Investigating Mobility-aware Strategies for IoT Services Placement in the Fog under Energy and QoS Constraints

Published online: Apr 16, 2021
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DOI: 10.24138/jcomss-2020-0024
Tanissia Djemai, Patricia Stolf, Thierry Monteil, Jean-Marc Pierson


Mobility of Internet of Things (IoT) objects is a key characteristic of IoT environments. It brings dynamicity, uncertainty and raises many challenges when it is associated with computation and network resources management for IoT applications. The resources management problem under objects mobility consideration is even more sensitive if we consider that various IoT applications have stringent Quality of Service (QoS) needs. Fog Computing is a distributed computation paradigm that increases data centers computation and storage abilities with nodes between end-users and the Cloud. Fog computing offers a large distributed infrastructure to support IoT applications needs by bringing services closer to end users. However, Fog infrastructures inherit the energy greediness characteristics of both data centers and network infrastructures. This work investigates the IoT services placement problem in the Fog as an optimization problem to minimize energy consumption and enhance QoS while considering mobility of IoT objects. We model the placement problem as a multi-objective optimization problem and we propose a location history based mobility model (HTM) to estimate future locations of IoT mobile nodes. We propose a framework composed of online strategies for IoT services placement and a Mobility-aware Genetic Algorithm (MGA) for services migrations. We evaluate our strategies through iFogSim simulator and compare the proposed framework to migrations and placement strategies from the literature based on Shortest Access Point migration strategy (SAP) and with Penguins Search Optimization Algorithm (PeSOA). Experiments show that the proposed framework outperforms literature approaches for the considered objectives and for various configurations of the mobile environment.

Creative Commons License 4.0
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.