海洋观监测传感器网络多接入边缘计算卸载方法

doi:10.11959/j.issn.2096-3750.2021.00205

Abstract

Abstract:

Multi-access edge computing can effectively guarantee the low-latency, high-reliability data transmission of ocean monitoring sensor networks and various related maritime applications.In the offshore scenario, two offloading models of multi-user single-hop unicast and multi-user multi-hop unicast were established in combination with the distribution of edge computing resources.The mixed integer nonlinear programming was used to separate optimization targets and effectively allocate transmission power.The unloading decisions were made by improving the traditional artificial fish swarms algorithm.The results show that the proposed optimization algorithm can reduce the network delay by nearly 19% compared with the traditional scheme.In the far-sea scenario, a multi-user single-hop unicast offloading model was established, and a reasonable channel allocation algorithm was proposed based on the network connection probability.The results show that when the network connection time is sufficient, the number of allowable sub-channels can be increased to reduce the network delay.When the network connection time is limited, the number of unloaded marine user equipment can be controlled to ensure the network transmission delay.

Key words: maritime monitoring sensor network, multi-access edge computing, artificial fish swarm algorithm, channel allocation

CLC Number:

TN929.52

Xin SU, Ziyi WANG, Yupeng WANG, Siyuan ZHOU. Multi-access edge computing offloading in maritime monitoring sensor networks[J]. Chinese Journal on Internet of Things, 2021, 5(1): 36-52.

Figures/Tables 11

参数	取值
平均计算密度 $a$	[15, 25]cycles/bit
SBS计算能力 $f_{c}$	[20, 30]GHz
子任务最大能耗E_max	0.2 J
子任务额定功率 $p_{\max}$	[0.5, 1]W
OUE计算能力 $f l$	[0.05, 0.1]GHz
子信道带宽 $B$	[1, 2]MHz
信道增益噪声比 $\frac{g}{σ^{2}}$	[10, 200]W^-1

参数	取值
平均计算密度 $\tilde{a}$	[5, 20]cycles/bit
SBS计算能力 ${\tilde{f}}_{c}$	50 GHz
子任务最大能耗 ${\tilde{E}}_{max}$	0.2 J
子任务额定功率 ${\tilde{p}}_{max}$	[0.5, 1]W
移动节点计算能力 ${\tilde{f}}_{m}$	[1, 15]GHz
OUE计算能力 $\tilde{f} l$	[0.01, 0.1]GHz
子信道带宽 $\tilde{B}$	[1, 2]MHz
信道增益噪声比 $\frac{\tilde{g}}{{\tilde{σ}}^{2}}$	[10, 200]W^{- 1}
任务数据量 $\tilde{D}$	[500, 700]KB
移动节点到固定节点的传输时延tts	0.008 s

参数	取值
平均计算密度 $\tilde{a}$	[5, 15]cycles/bit
芯片结构参数 $\bar{l}$	10^-26
OUE最大能耗 ${\bar{E}}_{max}$	0.2 J
传输功率 $\bar{p}$	$[\frac{0.5}{\bar{N}}, \frac{1}{\bar{N}}] W$
移动节点计算能力 ${\bar{f}}_{c}$	[5, 8]GHz
本地最大计算速率 $f {\bar{l}}_{max}$	[0.01, 0.1]GHz
子信道带宽 $\bar{B}$	1 MHz
信道增益噪声比 $\frac{\bar{g}}{{\bar{σ}}^{2}}$	[10, 200]W^{- 1}
任务数据量 $\bar{D}$	[500, 700]KB
处理1 bit数据返回数据比β	0.3

References 23

[1]	PORAMBAGE P , OKWUIBE J , LIYANAGE M ,et al. Survey on multi-access edge computing for Internet of things realization[J]. IEEE Communications Surveys and Tutorials, 2018,20(4): 2961-2991.
[2]	TALEB T , SAMDANIS K , MADA B ,et al. On multi-access edge computing:a survey of the emerging 5G network edge cloud architecture and orchestration[J]. IEEE Communications Surveys and Tutorials, 2017,19(3): 1657-1681.
[3]	SAFAVAT S , SAPAVATH N N , RAWAT D B ,et al. Recent advances in mobile edge computing and content caching[J]. Digital Communications Networks, 2020,6(2): 189-194.
[4]	HUANG L , FENG X , ZHANG C ,et al. Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing[J]. Digital Communications Networks, 2019,5(1): 10-17.
[5]	ABBAS N , ZHANG Y , TAHERKORDI A ,et al. Mobile edge computing:a survey[J]. IEEE Internet of Things Journal, 2017,5(1): 450-465.
[6]	ZHOU Y , LIU L , WANG L ,et al. Service aware 6G:an intelligent and open network based on convergence of communication,computing and caching[J]. Digital Communications Networks, 2020,6(3): 253-260.
[7]	KEWEI S , ANDREW Y T , WEI W ,et al. A survey of edge computing-based designs for IoT security[J]. Digital Communications Networks, 2020,6(2): 195-202.
[8]	KAMOUN M , LABIDI W , SARKISS M . Joint resource allocation and offloading strategies in cloud enabled cellular networks[C]// Proceedings of IEEE International Conference on Communications (ICC). Piscataway:IEEE Press, 2018.
[9]	WANG J Y , PENG J , WEI Y H ,et al. Adaptive application offloading decision and transmission scheduling for mobile cloud computing[J]. China Communications, 2017,14(3): 169-181.
[10]	CAO S W , TAO X F , HOU Y Z ,et al. An energy-optimal offloading algorithm of mobile computing based on HetNets[C]// Proceedings of 2015 International Conference on Connected Vehicles and Expo (ICCVE). Piscataway:IEEE Press, 2015.
[11]	MAO Y Y , ZHANG J , LETAIEF K B . Joint task offloading scheduling and transmit power allocation for mobile-edge computing systems[C]// Proceedings of 2017 IEEE Wireless Communications and Networking Conference (WCNC). Piscataway:IEEE Press, 2017.
[12]	WANG Y T , SHENG M , WANG X J ,et al. Mobile-edge computing:partial computation offloading using dynamic voltage scaling[J]. IEEE Transactions on Communications, 2016,64(10): 4268-4282.
[13]	MAO Y Y , ZHANG J , SONG S H ,et al. Stochastic joint radio and computational resource management for multi-user mobile-edge computing systems[J]. IEEE Transactions on Wireless Communications, 2017,16(9): 5994-6009.
[14]	REN J K , YU G D , CAI Y L ,et al. Latency optimization for resource allocation in mobile-edge computation offloading[J]. IEEE Transactions on Wireless Communications, 2018,17(8): 5506-5519.
[15]	LI Q P , ZHAO J H , GONG Y ,et al. Energy-efficient computation offloading and resource allocation in fog computing for Internet of everything[J]. China Communications, 2019,16(3): 32-41.
[16]	YOU C S , HUANG K B , CHAE H ,et al. Energy-efficient resource allocation for mobile-edge computation offloading[J]. IEEE Transactions on Wireless Communications, 2017,16(3): 1397-1411.
[17]	HE W , ZHANG Y Z , HUANG Y H ,et al. latency minimization for full-duplex mobile-edge computing system[C]// Proceedings of 2019 IEEE International Conference on Communications (ICC). Piscataway:IEEE Press, 2019.
[18]	PAN Y J , CHEN M , YANG J H ,et al. Energy-efficient NOMA-based mobile edge computing offloading[J]. IEEE Communications Letters, 2019,23(2): 310-313.
[19]	周晓敏 . 面向节能的移动边缘计算的卸载策略研究[D]. 北京:北京邮电大学, 2019.
	ZHOU X M . Research on offloading strategy in energy-saving mobile edge computing system[D]. Beijing:Beijing University of Posts and Telecommunications, 2019.
[20]	SU X , MENG L L , HUANG J ,et al. Intelligent maritime networking with edge services and computing capability[J]. IEEE Transactions on Vehicular Technology, 2020,69(11): 13606-13620.
[21]	DAI Y P , SHENG M , LIU J Y ,et al. Resource allocation for low latency mobile edge computation offloading in NOMA networks[C]// Proceedings of 2018 IEEE Global Communications Conference (GLOBECOM). Piscataway:IEEE Press, 2018.
[22]	WANG Y T , SHENG M , WANG X J ,et al. Cooperative dynamic voltage scaling and radio resource allocation for energy-efficient multiuser mobile edge computing[C]// Proceedings of 2018 IEEE International Conference on Communications (ICC). Piscataway:IEEE Press, 2018: 1938-1883.
[23]	亓晋, 孙海蓉, 巩锟 ,等. 移动边缘计算中基于信誉值的智能计算卸载模型研究[J]. 通信学报, 2020,41(7): 141-151.
	QI J , SUN H R , GONG K ,et al. Research on intelligent offloading model based on reputation value in mobile edge computing[J]. Journal on Communications, 2020,41(7): 141-151.

Metrics

Recommended 0

No Suggested Reading articles found!

AFSA	近海MSU卸载决策优化问题
人工鱼的位置	卸载决策向量
解空间	卸载决策的取值范围
适应度函数	优化目标函数

Multi-access edge computing offloading in maritime monitoring sensor networks

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 11

References 23

Related Articles 3

Metrics

Recommended 0

[1]	Meinan ZHANG, Mingqi ZHANG, Fei DING, Hengheng ZHUANG, Hairong MA. Offloading strategy with edge optimization of time delay and energy consumption in integrated satellite-terrestrial relay network [J]. Chinese Journal on Internet of Things, 2022, 6(3): 124-132.
[2]	Xin SU, Su JIANG, Yiqing ZHOU. Research on location privacy protection of mobile terminals for maritime monitoring sensor networks [J]. Chinese Journal on Internet of Things, 2021, 5(4): 26-36.
[3]	Hongyi ZHU,Xiaoyuan LU,Yi LI. Research on distributed multi-access edge computing based on mimic defense theory [J]. Chinese Journal on Internet of Things, 2019, 3(3): 76-83.