TY - GEN
T1 - Enabling IoT Continuous Connectivity in Smart Spaces
AU - Andreou, Andreas
AU - Mavromoustakis, Constandinos X.
AU - Batalla, Jordi Mongay
AU - Dobre, Ciprian
AU - Markakis, Evangelos
AU - Mastorakis, George
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Smart spaces are a rapidly emerging concept in technology. They result from the convergence of various novel technologies, such as the Internet of Things, Machine Learning and Artificial Intelligence, which allow for greater levels of automation and control within physical environments. The devices which are connected to the IoT network are equipped with sensors to acquire and exchange data. As a result, the IoT has transformed how we live, work, and play. However, the deployment in smart spaces is not always the best due to the issues arising from network node positioning. Therefore, we are investigating solutions to this problem with a novel approach which utilises Voronoi diagrams in conjunction with the algorithmic genetic technique. First, the initial positions of the IoT nodes will be determined by simulating a homogeneous Poisson point process in the smart space environment. Then, after dividing the area into the Voronoi cells, the genetic algorithm will optimise the position towards achieving full network coverage within the smart space. Experimental results prove the 100% network coverage within the specified area.
AB - Smart spaces are a rapidly emerging concept in technology. They result from the convergence of various novel technologies, such as the Internet of Things, Machine Learning and Artificial Intelligence, which allow for greater levels of automation and control within physical environments. The devices which are connected to the IoT network are equipped with sensors to acquire and exchange data. As a result, the IoT has transformed how we live, work, and play. However, the deployment in smart spaces is not always the best due to the issues arising from network node positioning. Therefore, we are investigating solutions to this problem with a novel approach which utilises Voronoi diagrams in conjunction with the algorithmic genetic technique. First, the initial positions of the IoT nodes will be determined by simulating a homogeneous Poisson point process in the smart space environment. Then, after dividing the area into the Voronoi cells, the genetic algorithm will optimise the position towards achieving full network coverage within the smart space. Experimental results prove the 100% network coverage within the specified area.
KW - genetic algorithm
KW - IoT
KW - Poisson point process
KW - Smart Spaces
KW - Voronoi diagrams
UR - http://www.scopus.com/inward/record.url?scp=85175236309&partnerID=8YFLogxK
U2 - 10.1109/ISPDC59212.2023.00011
DO - 10.1109/ISPDC59212.2023.00011
M3 - Conference contribution
AN - SCOPUS:85175236309
T3 - Proceedings - 2023 22nd International Symposium on Parallel and Distributed Computing, ISPDC 2023
SP - 110
EP - 115
BT - Proceedings - 2023 22nd International Symposium on Parallel and Distributed Computing, ISPDC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd International Symposium on Parallel and Distributed Computing, ISPDC 2023
Y2 - 10 July 2023 through 12 July 2023
ER -