Improved artificial bee colony optimization based clustering algorithm for SMART sensor environments

Famila, S.; Jawahar, A.; Sariga, A.; Shankar, K.

doi:10.1007/s12083-019-00805-4

Cited by 71 publications

(39 citation statements)

References 17 publications

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“…Famila et al [43] proposed an improved artificial bee colony optimization (IABCOCT) algorithm in WSN. The cluster creation considers the ABC algorithm and addition, which incorporates the parameters such as Cauchy operator and grenade explosion method.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…ABC Algorithm simulates e food source search strategy of a honey bee swarm [12]. Due to its fast and strong convergence ability, besides many optimization problems in computer science such as feature selection, intrusion detection, clustering, missing value imputation, it is also applied to optimize many engineering problems [13][14][15][16]. Disributed generation allocation problem, composite design optimization problem, photovoltaic parameter estimation problem, flow shop scheduling problem, estimation of the fundamental period of vibration are just some of the application areas.…”

Section: Introductionmentioning

confidence: 99%

Estimating The Aeration Performance Of Venturi-Conduit By Artificial Bee Colony Programming

2022

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 Aeration performance of venturi-conduit was modeled by Artificial Bee Colony Programming.  Functions that can model aeration performance are defined for 2 different contraction ratios, 0.75 and 0.90.  Defined functions can predict the ventilation performance of conduits with venture-conduit without the need to build an experimental setup. Using these functions, the outputs (aeration performance) of different input values (Reynold, beta and length) can be calculated without the need for a training step.  The average RMSE value of the developed functions was measured as 1.64 for the contraction ratio 0.75, and 2.66 for 0.9.

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“…The cluster 33‐36 is the crucial step in the clustering algorithms. It is the cluster head node's function to collect data from the cluster members and forward it to the sink node.…”

Section: Introductionmentioning

confidence: 99%

An energy‐efficient clustering algorithm with self‐diagnosis data fault detection and prediction for wireless sensor networks

Loganathan¹,

Arumugam²,

Chinnababu³

2021

Concurrency and Computation

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Wireless sensor networks are used to track and regulate physical conditions like temperature and the environment's humidity. Wireless sensor networks with their advanced features are adopted for many real‐time applications. The limited capacity batteries usually power the sensor nodes. But the big challenge of limited battery capacity obstructs remote and inaccessible areas where their use is the most favorable. For extending the lifetime of the network, the battery should optimally utilize it for different operations. The requirement toward low complexity and low energy consumption motivate the wireless sensor networks' efficient clustering algorithm. The sensor nodes group into clusters; one sensor node is chosen as a cluster head, and communication to the sink node from the sensor nodes occurs through the cluster head (CH) node. In the proposed method, cluster heads are determined based on the sensor node's weighted metric. The sensor nodes are then self‐adaptive by making correct decisions in real‐time based on the sensed data, but detected information is often inaccurate due to some mechanical, wireless loss, and battery problems. The erroneous or irrelevant data should be overlooked to avoid unnecessary data transmission, which contributes to reducing the network's lifetime. In the neighborhood‐dependent self‐diagnosis fault detection technique, the faulty sensed data are filtered at the sensor node itself. In the data prediction algorithm, the filtered data are predicted at the cluster head. All the factors collectively contribute to enhance the network's lifetime. The lifetime improvement of the proposed approach is almost doubled compared with LEACH one time better than QLEACH and ECH, and 51% better than temporal approach.

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Improved artificial bee colony optimization based clustering algorithm for SMART sensor environments

Cited by 71 publications

References 17 publications

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

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

Estimating The Aeration Performance Of Venturi-Conduit By Artificial Bee Colony Programming

An energy‐efficient clustering algorithm with self‐diagnosis data fault detection and prediction for wireless sensor networks

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