Multi-Layer Cluster Based Energy Aware Routing Protocol for Internet of Things

Sankar, S.; Srinivasan, Padmini

doi:10.2478/cait-2018-0040

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…(1) Cluster-based mechanisms: Cluster-based networks can reduce the performance load in terms of reducing the aggregate computation and energy consumption of all nodes [22]. In cluster-based mechanisms, the network environment is divided into different clusters while each cluster comprises a number of sensor nodes [23,24]. In each cluster, one node is selected as the head of all other nodes in a cluster.…”

Section: System Model and Related Workmentioning

confidence: 99%

An Enhanced Distributed Data Aggregation Method in the Internet of Things

Homaei¹,

Salwana

Shamshirband

2019

Sensors

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“Internet of Things (IoT)” has emerged as a novel concept in the world of technology and communication. In modern network technologies, the capability of transmitting data through data communication networks (such as Internet or intranet) is provided for each organism (e.g. human beings, animals, things, and so forth). Due to the limited hardware and operational communication capability as well as small dimensions, IoT undergoes several challenges. Such inherent challenges not only cause fundamental restrictions in the efficiency of aggregation, transmission, and communication between nodes; but they also degrade routing performance. To cope with the reduced availability time and unstable communications among nodes, data aggregation, and transmission approaches in such networks are designed more intelligently. In this paper, a distributed method is proposed to set child balance among nodes. In this method, the height of the network graph increased through restricting the degree; and network congestion reduced as a result. In addition, a dynamic data aggregation approach based on Learning Automata was proposed for Routing Protocol for Low-Power and Lossy Networks (LA-RPL). More specifically, each node was equipped with learning automata in order to perform data aggregation and transmissions. Simulation and experimental results indicate that the LA-RPL has better efficiency than the basic methods used in terms of energy consumption, network control overhead, end-to-end delay, loss packet and aggregation rates.

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Section: System Model and Related Workmentioning

confidence: 99%

An Enhanced Distributed Data Aggregation Method in the Internet of Things

Homaei¹,

Salwana

Shamshirband

2019

Sensors

View full text Add to dashboard Cite

show abstract

“…(1) Cluster-based mechanisms: Cluster-based networks can reduce the performance load in terms of reducing the aggregate computation and energy consumption of all nodes [22]. In clusterbased mechanisms, the network environment is divided into different clusters while each cluster comprises a number of sensor nodes [23,24]. In each cluster, one node is selected as the head of all other nodes in a cluster.…”

Section: Data Aggregation Strategies In Iotmentioning

confidence: 99%

An Enhanced Distributed Data Aggregation Method in the Internet of Things

Homaei¹,

Rafiee²

2019

Preprint

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As a novel concept in technology and communication world, “Internet of Things (IoT)” has been emerged. In such modern technology, the capability to transmit data through data communication networks (such as Internet or Intranet) is provided for each organism (e.g. human being, animals, things, and so forth). Due to the limited hardware and communication operational capability as well as small dimensions, IoT undergoes quite a few challenges. Such inherent challenges not only cause fundamental restrictions in the efficiency of aggregation, transmission, and communication between nodes; but they also degrade routing performance. To cope with the reduced avail- ability time and unstable communications among nodes, data aggregation and transmission approaches in such networks are designed more intelligently. In this paper, a distributed method is proposed to set child balance among nodes. In this method, the height of the network graph is increased through restricting the degree; and network congestion is reduced as a result. Besides, a dynamic data aggregation approach -named as LA-RPL- is proposed for RPL networks. More specifically, each node is equipped with learning automata in order to perform data aggregation and transmissions. Simulation results demonstrate that the proposed approach outperforms previously suggested base approaches in terms of energy consumption, network control overhead, and packet loss rate.

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“…This method offers the aggregation facility in the cluster, which reduces the duplicate data from the CM and only the aggregated data are passed to the BS. In a clustering protocol, the essential processes are cluster creation, cluster head (CH) selection, and route establishment between nodes and BS [19][20][21][22][23][24][25][26][27]. The CH selection plays a crucial role in increasing the network lifetime.…”

Section: Introductionmentioning

confidence: 99%

T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things

Sankar¹,

Ramasubbareddy

Balasubramaniyam³

et al. 2021

IEEE Access

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In recent years, the Internet of Things (IoT) has evolved as a research field that transforms human lifestyle from traditional to sophisticated. In IoT, the network plays a crucial role in collecting data from sensors and moving to the sink. Increasing the network lifetime is a challenging task in IoT, which is connected to devices that are limited by resource. Clustering is one of the effective methods of increasing the network lifetime. However, improper cluster head (CH) selection easily drains the energy early in network nodes. With the aim to overcome the issue, this paper proposes the Type-2 Fuzzy Logic-based Particle Swarm Optimization (T2FL-PSO) algorithm to select the optimal CH to extend the network lifetime. The T2FL is highly useful in providing the accurate solution in uncertain network environments. Hence, T2FL is applied on the network parameters, residual energy, and the distance between sensor node and base station to determine the fitness value. Later, virtual clusters are formed on the basis of distance between sensor node and CH and between node and base station. To validate the performance of the proposed T2FL-PSO algorithm, extensive simulations are carried out using MATLAB 2019a. Furthermore, the proposed T2FL-PSO algorithm is compared with Particle Swarm Optimization Clustering (PSO-C) and Particle Swarm Optimization Wang Zhang (PSO-WZ). The result confirms that the proposed T2FL-PSO increases the network lifetime by 10%-15% and the packet transmission ratio by 10%. Compared with similar algorithms, the proposed T2FL-PSO also causes a higher increase of network lifetime.

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Multi-Layer Cluster Based Energy Aware Routing Protocol for Internet of Things

Cited by 8 publications

References 23 publications

An Enhanced Distributed Data Aggregation Method in the Internet of Things

An Enhanced Distributed Data Aggregation Method in the Internet of Things

An Enhanced Distributed Data Aggregation Method in the Internet of Things

T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things

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