Energy-efficient cooperative resource allocation and task scheduling for Internet of Things environments

Al‐Masri, Eyhab; Souri, Alireza; Mohamed, Habiba; Yang, Wei; Olmsted, James; Zhang, Minjia; Kotevska, Olivera

doi:10.1016/j.iot.2023.100832

Cited by 20 publications

(3 citation statements)

References 37 publications

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“…Constrained by the size and battery capacity, the IoT terminal devices' computing resources and storage capabilities are limited. To this end, some solutions [24][25][26] that offload computational tasks to edge or clouds for execution have been well investigated. Although promising, it will fail in remote regions due to the lack of edge or cloud nodes.…”

Section: Task Offloading In Satellite-terrestrial Cooperative Networkmentioning

confidence: 99%

Privacy-Friendly Task Offloading for Smart Grid in 6G Satellite–Terrestrial Edge Computing Networks

Zou,

Yuan,

Xin

et al. 2023

Electronics

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Through offloading computing tasks to visible satellites for execution, the satellite edge computing architecture effectively issues the high-delay problem in remote grids (e.g., mountain and desert) when tasks are offloaded to the urban terrestrial cloud (TC). However, existing works are usually limited to offloading tasks in pure satellite networks and make offloading decisions based on the predefined models. Additionally, runtime consumption for offloading decisions is rather high. Furthermore, privacy information may be maliciously sniffed since computing tasks are transmitted via vulnerable satellite networks. In this paper, we study the task-offloading problem in satellite–terrestrial edge computing networks, where tasks can be executed by satellite or urban TC. A privacy leakage scenario is described, and we consider preserving privacy by sending extra random dummy tasks to confuse adversaries. Then, the offloading cost with privacy protection consideration is modeled, and the offloading decision that minimizes the offloading cost is formulated as a mixed-integer programming (MIP) problem. To speed up solving the MIP problem, we propose a deep reinforcement learning-based task-offloading (DRTO) algorithm. In this case, offloading location and bandwidth allocation only depend on the current channel states. Simulation results show that the offloading overhead is reduced by 17.5% and 23.6% compared with pure TC computing and pure SatEC computing, while the runtime consumption of DRTO is reduced by at least 42.6%. The dummy tasks are exhibited to effectively mitigate privacy leakage during offloading.

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Section: Task Offloading In Satellite-terrestrial Cooperative Networkmentioning

confidence: 99%

Privacy-Friendly Task Offloading for Smart Grid in 6G Satellite–Terrestrial Edge Computing Networks

Zou,

Yuan,

Xin

et al. 2023

Electronics

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show abstract

“…This is because conventional optimization algorithms are mainly based on deterministic assumptions and constraints. At the same time, there are always uncertainties and randomness in areas such as venture capital (Xu et al 2023a, b), supply chain management (Zaman et al 2023), and resource scheduling (Al-Masri et al 2023). Finally, traditional optimization algorithms typically rely on the analytical form of the problem, which requires the problem to be clearly defined and described in mathematical form (Kumar et al 2023).…”

Section: Introductionmentioning

confidence: 99%

Black-winged kite algorithm: a nature-inspired meta-heuristic for solving benchmark functions and engineering problems

Wang,

et al. 2024

Artif Intell Rev

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This paper innovatively proposes the Black Kite Algorithm (BKA), a meta-heuristic optimization algorithm inspired by the migratory and predatory behavior of the black kite. The BKA integrates the Cauchy mutation strategy and the Leader strategy to enhance the global search capability and the convergence speed of the algorithm. This novel combination achieves a good balance between exploring global solutions and utilizing local information. Against the standard test function sets of CEC-2022 and CEC-2017, as well as other complex functions, BKA attained the best performance in 66.7, 72.4 and 77.8% of the cases, respectively. The effectiveness of the algorithm is validated through detailed convergence analysis and statistical comparisons. Moreover, its application in solving five practical engineering design problems demonstrates its practical potential in addressing constrained challenges in the real world and indicates that it has significant competitive strength in comparison with existing optimization techniques. In summary, the BKA has proven its practical value and advantages in solving a variety of complex optimization problems due to its excellent performance. The source code of BKA is publicly available at https://www.mathworks.com/matlabcentral/fileexchange/161401-black-winged-kite-algorithm-bka.

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“…To solve the resource allocation problem with opportunity constraints, they utilized hybrid ant colony optimization and a support vector machine (SVM). Al-Masri et al suggested a collaborative energy-aware resource allocation and scheduling strategy based on the TOPSIS multi-criteria decision-making method to maximize resource sharing and utilization efficiency in offloading IoT tasks [8]. Ari et al used an improved ACA to address resource allocation for 5G C-RAN [9].…”

Section: Introductionmentioning

confidence: 99%

Design and Application of a Resource Allocation Method for CAEVs Internet of Things Based on Swarm Intelligence Computing

et al. 2023

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The Internet of Things (IoT) faces significant challenges in the requirements of sensitive task latency, reasonable resource allocation and reliability for resource transactions. This paper introduces a novel method for road resource allocation in the IoT context of connected and autonomous electric vehicles (CAEVs). The proposed algorithm leverages the ant colony algorithm (ACA) to effectively allocate and coordinate road resources within groups of CAEVs. By considering the energy consumption and pheromone volatilization, the allocation and coordination process of road resources are optimized. To improve the linear packet loss of RED, we adopt the advanced ACA and CRED in the NS2 platform. The experimental results demonstrate that the proposed method outperforms the RED algorithm in packet loss rate and delay time, significantly enhancing system efficiency and performance. Furthermore, the combination of the CRED algorithm and ant colony algorithm successfully mitigates short-term congestion and identifies optimized paths with minimal delay.

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Energy-efficient cooperative resource allocation and task scheduling for Internet of Things environments

Cited by 20 publications

References 37 publications

Privacy-Friendly Task Offloading for Smart Grid in 6G Satellite–Terrestrial Edge Computing Networks

Privacy-Friendly Task Offloading for Smart Grid in 6G Satellite–Terrestrial Edge Computing Networks

Black-winged kite algorithm: a nature-inspired meta-heuristic for solving benchmark functions and engineering problems

Design and Application of a Resource Allocation Method for CAEVs Internet of Things Based on Swarm Intelligence Computing

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