A GA-based multi-purpose optimization algorithm for QoSrouting

Kôyama, Akio; Barolli, Leonard; Matsumoto, Keinosuke; Apduhan, Bernady O.

doi:10.1109/aina.2004.1283882

Cited by 25 publications

(21 citation statements)

References 4 publications

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“…Many GA-based routing protocols with multiple QoS constraints have been proposed in the past decade, e.g., [28], [29], [30]. However, we argue that our approaches have made the following key contributions in the IoT settings: a) existing approaches only examined single flow performance, while multiple flows with different QoS requirements coexist in an IoT environment.…”

Section: B Genetic Algorithm-based Multi Constraints Flow Schedulingmentioning

confidence: 99%

A Software Defined Networking architecture for the Internet-of-Things

Qin

Denker

Giannelli

et al. 2014

2014 IEEE Network Operations and Management Symposium (NOMS)

300

122

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Abstract-The growing interest in the Internet of Things (IoT) has resulted in a number of wide-area deployments of IoT subnetworks, where multiple heterogeneous wireless communication solutions coexist: from multiple access technologies such as cellular, WiFi, ZigBee, and Bluetooth, to multi-hop ad-hoc and MANET routing protocols, they all must be effectively integrated to create a seamless communication platform. Managing these open, geographically distributed, and heterogeneous networking infrastructures, especially in dynamic environments, is a key technical challenge. In order to take full advantage of the many opportunities they provide, techniques to concurrently provision the different classes of IoT traffic across a common set of sensors and networking resources must be designed. In this paper, we will design a software-defined approach for the IoT environment to dynamically achieve differentiated quality levels to different IoT tasks in very heterogeneous wireless networking scenarios. For this, we extend the Multinetwork INformation Architecture (MINA), a reflective (self-observing and adapting via an embodied Observe-Analyze-Adapt loop) middleware with a layered IoT SDN controller. The developed IoT SDN controller originally i) incorporates and supports commands to differentiate flow scheduling over task-level, multi-hop, and heterogeneous ad-hoc paths and ii) exploits Network Calculus and Genetic Algorithms to optimize the usage of currently available IoT network opportunities. We have applied the extended MINA SDN prototype in the challenging IoT scenario of wide-scale integration of electric vehicles, electric charging sites, smart grid infrastructures, and a wide set of pilot users, as targeted by the Artemis Internet of Energy and Arrowhead projects. Preliminary simulation performance results indicate that our approach and the extended MINA system can support efficient exploitation of the IoT multinetwork capabilities.

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Section: B Genetic Algorithm-based Multi Constraints Flow Schedulingmentioning

confidence: 99%

A Software Defined Networking architecture for the Internet-of-Things

Qin

Denker

Giannelli

et al. 2014

2014 IEEE Network Operations and Management Symposium (NOMS)

300

122

View full text Add to dashboard Cite

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“…Here the metric E is similar to the metric T defined in [18]. Figure 1 shows an ad hoc network sample.…”

Section: Problem Representationmentioning

confidence: 99%

Simulated Annealing Based Multi-constrained QoS Routing in Mobile ad hoc Networks

Liu

Feng

2006

Wireless Pers Commun

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Multi-constrained quality-of-service routing (QoSR) is to find a feasible path that satisfies multiple constraints simultaneously, which is a big challenge for mobile ad hoc networks (MANETs) where the topology may change constantly. It has been proved that such a problem is NP-complete. Heuristic algorithms with polynomial and pseudo-polynomial-time complexities are often used to deal with this problem. However, existing solutions, most of which suffered either from excessive computational complexities or from low performance were proposed only for wired networks and cannot be used directly in wireless MANETs. In this paper a novel QoS routing algorithm based on Simulated Annealing (SA_RA) is proposed. This algorithm first uses an energy function to translate multiple QoS weights into a single mixed metric and then seeks to find a feasible path by simulated annealing. The paper outlines simulated annealing algorithm and analyzes the problems met when we apply it to QoSR in MANETs. Theoretical analysis and experiment results demonstrate that the proposed method is an effective approximation algorithms showing better performance than the other pertinent algorithm in seeking the (approximate) optimal configuration within a period of polynomial time.

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“…They seek the optimal tradeoff (or Pareto-optimal) solutions and have no weight parameters to manually configure in their fitness functions [15][16][17][18]. Unlike existing MOGAs, EVOLT is designed to handle high-dimensional parameter and objective spaces well, minimize the number of manually-configured constants in genetic operators and visualize non-dominated individuals in a low-dimensional (two dimensional) SOM space.…”

Section: Related Workmentioning

confidence: 99%

“…High dimensionality in the objective space often leads to premature convergence, which fails to improve the optimization quality (or optimality) of QoS parameter sets. Traditional QoS optimization algorithms tend to deal with a limited number of parameters and optimization objectives; for example, less than 20 QoS parameters and three or less optimization objectives 1 [3][4][5][6][7][8][9][10][11][15][16][17][18][19][20][21][22].…”

mentioning

confidence: 99%

Evolutionary high-dimensional QoS optimization for safety-critical utility communication networks

2011

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This paper proposes and evaluates an evolutionary multiobjective optimization algorithm, called EVOLT, which heuristically optimizes QoS (quality of service) parameters in communication networks. EVOLT uses a population of individuals, each of which represents a set of QoS parameters, and evolves the individuals via genetic operators such as crossover, mutation and selection for satisfying given QoS requirements. For evaluating EVOLT in real-world settings that have high-dimensional parameter and optimization objective spaces, this paper focuses on QoS optimization in safety-critical communication networks for electric power utilities. Simulation results show that EVOLT outperforms a well-known existing evolutionary algorithm for multiobjective optimization and efficiently obtains quality QoS parameters with acceptable computational costs. Moreover, EVOLT visualizes obtained QoS parameters in a Self-Organizing Map in order to aid network administrators to intuitively understand the QoS parameters and the tradeoffs among optimization objectives.

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A GA-based multi-purpose optimization algorithm for QoSrouting

Cited by 25 publications

References 4 publications

A Software Defined Networking architecture for the Internet-of-Things

A Software Defined Networking architecture for the Internet-of-Things

Simulated Annealing Based Multi-constrained QoS Routing in Mobile ad hoc Networks

Evolutionary high-dimensional QoS optimization for safety-critical utility communication networks

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