Applying Genetic Algorithms for Multiradio Wireless Mesh Network Planning

Lin, Ting-Yu; Hsieh, K.L.; Huang, H.

doi:10.1109/tvt.2012.2191166

Cited by 30 publications

(14 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Holland from the University of Michigan proposed the GA [14], which uses selection, crossover, and mutation operators to involve the evolutionary process of organisms [15][16][17]. With powerful parallel processing and global optimization ability, this study presented an improved AGA to realize the optimization of the constellation layout design of pseudolites under multiple constraints.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Study of Constellation Design of Pseudolites Based on Improved Adaptive Genetic Algorithm

Song¹,

Hou²,

Xue³

2016

JCM

View full text Add to dashboard Cite

Global Navigation Satellite System (GNSS) is vulnerable to interferences and has other shortcomings such as unreliable signals in locations that are indoors, in urban canyons, and deep mines. Therefore, the pseudolite (pseudo-satellite) positioning technology, which has gained wide attention in recent years, is used to complement and enhance GNSS. The constellation layout of pseudolites creates geometrical benchmarks in spatial positioning, which in turn affects the receiver positioning accuracy by impacting the Dilution of Precision (DOP) value of each location point within the service area. The constellation layout of pseudolites poses a combinatorial optimization problem with multiple constraints, given the service area and station layout area, such as the distance between the pseudolite and the receiver, making the indicators of spatial configuration and redundancy maintenance and the availability within the service area to achieve best. An improved Adaptive Genetic Algorithm (AGA) is presented based on the mathematical modeling of pseudolite positioning and constraints. The simulation results show that the novel algorithm can effectively solve the optimization problem associated with the constellation layout of pseudolites under a variety of constraints.

show abstract

Section: Proposed Algorithmmentioning

confidence: 99%

Study of Constellation Design of Pseudolites Based on Improved Adaptive Genetic Algorithm

Song¹,

Hou²,

Xue³

2016

JCM

View full text Add to dashboard Cite

show abstract

“…A genetic algorithm is developed in [11] to find an optimal configuration of channels and routes in the planning stage. The algorithm involves solving a linear program model of multichannel routing as a fitness function.…”

Section: Related Workmentioning

confidence: 99%

“…Constraints (8), (9) are the topology constraints that ensure that each logical link can only be mapped to at most one channel to reduce the complexity of the routing layer. Constraints (10), (11) are the flow-topology constraints to ensure that only links with traffics will be signed as an active links. Flow conservation at each mesh router is ensured in (12).…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…This is since channel assignment and routing determine the contention and interference level over the backhaul wireless link. Therefore, finding the joint configuration of both is considered as main challenges in multi-radio WMN (MR-WMN) and several joint solutions for channel assignment and routing were proposed and evaluated in the literature [6][7][8][9][10][11][12][13][14][15][16]. Some other issues is also been studied in the literature to improve the performance of multi-hop wireless networks such as developing routing metrics [17][18][19] , multi-channel MAC protocols [20], power and topology control [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

State-Aware Re-configuration Model for Multi-Radio Wireless Mesh Networks

Omar

Hashim

Hassan³

et al. 2016

KSII TIIS

View full text Add to dashboard Cite

Joint channel assignment and routing is a well-known problem in multi-radio wireless mesh networks for which optimal configurations is required to optimize the overall throughput and fairness. However, other objectives need to be considered in order to provide a high quality service to network users when it deployed with high traffic dynamic. In this paper, we propose a re-configuration optimization model that optimizes the network throughput in addition to reducing the disruption to the mesh clients' traffic due to the re-configuration process. In this multi-objective optimization model, four objective functions are proposed to be minimized namely maximum link-channel utilization, network average contention, channel re-assignment cost, and rerouting cost. The latter two objectives focus on reducing the re-configuration overhead. This is to reduce the amount of disrupted traffic due to the channel switching and path rerouting resulted from applying the new configuration. In order to adapt to traffic dynamics in the network which might be caused by many factors i.e. users' mobility, a centralized heuristic re-configuration algorithm called State-Aware Joint Routing and Channel Assignment (SA-JRCA) is proposed in this research based on our re-configuration model. The proposed algorithm reassigns channels to radios and re-configures flows' routes with aim of achieving a tradeoff between maximizing the network throughput and minimizing the re-configuration overhead. The ns-2 simulator is used as simulation tool and various metrics are evaluated. These metrics include channel-link utilization, channel re-assignment cost, rerouting cost, throughput, and delay. Simulation results show the good performance of SA-JRCA in term of packet delivery ratio, aggregated throughput and re-configuration overhead. It also shows higher stability to the

show abstract

“…The population continues to improve until a stopping criterion is achieved, with the simplest being a maximum number of generations. In addition, GA can be regarded as rapid steps for determining a 'good enough' solution to the problem; therefore they are interesting for practical problems [32]. GA is directly applicable, which allows them benefit with respect to the exact techniques.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Solving the Wireless Mesh Network Design Problem using Genetic Algorithm and Simulated Annealing Optimization Methods

Girgis¹,

Mahmoud²,

Abdullatif³

et al. 2014

IJCA

View full text Add to dashboard Cite

Mesh clients, mesh routers and gateways are components of Wireless Mesh Network (WMN). In WMN, gateways connect to Internet using wireline links and supply Internet access services for users. Multiple gateways are needed, which take time and cost budget to set up, due to the limited wireless channel bit rate. WMN is a highly developed technology that offers to end users a wireless broadband access. It offers a high degree of flexibility contrasted to conventional networks; however, this attribute comes at the expense of a more complex construction. Therefore, a challenge is the planning and optimization of WMNs. This paper concentrates on the challenge using a genetic algorithm and simulated annealing. The genetic algorithm and simulated annealing enable searching for a low-cost WMN configuration with constraints and determine the number of used gateways. Experimental results proved that the performance of the genetic algorithm and simulated annealing in minimizing WMN network costs while satisfying quality of service. The proposed models are presented to significantly outperform the existing solutions.

show abstract

Applying Genetic Algorithms for Multiradio Wireless Mesh Network Planning

Cited by 30 publications

References 22 publications

Study of Constellation Design of Pseudolites Based on Improved Adaptive Genetic Algorithm

Study of Constellation Design of Pseudolites Based on Improved Adaptive Genetic Algorithm

State-Aware Re-configuration Model for Multi-Radio Wireless Mesh Networks

Solving the Wireless Mesh Network Design Problem using Genetic Algorithm and Simulated Annealing Optimization Methods

Contact Info

Product

Resources

About