Multi-agent system to monitor oceanic environments

Abstract

The exchange of CO2 between the atmosphere and the ocean surface is a problem that has become increasingly important due to its impact on climatic behavior. Given the large quantity of sources of information available for studying the CO2 problem, it is necessary to provide innovative solutions that facilitate the automation of certain tasks and incorporate decision support systems to obtain a better understanding of this phenomenon. This paper presents a multiagent architecture aimed at providing solutions for monitoring the interaction between the atmosphere and the ocean. The ocean surface and the atmosphere exchange carbon dioxide. This process is can be modeled by a multiagent system with advanced learning and adaption capabilities. The proposed multiagent architecture incorporates CBR-agents that integrate novel strategies that both monitor the parameters that affect the interaction, and facilitate the creation of models. The system was tested and this paper presents the results obtained.

Agent-based cloud workflow execution

“…Receive tasks' outputs from previously contracted SPAs t 4 Receive a request from an SPA regarding a set of needed requirements to fulfill a previously assigned requirement (as explained in Section 3) t 5 Send an inform message to either a CA or an SPA containing CWs' outputs. st 1 Send a call for proposals (adopting the CNP) to SPAs to contract services to fulfill a given requirement st 2…”

“…Send workflow tasks' outputs to BAs t 3 Receive outputs from BAs regarding previous requests t 4 Send requirements to BAs (as explained in Section 3) st 5 Assign unfulfilled requirements by allocating available RAs st 6 Deallocate an RA by receiving its resolved requirement. This resets the RA in order to accept another requirement st 7…”

“…In addition, agent-based solutions have ISSN 1069-2509/12/$27.50  2012 -IOS Press and the author(s). All rights reserved proved to be efficient in a wide range of dynamic environments, from human tracking video surveillance systems [15], interactive artistic installations [9], energy management [26], shipboard systems [34], oceanic monitoring [5] to establishing service-level agreements in Cloud-computing environments [27].…”

A geospatial case‐based reasoning model for oil–gas reservoir evaluation

“…In recent years, CBR has been used to solve problems in the environmental sciences (Bajo, De Paz, Rodríguez, & González, ; Chazara, Negny, & Montastruc, ; Kaur & Kundra, ; Lee, Koo, Hong, & Park, ; Lekkas, Avouris, & Viras, ; Mata & Corchado, ; Mounce, Mounce, & Boxall, ), urban planning (Li & Liu, ; Liu et al, ; Yeh & Shi, ), land use change (Du, Wen, Cao, & Ji, ; Holt & Benwell, ; Shi, Zhu, Burt, Qi, & Simonson, ), geological engineering (Dou et al, ; Fyson & Toll, ), mineral resource assessment (Chen, He, & Cui, ; Chen, He, Cui, Liu, & He, ; He, Chen, Cui, & He, ), and oil and gas engineering (Bhushan & Hopkinson, ; Popa & Wood, ; Shokouhi, Skalle, & Aamodt, ; Skalle & Aamodt, ). In these studies, the advantages and strengths of CBR were adequately demonstrated; the applications and experimental results were predominantly efficient.…”

RETRACTED CHAPTER: Multi-agent Technology to Perform Odor Classification

“…There are several proposals to build smart environments that combine multi-agent systems and sensor networks [8], [9], [10], [11], [12], [13] , [14], [15], [16], [17], [18], [19], [20], [21]. New approaches are needed to support evolutional systems and to facilitate their growth and runtime updates.…”