Enhancing mobile data security using red panda optimized approach with chaotic fuzzy encryption in mobile cloud computing

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Concurrency and Computation-Practice & Experience Pub Date : 2024-07-30 DOI:10.1002/cpe.8243
Vishal Garg, Bikrampal Kaur, Surender Jangra
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Abstract

Smartphone devices have occupied an indispensable place in human life. These devices have some restrictions, like short lifetime of battery, imperfect computation power, less memory size and unpredictable network connectivity. Hence, a number of methods previously presented to decrease these restrictions as well as increase the battery lifespan with the help of offloading strategy. This manuscript proposes a new enhancing mobile data security using red panda optimized approach with chaotic fuzzy encryption in mobile cloud computing (RPO-CFE-SMC) to offload intensive computation tasks from mobile device to the cloud. The proposed model utilizes a red panda optimization algorithm (RPOA) to scale dynamically the offloading decision under energy consumption, CPU utilization, execution time, memory usage parameters. Before the work is transferred to the cloud, an innovative security layer is applied for encrypting the data using AES chaotic fuzzy encryption (CFE) technology. The proposed RPO-CFE-SMC method provides 20.63%, 25.25%, 25.28%, and 32.47% lower encryption time and 23.66%, 24.25%, and 26.47% lower energy consumption compared with existing EFFORT-SMC, EESH-SMC, and CP-ABE-SMC models respectively. In conclusion, the simulation results prove that the improved efficiency of proposed model in offloading computation to the cloud with enhanced data protection using chaotic fuzzy encryption.

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在移动云计算中使用混沌模糊加密的红熊猫优化方法增强移动数据安全性
摘要智能手机设备在人类生活中占据着不可或缺的地位。这些设备存在一些限制,如电池寿命短、计算能力不完善、内存容量小以及网络连接不可预测等。因此,以前曾提出过一些方法,借助卸载策略来减少这些限制并延长电池寿命。本手稿提出了一种新的增强移动数据安全性的方法,即在移动云计算中使用红熊猫优化方法和混沌模糊加密(RPO-CFE-SMC),将密集型计算任务从移动设备卸载到云中。所提出的模型利用红熊猫优化算法(RPOA)在能耗、CPU 利用率、执行时间、内存使用率等参数下动态调整卸载决策。在将工作转移到云端之前,使用 AES 混沌模糊加密(CFE)技术应用创新的安全层对数据进行加密。与现有的 EFFORT-SMC、EESH-SMC 和 CP-ABE-SMC 模型相比,所提出的 RPO-CFE-SMC 方法的加密时间分别缩短了 20.63%、25.25%、25.28% 和 32.47%,能耗分别降低了 23.66%、24.25% 和 26.47%。总之,仿真结果证明,所提模型利用混沌模糊加密技术提高了将计算卸载到云端的效率,并增强了数据保护能力。
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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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