Intelligent workload allocation in IoT–Fog–cloud architecture towards mobile edge computing

Abbasi, Mahdi; Mohammadi-Pasand, E.; Khosravi, Mohammad R.

doi:10.1016/j.comcom.2021.01.022

Cited by 72 publications

(44 citation statements)

References 47 publications

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“…In the future, the research community is suggested to extend their attention toward novel edge architecture that should be managed to fulfill the communication and security commitments associated with IoT applications used for COVID-19. While developing novel edge architecture, the following crucial aspect must be considered: patients embedded IoT devices and network components inside the edge platform must be configured to ensure validation, authentication, and secure communication with minimal network cost ( Abbasi, Mohammadi-Pasand, Khosravi, 2021 , Wang, Mei, Liu, Wang, Dai, Wang, 2021 ). In order to ensure the aforestated metrics, novel edge layered architecture could be extremely beneficial in the H-IoT application used in the COVID-19 context.…”

Section: Future Research Directionsmentioning

confidence: 99%

Emerging IoT Applications in Sustainable Smart Cities for COVID-19: Network Security and Data Preservation Challenges with Future Directions

Adil

Khan

2021

Sustainable Cities and Society

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COVID-19 is a global infectious disease that can be easily spread by the contiguity of infected people. To prevent from COVID-19 and reduce its impact in sustainable smart cities, the global research communities are working relentlessly by harnessing the emerging technologies to develop the safest diagnosis, evaluation, and treatment procedures, and Internet of Things (IoT) is one of the pioneers among them. IoT can perform a pivotal role to diminish its immense contagious rate by suitable utilization in emerging healthcare IoT applications in sustainable smart cities. Therefore, the focus of this paper is to outline a survey of the emerging healthcare IoT applications practiced in the perspective of COVID-19 pandemic in terms of network architecture security, trustworthiness, authentication, and data preservation followed by identifying existing challenges to set the future research directions. The salient contributions of this work deal with the accomplishment of a detailed and comprehensive literature review of COVID-19 starting from 2019 through 2021 in the context of emerging healthcare IoT technology. In addition, we extend the correlated contributions of this work by highlighting the weak aspects of the existing emerging healthcare IoT applications, security of different network layers and secure communication environment followed by some associated requirements to address these challenges. Moreover, we also identify future research directions in sustainable smart cities for emerging healthcare IoT utilization in the context of COVID-19 with the most productive results and least network implementation costs.

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Section: Future Research Directionsmentioning

confidence: 99%

Emerging IoT Applications in Sustainable Smart Cities for COVID-19: Network Security and Data Preservation Challenges with Future Directions

Adil

Khan

2021

Sustainable Cities and Society

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“…The use of an energy harvester can be another potential solution to enhance the significance of the system. Fog or edge computing [ 110 , 111 ] is also an exciting solution to mitigate the impact of resource-hungry ML algorithms. The processing at the edge can eliminate the computational load at the sensors.…”

Section: Discussionmentioning

confidence: 99%

Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review

Usmani

Saboor

Haris

et al. 2021

Sensors

120

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Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues.

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“…is algorithm improves the simple genetic algorithm, takes the cost of the service provider as the objective function, selects the optimal solution through basic genetic operations, and achieves obvious optimization in delay and cost. Abbasi et al introduced an improved genetic algorithm, which introduces fitness judgment into the parental mutation operation and overcomes the blindness of the basic genetic algorithm in the mutation operation [18]. When using this algorithm to schedule tasks under the fog computing architecture, the response time constraints in the service level goals are considered.…”

Section: Related Workmentioning

confidence: 99%

Use Linear Weighted Genetic Algorithm to Optimize the Scheduling of Fog Computing Resources

Rui-sheng

2021

Complexity

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This paper establishes a mathematical model for the resource management and scheduling of the fog node cluster and establishes the optimization goals of delay, communication load, and service cost. According to the idea of genetic algorithm for single-objective optimization, this paper proposes a linear weighted genetic algorithm based on linear weighting. The optimization weight is established according to the user’s preference for the target. We normalize the optimization objective function and merge it into one target, and then we proceed with genetic manipulation to get a better solution. The experimental results show that when the user specifies the preference weight, the optimal solution can be obtained by the genetic algorithm based on linear weighting, and the algorithm execution efficiency is high. With the increase of the single-objective weight, the optimization effect of this objective is better. When the preference weight tends to be average, its overall optimization effect is not ideal. When the user does not specify the preference weight, a set of optimal solutions can be obtained through the improved nondominated sorting genetic algorithm with elite strategy. Compared with the traditional algorithm, in addition to the overall optimization effect of the target being better, the algorithm itself also has higher efficiency.

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Intelligent workload allocation in IoT–Fog–cloud architecture towards mobile edge computing

Cited by 72 publications

References 47 publications

Emerging IoT Applications in Sustainable Smart Cities for COVID-19: Network Security and Data Preservation Challenges with Future Directions

Emerging IoT Applications in Sustainable Smart Cities for COVID-19: Network Security and Data Preservation Challenges with Future Directions

Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review

Use Linear Weighted Genetic Algorithm to Optimize the Scheduling of Fog Computing Resources

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