Energy-efficient computation offloading using hybrid GA with PSO in internet of robotic things environment

Menbawy, Noha El; Ali, Hesham A.; Saraya, Mohamed S.; Ali-Eldin, Amr; Abdelsalam, Mohamed M.

doi:10.1007/s11227-023-05387-w

Cited by 12 publications

(3 citation statements)

References 67 publications

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“…On the other hand, More research is needed to improve the networking performance with security aspects. El Menbawy et al 23 illustrated a synergy between autonomous robots and the Internet of Things (IoT) by focusing on energy-efficient environments and smart connectivity, to achieve intelligent connectivity and seamless communication for integrating IoRT with digital platforms, guaranteeing continuous commitment and energy proficiency in cutting-edge IoRT applications. To confirm the effectiveness of the schema, a comprehensive statistical simulation was conducted and compared with similar studies in the field.…”

Section: 4mentioning

confidence: 99%

HRF‐ExGB: Hybrid random forest‐extreme gradient boosting for mobile edge computing

Anuradha,

Jean Justus,

Vijayalakshmi

et al. 2024

Trans Emerging Tel Tech

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The development of increasingly cutting‐edge mobile apps like augmented reality, facial recognition, and natural language processing has been facilitated by the sharp rise in smartphone demand. The increased use of mobile devices like wireless sensors and wearable technology has led to a rapid increase in mobile applications. Due to the explosive growth of the Internet and distributed computing resources of edge devices in mobile edge computing (MEC), it is necessary to have a suitable controller to ensure effective utilization of distributed computing resources. However, the existing approaches can lead to more computation time, more consumption of energy, and a lack of security issues. To overcome these issues, this paper proposed a novel approach called Hybrid Random Forest‐Extreme Gradient Boosting (HRF‐XGBoost) to enhance the computation offloading and joint resource allocation predictions. In a wireless‐powered multiuser MEC system, the starling murmuration optimization model is utilized to figure out the ideal task offloading options. XGBoost is combined with a random forest classifier to form an HRF‐XGBoost architecture which is used to speed up the process while preserving the user's device's battery. An offloading method is created employing certain processes once the best computation offloading decision for Mobile Users (MUs) has been established. The experiment result shows that the method reduced system overhead and time complexity using the strategy of selecting fewer tasks alone by optimally eliminating other tasks. It optimizes the execution time even when the mobile user increases. The performance of the overall system can be greatly improved by our model compared to other existing techniques.

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Section: 4mentioning

confidence: 99%

HRF‐ExGB: Hybrid random forest‐extreme gradient boosting for mobile edge computing

Anuradha,

Jean Justus,

Vijayalakshmi

et al. 2024

Trans Emerging Tel Tech

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“…Noha El Menbawy et al, [36] proposed a model which utilized to determine the optimal way of task offloading for Internet of Robotic Things (IoRT) devices for reducing the amount of energy consumed in IoRT environment and achieving the task deadline constraints. The approach was implemented based on fog computing to reduce the communication overhead between edge devices and the cloud.…”

Section: Overview Of Integrating (Pso) With (Ga)mentioning

confidence: 99%

Metaheuristics Method for Computation Offloading In Mobile Edge Computing: Survey

Safaa Alali,

Abdulkarim Assalem

2023

ARASET

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In recent years, edge computing has emerged as a computing paradigm to support the computationally intensive and latency-critical applications for resource limited devices. The main feature of edge computing is to push computation, networking, and storage facilities closer to the network edge. This enables user equipment (UE) to profit from the edge computing paradigm by mainly offloading their intensive computation tasks to edge resources. Because of some rising problems such as inherent software and hardware heterogeneity, restrictions, dynamism, and stochastic behavior of the ecosystem, the computation offloading issues consider as the essential challenging problems in the MEC environment. However, to the best of the author's knowledge, in spite of its significance, in Metaheuristic -based computation offloading mechanisms, there is not any systematic, comprehensive, and detailed survey in the MEC environment. In this paper, we provide a review on the Metaheuristic -based computation offloading mechanisms in the MEC environment in the form of a classical taxonomy to identify the contemporary mechanisms on this crucial topic and to offer open issues as well. The proposed taxonomy is classified into three main fields: genetic algorithm (GA)-based mechanisms, Partical Swarm Optimization(PSO)-based mechanisms, and Hybrid GA with PSO -based mechanisms. Next, these classes are compared with each other based on the essential features such as performance metrics, case studies, utilized techniques, and evaluation tools, and their advantages and weaknesses are discussed, as well. Finally, open issues and uncovered or inadequately covered future research challenges are argued, and the survey is concluded.

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“…The PSO has been combined with various metaheuristics in several studies, including hybrid PSO with GA (PSOGA) [15,16], PSO with Ant Lion Optimization (PSO-AlO) [17]; PSO was also combined with the Bacterial Foraging Optimization (BFO) algorithm in [18]. These hybrid approaches are meant to complement one another's capabilities to enhance exploitation while reducing the possibility of a local optimum.…”

Section: Introductionmentioning

confidence: 99%

On the Cryptanalysis of a Simplified AES Using a Hybrid Binary Grey Wolf Optimization

Rizk-Allah,

Abdulkader,

Elatif

et al. 2023

Mathematics

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Cryptosystem cryptanalysis is regarded as an NP-Hard task in modern cryptography. Due to block ciphers that are part of a modern cipher and have nonlinearity and low autocorrelation in their structure, traditional techniques and brute-force attacks suffer from breaking the key presented in traditional techniques, and brute-force attacks against modern cipher S-AES (simplified-advanced encryption standard) are complex. Thus, developing robust and reliable optimization with high searching capability is essential. Motivated by this, this paper attempts to present a novel binary hybridization algorithm based on the mathematical procedures of the grey wolf optimizer (GWO) and particle swarm optimization (PSO), named BPSOGWO, to deal with the cryptanalysis of (S-AES). The proposed BPSOGWO employs a known plaintext attack that requires only one pair of plaintext–ciphertext pairs instead of other strategies that require more pairs (i.e., it reduces the number of messages needed in an attack, and secret information such as plaintext-ciphertext pairs cannot be obtained easily). The comprehensive and statistical results indicate that the BPSOGWO is more accurate and provides superior results compared to other peers, where it improved the cryptanalysis accurateness of S-AES by 82.5%, 84.79%, and 79.6% compared to PSO, GA, and ACO, respectively. Furthermore, the proposed BPSOGWO retrieves the optimal key with a significant reduction in search space compared to a brute-force attack. Experiments show that combining the suggested fitness function with HPSOGWO resulted in a 109-fold reduction in the search space. In cryptanalysis, this is a significant factor. The results prove that BPSOGWO is a promising and effective alternative to attack the key employed in the S-AES cipher.

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Energy-efficient computation offloading using hybrid GA with PSO in internet of robotic things environment

Cited by 12 publications

References 67 publications

HRF‐ExGB: Hybrid random forest‐extreme gradient boosting for mobile edge computing

HRF‐ExGB: Hybrid random forest‐extreme gradient boosting for mobile edge computing

Metaheuristics Method for Computation Offloading In Mobile Edge Computing: Survey

On the Cryptanalysis of a Simplified AES Using a Hybrid Binary Grey Wolf Optimization

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