A Reliable and Low-Latency Graph-Routing Approach for IWSN using Q-Routing

Kunzel, Gustavo; Cainelli, Gustavo; Müller, Ivan; Pereira, Carlos Eduardo; Indrusiak, Leandro Soares

doi:10.1109/sbesc51047.2020.9277857

Cited by 3 publications

(5 citation statements)

References 20 publications

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“…However, this model cannot be used for centralised networks because it does not provide path redundancy, and the nodes cannot select the routes. The Q-Learning Reliable Routing with Multiple Agents (QLRR-MA) approach is presented in [ 18 ], which builds routing graphs in a centralised way using the Q-Routing model. The approach results demonstrated that in a significant number of cases, average network latency is reduced.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…While the multipath routing algorithm prioritises the data transmission over the shortest path, the alternative paths are always ready to tolerate the transmission errors. The multipath routing algorithm was simulated according to three existing algorithms, including [ 18 ]. The MPR algorithm demonstrated results that the significant reduction in average network latency, enhancement of expected network lifetime, and the ratio of data packet deliver.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…However, previous solutions have typically focused on the suggestion of network routes based on traditional route optimisation methods, with the exception of two works [ 18 , 19 ] that suggested using Q-learning with graph routing of IWSNs. Therefore, the possibility remains open of applying a further stage of optimisation to IWSN GR routing to compute best paths that balance all requirements.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

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Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy

Alharbi

Mackenzie

Pezaros

2022

Sensors

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The emergence of the Industrial Internet of Things (IIoT) has accelerated the adoption of Industrial Wireless Sensor Networks (IWSNs) for numerous applications. Effective communication in such applications requires reduced end-to-end transmission time, balanced energy consumption and increased communication reliability. Graph routing, the main routing method in IWSNs, has a significant impact on achieving effective communication in terms of satisfying these requirements. Graph routing algorithms involve applying the first-path available approach and using path redundancy to transmit data packets from a source sensor node to the gateway. However, this approach can affect end-to-end transmission time by creating conflicts among transmissions involving a common sensor node and promoting imbalanced energy consumption due to centralised management. The characteristics and requirements of these networks encounter further complications due to the need to find the best path on the basis of the requirements of IWSNs to overcome these challenges rather than using the available first-path. Such a requirement affects the network performance and prolongs the network lifetime. To address this problem, we adopt a Covariance-Matrix Adaptation Evolution Strategy (CMA-ES) to create and select the graph paths. Firstly, this article proposes three best single-objective graph routing paths according to the IWSN requirements that this research focused on. The sensor nodes select best paths based on three objective functions of CMA-ES: the best Path based on Distance (PODis), the best Path based on residual Energy (POEng) and the best Path based on End-to-End transmission time (POE2E). Secondly, to enhance energy consumption balance and achieve a balance among IWSN requirements, we adapt the CMA-ES to select the best path with multiple-objectives, otherwise known as the Best Path of Graph Routing with a CMA-ES (BPGR-ES). A simulation using MATALB with different configurations and parameters is applied to evaluate the enhanced graph routing algorithms. Furthermore, the performance of PODis, POEng, POE2E and BPGR-ES is compared with existing state-of-the-art graph routing algorithms. The simulation results reveal that the BPGR-ES algorithm achieved 87.53% more balanced energy consumption among sensor nodes in the network compared to other algorithms, and the delivery of data packets of BPGR-ES reached 99.86%, indicating more reliable communication.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Section: Background and Literature Reviewmentioning

confidence: 99%

Section: Background and Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy

Alharbi

Mackenzie

Pezaros

2022

Sensors

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“…A number of auxiliary technologies have lately advanced and grown more efficient as a result of industrial development, improving performance in a variety of diverse industries. IWSNs have the potential to be a key component in improving data transmission within industries as a viable replacement for traditional connectivity 3 . IWSNs have been utilized to boost productivity and financial gains in a number of industrial sectors, including manufacturing, construction, agriculture, quarrying, timber harvesting, and others.…”

Section: Introductionmentioning

confidence: 99%

“…IWSNs have the potential to be a key component in improving data transmission within industries as a viable replacement for traditional connectivity. 3 IWSNs have been utilized to boost productivity and financial gains in a number of industrial sectors, including manufacturing, construction, agriculture, quarrying, timber harvesting, and others. Additionally, it plays a significant part in this change by enabling quick, easy, and reliable deployment to satisfy the needs of industrial contexts.…”

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confidence: 99%

A novel energy‐efficient routing protocol for industrial WSN using hybrid COOT‐LS algorithm with LSTM‐based DOM prediction

Ravikumar,

Kumar

2023

Int J Communication

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SummaryIndustrial wireless sensor networks (IWSNs) are very important to improve and simplify the way to manage, monitor, and control industrial factories. IWSNs have many benefits, but because of their vulnerability to extremely complex and variable industrial contexts, they also limit some of the potential that is currently available and present difficulties on numerous fronts. The proposed work develops energy‐efficient routing technique to minimize the overall energy consumption. Heterogeneous mobile nodes are randomly deployed in the experimental region; then, region‐based grid formation is done in the proposed method. In each grid, the base node is selected utilizing the parameters like residual energy and distance of mobile nodes. Hybrid COOT‐HOA optimization is used in the proposed method for selecting the optimal base node in each grid. A deep learning‐based machine learning algorithm called long short‐term memory (LSTM) is utilized to predict the future direction of movement (DOM) of each mobile node in the grid. Estimation of signal parameters via rotational invariance technique (ESPRIT) is utilized to find an active zone from the sender node to direction of base node. Then, the sender node transmits the data to its nearest node in the active region. This proposed energy‐efficient routing algorithm is tested with several metrics which attains better performance like 94% packet delivery ratio, 7% packet loss, average residual energy of 9.5 J, and 3.4 Mbps throughput. Thus, the energy‐efficient routing protocol used in the proposed approach transfers the data in an energy‐efficient manner for IWSN.

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Open research issues on management of industrial wireless sensor systems applied to process automation

Feldman

Cainelli

Kunzel³

et al. 2021

At - Automatisierungstechnik

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Industrial wireless networks are a key technology for the implementation of the Industry 4.0 concepts. Due to its advantages in relation to wired networks, such as flexibility and low cost of installation, these networks are gaining momentum in the recent years. Wireless networks are becoming even more attractive for industrial applications and consequently an increase is expected in the number of wireless devices connected to industrial applications. As a result, efficient network management becomes even more relevant, and consequently new techniques are necessary to cope with new demands. Network management tasks, such as routing, scheduling, coexistence awareness, and mobility, have been in focus of scientific community. However, several important research questions remain open. This paper presents recent works on industrial wireless networks management techniques in addition with the presentation of open research issues.

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A Reliable and Low-Latency Graph-Routing Approach for IWSN using Q-Routing

Cited by 3 publications

References 20 publications

Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy

Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy

A novel energy‐efficient routing protocol for industrial WSN using hybrid COOT‐LS algorithm with LSTM‐based DOM prediction

Open research issues on management of industrial wireless sensor systems applied to process automation

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