A Comprehensive Survey on Edge Computing for the IoT

2019 ◽  
pp. 37-45 ◽  
Author(s):  
Pravin A. ◽  
Prem Jacob ◽  
G. Nagarajan
Keyword(s):  
Cloud Computing ◽  
Power Consumption ◽  
Response Time ◽  
Real World ◽  
Data Security ◽  
Edge Computing ◽  
Consumption Data ◽  
Real World Applications ◽  
Comprehensive Survey ◽  
Overall Performance

The IoT concept is used in various applications and it uses different devices for collecting data and processing the data. Various sets of devices such as sensors generate a large amount of data and the data will be forwarded to the appropriate devices for processing. The devices used will range from small devices to larger devices. The edge computing becomes the major role in overcoming the difficulties in cloud computing, the nearby devices are used as servers for providing better services. Most of the issues such as power consumption, data security, and response time will be addressed. The IoT plays a major role in many real-world applications. In this chapter, the basics and the use of the Edge computing concept in different applications are discussed. Edge computing can be used to increase the overall performance of the IoT. The performance of various applications in terms of edge computing and other methodologies are analyzed.

scholarly journals A Survey on Mobile Edge Computing: Focusing on Service Adoption and Provision

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Kai Peng ◽  
Victor C. M. Leung ◽  
Xiaolong Xu ◽  
Lixin Zheng ◽  
Jiabin Wang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Mobile Networks ◽  
Edge Computing ◽  
Computation Offloading ◽  
Battery Life ◽  
Mobile Edge Computing ◽  
Service Adoption ◽  
Comprehensive Survey ◽  
And Storage ◽  
Execution Delay

Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). However, this mode may cause significant execution delay. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. In this paper, we present a comprehensive survey of the MEC research from the perspective of service adoption and provision. We first describe the overview of MEC, including the definition, architecture, and service of MEC. After that we review the existing MUs-oriented service adoption of MEC, i.e., offloading. More specifically, the study on offloading is divided into two key taxonomies: computation offloading and data offloading. In addition, each of them is further divided into single MU offloading scheme and multi-MU offloading scheme. Then we survey edge server- (ES-) oriented service provision, including technical indicators, ES placement, and resource allocation. In addition, other issues like applications on MEC and open issues are investigated. Finally, we conclude the paper.

scholarly journals Transfer Learning and Deep Domain Adaptation

2020 ◽  
Author(s):  
Wen Xu ◽  
Jing He ◽  
Yanfeng Shu
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Transfer Learning ◽  
Real World ◽  
Deep Neural Networks ◽  
Domain Adaptation ◽  
Fine Tuning ◽  
Real World Applications ◽  
Comprehensive Survey ◽  
Sample Reconstruction

Transfer learning is an emerging technique in machine learning, by which we can solve a new task with the knowledge obtained from an old task in order to address the lack of labeled data. In particular deep domain adaptation (a branch of transfer learning) gets the most attention in recently published articles. The intuition behind this is that deep neural networks usually have a large capacity to learn representation from one dataset and part of the information can be further used for a new task. In this research, we firstly present the complete scenarios of transfer learning according to the domains and tasks. Secondly, we conduct a comprehensive survey related to deep domain adaptation and categorize the recent advances into three types based on implementing approaches: fine-tuning networks, adversarial domain adaptation, and sample-reconstruction approaches. Thirdly, we discuss the details of these methods and introduce some typical real-world applications. Finally, we conclude our work and explore some potential issues to be further addressed.

scholarly journals Development of a Wireless Temperature Sensor Using Polymer-Derived Ceramics

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Ran Zhao ◽  
Gang Shao ◽  
Ni Li ◽  
Chengying Xu ◽  
Linan An
Keyword(s):  
High Temperature ◽  
Power Consumption ◽  
Embedded System ◽  
Real World ◽  
Temperature Sensor ◽  
Wireless Transmission ◽  
Low Power Consumption ◽  
Compact Size ◽  
Real World Applications ◽  
The Embedded System

A temperature sensor has been developed using an embedded system and a sensor head made of polymer-derived SiAlCN ceramics (PDCs). PDC is a promising material for measuring high temperature and the embedded system features low-power consumption, compact size, and wireless transmission. The developed temperature sensor has been experimentally tested to demonstrate the possibility of using such sensors for real world applications.

scholarly journals Convolver Design and Convolve-Accumulate Unit Design for Low-Power Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5081
Author(s):  
Hsu-Yu Kao ◽  
Xin-Jia Chen ◽  
Shih-Hsu Huang
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
State Of The Art ◽  
Clock Cycle ◽  
The State ◽  
Edge Computing ◽  
Convolution Operation ◽  
Product Matrix ◽  
Unit Design ◽  
Overall Performance

Convolution operations have a significant influence on the overall performance of a convolutional neural network, especially in edge-computing hardware design. In this paper, we propose a low-power signed convolver hardware architecture that is well suited for low-power edge computing. The basic idea of the proposed convolver design is to combine all multipliers’ final additions and their corresponding adder tree to form a partial product matrix (PPM) and then to use the reduction tree algorithm to reduce this PPM. As a result, compared with the state-of-the-art approach, our convolver design not only saves a lot of carry propagation adders but also saves one clock cycle per convolution operation. Moreover, the proposed convolver design can be adapted for different dataflows (including input stationary dataflow, weight stationary dataflow, and output stationary dataflow). According to dataflows, two types of convolve-accumulate units are proposed to perform the accumulation of convolution results. The results show that, compared with the state-of-the-art approach, the proposed convolver design can save 15.6% power consumption. Furthermore, compared with the state-of-the-art approach, on average, the proposed convolve-accumulate units can reduce 15.7% power consumption.

scholarly journals An Improved Model to Increase Retrieval Time and Security by Data Fragmentation and Replication Process in Cloud

2019 ◽  
Vol 9 (2) ◽  
pp. 3830-3834
Keyword(s):  
Cloud Computing ◽  
Data Security ◽  
Data Replication ◽  
Security Issue ◽  
Replication Process ◽  
Retrieval Time ◽  
Novel Approach ◽  
Data Fragmentation ◽  
Overall Performance ◽  
Improved Model

Cloud computing provides several features to users as well as to the organizations. Even though, there are some issues faced by the user while usingthe cloud. Security is a major concern that is always considered. Likewise Data replication is a significant technique to be consideredfor retrieval time. Replication helps to fetch the data from remote which is a hightime consuming process.To overcome the security issue along with data replication a novel approach is proposed in this paper.Dynamic fragmentation is utilized for the division of a file into fragments. Each cloud nodes has a different fragment to enhance the data security of the system. Blowfish technique is used for encrypt the files before storing in cloudthatdivides messages into 64 bits blocks then encrypts them separately. The result of experimental evaluation shows that this schemes increase the overall performance

scholarly journals Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Zhiguo Qu ◽  
Yilin Wang ◽  
Le Sun ◽  
Dandan Peng ◽  
Zheng Li
Keyword(s):  
Cloud Computing ◽  
Energy Saving ◽  
Fog Computing ◽  
Edge Computing ◽  
Quality Of Services ◽  
Service Computing ◽  
Comprehensive Survey ◽  
Qos Optimization ◽  
Computing Models

With an increase of service users’ demands on high quality of services (QoS), more and more efficient service computing models are proposed. The development of cloud computing, fog computing, and edge computing brings a number of challenges, e.g., QoS optimization and energy saving. We do a comprehensive survey on QoS optimization and energy saving in cloud computing, fog computing, edge computing, and IoT environments. We summarize the main challenges and analyze corresponding solutions proposed by existing works. This survey aims to help readers have a deeper understanding on the concepts of different computing models and study the techniques of QoS optimization and energy saving in these models.

scholarly journals Extended VEOT Framework – Implemented in a Smart Boutique

2019 ◽  
Vol 8 (9S3) ◽  
pp. 762-767
Keyword(s):  
Cloud Computing ◽  
Virtual Reality ◽  
Augmented Reality ◽  
Virtual Environment ◽  
Real World ◽  
New Technologies ◽  
Smart Homes ◽  
Touch Screen ◽  
Edge Computing ◽  
Base Paper

With advent of new technologies in communication and security in IoT and advantages of Cloud computing, have multiplied, the opportunities in Smart homes, Offices and Equipments. Augmented Reality (AR) adds another dimension to the experience. The proposed system makes use of all these concepts including Edge computing to design a Smart Boutique. A boutique is a unique store that sells stylish, luxury and fashion clothing, jewelry, or other exquisite goods. Considering the richness to be experienced by the Customers, the usage of these techniques would amply complement their senses. VEoT or Virtual Environment of Things is a framework for combining all these technologies to mix both real world objects and Virtual Reality. It is very important to cater to the needs of customer in a unique way, giving personal touch to the business. This would create repeat orders and would attract more customer footfalls. The base paper gives a concrete architecture for combining IoT and AR with the help of Edge server. We have tried to make the system robust, including a kiosk touch screen to enable the customer to have more information on Boutique and its articles. The goal of the system is to produce renewed customer experience and create a Smart Boutique with technology.

scholarly journals Emerging Paradigm of Edge Computing in the Context Of Iot

2018 ◽  
Vol 7 (3.3) ◽  
pp. 122
Author(s):  
BETHALA SHIRISHA ◽  
DEGALA DIVYA PRIYA ◽  
MAHALAKSHMI . ◽  
MAHENDER REDDY CHILUKALA ◽  
BETHALA PRAVALLIKA
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Cost Saving ◽  
Response Time ◽  
Spectral Efficiency ◽  
Smart Home ◽  
Edge Computing ◽  
Battery Life ◽  
Time Requirement ◽  
Machine Type

Centralised Cloud computing is having many challenges with the rapid increasing in IoT( Internet Of Things) applications. The challenges are high latency, low spectral efficiency (SE), and non-adaptive machine type of communication. In order to solve these challenges have moved to the concept of new computing concept that is nothing but edge computing, which calls for moving the data at the edge of the network. Edge computing has the possible to deal with the concerns of battery life constraint, response time requirement, data safety, bandwidth cost saving and privacy. In this paper, we started the explanation with IoT and solution of IOT i.e. edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing.  

Data Security and Protection in Cloud Computing

2012 ◽  
Vol 1 (2) ◽  
pp. 31-34
Author(s):  
Shameena Begum ◽  
◽  
V.Ratna Vasuki ◽  
K.V.V.Srinivas K.V.V.Srinivas
Keyword(s):  
Cloud Computing ◽  
Data Security ◽  
Security And Protection
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