TY - GEN
T1 - The Spatio-temporal Hybrid Development Methodology for Smart IoT
T2 - 2022 IEEE International Conference on Intelligent Education and Intelligent Research, IEIR 2022
AU - Alzahrani, Yazeed
AU - Shen, Jun
AU - Yan, Jun
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper deals with a review-based study on the efficient development methodologies for the deployment of IoT systems. Efficient hardware and software development reduces the risk of system bugs and faults. However, the optimal placement of the IoT devices is one of the major challenges for the monitoring applications. In this paper, a combined Qualitative Spatial Reasoning (QSR) and Qualitative Temporal Reasoning (QTR) methodology is proposed to build IoT software systems. The proposed hybrid methodology includes the features of QSR, QTR, and traditional data-oriented methodologies. This methodology directs software systems to the specific goal in obtaining outputs inherent to the process. The hybrid methodology includes the support of tools integrated, and also fits the general purpose. This methodology repeats the structure of spatio-temporal reasoning. Segmentation and object detection are used for the division of the region into sub-regions. Furthermore, the coverage and connectivity are maintained by the optimal placement of the IoT devices using RCC8 and TPCC algorithms.
AB - This paper deals with a review-based study on the efficient development methodologies for the deployment of IoT systems. Efficient hardware and software development reduces the risk of system bugs and faults. However, the optimal placement of the IoT devices is one of the major challenges for the monitoring applications. In this paper, a combined Qualitative Spatial Reasoning (QSR) and Qualitative Temporal Reasoning (QTR) methodology is proposed to build IoT software systems. The proposed hybrid methodology includes the features of QSR, QTR, and traditional data-oriented methodologies. This methodology directs software systems to the specific goal in obtaining outputs inherent to the process. The hybrid methodology includes the support of tools integrated, and also fits the general purpose. This methodology repeats the structure of spatio-temporal reasoning. Segmentation and object detection are used for the division of the region into sub-regions. Furthermore, the coverage and connectivity are maintained by the optimal placement of the IoT devices using RCC8 and TPCC algorithms.
KW - Development Methodology
KW - IoT
KW - QSR
KW - QTR
UR - http://www.scopus.com/inward/record.url?scp=85150049091&partnerID=8YFLogxK
U2 - 10.1109/IEIR56323.2022.10050075
DO - 10.1109/IEIR56323.2022.10050075
M3 - Conference contribution
AN - SCOPUS:85150049091
T3 - IEIR 2022 - IEEE International Conference on Intelligent Education and Intelligent Research
SP - 169
EP - 176
BT - IEIR 2022 - IEEE International Conference on Intelligent Education and Intelligent Research
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 December 2022 through 20 December 2022
ER -