APTEEN routing protocol optimization in wireless sensor networks based on combination of genetic algorithms and fruit fly optimization algorithm

Wang, Minghao; Wang, Shubin; Zhang, Bowen

doi:10.1016/j.adhoc.2020.102138

Cited by 37 publications

(14 citation statements)

References 7 publications

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“…To solve the problem of uneven energy consumption, the authors in [ 23 ] use genetic algorithms and fruit fly algorithms. These algorithms are applied to cluster the nodes in the network, while the Dijkstra algorithm [ 24 ] is used to determine the specific best path. This combination of algorithms optimises the whole network, improving the network lifetime by 50% and boosting the whole network coverage by 10%.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

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%

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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“…Wang et al 25 developed APTEEN by integrating GA with fruit fly optimization for obtaining minimum energy consumption, reducing premature death of nodes, and increasing the efficient coverage area of the networks. The measures utilized for the analysis were residual energy, energy consumption, number of nodes covered.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy‐efficient model using optimal route discovery based on adaptive spider monkey optimization model

Justus

Muthukrishnan

Jothi

2022

Int J Communication

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Summary During the past few years, wireless sensor networks (WSNs) have created a substantial interest because of their capabilities namely, self‐healing and self‐organizing under restricted network resources. Numerous WSN‐based routing algorithms are implemented to perform well, but the energy consumption of every data packet and information loss is more. Due to the power restriction in the sensor nodes, energy consumption is regarded as the major issue in networks that need to be minimized to improve the network lifetime. Hence, to conquer these consequences, this paper aims to propose an adaptive spider monkey optimization (ASMO)‐based effective optimal routing path from various sensor nodes with enhanced network lifetime. Here an ASMO is employed in selecting an optimal routing path. In addition to this, the proposed ASMO algorithm is capable of selecting energy‐aware routes by energy consumption. It also chooses the routes with minimum end‐to‐end delay using the bare least hop count criteria, thus enhancing the network life span. The results of the proposed method are compared with other methods such as DMEERP, EGSMRP, EEIABC, and RACO. The results reveal that the proposed approach consumed less amount of energy; hence, network lifetime is more when compared with other approaches.

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“…The problem of CH selection in APTEEN using artificial intelligence has also attracted the interest of researchers in recent years: using PSO [ 174 ], a combination of genetic algorithms and fruit fly optimization algorithm [ 175 ], or ACO [ 176 , 177 ].…”

Section: Energy Conservationmentioning

confidence: 99%

Current Trends on Green Wireless Sensor Networks

López-Ardao

Rodríguez-Rubio

Suárez-González

et al. 2021

Sensors

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The issue of energy balancing in Wireless Sensor Networks is a pivotal one, crucial in their deployment. This problem can be subdivided in three areas: (i) energy conservation techniques, usually implying minimizing the cost of communication at the nodes since it is known that the radio is the biggest consumer of the available energy; (ii) energy-harvesting techniques, converting energy from not full-time available environmental sources and usually storing it; and (iii) energy transfer techniques, sharing energy resources from one node (either specialized or not) to another one. In this article, we survey the main contributions in these three areas and identify the main trending topics in recent research. A discussion and some future directions are also included.

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APTEEN routing protocol optimization in wireless sensor networks based on combination of genetic algorithms and fruit fly optimization algorithm

Cited by 37 publications

References 7 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

Energy‐efficient model using optimal route discovery based on adaptive spider monkey optimization model

Current Trends on Green Wireless Sensor Networks

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