Resource Allocation for Edge-enhanced Distributed Power Wireless Sensor Network

doi:10.11930/j.issn.1004-9649.202304069

Abstract

Abstract: Aiming at the problems of high access latency, high energy consumption, and deteriorating user service quality in traditional wireless power sensor networks, the mobile edge computing (MEC) is introduced to establish a joint optimization model of access latency and network energy consumption, with objective to minimize the joint function of latency and energy consumption. Then, the total resource processing capacity of the central unit/distribution unit is determined by controlling the waiting latency in the load transfer process under the common constraints of the remaining resources of the RF remote unit, the load queuing latency, and the remaining resources of the central unit/distribution unit. And then, based on the resource utilization of the MEC server, the energy consumption of the sensor devices is reduced by reducing the number of active MEC servers. The influence of the weight coefficient on the objective function and the comparison of the algorithm under different traffic flows and times are analyzed. The results show that the proposed allocation mechanism is better than other comparison algorithms in reducing access latency and energy consumption. Therefore, this mechanism can effectively improve the communication quality of the sensors and the efficiency of the system, extend the life of the sensor equipment, and reduce the network cost.

Key words: power wireless sensor networks, mobile edge computing, energy consumption aware, delay aware, load balancing

WU Gang, ZHOU Jinhui, LI Hui. Resource Allocation for Edge-enhanced Distributed Power Wireless Sensor Network[J]. Electric Power, 2023, 56(8): 77-85,98.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202304069

https://www.electricpower.com.cn/EN/Y2023/V56/I8/77

References

[1] 叶远波, 刘宏君, 张兆云, 等. 基于广域信息的继电保护实时评价研究[J]. 电力系统保护与控制, 2021, 49(13): 150–157
YE Yuanbo, LIU Hongjun, ZHANG Zhaoyun, et al. Research on real-time evaluation of relay protection based on wide area information[J]. Power System Protection and Control, 2021, 49(13): 150–157
[2] 吴庆, 王云棣, 曾令康, 等. 基于层次分析法的“十四五”终端通信接入网通信技术规划[J]. 中国电力, 2022, 55(5): 174–181
WU Qing, WANG Yundi, ZENG Lingkang, et al. Communication technology planning for power terminal communication access networks in the “14th five-year plan” based on analytic hierarchy process[J]. Electric Power, 2022, 55(5): 174–181
[3] 徐江涛, 姜海波, 刘洋, 等. 电力物联网D2D通信中模式预选的多播重传技术[J]. 电力科学与技术学报, 2021, 36(4): 196–202
XU Jiangtao, JIANG Haibo, LIU Yang, et al. A multicast retransmission scheme based on the pre-selected mode for D2D communications of electric Internet of Things networks[J]. Journal of Electric Power Science and Technology, 2021, 36(4): 196–202
[4] 赖业宁, 封科, 于同伟, 等. 基于DHT和区块链技术的电网安全稳定控制终端分布式认证[J]. 中国电力, 2022, 55(4): 44–53
LAI Yening, FENG Ke, YU Tongwei, et al. Distributed authentication of power grid safety and stability control terminals based on DHT and blockchain[J]. Electric Power, 2022, 55(4): 44–53
[5] 张怡, 张锋, 李有春, 等. 基于智慧输电线路的动态增容辅助决策系统[J]. 电力系统保护与控制, 2021, 49(4): 160–168
ZHANG Yi, ZHANG Feng, LI Youchun, et al. Assistant decision system to improve transmission capacity based on a smart transmission line[J]. Power System Protection and Control, 2021, 49(4): 160–168
[6] CHANAK P, BANERJEE I. Congestion free routing mechanism for IoT-enabled wireless sensor networks for smart healthcare applications[J]. IEEE Transactions on Consumer Electronics, 2020, 66(3): 223–232.
[7] VERMA S, ZEADALLY S, KAUR S, et al. Intelligent and secure clustering in wireless sensor network(WSN)-based intelligent transportation systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(8): 13473–13481.
[8] NGUYEN T T, OH H. A smart multichannel slotted sense multiple access protocol for industrial wireless sensor networks[J]. IEEE Internet of Things Journal, 2022, 9(14): 12460–12471.
[9] AL-RAZGAN M, ALFAKIH T. Wireless sensor network architecture based on mobile edge computing[J]. Security and Communication Networks, 2022, 2022: 1–16.
[10] ALFAKIH T, HASSAN M M, AL-RAZGAN M. Multi-objective accelerated particle swarm optimization with dynamic programing technique for resource allocation in mobile edge computing[J]. IEEE Access, 2021, 9: 167503–167520.
[11] HARUTYUNYAN D, RIGGIO R. How to migrate from operational LTE/LTE–a networks to C–RAN with minimal investment?[J]. IEEE Transactions on Network and Service Management, 2018, 15(4): 1503–1515.
[12] BIHNAM A N, LIU X, AL-DOORI B. Analytical formulas for the ergodic capacity in association of RRHs in cloud radio access networks[C]//2020 IEEE 17 th Annual Consumer Communications & Networking Conference (CCNC). Las Vegas, NV, USA. IEEE, 2020: 1–4.
[13] 韦磊, 徐江涛, 郭雅娟, 等. 基于信任机制的电力无线传感网络安全簇头选举算法[J/OL]. 中国电力: 1–7[2023-03-24]. http://kns.cnki.net/kcms/detail/11.3265.TM.20230324.0832.002.html.
WEI Lei, XU Jiangtao, GUO Yajuan, et al. Secure cluster head election algorithm based on trust mechanism in electric power wireless sensor networks [J/OL]. Electric Power: 1–7[2023-03-24]. http://kns.cnki.net/kcms/detail/11.3265.TM.20230324.0832.002.html.
[14] LIU J E, ZHU L. Joint resource allocation optimization of wireless sensor network based on edge computing[J]. Complexity, 2021, 2021: 1–11.
[15] 鄢丽娟, 张彦虎. 舰船航行信息传送的无线传感器网络能耗优化[J]. 舰船科学技术, 2021, 43(20): 82–84
YAN Lijuan, ZHANG Yanhu. Energy consumption optimization of wireless sensor networks for ship navigation information transmission[J]. Ship Science and Technology, 2021, 43(20): 82–84
[16] 马步云, 马新策, 黄松, 等. WSN低功耗低时延路径式协同计算方法[J]. 无线电通信技术, 2021, 47(2): 168–177
MA Buyun, MA Xince, HUANG Song, et al. Low-power low-latency path-based collaborative computing scheme for WSN[J]. Radio Communications Technology, 2021, 47(2): 168–177
[17] YANG J, ZHANG H S, LING Y, et al. Task allocation for wireless sensor network using modified binary particle swarm optimization[J]. IEEE Sensors Journal, 2014, 14(3): 882–892.
[18] 牛祺君, 张永辉. 基于蜂群算法的无线传感器网络层次路由优化[J]. 计算机仿真, 2018, 35(12): 229–232, 420
NIU Qijun, ZHANG Yonghui. Optimization of WSN hierarchical routing based on artificial bee colony algorithm[J]. Computer Simulation, 2018, 35(12): 229–232, 420
[19] 朱鹏, 任继军, 任智源. 基于路径计算方法的WSN时延优化研究[J]. 西北工业大学学报, 2022, 40(6): 1394–1403
ZHU Peng, REN Jijun, REN Zhiyuan. WSN latency optimization based on path calculation method[J]. Journal of Northwestern Polytechnical University, 2022, 40(6): 1394–1403
[20] KIM H S, KIM H, PAEK J, et al. Load balancing under heavy traffic in RPL routing protocol for low power and lossy networks[J]. IEEE Transactions on Mobile Computing, 2017, 16(4): 964–979.
[21] LI Z D, ZHONG A L. Resource allocation in wireless powered virtualized sensor networks[J]. IEEE Access, 2020, 8: 40327–40336.
[22] HAN S Y, MA D W, KANG C, et al. Optimization of mobile edge computing offloading model for distributed wireless sensor devices[J]. Journal of Sensors, 2022, 2022: 1–9.
[23] WANG X, JI Y F, ZHANG J W, et al. Joint optimization of latency and deployment cost over TDM-PON based MEC-enabled cloud radio access networks[J]. IEEE Access, 2019, 8: 681–696.
[24] FAN W H, LIU Y A, TANG B H, et al. Computation offloading based on cooperations of mobile edge computing-enabled base stations[J]. IEEE Access, 2017, 6: 22622–22633.
[25] HAWILO H, JAMMAL M, SHAMI A. Network function virtualization-aware orchestrator for service function chaining placement in the cloud[J]. IEEE Journal on Selected Areas in Communications, 2019, 37(3): 643–655.
[26] HOUTSMA V, VAN VEEN D. Bi-directional 25 G/50 G TDM-PON with extended power budget using 25 G APD and coherent detection[J]. Journal of Lightwave Technology, 2018, 36(1): 122–127.
[27] RIMAL B P, VAN D P, MAIER M. Mobile-edge computing vs. centralized cloud computing in fiber-wireless access networks[C]//2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). San Francisco, CA, USA. IEEE, 2016: 991–996.