DRL-Based Computation Offloading and Resource Allocation in Green MEC-Enabled Maritime-IoT Networks

被引:6
作者
Wei, Ze [1 ]
He, Rongxi [1 ]
Li, Yunuo [1 ]
Song, Chengzhi [1 ]
机构
[1] Dalian Maritime Univ, Coll Informat Sci & Technol, Dalian 116026, Peoples R China
基金
中国国家自然科学基金;
关键词
maritime Internet of things (MIoT); mobile edge computing (MEC); computation offloading; carbon emissions; renewable energy; deep deterministic policy gradient (DDPG); ENERGY;
D O I
10.3390/electronics12244967
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The maritime Internet of Things (MIoT), a maritime version of the Internet of Things (IoT), is envisioned as a promising solution that can provide ubiquitous connectivity over land and sea. Due to the rapid development of maritime activities and the maritime economy, there is a growing demand for computing-intensive and latency-sensitive maritime applications requiring various energy consumption, communication, and computation resources, posing a significant challenge to MIoT devices due to their limited computational ability and battery capacity. Mobile Edge Computing (MEC), which can handle computation tasks at the network's edge more efficiently and with less latency, is emerging as a paradigm for fulfilling the ever-increasing demands of MIoT applications. However, the exponential increase in the number of MIoT devices has increased the system's energy consumption, resulting in increased greenhouse gas emissions and a negative impact on the environment. As a result, it is vital for MIoT networks to take traditional energy usage minimization into account. The integration of renewable energy-harvesting capabilities into base stations or MIoT devices possesses the potential to reduce grid energy consumption and carbon emissions. However, making an effective decision regarding task offloading and resource allocation is crucial for maximizing the utilization of the system's potential resources and minimizing carbon emissions. In this paper, we first propose a green MEC-enabled maritime IoT network architecture to flexibly provide computing-intensive and latency-sensitive applications for MIoT users. Based on the architecture, we formulate the joint task offloading and resource allocation problem by optimizing the total system execution efficiency (including the total size of completed tasks, task execution latency, and the system's carbon emissions) and then propose a deep-deterministic-policy-gradient-based joint optimization strategy to solve the problem, eventually obtaining an effective resolution through continuous action space learning in the changing environment. Finally, simulation results confirm that our proposal can yield good performance in system execution efficiency compared to other benchmarks; that is, it can significantly reduce the system's carbon emissions and tasks' delay and improve the total size of completed tasks.
引用
收藏
页数:27
相关论文
共 48 条
  • [1] SDN assisted Stackelberg Game model for LTE-WiFi offloading in 5G networks
    Anbalagan, Sudha
    Kumar, Dhananjay
    Raja, Gunasekaran
    Balaji, Alkondan
    [J]. DIGITAL COMMUNICATIONS AND NETWORKS, 2019, 5 (04) : 268 - 275
  • [2] Communicating While Computing [Distributed mobile cloud computing over 5G heterogeneous networks]
    Barbarossa, Sergio
    Sardellitti, Stefania
    Di Lorenzo, Paolo
    [J]. IEEE SIGNAL PROCESSING MAGAZINE, 2014, 31 (06) : 45 - 55
  • [3] Scheduling in wireless networks with spatial reuse of spectrum as restless bandits
    Borkar, Vivek S.
    Choudhary, Shantanu
    Gupta, Vaibhav Kumar
    Kasbekar, Gaurav S.
    [J]. PERFORMANCE EVALUATION, 2021, 149
  • [4] HCOME: Research on Hybrid Computation Offloading Strategy for MEC Based on DDPG
    Cao, Shaohua
    Chen, Shu
    Chen, Hui
    Zhang, Hanqing
    Zhan, Zijun
    Zhang, Weishan
    [J]. ELECTRONICS, 2023, 12 (03)
  • [5] A DRL Agent for Jointly Optimizing Computation Offloading and Resource Allocation in MEC
    Chen, Juan
    Xing, Huanlai
    Xiao, Zhiwen
    Xu, Lexi
    Tao, Tao
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (24) : 17508 - 17524
  • [6] Joint Optimization of Computational Cost and Devices Energy for Task Offloading in Multi-Tier Edge-Clouds
    El Haber, Elie
    Tri Minh Nguyen
    Assi, Chadi
    [J]. IEEE TRANSACTIONS ON COMMUNICATIONS, 2019, 67 (05) : 3407 - 3421
  • [7] NOMA-Based Hybrid Satellite-UAV-Terrestrial Networks for 6G Maritime Coverage
    Fang, Xinran
    Feng, Wei
    Wang, Yanmin
    Chen, Yunfei
    Ge, Ning
    Ding, Zhiguo
    Zhu, Hongbo
    [J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2023, 22 (01) : 138 - 152
  • [8] Giannopoulos A., 2023, P INT C ARTIFICIAL I
  • [9] Giannopoulos A., 2021, P 2021 IEEE 22 INT S
  • [10] Green Energy Powered Cognitive Sensor Network With Cooperative Sensing
    Hu, Hang
    Da, Xinyu
    Ni, Lei
    Huang, Yangchao
    Zhang, Hang
    [J]. IEEE ACCESS, 2019, 7 : 17354 - 17364