Clonal-Selection-Based Minimum-Interference Channel Assignment Algorithms for Multiradio Wireless Mesh Networks

Tan, Su Wei; Lee, Sheng Chyan; Chan, Cheong Loong

doi:10.1016/b978-0-12-801538-4.00013-6

Cited by 3 publications

(4 citation statements)

References 24 publications

(60 reference statements)

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“…( 2), α is the rate of mutation, ρ is a decay controller, and f is the affinity of the antibody normalized in [0,1]. Given L as the length of the clone, then the number of genes to be mutated N M can be calculated as follow [75]:…”

Section: ) Mutationmentioning

confidence: 99%

Multi-Layer Perceptron Training Optimization Using Nature Inspired Computing

2022

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Although the multi-layer perceptron (MLP) neural networks provide a lot of flexibility and have proven useful and reliable in a wide range of classification and regression problems, they still have limitations. One of the most common is associated with the optimization algorithm used to train them. The most commonly used training method is stochastic gradient descent with backpropagation (or backpropagation for short) because it is mathematically tractable (given that the activation functions are differentiable). However, backpropagation is not guaranteed to find the globally optimal set of weights and biases. As a result, the MLP is often incapable of obtaining a desirable solution to the problem. Clonal selection algorithms (CSA) are optimization procedures that effectively explore a complex and large space to find values near the global optimum. Consequently, CSA can be used to solve the problem of training MLP networks. This paper presents a novel implementation of CSA for training MLP architectures to solve real-world problems such as breast cancer diagnosis, active sonar target classification, wheat classification, and flower classification. The CSA is used to find the optimal weights and biases that will significantly increase the classification accuracy of the MLP. The performance of our proposed approach is compared with other popular training methods: genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), Harris hawks optimization (HHO), moth-flame optimization (MFO), flower pollination algorithm (FPA), and backpropagation (BP). The comparison is benchmarked using five classification datasets: Iris Flower, Sonar, Wheat Seeds, Breast Cancer Wisconsin, and Haberman's Survival. Comparative study results illustrate the improvements in MLP performance gained by using CSA over other training methods, and hence it can be considered a competitive approach to training MLP networks when solving real-world applications in various disciplines.

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Section: ) Mutationmentioning

confidence: 99%

Multi-Layer Perceptron Training Optimization Using Nature Inspired Computing

2022

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Section: Introductionunclassified

“…As redes em malha sem fios (do inglês, wireless mesh networks [5]) assumem um papel determinante no paradigma IoT. São caracterizadas por uma topologia em que os nós se agregam numa malha sem uma hierarquia definida, permitindo que todos os dispositivos possam ser uma parte ativa na transmissão dos pacotes [5]. Esta topologia oferece maior tolerância a falhas, visto que os nós não estão dependentes uns dos outros, e facilita o processo de adicionar novos nós à rede sem a necessidade desta ser alterada ou reconfigurada [6].…”

Section: Introductionunclassified

Performance Assessment of a Wireless Mesh Network for Post-harvest Food Quality Traceability of Fruit Products: A Case Study

Caldeira

Soares

Gaspar

2023

RITA

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This paper presents the performance evaluation of a wireless sensor mesh network, for monitoring temperature and humidity in horticultural products when transported in truck galleys. For this purpose a software solution was proposed using ESP8266 devices powered by batteries. The mesh network was managed by the painlessMesh library. The proposed solution aims to minimize the energy consumption of the sensor nodes. The validation of the solution was performed in an area simulating a galley of a truck, where five sensor nodes and a root node were distributed. The tests were developed considering four different models involving variations in messages delivery confirmation, number of attempts until successful delivery and duty cycle duration of the nodes. The performance evaluation of the solution aimed to determine, connectivity rate, sending rate after connection and delivery rates of the first and second attempts. The results obtained show that the message delivery confirmation does not bring added value to the solution, contributing only to increase energy consumption. The use of synchronous duty cycles also showed worse results than the asynchronous use. These results allow the creation of a knowledge base for the use of this solution in a real context.

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“…F I G U R E 1 Wireless mesh network 8 F I G U R E 2 Learning agent in reinforcement learning 12 The WMN can be connected to wired and wireless customers. Specific heterogeneous networks, such as Wi-Fi, Wi-Max, sensor, and mobile networks are connected via their access point to the mesh networks.…”

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confidence: 99%

Optimization of reinforcement routing for wireless mesh network using machine learning and high‐performance computing

Singh

Prakash

Singh

2022

Concurrency and Computation

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In the modern world, wireless mesh networks (WMN) are widely used in Internet of Things (IoT) applications. It is a dynamic, infrastructure-less multihop network with mobility features. In WMN, to design an effective and strong routing protocol is a challenge. Conventional protocols for routing using a remote vector or link-state routing are not ideal for a wireless network. The shortest routing path is used with all current traditional routing protocols to pick a route with a minimum number of hops. The decisions must be done in real-time in high-traffic networks for the route selection and for the packets to be diversified into other routes. The routing protocols should adapt to the traffic conditions, that is, the network load changes and the route must be selected accordingly. The route may not be the shortest in number, but the best in terms of the actual time a packet requires to reach the target. The key objective of this research work is to design such an adaptive routing algorithm, while minimizing the delivery time and preventing the network from congestion. The rationale for this research work is to optimize adaptive routing protocol based on reinforcement learning, which efficiently delivers packets in the WMN. The experimental evaluation demonstrates that the obtained results significantly improve the system performance compared to the benchmark routing protocols. The Quality of Service (QoS) parameters such as packet delivery ratio (PDR), delay, and throughput metrics are used to assess the performance of the proposed technique using the NS-3 simulator.

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Clonal-Selection-Based Minimum-Interference Channel Assignment Algorithms for Multiradio Wireless Mesh Networks

Cited by 3 publications

References 24 publications

Multi-Layer Perceptron Training Optimization Using Nature Inspired Computing

Multi-Layer Perceptron Training Optimization Using Nature Inspired Computing

Performance Assessment of a Wireless Mesh Network for Post-harvest Food Quality Traceability of Fruit Products: A Case Study

Optimization of reinforcement routing for wireless mesh network using machine learning and high‐performance computing

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