scholarly journals A novel trust management model for edge computing

2021 ◽  
Author(s):  
Rabia Latif ◽  
Malik Uzair Ahmed ◽  
Shahzaib Tahir ◽  
Seemab Latif ◽  
Waseem Iqbal ◽  
...  
Keyword(s):  
Trust Management ◽  
Service Level ◽  
Edge Computing ◽  
Management Model ◽  
Security Risks ◽  
Computing Paradigm ◽  
Malicious Behavior ◽  
Processing Power ◽  
Qos Parameters ◽  
Iot Devices

AbstractEdge computing is a distributed architecture that features decentralized processing of data near the source/devices, where data are being generated. These devices are known as Internet of Things (IoT) devices or edge devices. As we continue to rely on IoT devices, the amount of data generated by the IoT devices have increased significantly due to which it has become infeasible to transfer all the data over to the Cloud for processing. Since these devices contain insufficient storage and processing power, it gives rise to the edge computing paradigm. In edge computing data are processed by edge devices and only the required data are sent to the Cloud to increase robustness and decrease overall network overhead. IoT edge devices are inherently suffering from various security risks and attacks causing a lack of trust between devices. To reduce this malicious behavior, a lightweight trust management model is proposed that maintains the trust of a device and manages the service level trust along with quality of service (QoS). The model calculates the overall trust of the devices by using QoS parameters to evaluate the trust of devices through assigned weights. Trust management models using QoS parameters show improved results that can be helpful in identifying malicious edge nodes in edge computing networks and can be used for industrial purposes.

scholarly journals Trust Assessment in Internet of Things Using Blockchain and Machine Learning

2020 ◽  
Author(s):  
Liming Wang ◽  
Hongqin Zhu ◽  
Jiawei Sun ◽  
Ran Dai ◽  
Qi Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Trust Management ◽  
Identity Management ◽  
Edge Computing ◽  
Management Model ◽  
Learning Methods ◽  
Transaction Model ◽  
Machine Learning Methods ◽  
Iot Devices

Abstract Since IoT devices are strengthened, edge computing with multi-center cooperation becomes a trend. Considering that edge nodes may belong to different center, they have different trust management model, it’s hard to assess trust among edge nodes. In this paper, we take blockchain to coordinate differences among centers, construct a trust environment for transactions in IoT. In detail, we propose a blockchain based identity management for IoT to ensure identity is credible, then design a transaction model to provide certification for IoT transactions. And, we take machine learning methods to analyze IoT transaction log, thus decide trust nodes or not. Experiment results show that our mechanism could effectively identify trustworthy edges in IoT.

scholarly journals DM-MQTT: An Efficient MQTT Based on SDN Multicast for Massive IoT Communications

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3071 ◽  
Author(s):  
Jun-Hong Park ◽  
Hyeong-Su Kim ◽  
Won-Tae Kim
Keyword(s):  
Cloud Computing ◽  
Traffic Congestion ◽  
Data Exchange ◽  
Data Transfer ◽  
Edge Computing ◽  
Computing Paradigm ◽  
Transfer Delay ◽  
Network Usage ◽  
Iot Devices ◽  
Edge Networks

Edge computing is proposed to solve the problem of centralized cloud computing caused by a large number of IoT (Internet of Things) devices. The IoT protocols need to be modified according to the edge computing paradigm, where the edge computing devices for analyzing IoT data are distributed to the edge networks. The MQTT (Message Queuing Telemetry Transport) protocol, as a data distribution protocol widely adopted in many international IoT standards, is suitable for cloud computing because it uses a centralized broker to effectively collect and transmit data. However, the standard MQTT may suffer from serious traffic congestion problem on the broker, causing long transfer delays if there are massive IoT devices connected to the broker. In addition, the big data exchange between the IoT devices and the broker decreases network capability of the edge networks. The authors in this paper propose a novel MQTT with a multicast mechanism to minimize data transfer delay and network usage for the massive IoT communications. The proposed MQTT reduces data transfer delays by establishing bidirectional SDN (Software Defined Networking) multicast trees between the publishers and the subscribers by means of bypassing the centralized broker. As a result, it can reduce transmission delay by 65% and network usage by 58% compared with the standard MQTT.

Feedback-Based Fuzzy Resource Management in IoT-Based-Cloud

2020 ◽  
Vol 3 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Basetty Mallikarjuna
Keyword(s):  
Fog Computing ◽  
High Volume ◽  
Cloud Services ◽  
Dynamic Feedback ◽  
Smart Cameras ◽  
Computing Paradigm ◽  
Qos Parameters ◽  
Speed Up ◽  
Iot Devices ◽  
Smart Home Appliances

The main aim of Internet of Things (IoT) is to get every “thing” (sensors, smart cameras, wearable devices, and smart home appliances) to connect to the internet. Henceforth to produce the high volume of data required for data processing between IoT devices, large storage and the huge number of applications to offer cloud computing as a service. The purpose of IoT-based-cloud is to manage the resources, and effective utilization of tasks in cloud. The end user applications are essential to enhance the QoS parameters. As per the QoS parameters, the service provider makes the speed up of tasks. There is a requirement for assigning responsibilities based on priority. The cloud services are increased to the network edge, and the planned model is under the Fog computing paradigm to reduce the makespan of time. The priority based fuzzy scheduling approach is brought by the dynamic feedback-based mechanism. The planned mechanism is verified with the diverse prevailing algorithms and evidenced that planned methodology is supported by effective results.

scholarly journals An integrated three-tier trust management framework in mobile edge computing using fuzzy logic

2021 ◽  
Vol 7 ◽  
pp. e700
Author(s):  
Merrihan B.M. Mansour ◽  
Tamer Abdelkader ◽  
Mohamed Hashem ◽  
El-Sayed M. El-Horbaty
Keyword(s):  
Service Provider ◽  
Trust Management ◽  
Service Providers ◽  
Service Level Agreement ◽  
Cloud Service ◽  
Service Level ◽  
Edge Computing ◽  
Trust Value ◽  
Management Framework ◽  
Cloud Service Providers

Mobile edge computing (MEC) is introduced as part of edge computing paradigm, that exploit cloud computing resources, at a nearer premises to service users. Cloud service users often search for cloud service providers to meet their computational demands. Due to the lack of previous experience between cloud service providers and users, users hold several doubts related to their data security and privacy, job completion and processing performance efficiency of service providers. This paper presents an integrated three-tier trust management framework that evaluates cloud service providers in three main domains: Tier I, which evaluates service provider compliance to the agreed upon service level agreement; Tier II, which computes the processing performance of a service provider based on its number of successful processes; and Tier III, which measures the violations committed by a service provider, per computational interval, during its processing in the MEC network. The three-tier evaluation is performed during Phase I computation. In Phase II, a service provider total trust value and status are gained through the integration of the three tiers using the developed overall trust fuzzy inference system (FIS). The simulation results of Phase I show the service provider trust value in terms of service level agreement compliance, processing performance and measurement of violations independently. This disseminates service provider’s points of failure, which enables a service provider to enhance its future performance for the evaluated domains. The Phase II results show the overall trust value and status per service provider after integrating the three tiers using overall trust FIS. The proposed model is distinguished among other models by evaluating different parameters for a service provider.

Fog Computing and Its Challenges

2019 ◽  
pp. 36-52
Author(s):  
Vighnesh Srinivasa Balaji
Keyword(s):  
Cloud Computing ◽  
Fog Computing ◽  
Service Level ◽  
Future Research ◽  
Research Directions ◽  
Security Issues ◽  
Computing Paradigm ◽  
Future Research Directions ◽  
Time Latency ◽  
Iot Devices

In recent times, the number of internet of things (IoT) devices/sensors increased tremendously. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named fog computing has been introduced. In this chapter, the authors will introduce fog computing, its difference in comparison to cloud computing, and issues related to fog. Among the three issues (i.e. service, structural, and security issues), this chapter scrutinizes and comprehensively discusses the service and structural issues also providing the service level objectives of the fog. They next provide various algorithms for computing in fog, the challenges faced, and future research directions. Among the various uses of fog, two scenarios are put to use.

A Survey on Service Level Components in Big-Cloud-IoT Systems with Hybrid Meta-heuristic Techniques

2020 ◽  
pp. 95-101
Author(s):  
Xueqiang Yin ◽  
Athreya Tao Chen
Keyword(s):  
Big Data ◽  
Ant Colony Algorithm ◽  
Data Transfer ◽  
Processing System ◽  
Large Data ◽  
Computing System ◽  
Service Level ◽  
Edge Computing ◽  
Service Components ◽  
Iot Devices

Big data is one such technology. When we receive huge volume of data, there will be high demand in processing the huge data. It can also be said challenging task in big data processing. The increases in IoT devices in the network system collect more data to be processed in centralized devices called cloud storage. Every big data is processed and stored in the cloud. To overcome the performance and latency issues in large data computation, big cloud processing system uses edge computing in it. One of the key components of IoT is edge computing. We combine big data with cloud and edge computing in this paper as hybrid edge computing system. In the edge computing system, huge number of IoT devices computes services in its nearby network edge. Data sharing and transmission between the various service components may affect performance of the system. The main aim of this research article is to reduce the delay in data transfer between the components. This optimization goal is achieved by new Hybrid Meta-heuristic optimization (HMeO) algorithm. New HMeO algorithm designed for IoT devices to deploy the service components. MHO model is design to optimize the process by selecting the edge computing with minimum latency. Our proposed HMeO algorithm is compared with existing genetic algorithm and ant colony algorithm. The result shows HMeO algorithm provides more performance and efficient in in-depth data analysing and locating the component in big databased cloud environment.

scholarly journals A novel approach for IoT tasks offloading in edge-cloud environments

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Jaber Almutairi ◽  
Mohammad Aldossary
Keyword(s):  
Service Time ◽  
Edge Computing ◽  
Cloud System ◽  
Novel Approach ◽  
Delay Sensitivity ◽  
Cloud Environments ◽  
Application Characteristics ◽  
Iot Devices ◽  
Computational Resources ◽  
And Storage

AbstractRecently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. Although Edge Computing is a promising enabler for latency-sensitive related issues, its deployment produces new challenges. Besides, different service architectures and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents a novel approach for task offloading in an Edge-Cloud system in order to minimize the overall service time for latency-sensitive applications. This approach adopts fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand and delay sensitivity) as well as resource utilization and resource heterogeneity. A number of simulation experiments are conducted to evaluate the proposed approach with other related approaches, where it was found to improve the overall service time for latency-sensitive applications and utilize the edge-cloud resources effectively. Also, the results show that different offloading decisions within the Edge-Cloud system can lead to various service time due to the computational resources and communications types.

scholarly journals Energy Efficient SWIPT Based Mobile Edge Computing Framework for WSN-Assisted IoT

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4798
Author(s):  
Fangni Chen ◽  
Anding Wang ◽  
Yu Zhang ◽  
Zhengwei Ni ◽  
Jingyu Hua
Keyword(s):  
Energy Consumption ◽  
Transmission Rate ◽  
Edge Computing ◽  
Energy Harvest ◽  
Total System ◽  
Mobile Edge Computing ◽  
Weight Factor ◽  
Iot Devices ◽  
System Variables ◽  
The Relationship

With the increasing deployment of IoT devices and applications, a large number of devices that can sense and monitor the environment in IoT network are needed. This trend also brings great challenges, such as data explosion and energy insufficiency. This paper proposes a system that integrates mobile edge computing (MEC) technology and simultaneous wireless information and power transfer (SWIPT) technology to improve the service supply capability of WSN-assisted IoT applications. A novel optimization problem is formulated to minimize the total system energy consumption under the constraints of data transmission rate and transmitting power requirements by jointly considering power allocation, CPU frequency, offloading weight factor and energy harvest weight factor. Since the problem is non-convex, we propose a novel alternate group iteration optimization (AGIO) algorithm, which decomposes the original problem into three subproblems, and alternately optimizes each subproblem using the group interior point iterative algorithm. Numerical simulations validate that the energy consumption of our proposed design is much lower than the two benchmark algorithms. The relationship between system variables and energy consumption of the system is also discussed.

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