Workload Allocation in IoT-Fog-Cloud Architecture Using a Multi-Objective Genetic Algorithm

Abbasi, Mahdi; Pasand, Ehsan Mohammadi; Khosravi, Mohammad R.

doi:10.1007/s10723-020-09507-1

Cited by 72 publications

(36 citation statements)

References 29 publications

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“…Feedback delay, 4.V2V propagation delay. Where, the upload delay of i task can be expressed as Equation (7).…”

Section: Time Consumption Modelmentioning

confidence: 99%

“…Literature [7] solves the problem of workload distribution in the Internet of Things -fog -cloud architecture, and makes a tradeoff between energy consumption and propagation delay. NSGA-II algorithm is used to process the multi-objective model, and simulation calculation is carried out in three scenarios of fog, pure cloud and fog cloud, which proves that this scheme has significant differences.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Optimized Immune Algorithm For Computing Offloading Problem In Edge Computing Scenes of Internet of Vehicles

Zhu

Cai

Sun

et al. 2021

Preprint

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With the development of 5G technology, the Internet of Vehicles (IoV) has also received worldwide attention. IoV edge computing achieves the goal of low latency by offloading tasks to the Mobile Edge Computing Server (MECS). However, it is still a challenge to reduce the computing delay of mobile terminal devices while ensuring the low energy consumption and load balancing of servers. In order to solve this problem, the system model, delay model, load balancing model, energy consumption model and objective optimization model are established in this paper. A computational unloading scheme based on multi-objective immune optimization algorithm is proposed. Finally, this scheme is compared with the reference scheme and the literature scheme. Simulation experiments show that the proposed scheme can effectively reduce the average unload delay of users, optimize the workload between servers, and effectively reduce energy consumption. Its performance is better than the schemes in the literature.

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“…Feedback delay, 4.V2V propagation delay. Where, the upload delay of i task can be expressed as Equation (7).…”

Section: Time Consumption Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimized Immune Algorithm For Computing Offloading Problem In Edge Computing Scenes of Internet of Vehicles

Zhu

Cai

Sun

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The selection of optimal nodes at each step leads to global optimization of reliable routing in healthcare systems on the IoT platform. The application of multiobjective genetic optimization algorithms has been proven in many fields of research, including the energy-efficient routing in wireless sensor networks [15], resource allocation in the cloud-fog-IoT infrastructure [16], service placement, and load distribution in edge computing [17]. In general, the contribution of this paper can be summarized in four steps:…”

Section: Introductionmentioning

confidence: 99%

Presenting a Reliable Routing Approach in IoT Healthcare Using the Multiobjective‐Based Multiagent Approach

Javid

Mirzaei

2021

Wireless Communications and Mobile Computing

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Developments in information and related technologies have led to a wider use of the Internet of things (IoT). By integrating both virtual and physical worlds, IoT creates an integrated communication framework of interrelated things and operating systems. With the advent of IoT systems based on digital remote care, transferring medical data is becoming a daily routine. Healthcare is one of the most popular IoT applications and tries to monitor patients’ vital signs during the day for weeks and to eliminate the need for hospitalization. In a healthcare system, many sensors are installed to collect the patient’s information, including environmental monitoring sensors and vital and unstructured message sensors in order to reduce the patients’ expenses. The IoT network contains flexible sensors in dynamically changing environments where sensors collect environmental information and send it to nursing stations for healthcare applications. Due to the wireless nature of IoT networks, secure data transmission in the healthcare context is very important. Data collected from sensors embedded in healthcare devices may be lost for various reasons along the transmission path. Therefore, establishing a secure communication path in IoT networks in the context of healthcare is of great importance. In this paper, in order to provide a reliable data transfer protocol in the context of healthcare, a reliable routing using multiobjective genetic algorithm (RRMOGA) method is presented. The contribution of this paper can be summarized in two steps: (i) using a multiobjective optimization approach to find near-optimal paths and (ii) using reliable agents in the network to find backup paths. The simulation outcomes reveal that the proposed approach, based on the use of the multiobjective optimization approach, tries to find optimal paths for information transfer that improve the main parameters of the network. Also, the use of secure agents leads to a secure information transfer in the network in the context of healthcare. Experimental results show that the proposed method has achieved reliability and data delivery rates, 99% and 99.9%, respectively. The proposed method has improved network lifetime, delivery rate, and delay by 14%, 2%, and 5.6%, respectively.

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“…Literature [12] proposes a new cross-task knowledge transfer mode based on denoising autoencoder for single-objective and multi-objective multi-task optimization. Literature [13] balances the relationship between energy consumption and time delay when the fog and cloud process the workload, and adopts NSGAII algorithm for effective processing.…”

Section: Introductionmentioning

confidence: 99%

“…Literature [7] conducts a targeted study on the dependency relationship among various tasks, proposes a offloading strategy combining workflow scheduling, and uses genetic algorithm to optimize it. Literature [8] solves the problem of workload distribution in the fog cloud architecture of the Internet of Things. NSGAII algorithm is used as the optimization method to reduce the time delay and meet the energy consumption requirements under the three-layer architecture of the Internet of Things, fog and cloud.…”

Section: Introductionmentioning

confidence: 99%

An Immune Algorithm Based on Entropy For Computing Offloading Problem in Edge Computing Scenes

Sifeng

Cai

Enlin

et al. 2021

Preprint

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By offloading computing tasks to the edge of mobile network, mobile edge computing(MEC) solves the shortcomings of mobile devices in computing capacity and storage capacity. In order to reduce the computing delay of mobile terminal devices and the degree of workload balancing in MEC servers, a computing offloading solution based on information entropy immune algorithm is proposed. Firstly, the system model, delay model, load model and objective optimization model are constructed. Then, a task offloading scheme based on immune optimization is proposed .Finally, this scheme is compared with the Benchmark scheme and the offloading scheme in the literature. Simulation experiments show that the proposed scheme can effectively reduce the average offloading delay, and can effectively balance the load between MEC servers. At the same time, compared with the schemes in the literature, the performance of the scheme is improved to a certain extent.

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Workload Allocation in IoT-Fog-Cloud Architecture Using a Multi-Objective Genetic Algorithm

Cited by 72 publications

References 29 publications

Multi-Objective Optimized Immune Algorithm For Computing Offloading Problem In Edge Computing Scenes of Internet of Vehicles

Multi-Objective Optimized Immune Algorithm For Computing Offloading Problem In Edge Computing Scenes of Internet of Vehicles

Presenting a Reliable Routing Approach in IoT Healthcare Using the Multiobjective‐Based Multiagent Approach

An Immune Algorithm Based on Entropy For Computing Offloading Problem in Edge Computing Scenes

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