Department of Computer Engineering, College of Computer Science and Engineering, Taibah University, Al-Madinah 42353, Saudi Arabia.
Department of Computer Science, College of Arts and Science, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia.
Sensors (Basel). 2023 Mar 20;23(6):3254. doi: 10.3390/s23063254.
Nowadays, Unmanned Aerial Vehicle (UAV) devices and their services and applications are gaining popularity and attracting considerable attention in different fields of our daily life. Nevertheless, most of these applications and services require more powerful computational resources and energy, and their limited battery capacity and processing power make it difficult to run them on a single device. Edge-Cloud Computing (ECC) is emerging as a new paradigm to cope with the challenges of these applications, which moves computing resources to the edge of the network and remote cloud, thereby alleviating the overhead through task offloading. Even though ECC offers substantial benefits for these devices, the limited bandwidth condition in the case of simultaneous offloading via the same channel with increasing data transmission of these applications has not been adequately addressed. Moreover, protecting the data through transmission remains a significant concern that still needs to be addressed. Therefore, in this paper, to bypass the limited bandwidth and address the potential security threats challenge, a new compression, security, and energy-aware task offloading framework is proposed for the ECC system environment. Specifically, we first introduce an efficient layer of compression to smartly reduce the transmission data over the channel. In addition, to address the security issue, a new layer of security based on an Advanced Encryption Standard (AES) cryptographic technique is presented to protect offloaded and sensitive data from different vulnerabilities. Subsequently, task offloading, data compression, and security are jointly formulated as a mixed integer problem whose objective is to reduce the overall energy of the system under latency constraints. Finally, simulation results reveal that our model is scalable and can cause a significant reduction in energy consumption (i.e., 19%, 18%, 21%, 14.5%, 13.1% and 12%) with respect to other benchmarks (i.e., local, edge, cloud and further benchmark models).
如今，无人机（UAV）设备及其服务和应用在我们日常生活的不同领域越来越受欢迎，引起了广泛关注。然而，这些应用和服务大多数都需要更强大的计算资源和能量，而它们有限的电池容量和处理能力使得在单个设备上运行它们变得困难。边缘云计算（ECC）作为一种新的范例出现，以应对这些应用的挑战，它将计算资源移动到网络和远程云的边缘，从而通过任务卸载来减轻开销。尽管 ECC 为这些设备提供了实质性的好处，但在通过同一通道同时卸载的情况下，随着这些应用的数据传输量的增加，有限的带宽条件尚未得到充分解决。此外，通过传输保护数据仍然是一个需要解决的重大问题。因此，在本文中，为了绕过有限的带宽并解决潜在的安全威胁挑战，我们针对 ECC 系统环境提出了一种新的压缩、安全和节能感知任务卸载框架。具体来说，我们首先引入了一种高效的压缩层，巧妙地减少了通过通道传输的数据。此外，为了解决安全问题，我们提出了一种新的基于高级加密标准（AES）加密技术的安全层，以保护卸载和敏感数据免受不同的漏洞。随后，任务卸载、数据压缩和安全性被联合制定为一个混合整数问题，其目标是在延迟约束下降低系统的总能量。最后，仿真结果表明，我们的模型是可扩展的，可以显著降低能量消耗（即 19%、18%、21%、14.5%、13.1%和 12%），与其他基准（即本地、边缘、云以及进一步的基准模型）相比。
