Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks Based on Yellow Saddle Goatfish Algorithm

Rodríguez, Alma; Del-Valle-Soto, Carolina; Velázquez, Ramiro

doi:10.3390/math8091515

Cited by 52 publications

(48 citation statements)

References 37 publications

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“…Following the setting of configuration parameters as listed in Table 1. The proposed energy-aware routing scheme has been successfully implemented and tested with Low Energy Adaptive Clustering Hierarchy (LEACH) [14], Power-Efficient Gathering in Sensor Information Systems (PEGASIS) [14], threshold-sensitive energy efficient sensor network (TEEN) [14] routing algorithm, EMEER [14] and YSGA [17] as well as superior performance observed in terms of packet delivery ratio, energy consumption, average energy consumption of each node and throughput.…”

Section: Resultsmentioning

confidence: 99%

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Energy-Efficient Cluster based Routing for Hybrid Wireless Sensor Networks using Adaptive Hybrid Cuckoo Search and Grey Wolf Optimization Algorithm

Rangappa¹,

Dyamanna²

2021

IJIES

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The combination of both static sensors and mobile sensors is called as Hybrid Wireless Sensor Network (HWSN). Static sensors monitor the environment and report events occurring in the sensing field. Mobile sensors are then dispatched to visit these event locations to conduct more advanced analysis. These sensor nodes have major energy constraints as they are small and their battery can't be replaced and collaborate together in order to gather, transmit/forward the sensed data to the base station. Consequently, data transmission is one of the biggest reasons for energy depletion in HWSN. Clustering is one of the most effective techniques for energy efficient data transmission in HWSN. This paper proposes an Adaptive Hybrid Cuckoo Search (AHCS) algorithm. AHCS improves the Lévy flight method and population evolution strategy of the cuckoo search algorithm, and introduced a mutation operation operator. Inspired by the idea of position update of Grey Wolf Optimization (GWO) algorithm, this paper introduces the inertia weight w in the Lévy flight method of CS algorithm, and gives the new dynamic adjustment methods of parameters α and β respectively. The proposed algorithm is evaluated in terms of response time, packet drop ratio, energy consumption, end to end delay and dead time. Furthermore, the performance of AHCS-GWO is evaluated with existing methods such as classical algorithms, Evolutionary Multipath Energy-Efficient Routing protocol (EMEER) and Yellow Saddle Goatfish Algorithm (YSGA). The residual energy of the AHCS-GWO method for 200 nodes is 0.57J, it is high when compared to the existing methods.

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Section: Resultsmentioning

confidence: 99%

“…Rodríguez, Del-Valle-Soto, and Velázquez [17] presented a novel energy-efficient clustering routing protocol based on Yellow Saddle Goatfish Algorithm (YSGA). The developed protocol was mainly introduced to strengthen the network lifespan by minimizing the energy consumption of the system.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy-Efficient Cluster based Routing for Hybrid Wireless Sensor Networks using Adaptive Hybrid Cuckoo Search and Grey Wolf Optimization Algorithm

Rangappa¹,

Dyamanna²

2021

IJIES

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“…Alma Rodríguez et al, [16] have introduced the Energy-efficient Cluster Steering Conference for WSN, which was related on the Yellow Saddle Goat Fish Algorithm (YSGA). The conference was planned to increase the life of the company by decreasing energy use.…”

Section: Literature Reviewmentioning

confidence: 99%

An Adaptive Fuzzy C Means With Seagull Optimization Algorithm for Analysis of WSNs in Agricultural Field With IoT

Karunkuzhali¹,

Meenakshi²,

Lingam

2021

Preprint

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In recent years, the environmental monitoring in agriculture field is an essential required application. To achieve the environmental monitoring of agriculture fields, the wireless sense networks (WSN) and Internet of Things (IoT) is utilized. In the WSN, the energy consumption is a main issue to access the medium and transfer the networks. Hence, in this paper, Adaptive Fuzzy C means clustering and Seagull Optimization Algorithm (AFCM-SOA) is developed for monitoring environmental conditions in agriculture field. Two main objective functions are utilized to empower the presentation of the WSN such as load balancing and energy efficient operation. The proposed method is a combination of Fuzzy C Means Clustering and Seagull Optimization Algorithm (SOA). The energy efficient and load balancing is achieved by optimal routing scheme by proposed method. The Fuzzy C-Means Clustering is utilized to empower the energy efficient operation and load balancing. In the Fuzzy C-Means Clustering, the SOA is utilized to select the optimal path selection. The proposed method is executed by NS2 simulator and performances are compared with existing methods such as Atom Search Optimization (ASO) and Emperor Penguin Optimization (EPO) respectively. The performance metrics are delay, drop, throughput, energy consumption, network lifetime, overhead and delivery ratio.

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“…Hierarchical path selection mechanisms allow to control the overall energy consumption versus flat arrangements where all nodes share the same unchangeable role [67]. Both grid layout and clustered organization are employed for this purpose [66,68]. In this work we employ the cluster-based architecture defined above.…”

Section: Routing Algorithm and Dynamic Link Selectionmentioning

confidence: 99%

Model of a Device-Level Combined Wireless Network Based on NB-IoT and IEEE 802.15.4 Standards for Low-Power Applications in a Diverse IoT Framework

García-Martín

Torralba

2021

Sensors

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With the development of the Internet of Things (IoT), Low Data Rate-Personal Area Networks (LR-WPAN) have been deployed for different applications. Now comes the need to integrate these networks in search of greater connectivity, performances, and geographic coverage. This integration is facilitated by the recent deployment of low power wide area networks (LPWAN) in the licensed bands, especially narrowband IoT (NB-IoT) and long-term evolution for machine-type communications (LTE-M), which are standardized technologies that will continue evolving as part of the fifth generation (5G) specifications. This paper proposes a design methodology for combined networks using LR-WPAN and LPWAN technologies. These networks are combined at the device level using a cluster-tree topology. An example is shown here, where an existing IEEE 802.15.4 network is combined with NB-IoT. To this end, new dual nodes are incorporated, acting as cluster heads. The paper discusses the different aspects of formation and operation of the combined network. A dynamic link selection (DLS) algorithm is also proposed, based on which cluster headers dynamically determine the preferred link, depending on link quality and type of traffic. Extensive simulations show that the DLS algorithm significantly increases battery life on dual nodes, which are the nodes with the highest power demands.

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Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks Based on Yellow Saddle Goatfish Algorithm

Cited by 52 publications

References 37 publications

Energy-Efficient Cluster based Routing for Hybrid Wireless Sensor Networks using Adaptive Hybrid Cuckoo Search and Grey Wolf Optimization Algorithm

Energy-Efficient Cluster based Routing for Hybrid Wireless Sensor Networks using Adaptive Hybrid Cuckoo Search and Grey Wolf Optimization Algorithm

An Adaptive Fuzzy C Means With Seagull Optimization Algorithm for Analysis of WSNs in Agricultural Field With IoT

Model of a Device-Level Combined Wireless Network Based on NB-IoT and IEEE 802.15.4 Standards for Low-Power Applications in a Diverse IoT Framework

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