The growth of Internet of Thing (IoT) implies the availability of a very large number of services which may be similar or the same, managing the Quality of Service (QoS) helps to differentiate one service from another. The service composition provides the ability to perform complex activities by combining the functionality of several services within a single process. Very few works have presented an adaptive service composition solution managing QoS attributes, moreover in the field of healthcare, which is one of the most difficult and delicate as it concerns the precious human life.In this paper, we will present an adaptive QoS-Aware Service Composition Approach (P-MPGA) based on multi-population genetic algorithm in Fog-IoT healthcare environment. To enhance Cloud-IoT architecture, we introduce a Fog-IoT 5-layared architecture. Secondly, we implement a QoS-Aware Multi-Population Genetic Algorithm (P-MPGA), we considered 12 QoS dimensions, i.e., Availability (A), Cost (C), Documentation (D), Location (L), Memory Resources (M), Precision (P), Reliability (R), Response time (Rt), Reputation (Rp), Security (S), Service Classification (Sc), Success rate (Sr), Throughput (T). Our P-MPGA algorithm implements a smart selection method which allows us to select the right service. Also, P-MPGA implements a monitoring system that monitors services to manage dynamic change of IoT environments. Experimental results show the excellent results of P-MPGA in terms of execution time, average fitness values and execution time / best fitness value ratio despite the increase in population. P-MPGA can quickly achieve a composite service satisfying user’s QoS needs, which makes it suitable for a large scale IoT environment

The Internet of things (IoT) is the integration of information space and physical space, becoming more and more popular in several places. In this paper, we will present QoS service composition approach based on multi-population genetic algorithm based on Fog-IoT computing, IoT-cloud architecture problems led us to use the 5-layared architecture implemented on a Fog computing system especially the transport layer. Our work was focus on this transport layer where we divided it into four sub-layers (security, storage, pre-processing & monitoring), it allows us to have promising advantages. Secondly, we implemented a multi-population genetic algorithm (MPGA) based on a QoS model, we considered seven QoS dimensions, i.e. Cost, response time, reliability, reputation, location, security and availability. Experimental results show the excellent results of MPGA in terms of fitness value and execution time to handle our ambulance emergency study case.