A Reliable Trust Computing Mechanism in Fog Computing

2021 ◽  
Vol 11 (1) ◽  
pp. 1-20
Author(s):  
Vijay Lingaraddi Hallappanavar ◽  
Mahantesh N. Birje
Keyword(s):  
Fog Computing ◽  
Multiple Sources ◽  
Individual Interaction ◽  
Punishment Mechanism ◽  
Iot Devices ◽  
Time Required ◽  
Subjective Trust ◽  
Trust Computing ◽  
Better Than

Due to the lack of trust on IoT devices, the integration of fog computing and IoT devices is hindered. Trust is considered to have two notions: subjective trust where the user puts his individual interests to the interactions and objective trust which depends only on individual interaction experiences. This paper proposes a reliable trust computing mechanism based on subjective and objective trust. The subjective trust is calculated from feedback of multiple sources. The incentive and punishment mechanism is applied to the subjective trust to avoid malicious devices. The objective trust is calculated based on quality of services. The overall trust helps the IoT devices to determine the trustworthiness of other IoT devices and in turn helps to establish a trusted environment. The experimental results show that the performance is better than existing methods in terms of time required to calculate the overall trust, reliability, and trustworthiness of IoT devices.

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.

Fog Computing Qos Review and Open Challenges

2021 ◽  
pp. 1147-1157
Author(s):  
R. Babu ◽  
K. Jayashree ◽  
R. Abirami
Keyword(s):  
Programming Model ◽  
Fog Computing ◽  
Big Data Analytics ◽  
Security And Privacy ◽  
Computation Offloading ◽  
Future Research ◽  
Future Research Directions ◽  
Iot Devices ◽  
Research Challenge

Internet of Things (IoT) enables inters connectivity among devices and platforms. IoT devices such as sensors, or embedded systems offer computational, storage, and networking resources and the existence of these resources permits to move the execution of IoT applications to the edge of the network and it is known as fog computing. It is able to handle billions of Internet-connected devices and is well situated for real-time big data analytics and provides advantages in advertising and personal computing. The main issues in fog computing includes fog networking, QoS, interfacing and programming model, computation offloading, accounting, billing and monitoring, provisioning and resource management, security and privacy. A particular research challenge is the Quality of Service metric for fog services. Thus, this paper gives a survey of cloud computing, discusses the QoS metrics, and the future research directions in fog computing.

scholarly journals Dynamic Fault Tolerance Aware Scheduling for Healthcare System on Fog Computing

2021 ◽  
pp. 308-318
Author(s):  
Hadeel T. Rajab ◽  
Manal F. Younis
Keyword(s):  
Fault Tolerance ◽  
Fog Computing ◽  
Middle Layer ◽  
Massive Data ◽  
Data Filtering ◽  
Iot Devices ◽  
Processing And Storage ◽  
Cloud Network ◽  
And Storage

 Internet of Things (IoT) contributes to improve the quality of life as it supports many applications, especially healthcare systems. Data generated from IoT devices is sent to the Cloud Computing (CC) for processing and storage, despite the latency caused by the distance. Because of the revolution in IoT devices, data sent to CC has been increasing. As a result, another problem added to the latency was increasing congestion on the cloud network. Fog Computing (FC) was used to solve these problems because of its proximity to IoT devices, while filtering data is sent to the CC. FC is a middle layer located between IoT devices and the CC layer. Due to the massive data generated by IoT devices on FC, Dynamic Weighted Round Robin (DWRR) algorithm was used, which represents a load balancing (LB) algorithm that is applied to schedule and distributes data among fog servers by reading CPU and memory values of these servers in order to improve system performance. The results proved that DWRR algorithm provides high throughput which reaches 3290 req/sec at 919 users. A lot of research is concerned with distribution of workload by using LB techniques without paying much attention to Fault Tolerance (FT), which implies that the system continues to operate even when fault occurs. Therefore, we proposed a replication FT technique called primary-backup replication based on dynamic checkpoint interval on FC. Checkpoint was used to replicate new data from a primary server to a backup server dynamically by monitoring CPU values of primary fog server, so that checkpoint occurs only when the CPU value is larger than 0.2 to reduce overhead. The results showed that the execution time of data filtering process on the FC with a dynamic checkpoint is less than the time spent in the case of the static checkpoint that is independent on the CPU status.

PENGARUH LAMA PENYULINGAN DAN KOMPOSISI BAHAN BAKU TERHADAP RENDEMEN DAN MUTU MINYAK ATSIRI DARI DAUN DAN BATANG NILAM (Pogostemon cablin Benth)

2012 ◽  
Vol 4 (2) ◽  
pp. 16
Author(s):  
A Sulaiman
Keyword(s):  
Essential Oil ◽  
Raw Materials ◽  
Raw Material ◽  
Composition Effect ◽  
High Quality ◽  
Yield And Quality ◽  
Pogostemon Cablin ◽  
Time Required ◽  
Better Than

The research of Distillation And Raw Material Composition Effect of Yield And Quality EssentialOil of Leaves And Stem Patchouli (Pogostemon cablin Benth). This study aimed to examine the influence of the length of distillation and composition of raw materials to the yield and quality of essential oil of patchouli leaves and stems to produce essential oils that have a high quality and yield. The time required to obtain the highest yield of patchouli oil is 8 hours, by composition of 100% leaf (1:0), that is equal to 3.631%, while the lowest yield of patchouli oil are produced from 100% stem (1:0) by distillation of 4 hours, in the amount of 0.10%. Composition that produces patchouli oil with the best quality is 100% stems (0:1) but that yield is lower, while the quality of patchouli oil produced by 100% leaf (1:0) and a mixture of leaf-stem (1:1) quality is still lower than the patchouli oil from the stem, but its yield is better than the yield of oil patchouli by 100% composition of the stem (0:1).Keywords:  essential oil, pogostemon cablin benth, yield

Fog Computing Qos Review and Open Challenges

2018 ◽  
Vol 1 (2) ◽  
pp. 109-118 ◽  
Author(s):  
R. Babu ◽  
K. Jayashree ◽  
R. Abirami
Keyword(s):  
Programming Model ◽  
Fog Computing ◽  
Big Data Analytics ◽  
Security And Privacy ◽  
Computation Offloading ◽  
Future Research ◽  
Future Research Directions ◽  
Iot Devices ◽  
Research Challenge

Internet of Things (IoT) enables inters connectivity among devices and platforms. IoT devices such as sensors, or embedded systems offer computational, storage, and networking resources and the existence of these resources permits to move the execution of IoT applications to the edge of the network and it is known as fog computing. It is able to handle billions of Internet-connected devices and is well situated for real-time big data analytics and provides advantages in advertising and personal computing. The main issues in fog computing includes fog networking, QoS, interfacing and programming model, computation offloading, accounting, billing and monitoring, provisioning and resource management, security and privacy. A particular research challenge is the Quality of Service metric for fog services. Thus, this paper gives a survey of cloud computing, discusses the QoS metrics, and the future research directions in fog computing.

scholarly journals Minimising Delay and Energy in Online Dynamic Fog Systems

2020 ◽  
Author(s):  
Faten Alenizi ◽  
Omer Rana
Keyword(s):  
Energy Consumption ◽  
Fog Computing ◽  
Cloud Services ◽  
Dynamic Task ◽  
Delay Sensitive ◽  
Reduce Energy Consumption ◽  
Iot Devices ◽  
User Tasks ◽  
Use Of Internet

The increasing use of Internet of Things (IoT) devices generates a greater demand for data transfers and puts increased pressure on networks. Additionally, connectivity to cloud services can be costly and inefficient. Fog computing provides resources in proximity to user devices to overcome these drawbacks. However, optimisation of quality of service (QoS) in IoT applications and the management of fog resources are becoming challenging problems. This paper describes a dynamic online offloading scheme in vehicular traffic applications that require execution of delay-sensitive tasks. This paper proposes a combination of two algorithms: dynamic task scheduling (DTS) and dynamic energy control (DEC) that aim to minimise overall delay, enhance throughput of user tasks and minimise energy consumption at the fog layer while maximising the use of resource-constrained fog nodes. Compared to other schemes, our experimental results show that these algorithms can reduce the delay by up to 80.79% and reduce energy consumption by up to 66.39% in fog nodes. Additionally, this approach enhances task execution throughput by 40.88%.

scholarly journals Factors Influencing Fog Computing Adoption based on Quality of Results (QoR) for Heterogeneous Data Analysis: A Proposed Framework

2019 ◽  
Vol 8 (2) ◽  
pp. 2760-2766
Keyword(s):  
Data Analysis ◽  
Data Quality ◽  
Fog Computing ◽  
Heterogeneous Data ◽  
Quality Level ◽  
Quality Of Results ◽  
Iot Devices ◽  
Dynamic Execution ◽  
Systems Framework

The rapid increase of data generated has brought challenges on data quality level. Fog computing in general has been supporting the requirements of end user devices that could not be met by cloud computing solution and it is acknowledged to have a major impact on how an organisation decides to adopt for preprocessing a huge amount of data being generated by the devices. Since IoT devices generating very heterogeneous and dynamic data, there are challenges for the level of data quality. The limitation has hindered the development of fog systems framework that capable operating the dynamic execution of edge devices that handling generation and collection large amounts of data on-premise and off-premise. Thus,sufficient operations of identifying Quality of Result enable user to detect any problems when conducting the decision making. The aim of this paper is to address the factors that perceived likely to influence the adoption of fog computing in evaluating the data analysis on data transmitted from the ever increases devices.A conceptual framework has been constructed considering attributes such as heterogeneous data analysis (on-premise and off-premise) and Quality of Results (quality indicators, quality control, validity outcome and reliability outcome).Potential benefits from the implementation of this framework to organisation is it enable to provide greater value and benefits to the business process. The framework of this study could also be influencing and inhibiting the adoption of fog computing.Quality of result has higher chances to satisfy the defined industrial’s requirement. In addition, fog-computing adoption is important for serving an environment for industry to execute, monitor, and analyze a large form of data in a fog landscape.

A framework for scheduling IoT application jobs on fog computing infrastructure based on QoS parameters

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mandeep Kaur ◽  
Rajinder Sandhu ◽  
Rajni Mohana
Keyword(s):  
Fog Computing ◽  
Physical Distance ◽  
Coarse Grained ◽  
Content Type ◽  
Fine Grained ◽  
Specific Category ◽  
Qos Parameters ◽  
Bayes Algorithm ◽  
Better Than

Purpose The purpose of this study is to verify that if applications categories are segmented and resources are allocated based on their specific category, how effective scheduling can be done?. Design/methodology/approach This paper proposes a scheduling framework for IoT application jobs, based upon the Quality of Service (QoS) parameters, which works at coarse grained level to select a fog environment and at fine grained level to select a fog node. Fog environment is chosen considering availability, physical distance, latency and throughput. At fine grained (node selection) level, a probability triad (C, M, G) is anticipated using Naïve Bayes algorithm which provides probability of newly submitted application job to fall in either of the categories Compute (C) intensive, Memory (M) intensive and GPU (G) intensive. Findings Experiment results showed that the proposed framework performed better than traditional cloud and fog computing paradigms. Originality/value The proposed framework combines types of applications and computation capabilities of Fog computing environment, which is not carried out to the best of knowledge of authors.

scholarly journals Minimizing Deadline Misses and Total Run-time with Load Balancing for a Connected Car Systems in Fog Computing

2020 ◽  
Vol 21 (1) ◽  
pp. 73-84
Author(s):  
K Jairam Naik ◽  
D Hanumanth Naik
Keyword(s):  
Load Balancing ◽  
Fog Computing ◽  
Problem Formulation ◽  
Mobility Patterns ◽  
Active Monitoring ◽  
Connected Cars ◽  
Time Required ◽  
Load Balancer ◽  
Optimization Problem Formulation ◽  
Better Than

Cloud computing helps in providing the applications with a few number of resources that are used to unload the tasks. But there are certain applications like coordinated lane change assistance which are helpful in cars that connects to internet has strict time constraints, and it may not be possible to get the job done just by unloading the tasks to the cloud. Fog computing helps in reducing the latency i.e the computation is now done in local fog servers instead of remote datacentres and these fog servers are connected to the nearby distance to clients. To achieve better timing performance in fog computing load balancing in these fog servers is to be performed in an efficient manner.The challenges in the proposed application includes the number of tasks are high, client mobility and heterogeneous nature of fog servers. We use mobility patterns of connected cars and load balancing is done periodically among fog servers. The task model presented here in this paper solves scheduling problem and this is done at the server level and not on the device level. And at last, we present an optimization problem formulation for balancing the load and for reducing the misses in deadline, also the time required for running the task in these cars will be minimized with the help of fog computing. It also performs better than somecommon algorithms such as active monitoring, weighted round robin and throttled load balancer.

scholarly journals Design and develop Quality of Service Framework using Fog Computing for Smart City Applications

2019 ◽  
Vol 9 (1S) ◽  
pp. 343-348
Keyword(s):  
Quality Of Service ◽  
Energy Consumption ◽  
Power Consumption ◽  
Smart City ◽  
Fog Computing ◽  
Cloud Data ◽  
Efficient Reduction ◽  
Iot Devices ◽  
Service Framework

In today's world, Internet of Things (IoT) is has become the most promising and life-changing technology. In the past few years, IoT has become most productive in the area of healthcare, to improve the quality of care to the patients. This paper aims to reduce the delay, energy consumption of cloud data-centers and minimized the power consumption IoT devices using fog devices. To solve the problem mentioned above, we proposed the Quality of Service framework using fog computing for smart city applications named FATEH, a three-tier architecture for IoT-based application. Various quality of services parameters are optimized as For minimizing the power consumption of IoT devices, the Routing Protocol for Low power and Lossy network (RPL). The other QoS parameter is computing the performance of the proposed framework which has been evaluated through the iFogsim toolkit and the Cooja simulator. Results show the efficient reduction in the delay as well as energy consumption in the proposed scenario and provide better QoS framework

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