TY - JOUR
T1 - Design of a general complex problem-solving architecture based on task management and predictive optimization
AU - Ahmad, Shabir
AU - Khan, Salman
AU - Jamil, Faisal
AU - Qayyum, Faiza
AU - Ali, Abid
AU - Kim, Do Hyeun
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by Energy Cloud R&D Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT (2019M3F2A1073387), and this work was conducted by Center for Applied Research and This work was supported by the Institute for Information & communications Technology Promotion (IITP) (NO. 2022-0-00980, Cooperative Intelligence Framework of Scene Perception for Autonomous IoT Device) and by the National Research Foundation of Korea (NRF) Grant funded by the Ministry of Basic Education under Grant 2021R1I1A1A01045177.
Publisher Copyright:
© The Author(s) 2022.
PY - 2022/6/28
Y1 - 2022/6/28
N2 - Many real-life problems have different contradicting goals and no simple solution. Therefore, an analysis is made to select the appropriate solution based on the scenario, which is considered the best compromise toward the achievement of a goal. In literature, it is known as complex problem-solving and is a kind of paradigm that has been around since the last century, but the cognition involved in complex problem-solving has purely relied on experts in the field. However, with the evolution of the current stack of technologies such as artificial intelligence and the Internet of Things, it is quite possible to perform the cognition process with the help of machines based on the previously-trained historical data. Our previous work proposed the complex problem-solving as a service for smart cities. In this article, we extend this work and propose a generic architecture for complex problem-solving using task orchestration and predictive optimization in Internet of Things–enabled generic smart space. The proposed framework makes use of historical data for artificial cognition of the complexity of the given problem. For this, predictive optimization is used, which identifies the problem and intelligently predict the solution based on the given constraints. The task orchestration architecture is used to decompose the complex problem into small tasks for real-world deployment into sensors and actuators. The architecture is evaluated against different load conditions and different categories of problems, and the results suggest that the proposed architecture can be used a commonplace for different smart space solutions.
AB - Many real-life problems have different contradicting goals and no simple solution. Therefore, an analysis is made to select the appropriate solution based on the scenario, which is considered the best compromise toward the achievement of a goal. In literature, it is known as complex problem-solving and is a kind of paradigm that has been around since the last century, but the cognition involved in complex problem-solving has purely relied on experts in the field. However, with the evolution of the current stack of technologies such as artificial intelligence and the Internet of Things, it is quite possible to perform the cognition process with the help of machines based on the previously-trained historical data. Our previous work proposed the complex problem-solving as a service for smart cities. In this article, we extend this work and propose a generic architecture for complex problem-solving using task orchestration and predictive optimization in Internet of Things–enabled generic smart space. The proposed framework makes use of historical data for artificial cognition of the complexity of the given problem. For this, predictive optimization is used, which identifies the problem and intelligently predict the solution based on the given constraints. The task orchestration architecture is used to decompose the complex problem into small tasks for real-world deployment into sensors and actuators. The architecture is evaluated against different load conditions and different categories of problems, and the results suggest that the proposed architecture can be used a commonplace for different smart space solutions.
KW - artificial cognition
KW - Complex problem-solving
KW - embedded Internet of Things systems
KW - predictive optimization
KW - task modeling
KW - task orchestration
UR - http://www.scopus.com/inward/record.url?scp=85133303054&partnerID=8YFLogxK
U2 - 10.1177/15501329221107868
DO - 10.1177/15501329221107868
M3 - Article
AN - SCOPUS:85133303054
VL - 18
SP - 1
EP - 14
JO - International Journal of Distributed Sensor Networks
JF - International Journal of Distributed Sensor Networks
SN - 1550-1329
IS - 6
ER -