Edge computing proposes a novel model for providing computational resources close to end devices that are connected to the network It has numerous applications in Internet of Things, as well as smart grids, healthcare...
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ISBN:
(纸本)9781538618394
Edge computing proposes a novel model for providing computational resources close to end devices that are connected to the network It has numerous applications in Internet of Things, as well as smart grids, healthcare, smart home, etc. This paper presents ongoing research regarding the use of blockchain technology as a platform hierarchical and distributed control systems based on IEC 61499 standard. Hyperledger Fabric was selected as the blockchain solution, where function blocks are to be implemented as smart contracts on a supervisor level. The integration with the edge nodes that perform on the executive level responsible for actual process control is based on a micro-services architecture where Docker containers implement function blocks, and Kubernetes platform is used for orchestrating the execution of containers across the edge resources.
With the rapid development of modern education, the current school teaching model needs to be improved and perfected to meet the needs of modern teaching. In order to improve the quality and efficiency of network teac...
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The Internet has been a great success but its architecture need relatively complete infrastructure construction to implement and operate. Especially, the situation worsens on resource-limited devices, so delay-toleran...
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Architecture models mainly have three functions;1) document, 2) analyze, and 3) improve the system under consideration. All three functions have suffered from being timeconsuming and expensive, mainly due to being man...
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Optimal fusion of estimates that are computed in a distributed fashion is a challenging task. In general, the sensor nodes cannot keep track of the cross-correlations required to fuse estimates optimally. In this pape...
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Optimal fusion of estimates that are computed in a distributed fashion is a challenging task. In general, the sensor nodes cannot keep track of the cross-correlations required to fuse estimates optimally. In this paper, a novel technique is presented that provides the means to reconstruct the required correlation structure. For this purpose, each node computes a set of deterministic samples that provides all the information required to reassemble the cross-covariance matrix for each pair of estimates. As the number of samples is increasing over time, a method to reduce the size of the sample set is presented and studied. In doing so, communication expenses can be reduced significantly, but approximation errors are possibly Introduced by neglecting past correlation terms. In order to keep approximation errors at a minimum, an appropriate set size can be determined and a trade-off between communication expenses and estimation quality can be found.
In research community, most of the research work is done by the group of researchers and the evaluation of scientific impact of individual is based on either citation-based metrics or centrality measures of social net...
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In now a day, the data in real time is increasing exponentially. This data is generating from every corner of the earth viz., social networks, sensors mainly from IoT (trending technology), e-commerce site, GPS signal...
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ISBN:
(纸本)9781538658543;9781538658536
In now a day, the data in real time is increasing exponentially. This data is generating from every corner of the earth viz., social networks, sensors mainly from IoT (trending technology), e-commerce site, GPS signals etc. This data may be in form of structured, semi -structured or unstructured. Currently, tech companies, for example, Facebook, Amazon, Twitter, You Tube and Google handle big data sets around terabytes or petabytes of data per day. Therefore, this data is to be analyzed or processed. It is not easy to process the whole data. The first solution to such big data problems is Hadoop. Hadoop uses Hadoop distributed File System (HDFS) to store the data. Though Hadoop gives solution to big data problems, it takes more time to produce the results. The most important constraint in this 21st century is time. In this paper, acceleration of Hadoop using Apache Ignite Filesystem that acts in in-memory instead of HDFS.
This paper focuses on reducing communication bandwidth and, consequently, energy consumption in the context of distributed target detection and tracking over a peer-to-peer sensor network. A consensus Bernoulli filter...
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This paper focuses on reducing communication bandwidth and, consequently, energy consumption in the context of distributed target detection and tracking over a peer-to-peer sensor network. A consensus Bernoulli filter with event-triggered communication is developed by enforcing each node to transmit its local information to the neighbors only when a suitable measure of discrepancy between the current local posterior and the one predictable from the last transmission exceeds a preset threshold. Two information-theoretic criteria, i.e. Kullback-Leibler divergence and Hellinger distance, are adopted in order to measure the discrepancy between random finite set densities. The performance of the proposed event-triggered consensus Bernoulli filter is evaluated through simulation experiments.
This paper gives a brief overview of the existing literature on Optimization in Internet of Things (IoT), which is a very critical technology in the 21st century. A few of the papers deal with the evolution of IoT as...
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This paper gives a brief overview of the existing literature on Optimization in Internet of Things (IoT), which is a very critical technology in the 21st century. A few of the papers deal with the evolution of IoT as a technology in the past 20 years, whereas a majority of the papers deal with the challenges faced in the communication, modelling and deployment of IoT applications, few of which use optimization and which are exploding in their numbers and the diversity of the type of devices.
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