Energy-Centered and QoS-Aware Services Selection for Internet of Things

An important challenge to be addressed in the domain of Internet of Things (IoT) is the development of efficient services selection algorithms for an optimal management of both energy and Quality of Service (QoS) in the context of IoT services composition. This issue becomes crucial in the case of large-scale IoT environments composed of thousands of distributed entities. In this paper, an energy-centered and QoS-aware services selection algorithm (EQSA) is proposed for IoT services composition. The proposed selection approach consists of preselecting the services offering the QoS level required for user's satisfaction using a lexicographic optimization strategy and QoS constraints relaxation technique. In order to reduce the energy consumption of a composite service without affecting the user's satisfaction, the most suitable services among the preselected ones are then selected using the concept of relative dominance of services in the sense of Pareto. The relative dominance of a candidate service depends on its energy profile and QoS attributes, and user's preferences. The proposed algorithm has been evaluated through several simulation scenarios. The obtained results show clearly the good performances of the EQSA algorithm in terms of selection time, energy efficiency, composition lifetime, and optimality and its added value in comparison with algorithms dealing separately with QoS and energy consumption. Note to Practitioners-This paper is motivated by services selection problem for an optimal management of energy and satisfaction of QoS requirements, in the context of IoT environments. The proposed selection algorithm aims at maximizing energy conservation to ensure a high services availability. It is based on the idea that it is always possible to make energy conservation by slightly reducing the QoS level without affecting user's satisfaction. Services selection is formulated and solved as a multi-objective optimization problem that consists to find a near-to-optimal solution: 1) minimizing the energy consumption of a composite service and 2) satisfying the user's requirements in terms of QoS of the composite service. Simulation results are provided to demonstrate the efficiency of the proposed approach in terms of energy conservation, time selection and optimality of found solutions, in the context of large-scale IoT environments.