Internet of Things (IoT) has been grown rapidly over the last years to connect a considerable number of spatially distributed objects or actuators. The connected objects create new functionality and provide various services to enhance and satisfy End-users daily lives. The issue is to provide the End-users with optimal services based on their requirements. The critical challenge is to select the optimal service from similar services functionally and various services non-functionality requirements (Quality of services). To achieve this challenge, this paper proposed a services selection model under QoS constraints in the IoT environment. The introduced model implements a meta-heuristic optimization algorithm with a friendly Likert scale measurement method. It aims to improve the performance of bio-inspired optimizing algorithms, called a Social Spider Optimization (SSO) Algorithm, by adding a reputation value to member's weight. The proposed model used a Likert scale measurement to evaluate the reputation of the services from the End-users. In the experiments, a comparative study was done between an original SSO and the proposed RI-SSO model. The results show the efficiency of the proposed RI-SSO model against the original SSO, in both maximization and minimization problems. It obtains a better outperform in terms of fitness values. ​​

Abstract Internet of Things (IoT) has been grown rapidly over the last years to connect a considerable number of spatially distributed objects or actuators. The connected objects create new functionality and provide various services to enhance and satisfy End-users daily lives. The issue is to provide the End-users with optimal services based on their requirements. The critical challenge is to select the optimal service from similar services functionally and various services non-functionality requirements (Quality of services). To achieve this challenge, this paper proposed a services selection model under QoS constraints in the IoT environment. The introduced model implements a meta-heuristic optimization algorithm with a friendly Likert scale measurement method. It aims to improve the performance of bio-inspired optimizing algorithms, called a Social Spider Optimization (SSO) Algorithm, by adding a reputation value to member's weight. The proposed model used a Likert scale measurement to evaluate the reputation of the services from the End-users. In the experiments, a comparative study was done between an original SSO and the proposed RI-SSO model. The results show the efficiency of the proposed RI-SSO model against the original SSO, in both maximization and minimization problems. It obtains a better outperform in terms of fitness values. ​​

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