The computing continuum is growing because multiple devices are added daily. Edge devices play a key role in this because computation is decentralized or distributed. Edge computing is advanced by using AI/ML algorith...
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(纸本)9798350304831
The computing continuum is growing because multiple devices are added daily. Edge devices play a key role in this because computation is decentralized or distributed. Edge computing is advanced by using AI/ML algorithms to become more intelligent. Besides, Edge data protocols are useful for transmitting or receiving data between devices. Since, computation efficiency is possible when the data is received at the Edge timely, and it is possible only when the data protocols are efficient, reliable and fast. Most edge data protocols are defined with static set of rules and their primary purpose is to provide standardized and reliable data communications. Edge devices need autonomous or dynamic protocols that enable interoperability, autonomous decision making, scalability, and adaptability. This paper examines the limitations of popular data protocols used in edge networks, the need for intelligent data protocols, and their implications. We also explore possible ways to simplify learning for edge devices and discuss how intelligent data protocols can mitigate challenges such as congestion, message filtering, message expiration, prioritization, and resource handling.
The recent advances in DNA sequencing technology, from first-generation sequencing (FGS) to third-generation sequencing (TGS), have constantly transformed the genome research landscape. Its data throughput is unpreced...
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The recent advances in DNA sequencing technology, from first-generation sequencing (FGS) to third-generation sequencing (TGS), have constantly transformed the genome research landscape. Its data throughput is unprecedented and severalfold as compared with past technologies. DNA sequencing technologies generate sequencing data that are big, sparse, and heterogeneous. This results in the rapid development of various data protocols and bioinformatics tools for handling sequencing data. In this review, a historical snapshot of DNA sequencing is taken with an emphasis on data manipulation and tools. The technological history of DNA sequencing is described and reviewed in thorough detail. To manipulate the sequencing data generated, different data protocols are introduced and reviewed. In particular, data compression methods are highlighted and discussed to provide readers a practical perspective in the real-world setting. A large variety of bioinformatics tools are also reviewed to help readers extract the most from their sequencing data in different aspects, such as sequencing quality control, genomic visualization, single-nucleotide variant calling, INDEL calling, structural variation calling, and integrative analysis. Toward the end of the article, we critically discuss the existing DNA sequencing technologies for their pitfalls and potential solutions.
Scientific and business communities are adopting large-scale distributed systems as a means to solve a wide range of resource-intensive tasks. These communities also have requirements in terms of provenance. We define...
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Scientific and business communities are adopting large-scale distributed systems as a means to solve a wide range of resource-intensive tasks. These communities also have requirements in terms of provenance. We define the provenance of a result produced by a distributed system as the process that led to that result. This paper describes a protocol for recording documentation of a distributed system's execution. The distributed protocol guarantees that documentation with characteristics suitable for accurately determining the provenance of results is recorded. These characteristics are confirmed through a number of proofs based on an abstract state machine formalization.
The availability of renewable energy sources along with the advancement of sensing and communication technologies has resulted in the sustainable operation of modern energy systems. An intelligent grid system is the i...
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The availability of renewable energy sources along with the advancement of sensing and communication technologies has resulted in the sustainable operation of modern energy systems. An intelligent grid system is the integration of sensors and actuators, which enables the system to connect and exchange energy-related data from renewable sources to a computer system and end-users in a communication network. This data can be monitored in real-time with the help of the Internet of Things (IoT). However, several challenges exist in IoT, such as security, bandwidth management, interfacing interoperability, connectivity, packet loss, and data processing. In this paper, the key challenges and outstanding issues with the IoT when incorporated with energy systems are reviewed. The objective of this paper is to assess the suitability of different data transfer and communication protocols of IoT for deployment in the modern grid system. Moreover, several wireless IoT communication technologies are compared for their suitability in the multilayer network architecture and applications of energy systems.
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