%0 Journal Article
	%A Fatima Z. Cherhabil and  Mammar Sedrati and  Sonia-Sabrina Bendib‎
	%D 2023
	%J International Journal of Information and Communication Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 194, 2023
	%T AI-Based Approaches for Task Offloading, ‎Resource ‎Allocation and Service Placement of ‎IoT Applications: State of the Art
	%U https://publications.waset.org/pdf/10012971
	%V 194
	%X In order to support the continued growth, critical latency of ‎IoT ‎applications and ‎various obstacles of traditional data centers, ‎Mobile Edge ‎Computing (MEC) has ‎emerged as a promising solution that extends the cloud data-processing and decision-making to edge devices. ‎By adopting a MEC structure, IoT applications could be executed ‎locally, on ‎an edge server, different fog nodes or distant cloud ‎data centers. However, we are ‎often ‎faced with wanting to optimize conflicting criteria such as ‎minimizing energy ‎consumption of limited local capabilities (in terms of CPU, RAM, storage, bandwidth) of mobile edge ‎devices and trying to ‎keep ‎high performance (reducing ‎response time, increasing throughput and service availability) ‎at the same ‎time‎. Achieving one goal may affect the other making Task Offloading (TO), ‎Resource Allocation (RA) and Service Placement (SP) complex ‎processes. ‎It is a nontrivial multi-objective optimization ‎problem ‎to study the trade-off between conflicting criteria. ‎The paper provides a survey on different TO, SP and RA recent Multi-‎Objective Optimization (MOO) approaches used in edge computing environments, particularly Artificial Intelligent (AI) ones, to satisfy various objectives, constraints and dynamic conditions related to IoT applications‎.
	%P 137 - 143