Using multi-objective domain optimization for routing in hierarchical networks

Manousakis, Kyriakos; McAuley, T.; Morera, R.; Baras, John S.

doi:10.1109/wirles.2005.1549628

Cited by 12 publications

(9 citation statements)

References 18 publications

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“…They encompass methods such as Probabilistic Roadmaps (PR); Rapidly-exploring Random Trees (RRT); Ant Colony Optimization (ACO), that relies on the foraging behavior of ants for finding the shortest path to the food source ( [32], [33], [34]); Simulated Annealing (SA), which is a heuristic random search approach that resembles the cooling process of molten metals through annealing ( [35], [36]); Neural Network [37]; Genetic Algorithms (GA), which are based on the mechanics of natural genetics and selection ( [38], [39], [40]); Particle Swarm Optimization (PSO), which are inspired by social behavior of bird flocking or fish schooling and are easier to implement than GA and with a fewer parameters to be adjusted ( [41], [42], [43], [44], [45], [46]); Stigmergy, which is a mechanism of indirect and Tabu Search, which is a local-search method used for mathematical optimization [36].…”

Section: Heuristic Approachesmentioning

confidence: 99%

Optimization approaches for robot trajectory planning

Llopis-Albert

Rubio

2018

Mult.J.Edu.Soc.Tec.Sci.

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The development of optimal trajectory planning algorithms for autonomous robots is a key issue in order to efficiently perform the robot tasks. This problem is hampered by the complex environment regarding the kinematics and dynamics of robots with several arms and/or degrees of freedom (dof), the design of collision-free trajectories and the physical limitations of the robots. This paper presents a review about the existing robot motion planning techniques and discusses their pros and cons regarding completeness, optimality, efficiency, accuracy, smoothness, stability, safety and scalability.

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Section: Heuristic Approachesmentioning

confidence: 99%

Optimization approaches for robot trajectory planning

Llopis-Albert

Rubio

2018

Mult.J.Edu.Soc.Tec.Sci.

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“…Even though we are not limited by SA's functionality, in this paper, without loss of generality, we focus on the improvement of routing overhead. In [9] we have concentrated on improving the routing overhead but when 2-layer hierarchy is applied. The solution to that problem was to balance out the size (e.g.…”

Section: Objective Function and Constraintsmentioning

confidence: 99%

“…Such a hierarchical design has the potential to result in lower routing overhead, which translates to lower control signaling and larger network capacity available for data. By applying (9) we are designing all the layers simultaneously. The representation involves the description of a layer-I domain as a set of network nodes and the description of a layer-/(>1) domain as a set of layer-(/-1) domains.…”

Section: Objective Function and Constraintsmentioning

confidence: 99%

“…When we refer to large networks, then it is unavoidable that the size of domains at the lower layer might be orders of magnitude larger compared to the size of an upper layer domain. This will result in the dominant contribution of lower layer optimality to the objective function (9). The side effect will be the balancing of lower level domains but with the cost of letting the upper layer domains unbalanced, because due to their smaller number and size, their contribution to the value of the objective function will be smaller.…”

Section: Objective Function and Constraintsmentioning

confidence: 99%

“…Specifically, we have applied the SA-based mechanism on forming different number of hierarchy layers subject to (9), which suggests the formation of balanced membership size domains on each layers of the multilayer hierarchy. This requirement as we mentioned above aims on the reduction of routing overhead.…”

Section: Performance Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

On the Formation of Multi-Layer Hierarchies

Manousakis¹,

McAuley²

2007

MILCOM 2007 - IEEE Military Communications Conference

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The application of hierarchy in large and dynamic networks improves scalability, survivability and robustness ofthese networks. Protocol designers have thus introduced mechanisms to automatically create and maintain hierarchical network structures. Existing work, however, is limited toforming only 2-layer structures, even though more than two layers are becoming increasingly necessary for larger networks. Furthermore, the majority of approaches focus on providing robustness in dynamic environments, and largely ignore the quality of the hierarchy formed. Such a design philosophy may result in hierarchy that actually degrades network performance. In this paper we present a general framework for the formation of multilayer hierarchies subject to network performance and hierarchy design requirements. We show the benefits of multilayer hierarchy in the automatic creation of more than two level routing domains. Furthermore, we show that our hierarchy framework can generate very good multilayer routing hierarchies that can minimize routing overhead. INTRODUCTION The application of hierarchical structures has many advantages on the network performance, survivability and robustness [3]. These benefits are most evident in large and dynamic network scenarios. In such scenarios the application of hierarchy results in improved manageability of the network and in more effective application of networking protocols (e.g. routing, security) since the network appears to be less dynamic and smaller in size, increasing the chances of the survivability and efficient application of these protocols. On the other hand the formation of hierarchical structures is not straightforward and may have opposite effects on the network performance from those expected. Specifically, a carelessly designed hierarchy can degrade the network performance significantly instead of improving it, since the hierarchy

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Monopati: A Multi-Objective Network Optimization and Analysis Tool Applied to Hierarchical Network Structures

McAuley¹,

Manousakis²

2006

Milcom 2006

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1)This paper describes a prototype configuration management tool applied to the design, planning, evaluation, and maintenance of network hierarchical structures. Specifically designedfor future ad hoc military networks, the versatile tool supports a) rapid deployment oflarge numbers ofnodes with heterogeneous links, and b) rapid adaptation to changing environment or mission needs. The tool, called Multi-Objective Network Optimization and Analysis Tool (MONOPATI), has three main components. the network environment simulator component, the network optimization component and the statistics collection and visualization component. The network environment simulator can generate diverse topologies and apply several random, group and custom mobility models. The statistics component collects and visualizes real-time network and performance metrics. The network optimization component uses an enhanced version of Simulated Annealing (SA) for rapid network optimization and assessment based on the objectives and constraints. A unique feature ofMONOPA TI is the flexible mathematical representation of goals in objective functions and constraints; thus quantifying hierarchy goodness. This paper highlights the application and effectiveness of MONOPATI in forming and evaluating structures that satisfy diverse hierarchy formation objectives in dynamic ad hoc network environments.

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Using multi-objective domain optimization for routing in hierarchical networks

Cited by 12 publications

References 18 publications

Optimization approaches for robot trajectory planning

Optimization approaches for robot trajectory planning

On the Formation of Multi-Layer Hierarchies

Monopati: A Multi-Objective Network Optimization and Analysis Tool Applied to Hierarchical Network Structures

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