Mobility Aware Blockchain Enabled Offloading and Scheduling in Vehicular Fog Cloud Computing

Lakhan, Abdullah; Ahmad, Muneer; Bilal, Muhammad; Jolfaei, Alireza; Mehmood, Raja Majid

doi:10.1109/tits.2021.3056461

Cited by 82 publications

(33 citation statements)

References 23 publications

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“…However, they did not consider the security mechanism in the study. Yan et al [11], De-Lara et al [12], Lakhan et al [13] and Li et al [14] and Li et al [15,16] suggested serverless and container-based application partitioning, resource allocation and scheduling methodology-based linear and dynamic optimization in fog-cloud. The objective is to minimize the execution, energy, response time, and delay and offloading cost of applications.…”

Section: Related Workmentioning

confidence: 99%

“…The Ethereum blockchain is the most widely used in the peer-to-peer network and applications for the distributed system. Ethereume blockchain is a group of blocks (data blocks) that are unalterable and well structured, and it records the logs of all transactions, in the form of a file system, the data blocks in the blockchain stored data in each node [11]. Each block contains data for many transactions, the number of which can vary between different blocks.…”

Section: Introductionmentioning

confidence: 99%

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Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

Lakhan

Mohammed

Rashid

et al. 2021

Sensors

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The Internet of Medical Things (IoMT) is increasingly being used for healthcare purposes. IoMT enables many sensors to collect patient data from various locations and send it to a distributed hospital for further study. IoMT provides patients with a variety of paid programmes to help them keep track of their health problems. However, the current system services are expensive, and offloaded data in the healthcare network are insecure. The research develops a new, cost-effective and stable IoMT framework based on a blockchain-enabled fog cloud. The study aims to reduce the cost of healthcare application services as they are processing in the system. The study devises an IoMT system based on different algorithm techniques, such as Blockchain-Enable Smart-Contract Cost-Efficient Scheduling Algorithm Framework (BECSAF) schemes. Smart-Contract Blockchain schemes ensure data consistency and validation with symmetric cryptography. However, due to the different workflow tasks scheduled on other nodes, the heterogeneous, earliest finish, time-based scheduling deals with execution under their deadlines. Simulation results show that the proposed algorithm schemes outperform all existing baseline approaches in terms of the implementation of applications.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

Lakhan

Mohammed

Rashid

et al. 2021

Sensors

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“…The purpose of these studies is to gain dynamic results for healthcare applications in distributed cloud data centers. The studies [9][10][11][12][13][14] proposed IoMT with a tuple of implementations, such as coarse-grained workload, and optimized the objectives' lateness and energy with particle swarm optimization schemes in distributed RSA-enabled fog virtual machines. Particle swarm optimization (PSO) is a computational method for solving problems by iteratively improving a potential solution against a set of quality criteria.…”

Section: Related Workmentioning

confidence: 99%

“…This resource-provisioning model is very costly for fine-grained tasks where they need only usage-based services instead of monthly and yearly contract-based services. (iii) Existing resource allocation policies of theses studies [2,3,7,9,10,[12][13][14] in the IoMT network only make offloading decisions at the user level and based on the threshold. However, optimal scheduling and failure aware mechanisms at the distributed computing node level are widely ignored.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network

et al. 2021

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The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan.

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“…It effectively and dynamically searches for optimal solutions for resource allocation among all candidate solutions, and this process continues until the final optimal solution is produced. An energy-efficient workflow and independent task scheduling based on cloud speed and power consumption-based frameworks is proposed in [11,[13][14][15][16]. The main goal of these studies is to minimize energy consumption and idle time while performing task scheduling.…”

Section: Task Allocation Schemes In Application Partitioningmentioning

confidence: 99%

Dynamic Application Partitioning and Task-Scheduling Secure Schemes for Biosensor Healthcare Workload in Mobile Edge Cloud

Lakhan

Grønli

et al. 2021

Electronics

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Currently, the use of biosensor-enabled mobile healthcare workflow applications in mobile edge-cloud-enabled systems is increasing progressively. These applications are heavyweight and divided between a thin client mobile device and a thick server edge cloud for execution. Application partitioning is a mechanism in which applications are divided based on resource and energy parameters. However, existing application-partitioning schemes widely ignore security aspects for healthcare applications. This study devises a dynamic application-partitioning workload task-scheduling-secure (DAPWTS) algorithm framework that consists of different schemes, such as min-cut algorithm, searching node, energy-enabled scheduling, failure scheduling, and security schemes. The goal is to minimize the energy consumption of nodes and divide the application between local nodes and edge nodes by applying the secure min-cut algorithm. Furthermore, the study devises the secure-min-cut algorithm, which aims to migrate data between nodes in a secure form during application partitioning in the system. After partitioning the applications, the node-search algorithm searches optimally to run applications under their deadlines. The energy and failure schemes maintain the energy consumption of the nodes and the failure of the system. Simulation results show that DAPWTS outperforms existing baseline approaches by 30% in terms of energy consumption, deadline, and failure of applications in the system.

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Mobility Aware Blockchain Enabled Offloading and Scheduling in Vehicular Fog Cloud Computing

Cited by 82 publications

References 23 publications

Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network

Dynamic Application Partitioning and Task-Scheduling Secure Schemes for Biosensor Healthcare Workload in Mobile Edge Cloud

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