基于鸟群人工鱼群算法的区块链移动边缘计算卸载模型

doi:10.16180/j.cnki.issn1007-7820.2024.08.004

摘要/Abstract

摘要：

计算密集型任务数量的增加导致智能移动设备(Smart Mobile Devices,SMD)计算任务过载,借助MEC(Mobile Edge Computing Servers)及利用网络中空闲边缘设备(Edge Devices,ED)可使计算能力受限的SMD将计算任务卸载到MEC和ED协作中,并基于委托信誉证明(Delegated Proof of Reputation,DPoR)共识机制增强系统的安全性。文中提出一种基于鸟群人工鱼群算法(Bird Swarm-Artificial Fish Swarm Algorithm,BS-AFSA)的区块链移动边缘计算卸载模型,将任务卸载问题转化为优化目标函数来降低计算开销。采用改进鸟群人工鱼群算法来优化任务时延和能量消耗,对算法中的行为参数进行针对性构造,并改进拥挤度因子来提高后期迭代中寻优的局部搜索精度。仿真结果表明,与其他基准算法相比,文中所提算法减少了陷入局部最优的可能性,并降低了联合卸载方案的系统总开销。

关键词: 区块链, 移动边缘计算, 计算卸载, 共识机制, 鸟群算法, 人工鱼群算法, 任务时延能耗, 优化问题

Abstract:

The rapid increase in the number of computing-intensive tasks has led to an overload of SMD(Smart Mobile Devices) computing tasks. By using MEC(Mobile Edge Computing Servers) and idle ED(Edge Devices) in the network, SMD with limited computing power can offload computing tasks to MEC and ED collaboration, and enhance system security based on the DPoR(Delegated Proof of Reputation) consensus mechanism. This study proposes a blockchain mobile edge computing offloading model based on BS-AFSA(Bird Swarm-Artificial Fish Swarm Algorithm), which transforms the task offloading problem into an optimization objective function to reduce the computational overhead. The improved BS-AFSA is used to optimize the task delay and energy consumption, and the behavior parameters in the algorithm are constructed and the crowding factor is improved to elevate the local search accuracy in the later iteration. The simulation results show that compared with other benchmark algorithms, the proposed algorithm reduces the possibility of falling into local optimum and effectively reduces the total system cost of the joint offloading scheme.

Key words: blockchain, mobile edge computing, computation offloading, consensus mechanism, bird swarm algorithm, artificial fish swarm algorithm, task delay and energy consumption, optimization problem

中图分类号:

TP391

孔小爽, 袁健. 基于鸟群人工鱼群算法的区块链移动边缘计算卸载模型[J]. 电子科技, 2024, 37(8): 26-33.

KONG Xiaoshuang, YUAN Jian. Blockchain Mobile Edge Computing Offloading Model Based on Bird Swarm Artificial Fish Swarm Algorithm[J]. Electronic Science and Technology, 2024, 37(8): 26-33.

图/表 8

图1

图2

表1

仿真参数"

参数描述	数值
SMD计算能力 $x i l o c$ /GHz	[0.1,1.0]
MEC和ED计算能力 $f i j s$ 和 $f i k e$ /GHz	10.0,5.0
SMD能耗系数ζ/W	10^-18
MEC和ED能耗系数e_j和e_k/W	5.0,2.0
信道带宽B_i/MHz	20.0
信道增益h_i	10.0~600.0
发射功率p_i/mW	500.0
噪声干扰I_i/dBm	-100.0
高斯白噪声功率B₀/dBm	-100.0
任务所需CPU周期数D_i/kB	5 000.0
计算任务CPU周期数C_i/Gcycle	1 000.0
任务允许最大延迟时间 $t i m a x$ /s	500.0
区块验证所需CPU周期数φ_i/cycle	10^-3
区块交易上行传输速率 $r i u$ /kbit·s^-1	150.0
区块交易下行传输速率 $r i d$ /kbit·s^-1	200.0
区块交易数据Tr_i/kB	[50.0-500.0]
区块验证结果 $T r i r e$ /kB	[10.0-50.0]
区块广播验证参数ξ	0.5
算法最大迭代次数	100.0

表1

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表2

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算法	任务数量20/s	任务数量40/s	任务数量60/s	任务数量80/s	任务数量100/s
BS-AFSA	11.55	13.30	15.32	17.91	20.42
AFSA	15.33	18.86	21.79	25.82	30.88
文献[18]	12.93	16.71	18.69	22.41	24.46
文献[19]	14.77	18.45	20.83	24.70	26.46