TY - GEN
T1 - Empirical Dataset Generation for AI-Optimized IoT Infrastructure Placement
AU - Taleb, Fayad
AU - Bouloukakis, Georgios
AU - Samrouth, Khouloud
AU - Hajj Hassan, Houssam
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - The strategic placement of nodes in Wireless IoT Networks (WIoTs) is crucial for ensuring optimal coverage, connectivity, and energy efficiency. Traditionally, node placement has relied on heuristic and manual methods, which often result in inefficiencies and suboptimal network performance. In this paper, we focus on optimizing the coverage performance of WIoTs, which play a pivotal role in environmental monitoring and event detection. In particular, we first develop a tool that allows IoT designers to simulate and generate datasets for multiple sensor deployment options. Then, we empirically generate a dataset that can contribute to the growing field of optimized sensor placement strategies by bridging algorithmic simulations with predictive modeling. Finally, we use the generated dataset to train a decision tree model for sensor node placement predictions. The prototype implementation of our tool and the generated datasets are publicly available for exploitation from the research community.
AB - The strategic placement of nodes in Wireless IoT Networks (WIoTs) is crucial for ensuring optimal coverage, connectivity, and energy efficiency. Traditionally, node placement has relied on heuristic and manual methods, which often result in inefficiencies and suboptimal network performance. In this paper, we focus on optimizing the coverage performance of WIoTs, which play a pivotal role in environmental monitoring and event detection. In particular, we first develop a tool that allows IoT designers to simulate and generate datasets for multiple sensor deployment options. Then, we empirically generate a dataset that can contribute to the growing field of optimized sensor placement strategies by bridging algorithmic simulations with predictive modeling. Finally, we use the generated dataset to train a decision tree model for sensor node placement predictions. The prototype implementation of our tool and the generated datasets are publicly available for exploitation from the research community.
KW - AI
KW - IoT
KW - Node placement
KW - smart environments
UR - https://www.scopus.com/pages/publications/105001670452
U2 - 10.1109/MENACOMM62946.2025.10911004
DO - 10.1109/MENACOMM62946.2025.10911004
M3 - Conference contribution
AN - SCOPUS:105001670452
T3 - 5th IEEE Middle East and North Africa Communications Conference: Breaking Boundaries: Pioneering the Next Era of Communication, MENACOMM 2025
BT - 5th IEEE Middle East and North Africa Communications Conference
A2 - Al-Shareeda, Sarah
A2 - Al-Shareeda, Sarah
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025
Y2 - 20 February 2025 through 22 February 2025
ER -
