Improved node localization using K-means clustering for Wireless Sensor Networks

Khediri, Salim El; Fakhet, Walid; Moulahi, Tarek; Khan, Rehanullah; Thaljaoui, Adel; Kachouri, Abdennaceur

doi:10.1016/j.cosrev.2020.100284

Cited by 69 publications

(28 citation statements)

References 8 publications

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“…Thus, here in this process the complete network consumes energy and for multiple rounds of simulation the nodes gradually start dying because of not enough energy to transmit the data packet. Comparing the obtained performance with existing techniques such as LEACH (Lowenergy adaptive clustering hierarchy), O-LEAC H(Optimization Low Energy Adaptive Clustering Hierarchy), Bee Cluster, and OK-Means (Optimal Kmeans) as mentioned in [37].…”

Section: Resultsmentioning

confidence: 99%

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An Efficient Routing Mechanism for Node Localization, Cluster Based Approach and Data Aggregation to Extend WSN Lifetime

2022

IJIES

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The Last decade the demand for wireless communication has increased massively. Wireless Sensor Networks (WSN) have turned out as a potential solution for various real-time applications namely environmental monitoring, health and military applications. These networks follow the random placement strategy hence, identifying the location of sensor nodes is a tedious task. Moreover, these networks suffer from various issues such as limited power resources. This work, focusses on the sensor node localization and network lifetime enhancement of Wireless Sensor Networks (WSN). In localization, security is an important issue, so a trust-based model to secure localization is investigated in this work. Further, energy aware clustering and Cluster Head (CH) selection has been undertaken to prolong the lifetime of the network. Finally, a data aggregation model which decreases the network energy depletion by discarding the redundant data is presented. The outcome of the model which is proposed has been assessed and expressed in the form of localization error, energy depletion and alive node. The comparative analysis shows that the localization error for varied anchor nodes is obtained as 1.51, 2.28, 2.52 and 1.25 using existing techniques DMA (Distance Mapping Algorithm), MDS-Map (Multidimensional Scaling), DV-Hop (Distance Vector-Hop) and Proposed Approach, respectively. Similarly, the average energy consumption is obtained as 1.02, 1.67, 1.90 and 0.82 using DMA (Distance Mapping Algorithm), MDS-Map (Multidimensional Scaling), DV-Hop (Distance Vector-Hop) and Proposed Hybrid Approach. The comparative analysis shows that proposed hybrid approach of localization, CH (Cluster Head) formation and aggregation attains better performance when compared with existing techniques in WSN(Wireless Sensor Networks).

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Section: Resultsmentioning

confidence: 99%

“…Salim El Khediri et al [37] presented K-means algorithm to deal with energy consumption related issues in WSN and improving the network lifetime. This is achieved by incorporating a sample space partition in k-means.…”

Section: Clustering and Cluster Head Selectionmentioning

confidence: 99%

An Efficient Routing Mechanism for Node Localization, Cluster Based Approach and Data Aggregation to Extend WSN Lifetime

2022

IJIES

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“…To tackle localization difficulties because of pairwise distance measurements, several centralized techniques have been developed. Author [13] used convex optimization and semi-definite programming to solve the localization problem. The network's interconnection is represented in the optimization problem by using convex localization constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

Distance Matrix and Markov Chain Based Sensor Localization in WSN

Bamasaq¹,

Alghazzawi²,

Bhatia³

et al. 2022

Computers, Materials &Amp; Continua

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Applications based on Wireless Sensor Networks (WSN) have shown to be quite useful in monitoring a particular geographic area of interest. Relevant geometries of the surrounding environment are essential to establish a successful WSN topology. But it is literally hard because constructing a localization algorithm that tracks the exact location of Sensor Nodes (SN) in a WSN is always a challenging task. In this research paper, Distance Matrix and Markov Chain (DM-MC) model is presented as node localization technique in which Distance Matrix and Estimation Matrix are used to identify the position of the node. The method further employs a Markov Chain Model (MCM) for energy optimization and interference reduction. Experiments are performed against two well-known models, and the results demonstrate that the proposed algorithm improves performance by using less network resources when compared to the existing models. Transition probability is used in the Markova chain to sustain higher energy nodes. Finally, the proposed Distance Matrix and Markov Chain model decrease energy use by 31% and 25%, respectively, compared to the existing DV-Hop and CSA methods. The experimental results were performed against two proven models, Distance Vector-Hop Algorithm (DV-HopA) and Crow Search Algorithm (CSA), showing that the proposed DM-MC model outperforms both the existing models regarding localization accuracy and Energy Consumption (EC). These results add to the credibility of the proposed DC-MC model as a better model for employing node localization while establishing a WSN framework.

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“…In CDAS (Devi et al, 2020), latency and packet loss reduction lessen the overhead and end-to-end delay while improving energy utilization and network lifetime. In (Khediri et al, 2020), intra-cluster communication employs single hop; in contrast, inter-cluster communication manages multi-hop communication mode and achieves energy utilization. Although the network lifetime is the most significant concern (Han et al, 2020), offline parameter optimization has a high-level complexity, creates computational overhead, and does not concern multi-hop communication.…”

Section: Related Workmentioning

confidence: 99%

QoS based congestion evasion clustering framework of wireless sensor networks

Saha¹,

Chaki²

2022

KJS publishes peer-review articles in Mathematics, Computer Sci

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Congestion is a significant issue for event-based applications due to the continuous data collection and transmission by the sensors constituting the network. The congestion control technique monitors the process of adjusting the data and intends to manage the network traffic level to the threshold value. The information gathered from an intensive study is required to strengthen the knowledge base for devising a QoS based congestion evasion clustering framework of wireless sensor networks. In this scheme, the cluster heads are optimally determined and dispersed over the network. The data aggregation approach has been applied in a clustered network and set out a crucial paradigm for WSN routing. The proposal employs to mitigate congestion while messages are being forwarded via an alternate route to distribute the traffic and increase the throughput. This technique aims to balance the energy ingestion among the sensor nodes, reduce energy consumption, improve network lifetime, and achieve the quality of services. The result analysis revealed that the proposed scheme recommends 22.5% better throughput, 21% lesser end-to-end delay, 25.5% better delivery ratio, and efficiently relieves congestion while preserving the network's performance for attaining QoS in wireless sensor networks.

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Improved node localization using K-means clustering for Wireless Sensor Networks

Cited by 69 publications

References 8 publications

An Efficient Routing Mechanism for Node Localization, Cluster Based Approach and Data Aggregation to Extend WSN Lifetime

An Efficient Routing Mechanism for Node Localization, Cluster Based Approach and Data Aggregation to Extend WSN Lifetime

Distance Matrix and Markov Chain Based Sensor Localization in WSN

QoS based congestion evasion clustering framework of wireless sensor networks

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