An improved K-means cluster-based routing scheme for wireless sensor networks

Lehsaini, Mohamed; Benmahdi, Meriem Bouchra

doi:10.1109/isps.2018.8379004

Cited by 18 publications

(15 citation statements)

References 9 publications

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“…K-means [ 30 ] is a clustering algorithm employed in machine learning. Several scholars [ 31 , 32 ] in the field of WSN research have attempted to obtain the optimal clustering number by improving this algorithm, so as to reduce the energy consumption of sensor nodes and extend the network life cycle. The K-means clustering algorithm was also adopted in the present study to establish a clustered network architecture, as shown in Figure 1 , aggregate the MC results from the clusters and send them to the sink through the CHs, and complete the data aggregation from the CHs.…”

Section: Methodsmentioning

confidence: 99%

A Byzantine Sensing Network Based on Majority-Consensus Data Aggregation Mechanism

Chang

Liu

2021

Sensors

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In the current Internet of Things era, digital devices form complex interconnections. The statuses of objects of interest are monitored using sensors, and distributed wireless sensor networks are formed from numerous sensor nodes. Many Byzantine fault tolerance mechanisms in wireless sensor networks (WSNs) were proposed from Byzantine agreement which even with a few faulty nodes in a sensor network, most healthy nodes can reach a consensus, perform data transmission tasks, and maintain network operation. In this study, this mechanism was utilized together with the majority function technique; in particular, the proposed method uses original sensor signals to define a threshold to assert a binary value of one or zero, thereby performing data judgment and aggregation. This approach reduces node energy consumption and enables the nodes to quickly reach a consensus. Moreover, the operating performance of the network can be maintained even when problems such as node failure and faults occur within the fault tolerance range. Compared with existing algorithms, the proposed data aggregation mechanism exhibits a better network life cycle and can effectively extend the flexibility of network operations.

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Section: Methodsmentioning

confidence: 99%

A Byzantine Sensing Network Based on Majority-Consensus Data Aggregation Mechanism

Chang

Liu

2021

Sensors

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“…After given the number of CHs, K-means [16]- [18] algorithm usually considers the Euclidean distance to determine the centroid position, and the node closest to the centroid serves as CH. Imp-K-means [19] is divided into two phases. The first phase is similar to K-means, except that imp-K-means considers the residual energy of each node to optimize CH selection.…”

Section: Related Workmentioning

confidence: 99%

“…where the standard deviation of the abscissa of node S(i) is given in (18), as shown at the bottom of this page, and the standard deviation of the ordinate of node S(i) is given in (19), as shown at the bottom of this page.…”

Section: ) Relative Distance To Bsmentioning

confidence: 99%

“…(22). (19) Each node adjusts the size of the cluster radius according to the static parameters, i.e., CR min ≤ CR(i) ≤ CR max . It can be seen that CR(i) is inversely proportional to the pilot estimation functionf H (l i | E, D).…”

Section: ) Relative Distance To Bsmentioning

confidence: 99%

“…EAMR [20] forms clusters in a LEACH-like manner, with the difference that EAMR utilizes fixed clustering, multi-hop routing, and threshold-based CH selection mechanism. The improved K-means routing scheme (imp-K-means) [19], based on the K-means, adopts the equal clustering mechanism to set up clusters. EAUCF [31] is based on a purely probabilistic model to select tentative CHs, and takes advantage of fuzzy logic to allocate competition radius.…”

Section: Introductionmentioning

confidence: 99%

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An Energy Aware Adaptive Kernel Density Estimation Approach to Unequal Clustering in Wireless Sensor Networks

Liu

Chang

2019

IEEE Access

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Energy conservation is one of the most important challenges in wireless sensor networks (WSNs). Therefore, compared with the traditional networks, the WSNs not only need high-quality services with high throughput or low transmission delay, but also pay greater attention to energy utilization to extend network lifetime. The clustering routing algorithm is considered to be among the effective ways to collect and transmit data in WSNs. Cluster head (CH) plays a vital role in the cluster which is in charge of data aggregation and data transmission, so their energy consumption is higher than non-CH nodes. The traditional clustering algorithm tends to have the same size in each cluster. However, due to the randomness of the node distribution, the equal clustering mechanism obviously cannot reduce energy consumption. In order to solve this problem, this paper contributes a new unequal clustering algorithm, an energy-aware adaptive kernel density estimation algorithm (EAKDE), which aims to balance the energy dissipation among the CHs. EAKDE utilizes fuzzy logic to determine the priority of nodes competing for CH. In order to adapt the dynamic change of node conditions, adaptive kernel density estimation algorithm is utilized to assign the appropriate unequal cluster radius to sensor nodes. The simulation results demonstrate that, in different scenarios, EAKDE outperforms the other well-known algorithms in terms of network stability, network lifetime, and energy efficiency. INDEX TERMS Unequal clustering, fuzzy logic, kernel density estimation, wireless sensor networks.

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Intra‐cluster aggregation aware routing for distributed training in wireless sensor networks

Chen

Long

Chen

et al. 2021

Concurrency and Computation

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In wireless sensor networks (WSNs), wireless sensor nodes can be equipped with deep neural network accelerators to deal with the computation challenges in distributed training. However, the communication overhead of distributed training and the limited battery capacity of sensor nodes still impedes the broad deployment of distributed training applications. This article investigates the distributed training in WSNs by formulating an aggregation-aware routing problem into a non-linear integer programming problem. The objective of the formulated problem is to reduce the training time using data aggregation-aware routing under the constraints of memory size and energy cost. Meanwhile, the NP-Hardness of the formulated problem is proved in this article. Then, an intra-cluster aggregation-aware routing algorithm is proposed. The proposed algorithm accelerates the transmission of the data packet by integrating the K-Means clustering and shortest path routing to choose the aggregators and the route paths. Extensive experiments demonstrate that the proposed algorithm outperforms two classical clustering routing algorithms UC-LEACH and K-Means by 29% and 37% in terms of average training time, and reducing the energy consumption by 21% and 15%, respectively.

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An improved K-means cluster-based routing scheme for wireless sensor networks

Cited by 18 publications

References 9 publications

A Byzantine Sensing Network Based on Majority-Consensus Data Aggregation Mechanism

A Byzantine Sensing Network Based on Majority-Consensus Data Aggregation Mechanism

An Energy Aware Adaptive Kernel Density Estimation Approach to Unequal Clustering in Wireless Sensor Networks

Intra‐cluster aggregation aware routing for distributed training in wireless sensor networks

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