scholarly journals Load Balancing at Fog Nodes using Scheduling Algorithms

2020 ◽  
Vol 8 (6) ◽  
pp. 4129-4134
Keyword(s):  
Load Balancing ◽  
Large Scale ◽  
Fog Computing ◽  
Scheduling Algorithms ◽  
Small Scale ◽  
Location Awareness ◽  
Time Cost ◽  
Cloud Server ◽  
Iot Devices ◽  
Cloud Servers

Cloud Computing proves to be most predominant innovative field in the area of Information technology. Cloud is best suited for small scale to large scale businesses and personal purposes such as storing, computing, managing data & resources, running applications and many more. Due to increasing large volumes of data over cloud servers created subsequent specific issues like data maintainability, network elasticity, managing Internet of Things (I.o.T’s) devices and many more. Recent progresses in Technology are given rise to fog computing or decentralized cloud to overcome cloud server issues called fog nodes. In this paper we present a brief note on how cloud issues can overcome using fog nodes benefits along with elaboration of load balancing factor. To maintain load balancing of fog nodes no much appreciable work took place in the field of fog computing. This paper proposes a scheduler which receives the devices in to a Job Queue to be connected over cloud. To apply scheduling algorithms like F.C.F.S, S.J.F, P.S, R.R and W.R.R. over fog nodes will be discussed along with their merits & demerits. At last we try to compare the various parameters of load balancing among various scheduling algorithms. In this paper we focus on how fog nodes perform functions like considerable storages, low latency, heterogeneity, allocation & interaction with limited IoT devices and Security along with architecture cloud to fog. During allocation of IoT devices to various fog nodes we will come across a serious issues i.e load balancing on fog nodes. Our detailed study presents the comparison of above mentioned scheduling algorithms load balancing factors such as rich resources allocations & Balancing among fog nodes, Identification of devices, Authentication of fog nodes, bandwidth consumption, location awareness, response time, cost maintenances, Intrusion detection, fault forbearances and maintainability.

scholarly journals Efficient Cognitive Fog Computing for Classification of Network Cyberattacks Using Machine Learning

2020 ◽  
pp. 176-184
Author(s):  
A. V. Deorankar ◽  
Shiwani S. Thakare
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Binary Classification ◽  
Fog Computing ◽  
Machine Learning Techniques ◽  
Machine Learning Classification ◽  
Detection Techniques ◽  
Learning Techniques ◽  
Iot Devices ◽  
Cloud Servers

IoT is the network which connects and communicates with billions of devices through the internet and due to the massive use of IoT devices, the shared data between the devices or over the network is not confidential because of increasing growth of cyberattacks. The network traffic via loT systems is growing widely and introducing new cybersecurity challenges since these loT devices are connected to sensors that are directly connected to large-scale cloud servers. In order to reduce these cyberattacks, the developers need to raise new techniques for detecting infected loT devices. In this work, to control over this cyberattacks, the fog layer is introduced, to maintain the security of data on a cloud. Also the working of fog layer and different anomaly detection techniques to prevent the cyberattacks has been studied. The proposed AD-IoT can significantly detect malicious behavior using anomalies based on machine learning classification before distributing on a cloud layer. This work discusses the role of machine learning techniques for identifying the type of Cyberattacks. There are two ML techniques i.e. RF and MLP evaluated on the USNW-NB15 dataset. The accuracy and false alarm rate of the techniques are assessed, and the results revealed the superiority of the RF compared with MLP. The Accuracy measures by classifiers are 98 and 53 of RF and MLP respectively, which shows a huge difference and prove the RF as most efficient algorithm with binary classification as well as multi- classification.

scholarly journals A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing

2019 ◽  
Vol 9 (1) ◽  
pp. 178 ◽  
Author(s):  
Belal Sudqi Khater ◽  
Ainuddin Wahid Bin Abdul Wahab ◽  
Mohd Yamani Idna Bin Idris ◽  
Mohammed Abdulla Hussain ◽  
Ashraf Ahmed Ibrahim
Keyword(s):  
Intrusion Detection ◽  
Detection System ◽  
Fog Computing ◽  
Raspberry Pi ◽  
Space Representation ◽  
Location Awareness ◽  
Resource Limitations ◽  
System Calls ◽  
Iot Devices ◽  
Hidden Layer

Fog computing is a paradigm that extends cloud computing and services to the edge of the network in order to address the inherent problems of the cloud, such as latency and lack of mobility support and location-awareness. The fog is a decentralized platform capable of operating and processing data locally and can be installed in heterogeneous hardware which makes it ideal for Internet of Things (IoT) applications. Intrusion Detection Systems (IDSs) are an integral part of any security system for fog and IoT networks to ensure the quality of service. Due to the resource limitations of fog and IoT devices, lightweight IDS is highly desirable. In this paper, we present a lightweight IDS based on a vector space representation using a Multilayer Perceptron (MLP) model. We evaluated the presented IDS against the Australian Defense Force Academy Linux Dataset (ADFA-LD) and Australian Defense Force Academy Windows Dataset (ADFA-WD), which are new generation system calls datasets that contain exploits and attacks on various applications. The simulation shows that by using a single hidden layer and a small number of nodes, we are able to achieve a 94% Accuracy, 95% Recall, and 92% F1-Measure in ADFA-LD and 74% Accuracy, 74% Recall, and 74% F1-Measure in ADFA-WD. The performance is evaluated using a Raspberry Pi.

scholarly journals Privacy-Preserving Data Aggregation against False Data Injection Attacks in Fog Computing

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2659 ◽  
Author(s):  
Yinghui Zhang ◽  
Jiangfan Zhao ◽  
Dong Zheng ◽  
Kaixin Deng ◽  
Fangyuan Ren ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Aggregation ◽  
Homomorphic Encryption ◽  
Fog Computing ◽  
Cloud Service ◽  
Privacy Preserving ◽  
Location Awareness ◽  
False Data Injection ◽  
Injection Attacks ◽  
Iot Devices

As an extension of cloud computing, fog computing has received more attention in recent years. It can solve problems such as high latency, lack of support for mobility and location awareness in cloud computing. In the Internet of Things (IoT), a series of IoT devices can be connected to the fog nodes that assist a cloud service center to store and process a part of data in advance. Not only can it reduce the pressure of processing data, but also improve the real-time and service quality. However, data processing at fog nodes suffers from many challenging issues, such as false data injection attacks, data modification attacks, and IoT devices’ privacy violation. In this paper, based on the Paillier homomorphic encryption scheme, we use blinding factors to design a privacy-preserving data aggregation scheme in fog computing. No matter whether the fog node and the cloud control center are honest or not, the proposed scheme ensures that the injection data is from legal IoT devices and is not modified and leaked. The proposed scheme also has fault tolerance, which means that the collection of data from other devices will not be affected even if certain fog devices fail to work. In addition, security analysis and performance evaluation indicate the proposed scheme is secure and efficient.

Agent-Based Improved Neuro-Fuzzy for Load Balancing in Sensor Cloud

2021 ◽  
Vol 10 (1) ◽  
pp. 16-35
Author(s):  
Prashant Sangulagi ◽  
Ashok Sutagundar
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Fuzzy Optimization ◽  
Great Accuracy ◽  
Agent Based ◽  
Neuro Fuzzy ◽  
Physical Sensors ◽  
Cloud Server ◽  
Sensor Cloud ◽  
Cloud Servers

Sensor cloud paradigm is a trending area for most of the applications. It collects the information from physical sensors and stores it in cloud servers, and it can be accessed anywhere. Energy optimization is one of the crucial issues in sensor cloud as sensed information are unprocessed and directly saved into cloud server thereby increasing energy consumption and delay which leads to unbalancing in the network. In this paper, agent-based improved neuro-fuzzy optimization is proposed to avoid transmission of redundant information into cloud along with load balancing among all nodes for equal energy consumption. The agents work on behalf of node, migrate to each node in the cluster, collect information, and submit to CH minimizing node energy consumption. Neuro-fuzzy along with weights is used to improve information accuracy and reducing energy consumption to improve overall network lifetime. Result shows that less energy is consumed along with minimum delay and information with great accuracy is saved into cloud server.

Load and Cost Aware Min-Min Workflow Scheduling Algorithm for Heterogeneous Resources in Fog, Cloud and Edge Scenarios

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Load Balancing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Fog Computing ◽  
Workflow Scheduling ◽  
Heterogeneous Environment ◽  
Cloud Servers ◽  
Heterogeneous Resources

Fog computing and Edge computing are few of the latest technologies which are offered as solution to challenges faced in Cloud Computing. Instead of offloading of all the tasks to centralized cloud servers, some of the tasks can be scheduled at intermediate Fog servers or Edge devices. Though this solves most of the problems faced in cloud but also encounter other traditional problems due to resource-related constraints like load balancing, scheduling, etc. In order to address task scheduling and load balancing in Cloud-fog-edge collaboration among servers, we have proposed an improved version of min-min algorithm for workflow scheduling which considers cost, makespan, energy and load balancing in heterogeneous environment. This algorithm is implemented and tested in different offloading scenarios- Cloud only, Fog only, Cloud-fog and Cloud-Fog-Edge collaboration. This approach performed better and the result gives minimum makespan, less energy consumption along with load balancing and marginally less cost when compared to min-min and ELBMM algorithms

Large-Scale Data Streaming in Fog Computing and Its Applications

2021 ◽  
pp. 50-65
Author(s):  
Oshin Sharma ◽  
Anusha S.
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Fog Computing ◽  
Data Streaming ◽  
Cloud Data ◽  
Large Scale Data ◽  
Emerging Trends ◽  
Iot Devices ◽  
Data Centres ◽  
Scale Data

The emerging trends in fog computing have increased the interests and focus in both industry and academia. Fog computing extends cloud computing facilities like the storage, networking, and computation towards the edge of networks wherein it offloads the cloud data centres and reduces the latency of providing services to the users. This paradigm is like cloud in terms of data, storage, application, and computation services, except with a fundamental difference: it is decentralized. Furthermore, these fog systems can process huge amounts of data locally and can be installed on hardware of different types. These characteristics make fog suitable for time- and location-based applications like internet of things (IoT) devices which can process large amounts of data. In this chapter, the authors present fog data streaming, its architecture, and various applications.

AntibIoTic: The Fog-enhanced distributed security system to protect the (legacy) Internet of Things

2021 ◽  
pp. 1-37
Author(s):  
Michele De Donno ◽  
Xenofon Fafoutis ◽  
Nicola Dragoni
Keyword(s):  
Internet Of Things ◽  
Fog Computing ◽  
Security System ◽  
Security And Privacy ◽  
Aggregated Data ◽  
Fine Grained ◽  
Computational Overhead ◽  
Distributed Security ◽  
Cloud Server ◽  
Iot Devices

The Internet of Things (IoT) is evolving our society; however, the growing adoption of IoT devices in many scenarios brings security and privacy implications. Current security solutions are either unsuitable for every IoT scenario or provide only partial security. This paper presents AntibIoTic 2.0, a distributed security system that relies on Fog computing to secure IoT devices, including legacy ones. The system is composed of a backbone, made of core Fog nodes and Cloud server, a Fog node acting at the edge as the gateway of the IoT network, and a lightweight agent running on each IoT device. The proposed system offers fine-grained, host-level security coupled with network-level protection, while its distributed nature makes it scalable, versatile, lightweight, and easy to deploy, also for legacy IoT deployments. AntibIoTic 2.0 can also publish anonymized and aggregated data and statistics on the deployments it secures, to increase awareness and push cooperations in the area of IoT security. This manuscript recaps and largely expands previous works on AntibIoTic, providing an enhanced design of the system, an extended proof-of-concept that proves its feasibility and shows its operation, and an experimental evaluation that reports the low computational overhead it causes.

Scheduling Algorithms Using Metaheuristics for Production Processes with Crane Interference

2018 ◽  
Vol 12 (3) ◽  
pp. 297-307 ◽  
Author(s):  
Takashi Tanizaki ◽  
Hideki Katagiri ◽  
António Oliveira Nzinga René ◽  
◽  
Keyword(s):  
Large Scale ◽  
Manufacturing Industry ◽  
Computer Experiments ◽  
Scheduling Algorithms ◽  
General Purpose ◽  
Small Scale ◽  
Production Processes ◽  
Large Scale Problems ◽  
Operational Constraints ◽  
Short Time

This paper proposes scheduling algorithms using metaheuristics for production processes in which cranes can interfere with each other. There are many production processes that involve cranes in manufacturing industry, such as in the steel industry, so a general purpose algorithm for this problem can be of practical use. The scheduling problem for this process is very complicated and difficult to solve because the cranes must avoid interfering with each other plus each machine has its own operational constraints. Although several algorithms have been proposed for a specific problem or small-scale problem, general purpose algorithms that can be solved in real time (about 30 minutes or less) in the company’s production planning work have not been developed for large-scale problems. This paper develops some metaheuristic algorithms to obtain suboptimal solutions in a short time, and it confirms their effectiveness through computer experiments.

scholarly journals Dynamically Controlling Offloading Thresholds in Fog Systems

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2512 ◽  
Author(s):  
Faten Alenizi ◽  
Omer Rana
Keyword(s):  
Energy Consumption ◽  
Fog Computing ◽  
Limited Resources ◽  
Location Awareness ◽  
Computing Systems ◽  
Dynamic Task ◽  
Delay Sensitive ◽  
Reduce Energy Consumption ◽  
Iot Devices ◽  
Dynamic Task Scheduling

Fog computing is a potential solution to overcome the shortcomings of cloud-based processing of IoT tasks. These drawbacks can include high latency, location awareness, and security—attributed to the distance between IoT devices and cloud-hosted servers. Although fog computing has evolved as a solution to address these challenges, it is known for having limited resources that need to be effectively utilized, or its advantages could be lost. Computational offloading and resource management are critical to be able to benefit from fog computing systems. We introduce a dynamic, online, offloading scheme that involves the execution of delay-sensitive tasks. This paper proposes an architecture of a fog node able to adjust its offloading threshold dynamically (i.e., the criteria by which a fog node decides whether tasks should be offloaded rather than executed locally) using two algorithms: dynamic task scheduling (DTS) and dynamic energy control (DEC). These algorithms seek to minimize overall delay, maximize throughput, and minimize energy consumption at the fog layer. Compared to other benchmarks, our approach could reduce latency by up to 95%, improve throughput by 71%, and reduce energy consumption by up to 67% in fog nodes.

Scalable Estimator for Multi-task Gaussian Graphical Models Based in an IoT Network

2021 ◽  
Vol 17 (3) ◽  
pp. 1-33
Author(s):  
Beilun Wang ◽  
Jiaqi Zhang ◽  
Yan Zhang ◽  
Meng Wang ◽  
Sen Wang
Keyword(s):  
Graphical Models ◽  
Large Scale ◽  
Graphical Model ◽  
Rapid Development ◽  
Heterogeneous Data ◽  
Joint Estimation ◽  
Gaussian Graphical Models ◽  
Novel Approach ◽  
Cloud Server ◽  
Iot Devices

Recently, the Internet of Things (IoT) receives significant interest due to its rapid development. But IoT applications still face two challenges: heterogeneity and large scale of IoT data. Therefore, how to efficiently integrate and process these complicated data becomes an essential problem. In this article, we focus on the problem that analyzing variable dependencies of data collected from different edge devices in the IoT network. Because data from different devices are heterogeneous and the variable dependencies can be characterized into a graphical model, we can focus on the problem that jointly estimating multiple, high-dimensional, and sparse Gaussian Graphical Models for many related tasks (edge devices). This is an important goal in many fields. Many IoT networks have collected massive multi-task data and require the analysis of heterogeneous data in many scenarios. Past works on the joint estimation are non-distributed and involve computationally expensive and complex non-smooth optimizations. To address these problems, we propose a novel approach: Multi-FST. Multi-FST can be efficiently implemented on a cloud-server-based IoT network. The cloud server has a low computational load and IoT devices use asynchronous communication with the server, leading to efficiency. Multi-FST shows significant improvement, over baselines, when tested on various datasets.

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