[NAFOSTED2020] Towards energy unlimited IoT networks by nature-inspired algorithms


  • PI: Assoc. Prof. Huynh Thi Thanh Binh
  • Time: 2020-2022
  • Funding: National Foundation of Science and Technology – NAFOSTED
  • Main areas: IoT, Optimization, Evolutionary Computing


Along with the development of microprocessor technology, and intelligent data collection and processing techniques, Internet of Things (IoT) have been used more widely and plays an important role in the fourth industrial revolution. Most IoT networks are self-operating networks, whose components are usually limited in energy. Therefore, one of the most critical problems in IoT networks is to optimize the energy consumption of the nodes, thereby maximizing the network’s lifetime. This problem has attracted a lot of attention from the academic community. The energy consumption of network nodes depends on many factors, from the installation to the operation. As a result, optimizing energy consumption is a difficult problem, especially in the context of new generations of IoT networks, which have complex configurations and uses advanced technologies. Although there have been many studies tackling energy optimization for IoT networks, the studies so far only focus on certain types of networks and solve the optimization problems under ideal assumptions. In this research, we aim at a comprehensive energy optimization solution for IoT networks. Specifically, we propose network models that are closer to reality and provide energy optimization solutions using the most advanced technologies. Energy optimization is an NP-hard problem and cannot find the exact solution for large datasets. Our approach is to exploit nature-inspired algorithms in order to obtain the most efficient solutions.