Energy efficient secured K means based unequal fuzzy clustering algorithm for efficient reprogramming in wireless sensor networks

Kumar, Sachin; Yogesh, P.; Selvi, M.; Ganapathy, Sannasi; Kannan, A.; Perumal, Sankar Pariserum

doi:10.1007/s11276-021-02660-9

Cited by 51 publications

(8 citation statements)

References 42 publications

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“…The fuzzy logic considers the cluster density, distance, and residual energy of the nodes present in the network. Santhosh Kumar et al, (2021) proposed a secure environment for a cluster-based wireless sensor network. For security, cryptography was adopted.…”

Section: Related Workmentioning

confidence: 99%

HCPFRP: Heterogeneous cluster prediction and formation routing protocol for wireless sensor network

Maheshwar

Veenadhari

2023

JCST

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Wireless sensor network (WSN) is composed of multiple sensors that are connected through a communication channel and communicate with each other. As these sensor nodes are battery-operated, therefore, as a consequence, battery life or energy is always an issue of concern. Therefore, researchers focus their work on optimizing the routing strategies to save energy wastage in WSNs. Among all routing strategies, cluster-based techniques proved to be quite able to successfully manage propagation from sender to receiver. Because it must gather all data and send it to the base station, each cluster's elected head is responsible for bearing the complete load. A cluster-based routing mechanism is established under this paper and termed a Heterogeneous Cluster Prediction and Formation Routing Protocol (HCPFR) in which the algorithm first creates the cluster and predicts the energy utilization or network lifetime, and then provides energy-efficient optimized clustering. In this method, the proposed HCPFR model is compared with different methods; LEACH PSO, LEACH-GWO, LEACH-EEGWO, and FZR, and the performance is compared with different parameters mainly First Dead Node (FDN), Network Longevity (NL) and throughput (THP) in term of packet delivered and residual energy. The result shows that the HCPFR model outperforms better over these approaches. The FDN, NL, and THP of the proposed HCPFR are nearly 8000,10000, and 30000. Also, the suggested model shows that as the number of rounds increases the residual energy drops to 0.1 from 3.8 as the rounds increases to 10000 from 2000.

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Section: Related Workmentioning

confidence: 99%

HCPFRP: Heterogeneous cluster prediction and formation routing protocol for wireless sensor network

Maheshwar

Veenadhari

2023

JCST

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“…S‐SELDRIP provides secure optimal path for data dissemination by finding the deliberate nodes for avoiding redundant broadcast strom problem. Kumar et al 2 has proposed a cluster based secured protocol (CSDP) for providing secure data dissemination process in the network based on public keys and private keys generation algorithm. An intelligent fuzzy based unequal clustering algorithm is proposed to provide efficient clustering process of nodes during data dissemination.…”

Section: Literature Surveymentioning

confidence: 99%

“…Reconfiguration and reprogramming are used to achieve data dissemination process in IoT. Reconfiguration 2 is a process of updating the array of the functionality of the node to make the minor notification in node behavior is called reconfiguration. In other hand, reprogramming is a process of programming the nodes from the scratch by installing the design framework to make the node to perform the respective activities.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive survey on data dissemination protocols for efficient reprogramming in Internet of Things

Shanmugapriya

Kumar

2022

Concurrency and Computation

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SUMMARY The Internet of Things (IoT) is a wide network of interconnected intelligent objects which can recognize the natural phenomenon of the deployed environment with the help of sensors and transmit the sensed information to other devices via internet as a backbone. Data dissemination is one of the important activities in IoT. Data dissemination is the process of updating the design framework of the sensor nodes to make the sensor nodes in IoT device to perform the intended services. Reprogramming is the process of entire change in the configuration parameters of the nodes and reconfiguration is the process of minor modification in the configuration parameter. In data dissemination, the design framework is typically disseminated through the sensor nodes to the base station using multi‐hop communication. In this article, a comprehensive survey on various dissemination approaches such as topology, reliability, scalability, structure, centralized, and de‐centralized are carried out to highlight with their advantages and limitations. Moreover, the proposed survey provides a detailed analysis on both reprogramming–related to data dissemination and reconfiguration‐related to data dissemination in order to improve the efficiency of data dissemination.

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“…RL algorithms are the two types: model‐based and model‐free. The model‐based approach predicts the best policy using transition and reward functions, whereas the model‐free technique uses a value‐based learning method 21 . In literature, the k‐means technique 22 is used to improve the CH selection process and balance the size of the cluster while actor‐critic algorithm, which is a model‐free policy‐based RL algorithm is used for path planning of UAVs 19 .…”

Section: Introductionmentioning

confidence: 99%

“…The model-based approach predicts the best policy using transition and reward functions, whereas the model-free technique uses a value-based learning method. 21 In literature, the k-means technique 22 is used to improve the CH selection process and balance the size of the cluster while actor-critic algorithm, which is a model-free policy-based RL algorithm is used for path planning of UAVs. 19 Q-learning based algorithm is used to find the optimal CHs and the shortest path for MS to reach all CHs based on the distance between the nodes.…”

mentioning

confidence: 99%

Double Q‐learning‐based adaptive trajectory selection for energy‐efficient data collection in wireless sensor networks

Rajagopal

Bhanumathi

Sakthivel

2023

Int J Communication

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This paper proposes a novel distributed stochastic routing strategy using mobile sink based on double Q-learning algorithm to improve the network performance in wireless sensor network with uncertain communication links. Furthermore, in order to extend network lifetime, a modified leach-based clustering technique is proposed. To balance the energy dissipation between nodes, the selected cluster head nodes are then rotated based on the newly suggested threshold energy value. The simulation results demonstrate that the proposed algorithms outperform the QWRP, QLMS, ESRP and HACDC in terms of network lifetime by 18.33%, 35.1%, 39.7% and 44.7%, respectively. Moreover, the proposed algorithms considerably enhances the learning rate and hence reduces the data collection latency.

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Energy efficient secured K means based unequal fuzzy clustering algorithm for efficient reprogramming in wireless sensor networks

Cited by 51 publications

References 42 publications

HCPFRP: Heterogeneous cluster prediction and formation routing protocol for wireless sensor network

HCPFRP: Heterogeneous cluster prediction and formation routing protocol for wireless sensor network

Comprehensive survey on data dissemination protocols for efficient reprogramming in Internet of Things

Double Q‐learning‐based adaptive trajectory selection for energy‐efficient data collection in wireless sensor networks

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