MSCO:区块链雾计算环境中的移动感知安全计算卸载

Veni Thangaraj, Thankaraja Raja Sree
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引用次数: 0

摘要

雾计算已发展成为一种前景广阔的计算模式,可支持执行对延迟敏感的物联网(IoT)应用。连接到雾环境中的移动设备资源有限,且不稳定。在这种环境下,有必要将移动用户的计算任务卸载到附近的雾服务器上,以满足时间关键型物联网应用的 QoS 要求。此外,雾服务器还容易受到许多攻击,从而引发安全和隐私问题。尽管有许多针对雾环境的完整性保护策略,但其中大多数都依赖于可能存在单点故障的可靠中央实体。区块链是一种很有前途的策略,它能以去中心化的方式维护数据完整性。最先进的区块链卸载机制没有考虑到安全卸载过程中的移动性。此外,在考虑用户设备移动性的同时,有必要确保物联网应用的 QoS 约束。因此,本文提出了区块链辅助移动感知安全计算卸载(MSCO)机制,以最小的计算和能源成本选择最佳授权雾服务器来完成卸载任务。为解决优化问题,采用了基于遗传算法的混合粒子群优化技术。实验结果表明,与现有方法相比,MSCO 的总成本(包括延迟和能耗参数)平均提高了 11%,取得了显著改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MSCO: Mobility-aware Secure Computation Offloading in blockchain-enabled Fog computing environments
Fog computing has evolved as a promising computing paradigm to support the execution of latency-sensitive Internet of Things (IoT) applications. The mobile devices connected to the fog environment are resource constrained and non-stationary. In such environments, offloading mobile user’s computational task to nearby fog servers is necessary to satisfy the QoS requirements of time-critical IoT applications. Moreover, the fog servers are also susceptible to numerous attacks which induce security and privacy issues.Offloading computation task to a malicious fog node affects the integrity of users’ data. Despite the fact that there are many integrity-preserving strategies for fog environments, the majority of them rely on a reliable central entity that might have a single point of failure. Blockchain is a promising strategy that maintains data integrity in a decentralized manner. The state-of-art blockchain offloading mechnanisms have not considered the mobility during secure offloading process. Besides, it is necessary to ensure QoS constraints of the IoT applications while considering mobility of user devices. Hence, in this paper, Blockchain assisted Mobility-aware Secure Computation Offloading (MSCO) mechanism is proposed to choose the best authorized fog servers for offloading task with minimal computational and energy cost. To address the optimization issue, a hybrid Genetic Algorithm based Particle Swarm Optimization technique is employed. Experimental results demonstrated the significant improvement of MSCO when compared to the existing approaches in terms of on average 11 % improvement of total cost which includes the parameters of latency and energy consumption.
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