A Novel RPL Algorithm Based on Chaotic Genetic Algorithm

Cao, Yanan; Muqing, Wu

doi:10.3390/s18113647

Cited by 29 publications

(18 citation statements)

References 24 publications

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“…where denotes the fitness function value of individual i, and denotes the maximum fitness function value, ε is a small positive number which equals 10 −5 in this paper like [27]. The individual with minimum fitness function value is selected from the population, which gives the optimal routing path for each CH.…”

Section: (4) Finding the Optimal Routing Pathsmentioning

confidence: 99%

“…An improved genetic algorithm is used to find the optimal routing path for each CH because the traditional genetic algorithm has some drawbacks such as premature convergence and local optimum [27]. Moreover, invalid individuals may be generated in traditional genetic algorithm due to its random operations of selection, crossover and mutation.…”

Section: Routing Paths Findingmentioning

confidence: 99%

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An Improved Genetic Algorithm Based Annulus-sector Clustering Routing Protocol for Wireless Sensor Networks

Wang

Liu

Han

et al. 2021

Preprint

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In wireless sensor networks, uniform cluster formation and optimal routing paths finding are always the two most important factors for clustering routing protocols to minimize the network energy consumption and balance the network load. In this paper, an improved genetic algorithm based annulus-sector clustering routing protocol called GACRP is proposed. In GACRP, the circular network is divided into sectors with the same size for each annulus, whose number is determined by calculating the minimum energy consumption of each annulus. Each annulus-sector forms a cluster and the best node in this annulus-sector is selected as cluster head. Moreover, an improved genetic algorithm with a novel fitness function considering energy and load balance is presented to find the optimal routing path for each CH, and an adaptive round time is calculated for maintaining the clusters. Simulation results show that GACRP can significantly improve the network energy efficiency and prolong the network lifetime as well as mitigate the hot spot problem.

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Section: (4) Finding the Optimal Routing Pathsmentioning

confidence: 99%

Section: Routing Paths Findingmentioning

confidence: 99%

An Improved Genetic Algorithm Based Annulus-sector Clustering Routing Protocol for Wireless Sensor Networks

Wang

Liu

Han

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The proposed method improves the packet delivery ratio and energy consumption, but it presents a looping in the network which increases the convergence time. The genetic algorithm-based method is also proposed which utilizes weighted queue length, delay, residual energy, ETX, and hop counts metrics [38]. The algorithm improves the packet's average success ratio, end-to-end delay, and remaining energy.…”

Section: Of 26mentioning

confidence: 99%

Reinforcement Learning-Enabled Cross-Layer Optimization for Low-Power and Lossy Networks under Heterogeneous Traffic Patterns

Musaddiq

Nain

Qadri

et al. 2020

Sensors

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The next generation of the Internet of Things (IoT) networks is expected to handle a massive scale of sensor deployment with radically heterogeneous traffic applications, which leads to a congested network, calling for new mechanisms to improve network efficiency. Existing protocols are based on simple heuristics mechanisms, whereas the probability of collision is still one of the significant challenges of future IoT networks. The medium access control layer of IEEE 802.15.4 uses a distributed coordination function to determine the efficiency of accessing wireless channels in IoT networks. Similarly, the network layer uses a ranking mechanism to route the packets. The objective of this study was to intelligently utilize the cooperation of multiple communication layers in an IoT network. Recently, Q-learning (QL), a machine learning algorithm, has emerged to solve learning problems in energy and computational-constrained sensor devices. Therefore, we present a QL-based intelligent collision probability inference algorithm to optimize the performance of sensor nodes by utilizing channel collision probability and network layer ranking states with the help of an accumulated reward function. The simulation results showed that the proposed scheme achieved a higher packet reception ratio, produces significantly lower control overheads, and consumed less energy compared to current state-of-the-art mechanisms.

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“…However, not all referential routing metrics can be made valid using this approach. The authors in [19] describe the problems of the additive based metric composition method and a new, chaotic genetic algorithm-based approach has been proposed to solve these problems.…”

Section: Related Workmentioning

confidence: 99%

A Resource Oriented Route Selection Framework Using Contextual Information Based on Fuzzy Logic

Bhandari

Cho

2019

Electronics

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Internet of Things (IoT) is expected to have a significant impact on city’s service provisioning and make a smart city more accessible and pragmatic since the deployment of heterogeneous smart devices in each infrastructure of cities is increasing. So far, the IPv6 routing protocol for low power and lossy networks (RPL) is considered to fit on IoT infrastructure for achieving the expected network requirements. While RPL meets the IoT network requirements quite well, there are some issues that need to be addressed, such as adaptability to network dynamics. This issue significantly limits the use of RPL in many smart city application scenarios, such as emergency alerts with high traffic flows. As part of a smart city vision, IoT applications are becoming more diverse, which requires context-awareness in routing protocols to support the behavior of the network. To address this issue, we design an objective function that performs the route selection based on fuzzy logic techniques while using contextual information from the application. For this, we present a new context-oriented objective function (COOF) that comprises both nodes as well as link metrics. Further, we suggest two new routing metrics, known as queue fluctuation index (QFI) and residual energy index (REI), which consider the status of queue utilization and remaining energy, respectively. The metrics used are designed to respond to the dynamic needs of the network. The proposed approach has been examined and evaluated in different scenarios when compared to other similar approach and default RPL objective functions. Simulation experiments are conducted in Cooja network simulator for Contiki OS. The evaluation results show that COOF can cope with network dynamics and IoT-based smart city application requirements.

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A Novel RPL Algorithm Based on Chaotic Genetic Algorithm

Cited by 29 publications

References 24 publications

An Improved Genetic Algorithm Based Annulus-sector Clustering Routing Protocol for Wireless Sensor Networks

An Improved Genetic Algorithm Based Annulus-sector Clustering Routing Protocol for Wireless Sensor Networks

Reinforcement Learning-Enabled Cross-Layer Optimization for Low-Power and Lossy Networks under Heterogeneous Traffic Patterns

A Resource Oriented Route Selection Framework Using Contextual Information Based on Fuzzy Logic

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