Title: ^DEVSServer: ambient intelligence and DEVS modelling-based simulation server for epidemic modelling

Authors: Mostefa Mokaddem; Baghdad Atmani; Abdelmalek Boularas; Chihab Eddine Mokaddem

Addresses: Laboratoire d'Informatique d'Oran (LIO), University of Oran 1 Ahmed Ben Bella, BP 1524 El M'Naouer 31000 Oran, Algeria ' Laboratoire d'Informatique d'Oran (LIO), University of Oran 1 Ahmed Ben Bella, BP 1524 El M'Naouer 31000 Oran, Algeria ' Computer Information System Department, Ahmed Bin Mohamed Military College, P.O. Box 22988, Doha, Qatar ' Département d'Informatique et de R. Opérationnelle, Groupe de Recherche GEODES, Université de Montréal, C.P. 6128 Succursale Centre-ville Montréal (QC) H3C 3J7, Canada

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 ^DEVSServer, a fully distributed TSC simulation server solution (middleware) designed to meet the needs of parallel and distributed discrete event simulation. ^DEVSServer 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 ^DEVSServer, we provide a tuberculosis epidemic model simulation in time-varying temporal network with genetic programming immunisation strategy approach.

Keywords: 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.

DOI: 10.1504/IJSPM.2018.095875

International Journal of Simulation and Process Modelling, 2018 Vol.13 No.6, pp.557 - 581

Received: 03 Aug 2017
Accepted: 11 Apr 2018
Published online: 25 Oct 2018 *

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