Abstract
Hybrid services use different protocols on various networks, such as WIFI networks, Bluetooth networks, 5G communications systems, and wireless sensor networks. Hybrid service compositions can be varied, representing an effective method of integrating into wireless scenarios context-aware applications that can sense mobility via changes in user location and combining services to support target functions. In this article, improved particle swarm optimization is introduced into the quality service evaluation of dynamic service composition to meet the mobility requirements of hybrid networks. First, this work classifies hybrid services into different task groups to generate candidate sets and then interface matching is used to compare the operations of candidate services with user requirements to select the appropriate services. Second, the service composition is determined by the particle swarm optimization simulation process, which aims to identify an optimal plan based on the calculated value from quality of service. Third, considering a change of service repository, when the quality of a composite service is lower than a predefined threshold, the local greedy algorithm and global reconfiguration method are adopted to dynamically restructure composite services. Finally, a set of experiments is conducted to demonstrate the effectiveness of the proposed method for determining the dynamic service composition, particularly when the scale of hybrid services is large. The method provides a technical reference for engineering practice that will fulfill mobile computing needs.
Highlights
A hybrid network consists of various sensors and wireless communication methods for supporting data transmission and message interaction, and these networks represent an important infrastructure for the Internet of things (IoT) and mobile computing
The local greedy algorithm is used to select new services to replace low-quality services, whereas the global reconfiguration method based on the improved particle swarm optimization (IPSO) is employed to rebuild composition plans when the total quality of the service composition is lower than the expected value of the user demands
The experiments indicate that IPSO is better than particle swarm optimization (PSO), as the service composition is returned by the IPSO method using the unified quality of service (QoS) formula, and the local greedy algorithm and the global reconfiguration method will execute QoS-aware services for service entities
Summary
A hybrid network consists of various sensors and wireless communication methods for supporting data transmission and message interaction, and these networks represent an important infrastructure for the Internet of things (IoT) and mobile computing. Sensors have the capacity to provide different sensing services based on the environment, collecting temperature, humidity, light intensity, and so on All of these services are special function modules. This research focuses on service selection to assign services to activities of the business process; the optimal service composition will be returned as a solution to the required tasks, and complex business processes are implemented in a wireless environment This method involves two steps: generation of the service composition and managing changes in the service: 1. The local greedy algorithm is used to select new services to replace low-quality services, whereas the global reconfiguration method based on the IPSO is employed to rebuild composition plans when the total quality of the service composition is lower than the expected value of the user demands. Section ‘‘Conclusion and future works’’ summarizes the paper and discusses future directions
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