Evolving clustering algorithms for wireless sensor networks with various radiation patterns to reduce energy consumption

Sheta, Alaa; Solaiman, Basma

doi:10.1109/sai.2015.7237270

Cited by 11 publications

(5 citation statements)

References 40 publications

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“…For the first factor, we prefer to choose the node which is as close as possible to the base station among the same cluster, since, as the data transmission distance between nodes increases, the data transmission energy consumption increases exponentially [36]. This will reduce the energy consumption of the vice cluster-head in forwarding packets to the base station.…”

Section: Elect the Primary Cluster-heads In Dck-leachmentioning

confidence: 99%

A Dual Cluster-Head Energy-Efficient Routing Algorithm Based on Canopy Optimization and K-Means for WSN

Min

et al. 2022

Sensors

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Wireless sensor networks (WSN) are widely used in various applications, such as environmental monitoring, healthcare, event detection, agriculture, disaster management, and so on. Due to their small size, sensors are limited power sources and are often deployed in special environments where frequent battery replacement is not feasible. Therefore, it is important to reduce the energy consumption of sensors and extend the network lifetime. An effective way to achieve this is clustering. This paper proposes a dual cluster-head energy-efficient algorithm (DCK-LEACH), which is based on K-means and Canopy optimization. Considering that the K-means algorithm is sensitive to the location of the initial clustering centers, this paper uses both the dynamic Canopy algorithm and K-means algorithm for clustering. For cluster-head election, this algorithm uses a hierarchy to minimize the cluster-head burden and balance the network load. The primary cluster-head is selected by two objectives: the node’s residual energy and the distance from the node to the clustering center. The vice cluster-head is selected by the residual energy of the node, and the distance from the nodes to the base station. Simulator results show that DCK-LEACH significantly prolongs the energy-critical node lifetime and the network lifetime compared with existing protocols.

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Section: Elect the Primary Cluster-heads In Dck-leachmentioning

confidence: 99%

A Dual Cluster-Head Energy-Efficient Routing Algorithm Based on Canopy Optimization and K-Means for WSN

Min

et al. 2022

Sensors

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“…The pheromone value on links between nodes that are neighbors is initialized using Equation (4). After choosing the next hop, the pheromone value on the path between sender and receiver is updated and increased using Equation (11) while the other unused paths the pheromone value is decreased using Equation ( 10). -Step 4 (Pheromone evaporation): After the ant chooses the link with the highest pheromone concentration, it deposits a certain quantity of pheromone, which increases the pheromone concentration in this link.…”

Section: Aco-based Routing Model (Routing Layer)mentioning

confidence: 99%

“…The routing protocols use several QoS mechanisms such as clustering, [1][2][3] routing, [4][5][6] sink mobility, 7,8 data aggregation, 9,10 and hybrid [11][12][13] that are based on three different categories, such as classical, optimization, and machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

ACOMAR: ACO‐based MAC‐aware routing protocol

Mechta

Mahgoun

Harous

2021

Int J Communication

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In this work, we present a new nature-inspired cross-layer routing protocol named ACO-based MAC-aware routing protocol (ACOMAR). An enhancement of time division multiple access (TDMA) algorithm is proposed where slots allocation algorithm considers the geographical distance between nodes and the sink. The nodes that are far away from the sink have better chance to be scheduled first. This strategy will result in more efficiency than a usual random TDMA schedule. This algorithm uses a metric based on the pheromone's value of Ant Colony Optimization (ACO) to choose the best next hop with the aim of reducing latency and energy and extending the network lifetime. Comparisons are made between ACOMAR, MAC-aware routing (MAR) and the latest cross-layer protocol latency and energy MAC-aware routing (LEMAR). The experimental results show that ACOMAR outperforms considerably LEMAR by 80%, 20%, 81%, and 54% in latency, energy, hop count, and throughput, respectively. Consequently, ACOMAR is a suitable solution for healthcare applications where WSN-based critical IoT devices interact quickly in emergence cases (high rate in latency optimization 80%).

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“…al. [19], in their work, they recommended a hybrid protocol to extend the network lifespan. It utilized the k-means algorithms in clustering the nodes into groups.…”

Section: Related Workmentioning

confidence: 99%

Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks

Hassan¹,

Shah²,

Othman³

et al. 2020

IJECE

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The clustering approach is considered as a vital method for wireless sensor networks (WSNs) by organizing the sensor nodes into specific clusters. Consequently, saving the energy and prolonging network lifetime which is totally dependent on the sensors battery, that is considered as a major challenge in the WSNs. Classification algorithms such as K-means (KM) and Fuzzy C-means (FCM), which are two of the most used algorithms in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces these algorithms to produce unbalanced clusters, which adversely affects the lifetime of the network. Based for our knowledge, there is no study has analyzed the performance of these algorithms in terms clusters construction in WSNs. In this study, we investigate in KM and FCM performance and which of them has better ability to construct balanced clusters, in order to enable the researchers to choose the appropriate algorithm for the purpose of improving network lifespan. In this study, we utilize new parameters to evaluate the performance of clusters formation in multi-scenarios. Simulation result shows that our FCM is more superior than KM by producing balanced clusters with the random distribution manner for sensor nodes.

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Evolving clustering algorithms for wireless sensor networks with various radiation patterns to reduce energy consumption

Cited by 11 publications

References 40 publications

A Dual Cluster-Head Energy-Efficient Routing Algorithm Based on Canopy Optimization and K-Means for WSN

A Dual Cluster-Head Energy-Efficient Routing Algorithm Based on Canopy Optimization and K-Means for WSN

ACOMAR: ACO‐based MAC‐aware routing protocol

Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks

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