Recently, the Internet of Things (IoT) has quickly risen as one of the most essential technologies of this century. IoT allows users to connect to a vast network of smart devices, services, and data. An important and challenging research problem in the Internet of Things applications is how to select an appropriate service selection (SS). In the SS problem, users can combine several services from diverse sources (things or devices) to satisfy their needs. On the other hand, the SS problem is known for its complexity and is categorized as an NP-hard problem; such problems are typically solved utilizing heuristics like bio-inspired algorithms. In this research a new bio-inspired algorithm called DDAPSO is created to solve the SS problem where a new strategy is proposed to maintain a balance between the exploration and exploitation abilities. This hybrid algorithm is the result of coupling a Discrete Dragonfly Algorithm (DDA) with the particle swarm optimization algorithm (PSO). The suggested algorithm was properly tested using a variety of scenarios with different numbers of services and with different numbers of concrete services per each service set or task. The proposed algorithm is compared with the main recent well-known algorithms, i.e. GA, PSO, DDA, ABC and MVO for service selection. In a large-scale setting, the results clearly show that the DDAPSO algorithm outperforms other services selection algorithms reported in the literature in terms of selection optimally as well as execution time.
In this workm we study the fault prediction in fuzzy discrete event systems. Fuzzy discrete event systems are proposed to deal with vagueness, impreciseness, and subjectivity in real-world problems. The verification is divided into two steps. In the first step, we give a method to construct a Diagnoser. And in the second step, based on the structure of diagnoser we give the necessary and sufficient conditions to verify the future occurrence of the fault. The newly proposed approach allows us to deal with the problem of fault prediction for both crisp DESs and FDESs. Finally, an example is provided to illustrate the efficiency of the proposed approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.