DEVSServer: ambient intelligence and DEVS modelling-based simulation server for epidemic modelling. (2018)

• Record Type: Journal Article
• Authors: Mokaddem, Mostefa; Atmani, Baghdad; Boularas, Abdelmalek; Mokaddem, Chihab Eddine
• Is Part Of: International journal of simulation and process modelling. Volume 13:Number 6(2018)
• Page Start: 557
• Page End: 581
• Publication Date: 2018
• Subjects: ambient intelligence -- AmI -- triple space-based computing -- TSC -- service oriented simulation -- parallel discrete-event simulation -- PDES -- disease surveillance system -- epidemic modelling -- temporal network -- genetic programming -- immunisation strategy; Management -- Computer simulation -- Periodicals; Mathematical models -- Periodicals; Operations research -- Periodicals; Simulation methods -- Periodicals; 003.05
• Languages: English
• ISSNs: 1740-2123

Abstract

To improve disease surveillance systems (DSS) with faster and accurate outbreak detection and epidemics propagation capabilities, the availability of fine-tuned models is required along with the design of server-based solutions that simulate the effects of public health authorities' measures and integrate ambient intelligence (AmI) capabilities to semantise epidemic models. Hosting discrete event system specifications (DEVS) models, these AmI servers and their communication protocols are different, miscellaneous and require interoperability. The triple-space computing (TSC) paradigm addresses interoperability by sharing information represented in a semantic format through a common virtual space. In this paper, we present DEVS Server, a fully distributed TSC simulation server solution (middleware) designed to meet the needs of parallel and distributed discrete event simulation. DEVS Server defines a service oriented architecture (SOA) interface for the TSC operations. This interface complies with DEVS formalism and focuses on simplicity, conviviality and modularity, so that a single or many simulations that support different models can still interact. To assess DEVS Server, we provide a tuberculosis epidemic model simulation in time-varying temporal network with genetic programming immunisation strategy approach.