There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

Neural networks

In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques.

Fog computing (FC) is used to reduce the energy consumption and latency for the heterogeneous communication approaches in the smart cities’ applications of the Internet of Everything (IoE). Fog computing nodes are connected through wired or wireless medium. The goal of smart city applications is to develop the transaction relationship of real-time response applications. There are various frameworks in real-world to support the IoE in smart-cities but they face the issues like security, platform Independence, multi-application assistance, and resource management. This article is motivated from the Blockchain and Fog computing technologies and presents a secured architecture Blockchain and Fog-based Architecture Network (BFAN) for IoE applications in the smart cities. The proposed architecture secures sensitive data with encryption, authentication, and Blockchain. It assists the System-developers and Architects to deploy the applications in smart city paradigm. The goal of the proposed architecture is to reduce the latency and energy, and ensure improved security features through Blockchain technology. The simulation results demonstrate that the proposed architecture performs better than the existing frameworks for smart-cities.

https://www.mdpi.com/1999-5903/12/4/61/pdf

Blockchain and Fog Based Architecture for Internet of Everything in Smart Cities

Cloud computing plays a vital role in processing a large amount of data. However, with the arrival of the Internet of Things, huge data are generated from these devices. Thus, there is the need for bringing characteristics of cloud closer to the request generator, so that processing of these huge data takes place at one-hop distance closer to that end user. This led to the emergence of fog computing with the aim to provide storage and computation at the edge of the network that reduces network traffic and overcomes many cloud computing drawbacks. Fog computing technology helps to overcome challenges of big data processing. The paper discusses the taxonomy of fog computing, how this is different from cloud computing and edge computing technologies, its applications, emerging key technologies (i.e., communication technologies and storage technologies) and various challenges involved in fog technology.

Fog computing: from architecture to edge computing and big data processing

Grid technologies enable the sharing of a wide variety of distributed resources. To utilize these resources, effective Resource Management systems are needed. Resource Management system performs resource discovery to obtain information about the available resources. However, the complex and dynamic nature of grid resources make sharing and discovery, a challenging issue. Resource Discovery is initiated by a network application to find suitable resources with in the Grid. Resource Discovery process is critical for efficient resource allocation and management. For making the Resource Discovery more efficient and reliable large numbers of Approaches are there. This paper analyses some of existing Approaches for Resource Discovery, which can search for the preferred resources quickly and efficiently (return the correct results quickly and reduce network complexity) in Grid computing. Finally a qualitative comparison between these Approaches based on the factors that affect Grid Resource Discovery process, has been done and results are presented.

Comparative Analysis of Resource Discovery Approaches in Grid Computing

Design and exploration of load balancers for fog computing using fuzzy logic

This paper discusses the Cloudanalyst tool. Cloudanalyst tool is used to determine the better load balancing algorithm from various scheduling and load balancing techniques e.g. round robin algorithm. This learning will help valued understanding to design infrastructure services of the Cloud. Different areas like coordination between one data center and other data center, algorithms of load balancing as well as other value-added services are also kept in mind, that are possible like service broker policies, which synchronize efficiently among data centers and enhance cost and throughput which is given to the owners.

http://www.sersc.org/journals/IJGDC/vol9_no9/2.pdf

Analysis of Load Balancing Algorithms using Cloud Analyst

Fault tolerance in cloud computing platform is a crucial issue as it guarantees the availability, performance and reliability of the applications. In order to achieve the availability, reliability, performance, robustness and dependability in cloud computing, failure should be accessed and handled effectively. This paper discusses the better understanding of different fault tolerance techniques which are used according to their policies and tools. This paper also describes the comprehensive taxonomy of faults, errors and failures. The usage of taxonomy and survey results are not only used to identify the similarities but also to identify the areas requiring for future research.

Anju Sharma

Papers

Fog computing: from architecture to edge computing and big data processing

Comparative Analysis of Resource Discovery Approaches in Grid Computing

Design and exploration of load balancers for fog computing using fuzzy logic

Analysis of Load Balancing Algorithms using Cloud Analyst

A comprehensive survey of Fault Tolerance techniques in Cloud Computing