论文标题
多阵容双重平衡的物联网资源分配：一种进化方法
Multi-Scenario Bimetric-Balanced IoT Resource Allocation: An Evolutionary Approach
论文作者
论文摘要
在本文中，我们将IoT设备分配为智能服务的资源，并具有时间约束的资源要求。称为Brad的分配方法可以在多种资源方案下运行，具有多种资源丰富性，可用性和成本，例如Harbin Technology Institute（HIT-IHC）部署的智能医疗保健系统。分配的目的是在多幕科案件下进行双光平衡，即，与服务满意度相关的利润和成本是共同优化和平衡的。此外，我们将IoT设备作为数字对象（DO）抽象，以使它们在资源分配过程中更易于与之互动。考虑到问题是NP-硬化，并且优化目标是无法差异的，因此我们利用灰狼优化（GWO）算法作为模型优化器。具体而言，我们通过引入三种新机制来形成Brad-GWA算法，从而解决了GWO的缺陷，并显着提高了其性能。对现实的HIT-IHC IoT测试床进行了全面的实验，并比较了几种算法，包括HIT-IHC系统最初使用的分配方法来验证BRAD-GWA的有效性。与HIT-IHC和原始GWO算法相比，BRAD-GWA的目标降低3.14倍和29.6％。
In this paper, we allocate IoT devices as resources for smart services with time-constrained resource requirements. The allocation method named as BRAD can work under multiple resource scenarios with diverse resource richnesses, availabilities and costs, such as the intelligent healthcare system deployed by Harbin Institute of Technology (HIT-IHC). The allocation aims for bimetric-balancing under the multi-scenario case, i.e., the profit and cost associated with service satisfaction are jointly optimised and balanced wisely. Besides, we abstract IoT devices as digital objects (DO) to make them easier to interact with during resource allocation. Considering that the problem is NP-Hard and the optimisation objective is not differentiable, we utilise Grey Wolf Optimisation (GWO) algorithm as the model optimiser. Specifically, we tackle the deficiencies of GWO and significantly improve its performance by introducing three new mechanisms to form the BRAD-GWA algorithm. Comprehensive experiments are conducted on realistic HIT-IHC IoT testbeds and several algorithms are compared, including the allocation method originally used by HIT-IHC system to verify the effectiveness of the BRAD-GWA. The BRAD-GWA achieves a 3.14 times and 29.6% objective reduction compared with the HIT-IHC and the original GWO algorithm, respectively.
