TY - GEN
T1 - EDICT
T2 - 19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023
AU - Hassan, Houssam Hajj
AU - Bouloukakis, Georgios
AU - Kattepur, Ajay
AU - Conan, Denis
AU - Belaid, Djamel
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - This paper presents EDICT, a tool for simulating Edge interactions in IoT-enhanced environments. Recently, ML and AI-based techniques have gained prominence to solve IoT-related challenges. However, such models require large and diverse datasets to perform well. Finding real-world datasets that capture the performance of IoT systems is a challenging task due to the cost of deploying devices and instrumenting environments, as well as privacy/security concerns. This task becomes more challenging when datasets for specific situations (e.g., overloaded system, emergency scenarios) are needed. EDICT enables IoT systems designers to evaluate the performance of their IoT systems at design time. EDICT is capable of generating performance metrics datasets for specific instances of IoT-enhanced environments under different configuration parameters. To support runtime adaptation of smart environments, EDICT enables rapid performance prediction using ML techniques.
AB - This paper presents EDICT, a tool for simulating Edge interactions in IoT-enhanced environments. Recently, ML and AI-based techniques have gained prominence to solve IoT-related challenges. However, such models require large and diverse datasets to perform well. Finding real-world datasets that capture the performance of IoT systems is a challenging task due to the cost of deploying devices and instrumenting environments, as well as privacy/security concerns. This task becomes more challenging when datasets for specific situations (e.g., overloaded system, emergency scenarios) are needed. EDICT enables IoT systems designers to evaluate the performance of their IoT systems at design time. EDICT is capable of generating performance metrics datasets for specific instances of IoT-enhanced environments under different configuration parameters. To support runtime adaptation of smart environments, EDICT enables rapid performance prediction using ML techniques.
KW - Data Exchange
KW - QoS
KW - Simulation
KW - Smart Environments
U2 - 10.1109/DCOSS-IoT58021.2023.00037
DO - 10.1109/DCOSS-IoT58021.2023.00037
M3 - Conference contribution
AN - SCOPUS:85174400653
T3 - Proceedings - 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023
SP - 161
EP - 165
BT - Proceedings - 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 June 2023 through 21 June 2023
ER -