Energy Saving and Edge Computing
▶ Introduction
 

Next-generation wireless networks are expected to significantly rely on edge applications and functions that include edge computing and edge artificial intelligence (edge AI). To successfully support such edge services within a wireless network with mobile edge computing (MEC) capabilities, energy management (i.e., demand and supply) is one of the most critical design challenges. It is imperative to equip next-generation wireless networks with alternative energy sources, such as renewable energy, in order to provide extremely reliable energy dispatch with less energy consumption cost

Figure 1. A System model for a self-powered wireless network with MEC capabilities

The stringent requirements of MEC applications and functions fathom the high capacity and dense deployment of MEC hosts to the upcoming wireless networks. However, operating such high capacity MEC hosts can significantly increase energy consumption. Thus, a base station (BS) unit can act as a self-powered BS. To provide sustainable edge computing for next-generation wireless systems, each base station (BS) with MEC capabilities unit can be equipped with renewable energy sources. Thus, the energy source of such a BS unit not only relies solely on the power grid, but also on the equipped renewable energy sources. In particular, in a self-powered network, wireless BSs with MEC capabilities is equipped with its own renewable energy sources that can generate renewable energy, consume, store, and share energy with other BS units.

Figure 2. A System model for a microgrid-enabled wireless network with MEC capabilities

The MEC is already included as an essential component in the smart infrastructures, such as the smart city, smart factory. Meanwhile, a microgrid is also considered to be prominent in those MEC infrastructures. As a result, microgrid can be useful energy supplement to MEC, while the necessity of the renewable energy powered base stations (BSs) operation is established more than a decade ago by the Ericsson (telecommunications company). MEC confronts with the sustainability issue to manage the computation with respect to energy consumption and the competence of MEC operation is depending on efficient energy supply for microgrid empowered MEC networks. However, the MEC receives tasks with the uncertainty, where the characteristic of the energy consumption relies on the computational payload size. Furthermore, the reliability and stability of the microgrid energy supply contingent on energy generation of the renewable (e.g., solar, wind, biofuels, etc.) and nonrenewable (e.g., diesel generator, coal power, and so on) energy sources.

Next-generation wireless networks with edge computing capabilities enabled high computational heterogonous MEC hosts with self-powered functionalities. To save the energy consumption of the MEC network, it is imperative to efficiently manage the energy generation and consumption for the network. Further, the high computational MEC tasks that include AI services and applications are needed to manage in such a way so that the quality of service (QoS), as well as quality of experience (QoE), are being fulfilled.

▶ Research Issues

 
  • Artificial intelligence-based energy demand-response management for MEC-enabled wireless network
  • Data-informed intelligent control system design for sustainable edge computing for the next-generation wireless network with hybrid energy supply
  • Stochastic modeling for a sustainable edge computing for next-generation wireless network with hybrid energy supply
  • Renewable energy-aware efficient and cost-effective MEC resource allocation
  • Energy and computational load balancing among the MEC host
  • Energy sustainable cyber-physical system design for coexisting between quantum computing enable MEC host and classical MEC host
  • Risk-aware energy demand-supply management for hybrid-powered MEC network
  • Renewable energy-aware computational and network service management for smart city, smart factory, mission-critical smart infrastructure, and other smart infrastructure
  • Economic and business model design for MEC-enabled self-powered wireless network
  • Security and privacy management of energy data in a self-powered wireless network


  • ▶ References

     
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    ▶ Achievements

     
    1. Md. Shirajum Munir, Sarder Fakhrul Abedin, Nguyen H. Tran, Hong, Choong Seon Hong, "When Edge Computing Meets Microgrid: A Deep Reinforcement Learning Approach," IEEE Internet of Things Journal, Vol.6, No. 5, pp.7360-7374, Oct. 2019.
    2. Md. Shirajum Munir, Sarder Fakhrul Abedin, Do Hyeon Kim, Nguyen H. Tran, Zhu Han, Choong Seon Hong, "A Multi-Agent System Toward the Green Edge Computing with Microgrid," The 2019 IEEE Global Communications Conference (GLOBECOM 2019), Dec. 9-13, 2019, Waikoloa, USA.
    3. Md. Shirajum Munir, Sarder Fakhrul Abedin and Choong Seon Hong, "Artificial Intelligence-based Service Aggregation for Mobile-Agent in Edge Computing," The 20th Asia-Pacific Network Operations and Management Symposium(APNOMS 2019), Sep. 18-21, 2019, Matsue, Japan.
    4. Md. Shirajum Munir, Sarder Fakrul Abedin, Anupam Kumar Bairagi, Sun Moo Kang, Choong Seon Hong, "Temporal Energy Demand Extrapolation for Mobile Edge based on Computational Task in Smart-Grid Framework", 2018년 한국컴퓨터종합학술대회(KCC 2018), 2017.6.20~6.22
    5. Md. Shirajum Munir, Sarder Fakhrul Abedin, Ashis Talukder, and Choong Seon Hong, "Energy Demand Scheduling for Autonomous and Connected Vehicle Swarms in Smart Transportation System", 2019년 한국소프트웨어종합학술대회(KSC 2019), 2019.12.18~12.20.
    6. Md. Shirajum Munir, Choong Seon Hong, "Meta-Reinforcement Learning for Proactive Energy Demand Scheduling in Smart City with Edge Computing", 2018년한국소프트웨어종합학술대회(KSC2018), 2018.12.19~12.21
    7. Sarder Fakhrul Abedin, Md. Shirajum Munir, Md. Golam Rabiul Alam, Choong Seon Hong, "Energy Efficient Crowdsourcing for Internet of Things Applications over NB-IoT Networks", 2017 한국소프트웨어종합학술대회(KSC 2017), 2017.12.20.~12.22.
    8. Md. Shirajum Munir, Sarder Fakhrul Abedin, Md. Golam Rabiul Alam, Do Hyeon Kim, Choong Seon Hong, "RNN based Energy Demand Prediction for Smart-Home in Smart-Grid Framework", 2017 한국소프트웨어종합학술대회(KSC 2017), 2017.12.20.~12.22.
    9. Md. Shirajum Munir, Do Hyeon Kim, Sun Moo Kang, Choong Seon Hong, "Intelligent Public DR Management for Smart Home with Interworking IoT Platform", 2018년 통신망 운용관리 학술대회(KNOM 2018), 2018.5.10~05.11
    10. Luyao Zou, Md. Shirajum Munir, Ki Tae Kim, Choong Seon Hong, "Day-ahead Energy Sharing Schedule for the P2P Prosumer Community Using LSTM and Swarm Intelligence," The International Conference on Information Networking (ICOIN 2020), January 7-10, 2020, Barcelona, Spain.
    11. Ye Lin Tun, Kyi Thar, Chu Myaet Thwal, Choong Seon Hong, "Federated Learning Based Energy Demand Prediction of Individual Households," 2020년 한국컴퓨터종합학술대회(KCC 2020), 2020.07.02~04.
    12. Luyao Zou, Choong Seon Hong, "Proactive P2P Energy Sharing for a Community via PSO-based Stochastic Optimization," 2020년 한국컴퓨터종합학술대회(KCC 2020), 2020.07.02~04.
    13. Luyao Zou, and Choong Seon Hong, "Intelligent Energy Scheduling with Considering Battery Storage System Using Particle Swarm Optimization", 2019년 한국소프트웨어종합학술대회(KSC 2019), 2019.12.18~12.20.