scholarly journals LDAF: Low-Bandwidth Distributed Applications Framework in a Use Case of Blockchain-Enabled IoT Devices

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2337 ◽  
Author(s):  
Matevž Pustišek ◽  
Dejan Dolenc ◽  
Andrej Kos
Keyword(s):  
Fog Computing ◽  
Distributed Applications ◽  
Use Case ◽  
Smart Meter ◽  
Resource Limited ◽  
Application Logic ◽  
Periodic Traffic ◽  
Communication Traffic ◽  
Iot Devices ◽  
Application Aware

In this paper, we present Low-Bandwidth Distributed Applications Framework (LDAF)—an application-aware gateway for communication-constrained Internet of things (IoT) devices. A modular approach facilitates connecting to existing cloud backend servers and managing message formats and APIs’ native application logic to meet the communication constraints of resource-limited end devices. We investigated options for positioning the LDAF server in fog computing architectures. We demonstrated the approach in three use cases: (i) a simple domain name system (DNS) query from the device to a DNS server, (ii) a complex interaction of a blockchain—based IoT device with a blockchain network, and (iii) difference based patching of binary (system) files at the IoT end devices. In a blockchain smart meter use case we effectively enabled decentralized applications (DApp) for devices that without our solution could not participate in a blockchain network. Employing the more efficient binary content encoding, we reduced the periodic traffic from 16 kB/s to ~1.1 kB/s, i.e., 7% of the initial traffic. With additional optimization of the application protocol in the gateway and message filtering, the periodic traffic was reduced to ~1% of the initial traffic, without any tradeoffs in the application’s functionality or security. Using a function of binary difference we managed to reduce the size of the communication traffic to the end device, at least when the binary patch was smaller than the patching file.

scholarly journals IoT Provisioning QoS based on Cloud and Fog Computing

2021 ◽  
Vol 2 (01) ◽  
pp. 29-40
Author(s):  
Fady Esmat Fathel Samann ◽  
Subhi R. M. Zeebaree ◽  
Shavan Askar
Keyword(s):  
Literature Review ◽  
Fog Computing ◽  
Academic Research ◽  
Traffic Patterns ◽  
Resource Limited ◽  
Life Tasks ◽  
Iot Devices ◽  
Primary Focus ◽  
Immense Potential ◽  
Qos Metrics

The wide-spread Internet of Things (IoT) utilization in almost every scope of our life made it possible to automate daily life tasks with no human intervention. This promising technology has immense potential for making life much easier and open new opportunities for newly developed applications to emerge. However, meeting the diverse Quality of Service (QoS) demands of different applications remains a formidable topic due to diverse traffic patterns, unpredictable network traffic, and resource-limited nature of IoT devices. In this context, application-tailored QoS provisioning mechanisms have been the primary focus of academic research. This paper presents a literature review on QoS techniques developed in academia for IoT applications and investigates current research trends. Background knowledge on IoT, QoS metrics, and critical enabling technologies will be given beforehand, delving into the literature review. According to the comparison presented in this work, the commonly considered QoS metrics are Latency, Reliability, Throughput, and Network Usage. The reviewed studies considered the metrics that fit their provisioning solutions.

scholarly journals An efficient pruning scheme of deep neural networks for Internet of Things applications

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chen Qi ◽  
Shibo Shen ◽  
Rongpeng Li ◽  
Zhifeng Zhao ◽  
Qing Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Internet Of Things ◽  
Deep Neural Networks ◽  
Computational Cost ◽  
Superior Performance ◽  
Compact Structure ◽  
Resource Limited ◽  
Benchmark Datasets ◽  
Iot Devices

AbstractNowadays, deep neural networks (DNNs) have been rapidly deployed to realize a number of functionalities like sensing, imaging, classification, recognition, etc. However, the computational-intensive requirement of DNNs makes it difficult to be applicable for resource-limited Internet of Things (IoT) devices. In this paper, we propose a novel pruning-based paradigm that aims to reduce the computational cost of DNNs, by uncovering a more compact structure and learning the effective weights therein, on the basis of not compromising the expressive capability of DNNs. In particular, our algorithm can achieve efficient end-to-end training that transfers a redundant neural network to a compact one with a specifically targeted compression rate directly. We comprehensively evaluate our approach on various representative benchmark datasets and compared with typical advanced convolutional neural network (CNN) architectures. The experimental results verify the superior performance and robust effectiveness of our scheme. For example, when pruning VGG on CIFAR-10, our proposed scheme is able to significantly reduce its FLOPs (floating-point operations) and number of parameters with a proportion of 76.2% and 94.1%, respectively, while still maintaining a satisfactory accuracy. To sum up, our scheme could facilitate the integration of DNNs into the common machine-learning-based IoT framework and establish distributed training of neural networks in both cloud and edge.

Survey on DDoS Attacks and Defense Mechanisms in Cloud and Fog Computing

2018 ◽  
Vol 10 (3) ◽  
pp. 61-83 ◽  
Author(s):  
Deepali Chaudhary ◽  
Kriti Bhushan ◽  
B.B. Gupta
Keyword(s):  
Cloud Computing ◽  
Defense Mechanisms ◽  
Low Cost ◽  
Fog Computing ◽  
Smart Devices ◽  
Cloud Environment ◽  
Security Issue ◽  
Process Data ◽  
Ddos Attack ◽  
Iot Devices

This article describes how cloud computing has emerged as a strong competitor against traditional IT platforms by offering low-cost and “pay-as-you-go” computing potential and on-demand provisioning of services. Governments, as well as organizations, have migrated their entire or most of the IT infrastructure to the cloud. With the emergence of IoT devices and big data, the amount of data forwarded to the cloud has increased to a huge extent. Therefore, the paradigm of cloud computing is no longer sufficient. Furthermore, with the growth of demand for IoT solutions in organizations, it has become essential to process data quickly, substantially and on-site. Hence, Fog computing is introduced to overcome these drawbacks of cloud computing by bringing intelligence to the edge of the network using smart devices. One major security issue related to the cloud is the DDoS attack. This article discusses in detail about the DDoS attack, cloud computing, fog computing, how DDoS affect cloud environment and how fog computing can be used in a cloud environment to solve a variety of problems.

scholarly journals IoT Notary : Attestable Sensor Data Capture in IoT Environments

2022 ◽  
Vol 3 (1) ◽  
pp. 1-30
Author(s):  
Nisha Panwar ◽  
Shantanu Sharma ◽  
Guoxi Wang ◽  
Sharad Mehrotra ◽  
Nalini Venkatasubramanian ◽  
...  
Keyword(s):  
Real Time ◽  
Location Based Services ◽  
Third Party ◽  
Sensor Data ◽  
Published Data ◽  
Resource Limited ◽  
User Data ◽  
Data Capturing ◽  
Iot Devices ◽  
The University

Contemporary IoT environments, such as smart buildings, require end-users to trust data-capturing rules published by the systems. There are several reasons why such a trust is misplaced—IoT systems may violate the rules deliberately or IoT devices may transfer user data to a malicious third-party due to cyberattacks, leading to the loss of individuals’ privacy or service integrity. To address such concerns, we propose IoT Notary , a framework to ensure trust in IoT systems and applications. IoT Notary provides secure log sealing on live sensor data to produce a verifiable “proof-of-integrity,” based on which a verifier can attest that captured sensor data adhere to the published data-capturing rules. IoT Notary is an integral part of TIPPERS, a smart space system that has been deployed at the University of California, Irvine to provide various real-time location-based services on the campus. We present extensive experiments over real-time WiFi connectivity data to evaluate IoT Notary , and the results show that IoT Notary imposes nominal overheads. The secure logs only take 21% more storage, while users can verify their one day’s data in less than 2 s even using a resource-limited device.

scholarly journals A Containerized Approach for Allocating Distributed Stream Queries to Fog Nodes

2021 ◽  
Author(s):  
Hamed Hasibi ◽  
Saeed Sedighian Kashi
Keyword(s):  
Fog Computing ◽  
Stream Processing ◽  
Stream Data ◽  
Process Data ◽  
Stream Query Processing ◽  
Tremendous Amount ◽  
Stream Processing Engines ◽  
Iot Devices ◽  
Distributed Stream Processing

Fog computing brings cloud capabilities closer to the Internet of Things (IoT) devices. IoT devices generate a tremendous amount of stream data towards the cloud via hierarchical fog nodes. To process data streams, many Stream Processing Engines (SPEs) have been developed. Without the fog layer, the stream query processing executes on the cloud, which forwards much traffic toward the cloud. When a hierarchical fog layer is available, a complex query can be divided into simple queries to run on fog nodes by using distributed stream processing. In this paper, we propose an approach to assign stream queries to fog nodes using container technology. We name this approach Stream Queries Placement in Fog (SQPF). Our goal is to minimize end-to-end delay to achieve a better quality of service. At first, in the emulation step, we make docker container instances from SPEs and evaluate their processing delay and throughput under different resource configurations and queries with varying input rates. Then in the placement step, we assign queries among fog nodes by using a genetic algorithm. The practical approach used in SQPF achieves a near-the-best assignment based on the lowest application deadline in real scenarios, and evaluation results are evidence of this goal.

scholarly journals Design a blockchain-based middleware layer in the Internet of Things Architecture

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Tanweer Alam
Keyword(s):  
Internet Of Things ◽  
Fog Computing ◽  
Smart Devices ◽  
The Internet ◽  
Novel Approach ◽  
Generation Computing ◽  
Critical Issues ◽  
Iot Devices ◽  
The Internet Of Things

In next-generation computing, the role of cloud, internet and smart devices will be capacious. Nowadays we all are familiar with the word smart. This word is used a number of times in our daily life. The Internet of Things (IoT) will produce remarkable different kinds of information from different resources. It can store big data in the cloud. The fog computing acts as an interface between cloud and IoT. The extension of fog in this framework works on physical things under IoT. The IoT devices are called fog nodes, they can have accessed anywhere within the range of the network. The blockchain is a novel approach to record the transactions in a sequence securely. Developing a new blockchains based middleware framework in the architecture of the Internet of Things is one of the critical issues of wireless networking where resolving such an issue would result in constant growth in the use and popularity of IoT. The proposed research creates a framework for providing the middleware framework in the internet of smart devices network for the internet of things using blockchains technology. Our main contribution links a new study that integrates blockchains to the Internet of things and provides communication security to the internet of smart devices.

Healthcare-Internet of Things and Its Components

2021 ◽  
pp. 258-277
Author(s):  
Aman Tyagi
Keyword(s):  
Internet Of Things ◽  
Fog Computing ◽  
Communication Technologies ◽  
The Internet ◽  
Healthcare Resources ◽  
Iot Security ◽  
Global Epidemic ◽  
Security Issues ◽  
Analyse Data ◽  
Iot Devices

Elderly population in the Asian countries is increasing at a very fast rate. Lack of healthcare resources and infrastructure in many countries makes the task of provding proper healthcare difficult. Internet of things (IoT) in healthcare can address the problem effectively. Patient care is possible at home using IoT devices. IoT devices are used to collect different types of data. Various algorithms may be used to analyse data. IoT devices are connected to the internet and all the data of the patients with various health reports are available online and hence security issues arise. IoT sensors, IoT communication technologies, IoT gadgets, components of IoT, IoT layers, cloud and fog computing, benefits of IoT, IoT-based algorithms, IoT security issues, and IoT challenges are discussed in the chapter. Nowadays global epidemic COVID19 has demolished the economy and health services of all the countries worldwide. Usefulness of IoT in COVID19-related issues is explained here.

The Challenges, Technologies, and Role of Fog Computing in the Context of Industrial Internet of Things

2021 ◽  
pp. 1-16
Author(s):  
Sasikala Chinthakunta ◽  
Shoba Bindu Chigarapalle ◽  
Sudheer Kumar E.
Keyword(s):  
Data Storage ◽  
Smart Grids ◽  
Traffic Management ◽  
Fog Computing ◽  
Poor Quality ◽  
Data Traffic ◽  
Cloud Data ◽  
Security Issues ◽  
Huge Data ◽  
Iot Devices

Typically, the analysis of the industrial big data is done at the cloud. If the technology of IIoT is relying on cloud, data from the billions of internet-connected devices are voluminous and demand to be processed within the cloud DCs. Most of the IoT infrastructures—smart driving and car parking systems, smart vehicular traffic management systems, and smart grids—are observed to demand low-latency, real-time services from the service providers. Since cloud includes data storage, processing, and computation only within DCs, huge data traffic generated from the IoT devices probably experience a network bottleneck, high service latency, and poor quality of service (QoS). Hence, the placement of an intermediary node that can perform tasks efficiently and effectively is an unavoidable requirement of IIoT. Fog can be such an intermediary node because of its ability and location to perform tasks at the premise of an industry in a timely manner. This chapter discusses challenges, need, and framework of fog computing, security issues, and solutions of fog computing for IIoT.

A Novel Resource Management Framework for Fog Computing by Using Machine Learning Algorithm

2020 ◽  
pp. 42-52
Author(s):  
Shanthi Thangam Manukumar ◽  
Vijayalakshmi Muthuswamy
Keyword(s):  
Machine Learning ◽  
Resource Management ◽  
Mobile Devices ◽  
Service Providers ◽  
Learning Algorithm ◽  
Fog Computing ◽  
Mobile Network ◽  
Resource Provisioning ◽  
Management Framework ◽  
Iot Devices

With the development of edge devices and mobile devices, the authenticated fast access for the networks is necessary and important. To make the edge and mobile devices smart, fast, and for the better quality of service (QoS), fog computing is an efficient way. Fog computing is providing the way for resource provisioning, service providers, high response time, and the best solution for mobile network traffic. In this chapter, the proposed method is for handling the fog resource management using efficient offloading mechanism. Offloading is done based on machine learning prediction technology and also by using the KNN algorithm to identify the nearest fog nodes to offload. The proposed method minimizes the energy consumption, latency and improves the QoS for edge devices, IoT devices, and mobile devices.

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