Clustering-based Optimized HEED protocols for WSNs using bacterial foraging optimization and fuzzy logic system

Gupta, Prateek; Sharma, Ajay K.

doi:10.1007/s00500-017-2837-7

Cited by 30 publications

(12 citation statements)

References 38 publications

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“…Du et al [ 17 ] presented an EESSC protocol that employed the residual energy parameter for clustering methods and used a specific packet header to update the sensor devices' residual energy during data transmission in the network while data transmission was taking place. Gupta and Sharma [ 18 ] suggested an ICHB method for the CH selection procedure that was based on the BFOA algorithm [ 19 ]. With the aid of artificial bacteria, ICHB is completely capable of looking for greater residual energy nodes in the network and locating them.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimized Artificial Intelligence System Using IoT Biosensors Networking for Healthcare Problems

Khan¹,

Singh

Singh³

et al. 2022

Computational Intelligence and Neuroscience

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In today’s environment, electronics technology is growing rapidly because of the availability of the numerous and latest devices which can be deployed for monitoring and controlling the various healthcare systems. Due to the limitations of such devices, there is a dire need to optimize the utilization of the devices. In healthcare systems, Internet of things (IoT) based biosensors networking has minimal energy during transmission and collecting data. This paper proposes an optimized artificial intelligence system using IoT biosensors networking for healthcare problems for efficient data collection from the deployed sensor nodes. Here, an optimized tunicate swarm algorithm is used for optimizing the route for data collection and transmission among the patient and doctor. The fitness function of the optimized tunicate swarm algorithm used the distance, proximity, residual, and average energy of nodes parameters. The proposed method is attributed to the optimal CH chosen under TSA operation having a lower energy consumption. The performance of the proposed method is compared to the existing methods in terms of various metrics like stability period, lifetime, throughput, and clusters per round.

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Section: Literature Reviewmentioning

confidence: 99%

An Optimized Artificial Intelligence System Using IoT Biosensors Networking for Healthcare Problems

Khan¹,

Singh

Singh³

et al. 2022

Computational Intelligence and Neuroscience

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“…To improve energy efficiency by reducing the distances between nodes due to multi‐hop communication number of routing protocols are proposed. In, 24 the authors applied chain‐based intra‐cluster and inter‐cluster communication in HEED to improve the CHs load distribution and enhance the energy dissipation among sensor nodes. For heterogeneous networks, the DEEC protocol is proposed in 25 in which the selection of CH can be decided by both initial and residual energy, and the CH selection probability is based on the ratio of the residual energy to the average energy of nodes of the network.…”

Section: Related Workmentioning

confidence: 99%

Modulated relay based stable election protocol for large‐scale wireless sensor networks

Hamad

Dağ

Gucluoglu

2022

Int J Communication

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Summary Internet of things (IoT) applications based on wireless sensor networks (WSNs) have recently gained vast momentum. These applications vary from health care, smart cities, and military applications to environmental monitoring and disaster prevention. As a result, energy consumption and network lifetime have become the most critical research area of WSNs. Through energy‐efficient routing protocols, it is possible to reduce energy consumption and extend the network lifetime for WSNs. Using hybrid routing protocols that incorporate multiple transmission methods is an effective way to improve network performance. This paper proposes modulated R‐SEP (MR‐SEP) for large‐scale WSN‐based IoT applications. MR‐SEP is based on the well‐known stable election protocol (SEP). MR‐SEP defines three initial energy levels for the nodes to improve the network energy distribution and establishes multi‐hop communication between the cluster heads (CHs) and the base station (BS) through relay nodes (RNs) to reduce the energy consumption of the nodes to reach the BS. In addition, MR‐SEP reduces the replacement frequency of CHs, which helps increase network lifetime and decrease power consumption. Simulation results show that MR‐SEP outperforms SEP, LEACH, and DEEC protocols by 70.2%, 71.58%, and 74.3%, respectively, in terms of lifetime and by 86.53%, 86.68%, and 86.93% in terms of throughput.

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“…Gupta et al [17] proposed an optimized hybrid energyefficient distributed (OHEED) protocol for heterogeneous WSN on different levels of node heterogeneity (i.e., 1-level, 2-level, 3-level, and multi-level) named as heterogeneous OHEED protocols. It can handle efficiently both homogeneous and heterogeneous nodes in the network.…”

Section: Literature Reviewmentioning

confidence: 99%

Pegasis Double Cluster Head Hybrid Congestion Control in Wireless Sensor Networks

Ali

Vadivel

2021

JCOMSS

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Wireless sensor network (WSN) is the fastest growing technology that dominates the future world into wireless communication. It is a collection of a number of self-governing sensor nodes responsible to sense, process and manipulate the nodes. The sensor nodes are regulated by a battery where the network gets failed if the battery is dead. Thus, energy is an important factor to be efficiently used. Furthermore, congestion occurs in WSN when the incoming traffic load exceeds the capacity of the network. The major factors that lead to congestion are buffer overflow, varying rates of transmission, packet collision, and many-to-one data transmission. Due to these, the network suffers from packet loss, queuing delay, end-to-end delay, decrease in network lifetime, and increase in energy consumption. Hence, a clustering-based routing protocol is introduced in this paper to improve the performance of the network and reduce congestion. In the proposed method, Power-Efficient Gathering in Sensor Information Systems (PEGASIS) double cluster head with artificial neural network (ANN) is utilized to analyze the overall network lifetime. The proposed technique is comprised of four phases: clustering the network nodes, cluster head (CH) selection, chain formation, and secondary CH (SCH) selection. The sensor nodes are initially clustered with the firefly algorithm in which the cluster heads of each node are elected via an artificial neural network. Meanwhile, chain formation is processed by PEGASIS double cluster head (PDCH) and the SCH is selected through grey wolf optimizer (GWO) to afford equivalent energy utilization between the sensor nodes. The simulation outcomes proved that the proposed method efficiently increases the lifetime of the network and reduces congestion level in WSN.

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Clustering-based Optimized HEED protocols for WSNs using bacterial foraging optimization and fuzzy logic system

Cited by 30 publications

References 38 publications

An Optimized Artificial Intelligence System Using IoT Biosensors Networking for Healthcare Problems

An Optimized Artificial Intelligence System Using IoT Biosensors Networking for Healthcare Problems

Modulated relay based stable election protocol for large‐scale wireless sensor networks

Pegasis Double Cluster Head Hybrid Congestion Control in Wireless Sensor Networks

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