take over the day to day life of human begin. No one can remain isolated from the adaptation of technology. Hence it is the need of the hour to introduce a technology which will do ...
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ISBN:
(纸本)9781509064717
It is believed that technology will take over the day to day life of human begin. No one can remain isolated from the adaptation of technology. Hence it is the need of the hour to introduce a technology which will do the day to day life tasks in human life with smart and intelligent way. Hence, thispaper critically discuss the emergence and progress of Internet of Every Things (IoET) as the next generation communication tool. Further, the various research avenues and open challenges in the area of IoET are discussed in detail.
Recent advancement in hardware, software and communication technologies have culminated in improvisation of safety aspects in vehicular communication networks. Vehicular Ad hoc NETworks (VANETs) is used to establish n...
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Recent advancement in hardware, software and communication technologies have culminated in improvisation of safety aspects in vehicular communication networks. Vehicular Ad hoc NETworks (VANETs) is used to establish network among the vehicles which paves way for intelligent Transportation System. In spite of myriad of benefits in VANETs, there exists enormous amount of concerns such as establishing privacy, safety monitoring, reliable connectivity, high bandwidth, less delay with guaranteed Quality of Service (QoS). Establishing security services in the VANET is the key to make any application successful in the safe driving condition. As part of intelligence in cognitive systems, Machine Learning algorithms make it possible for constantly mine data and acquire knowledge through advanced analytics. We use clustering, a machine learning algorithm for clustering the vehicles at intersection and grouping the messages in each cluster and filtering the redundant message. Naive Bayes algorithm is applied over the filtered message for classifying the messages and assigning priority before transmission. Further, our proposed Cognitive-intelligent Transportation System (C-ITS) model provides efficient channel utilization and the accuracy is improved compare to the existing methods of classification. Finally, performance and accuracy of proposed approach are evaluated.
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