Robustness Optimization for IoT Topology
portes grátis
Robustness Optimization for IoT Topology
Qiu, Tie; Zhang, Songwei; Chen, Ning
Springer Verlag, Singapore
06/2022
214
Dura
Inglês
9789811696084
15 a 20 dias
512
Descrição não disponível.
Chapter 1 Introduction 1.1 Context and motivation
1.2 Characteristics of IoT topology
1.3 Attack modes against network topology
1.4 Book organization
Chapter 2 Preliminaries of robustness optimization
2.1 Metrics of topology robustness
2.2 Related work
2.3 Existing challanges
Chapter 3 Robustness optimization based on self-organization
3.1 Path planning based on the greedy principle
3.2 Construction of highly robust topology
3.3 Robust time synchronization scheme
Chapter 4 Evolution-based robustness optimization
4.1 Robustness optimization scheme with multi-population co-evolution
4.2 An adaptive robustness evolution algorithm with self-competition
Chapter 5 Robustness optimization based on swarm intelligence
5.1 Topology optimization strategy with ant colony algorithm
5.2 Topology optimization strategy with particle swarm algorithm
Chapter 6 Robustness optimization based on multi-objective cooperation
6.1 Multi-objective optimization based on layered-cooperation
Chapter 7 Robustness optimization based on self-learning
7.1 Malicious node identification scheme based on gaussian mixture model
7.2 Highly robust topology learning model based on neural network
7.3 Highly robust topology generation strategy based on time series convolutional network
Chapter 8 Robustness optimization based on node self-learning
8.1 Node self-learning mechanism based on reinforcement learning
Chapter 9 Future research directions
9.1 Homogeneous networks
9.2 Heterogeneous networks
9.3 Smart IoT
1.2 Characteristics of IoT topology
1.3 Attack modes against network topology
1.4 Book organization
Chapter 2 Preliminaries of robustness optimization
2.1 Metrics of topology robustness
2.2 Related work
2.3 Existing challanges
Chapter 3 Robustness optimization based on self-organization
3.1 Path planning based on the greedy principle
3.2 Construction of highly robust topology
3.3 Robust time synchronization scheme
Chapter 4 Evolution-based robustness optimization
4.1 Robustness optimization scheme with multi-population co-evolution
4.2 An adaptive robustness evolution algorithm with self-competition
Chapter 5 Robustness optimization based on swarm intelligence
5.1 Topology optimization strategy with ant colony algorithm
5.2 Topology optimization strategy with particle swarm algorithm
Chapter 6 Robustness optimization based on multi-objective cooperation
6.1 Multi-objective optimization based on layered-cooperation
Chapter 7 Robustness optimization based on self-learning
7.1 Malicious node identification scheme based on gaussian mixture model
7.2 Highly robust topology learning model based on neural network
7.3 Highly robust topology generation strategy based on time series convolutional network
Chapter 8 Robustness optimization based on node self-learning
8.1 Node self-learning mechanism based on reinforcement learning
Chapter 9 Future research directions
9.1 Homogeneous networks
9.2 Heterogeneous networks
9.3 Smart IoT
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Internet of Things;Robustness Optimization;Evolutional Algorithm;Artificial Intelligence;Smart City
Chapter 1 Introduction 1.1 Context and motivation
1.2 Characteristics of IoT topology
1.3 Attack modes against network topology
1.4 Book organization
Chapter 2 Preliminaries of robustness optimization
2.1 Metrics of topology robustness
2.2 Related work
2.3 Existing challanges
Chapter 3 Robustness optimization based on self-organization
3.1 Path planning based on the greedy principle
3.2 Construction of highly robust topology
3.3 Robust time synchronization scheme
Chapter 4 Evolution-based robustness optimization
4.1 Robustness optimization scheme with multi-population co-evolution
4.2 An adaptive robustness evolution algorithm with self-competition
Chapter 5 Robustness optimization based on swarm intelligence
5.1 Topology optimization strategy with ant colony algorithm
5.2 Topology optimization strategy with particle swarm algorithm
Chapter 6 Robustness optimization based on multi-objective cooperation
6.1 Multi-objective optimization based on layered-cooperation
Chapter 7 Robustness optimization based on self-learning
7.1 Malicious node identification scheme based on gaussian mixture model
7.2 Highly robust topology learning model based on neural network
7.3 Highly robust topology generation strategy based on time series convolutional network
Chapter 8 Robustness optimization based on node self-learning
8.1 Node self-learning mechanism based on reinforcement learning
Chapter 9 Future research directions
9.1 Homogeneous networks
9.2 Heterogeneous networks
9.3 Smart IoT
1.2 Characteristics of IoT topology
1.3 Attack modes against network topology
1.4 Book organization
Chapter 2 Preliminaries of robustness optimization
2.1 Metrics of topology robustness
2.2 Related work
2.3 Existing challanges
Chapter 3 Robustness optimization based on self-organization
3.1 Path planning based on the greedy principle
3.2 Construction of highly robust topology
3.3 Robust time synchronization scheme
Chapter 4 Evolution-based robustness optimization
4.1 Robustness optimization scheme with multi-population co-evolution
4.2 An adaptive robustness evolution algorithm with self-competition
Chapter 5 Robustness optimization based on swarm intelligence
5.1 Topology optimization strategy with ant colony algorithm
5.2 Topology optimization strategy with particle swarm algorithm
Chapter 6 Robustness optimization based on multi-objective cooperation
6.1 Multi-objective optimization based on layered-cooperation
Chapter 7 Robustness optimization based on self-learning
7.1 Malicious node identification scheme based on gaussian mixture model
7.2 Highly robust topology learning model based on neural network
7.3 Highly robust topology generation strategy based on time series convolutional network
Chapter 8 Robustness optimization based on node self-learning
8.1 Node self-learning mechanism based on reinforcement learning
Chapter 9 Future research directions
9.1 Homogeneous networks
9.2 Heterogeneous networks
9.3 Smart IoT
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
