An efficient cluster head election based on optimized genetic algorithm for movable sinks in IoT enabled HWSNs

Nandan, Aridaman Singh; Singh, Samayveer; Awasthi, Lalit Kumar

doi:10.1016/j.asoc.2021.107318

Cited by 44 publications

(54 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of the proposed method is compared to GAOC [ 38 ] and OptiGACHS-StSS [ 38 ] protocols with different fitness functions depending on different fitness factors. These cutting-edge procedures GAOC [ 38 ] and OptiGACHS-StSS [ 38 ] protocols were chosen based on a comprehensive performance in the literature review. GA-based optimized clustering called GAOC is the existing work, and in this work, the clustering process is optimized using GA fitness function.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…The following is a discussion of the aforementioned procedures with the various performance metrics. When comparing the performance of GAOC [ 38 ], OptiGACHS-StSS [ 38 ], and proposed protocol certain important performance indicators are used. The following are the points that will be covered.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…This section describes the simulation environment used to simulate the GAOC [ 38 ] and OptiGACHS-StSS [ 38 ] protocols. The simulation programme MATLAB version 2016 is used for simulation, which runs on Windows 10 and has an Intel Core i7 CPU 540 processor running at 3.74 GHz and 8 GB of RAM.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…However, this work does not consider the various other parameters in CH election and construction, such as average energy and number of neighbor's nodes. Papers [ 36 – 38 ] consider the IoT environment for enhancing the lifetime of heterogeneous networks. Moreover, the works presented are efficient and prolong the lifetime.…”

Section: Literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations

An Optimized Artificial Intelligence System Using IoT Biosensors Networking for Healthcare Problems

Khan¹,

Singh

Singh³

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In today’s environment, electronics technology is growing rapidly because of the availability of the numerous and latest devices which can be deployed for monitoring and controlling the various healthcare systems. Due to the limitations of such devices, there is a dire need to optimize the utilization of the devices. In healthcare systems, Internet of things (IoT) based biosensors networking has minimal energy during transmission and collecting data. This paper proposes an optimized artificial intelligence system using IoT biosensors networking for healthcare problems for efficient data collection from the deployed sensor nodes. Here, an optimized tunicate swarm algorithm is used for optimizing the route for data collection and transmission among the patient and doctor. The fitness function of the optimized tunicate swarm algorithm used the distance, proximity, residual, and average energy of nodes parameters. The proposed method is attributed to the optimal CH chosen under TSA operation having a lower energy consumption. The performance of the proposed method is compared to the existing methods in terms of various metrics like stability period, lifetime, throughput, and clusters per round.

show abstract

Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

An Optimized Artificial Intelligence System Using IoT Biosensors Networking for Healthcare Problems

Khan¹,

Singh

Singh³

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…An OECRP utilizes the overall left-over energy of node and normal energy levels of a network to choose a cluster head. Here p optimal is the cluster head optimal proportionality variable (12)(13)(14) . Here, we have utilized n k to represent the quantity of the circles that are required for the cluster head in a node A s and we also refer to this as a revolving epoch.…”

Section: Selection Of Cluster Headmentioning

confidence: 99%

An Optimal Energy Utilization of Cluster Routing Protocol for Wireless Sensor Network in Restricted Area

Pavithra¹,

Babu²

2021

IJST

View full text Add to dashboard Cite

Objectives: To efficiently monitor the highly confined area by using security measurement methods to enhance the lifetime of a large area coverage network. Methods : the modified nodes are included in the normal execution process of the network to calculate the efficiency and lifetime of the WSN. With the help of the simulation and analysis process, the proposed protocol is found to be more powerful to impact the sustainability of the network by prolonging the lifetime of the nodes. To perform this operation, the following parameters are considered: Area of the monitoring area, Number nodes, Sink portion, Energies of radio amplifier systems, Data aggregation, node electronics, and data packet length. Findings: The proposed optimal energy cluster routing protocol finds the best topological structural protocol to select the cluster head with optimal connectivity path with other nodes in a group. The data of energy utilization, data communication rate between nodes and cluster head, and analysis of the number of alive nodes concerning time are obtained. Novelty: the proposed Optimal efficient cluster routing protocol (OECRP) selects the cluster head by considering energy consumption, cluster sustainability, and its proper communication with other nodes. OECRP is used to develop the optimal topological structural protocol to connectivity path with other nodes in a group.

show abstract

IoT based sensor network clustering for intelligent transportation system using meta‐heuristic algorithm

Malik,

Singh,

Manju

et al. 2024

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

SummaryInternet of Things (IoT) based sensor networks have been established as a pillar in intelligent communication systems for efficiently handling roadside congestion and accidents. These IoT networks sense, collect, and process data on a real‐time basis. However, IoT based sensor network clustering has various energy constraints such as inefficient routing due to long‐haul transmission, hot spot problem, network overhead, and unstable network whenever deployed along with the roadside that affect their architecture. In such networks, clustering techniques play a crucial role in extending the lifespan and optimizing the routes by integrating sensor devices through clusters. Therefore, a meta‐heuristic algorithm for clustering in IoT sensor networks for an intelligent transportation system is proposed. In this work, the seagull optimization algorithm is applied for clustering by considering residual and average energy, node spacing, and distance fitness parameters. Moreover, this work also considers the dynamic communication range of the cluster heads for increasing the stability period and lifetime of the proposed networks. The experiment results demonstrate that the proposed Seagull optimization algorithm for clustering in IoT networks (SOAC‐IoTNs) and Seagull optimization algorithm for clustering in IoT networks with dynamic communication range (SOAC‐IoTNs‐DR) achieve a significant increase in the stability period and network lifetime, with percentage increments of 55.68% and 71.47%, and 10.03% and 88.66% respectively, compared to the existing optimized genetic algorithm for cluster head selection with single static sink (OptiGACHS‐StSS).

show abstract

An efficient cluster head election based on optimized genetic algorithm for movable sinks in IoT enabled HWSNs

Cited by 44 publications

References 34 publications

An Optimized Artificial Intelligence System Using IoT Biosensors Networking for Healthcare Problems

An Optimized Artificial Intelligence System Using IoT Biosensors Networking for Healthcare Problems

An Optimal Energy Utilization of Cluster Routing Protocol for Wireless Sensor Network in Restricted Area

IoT based sensor network clustering for intelligent transportation system using meta‐heuristic algorithm

Contact Info

Product

Resources

About