Dynamic clustering approach based on wireless sensor networks genetic algorithm for IoT applications

Rani, Shalli; Ahmed, Syed Hassan; Rastogi, Ravi

doi:10.1007/s11276-019-02083-7

Cited by 132 publications

(41 citation statements)

References 19 publications

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“…The broker of the published side relays the transaction to the broker of the subscribed side. Figure 7 also illustrates the performance of dynamic clustering based on the genetic algorithm (GA) [44]. To optimize the clustering with historical data of network and IoT node behaviors, they built initial clustering solutions and try to converge to the optimal clustering.…”

Section: Results: Dynamic Iot Blockchain Configurationmentioning

confidence: 99%

Autonomous Operation Control of IoT Blockchain Networks

Kim

Lee

Hong³

2021

Electronics

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Internet of Things (IoT) networks are typically composed of many sensors and actuators. The operation controls for robots in smart factories or drones produce a massive volume of data that requires high reliability. A blockchain architecture can be used to build highly reliable IoT networks. The shared ledger and open data validation among users guarantee extremely high data security. However, current blockchain technology has limitations for its overall application across IoT networks. Because general permission-less blockchain networks typically target high-performance network nodes with sufficient computing power, a blockchain node with low computing power and memory, such as an IoT sensor/actuator, cannot operate in a blockchain as a fully functional node. A lightweight blockchain provides practical blockchain availability over IoT networks. We propose essential operational advances to develop a lightweight blockchain over IoT networks. A dynamic network configuration enforced by deep clustering provides ad-hoc flexibility for IoT network environments. The proposed graph neural network technique enhances the efficiency of dApp (distributed application) spreading across IoT networks. In addition, the proposed blockchain technology is highly implementable in software because it adopts the Hyperledger development environment. Directly embedding the proposed blockchain middleware platform in small computing devices proves the practicability of the proposed methods.

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Section: Results: Dynamic Iot Blockchain Configurationmentioning

confidence: 99%

Autonomous Operation Control of IoT Blockchain Networks

Kim

Lee

Hong³

2021

Electronics

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“…The model minimizes message replication, reduces communication overhead, and achieves better packet delivery performance. Other studies 35–37 showed how network congestion affects service quality of IoT applications. Here they presented a routing mechanism, namely, routing scheme for Internet of Things using shuffled frog leaping optimization algorithm (RISA).…”

Section: Related Workmentioning

confidence: 99%

REERS: Reliable and energy‐efficient route selection algorithm for heterogeneous Internet of things applications

Nayagi

Sivasankari

Ravi

et al. 2021

Int J Communication

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Summary Machine‐type communication (MTC) such as device‐to‐device and machine‐to‐machine communication is a technology used to realize the concept of Internet of things (IoT). The fifth generation (5G) IoT is composed of large density of wireless sensor device. The massive roll out of the MTC devices is a serious challenge for wireless communication networks from operational and management perspective, including massive access, route selection, energy efficiency and network congestion. Many proposals have been put forward by the research community to cater to the issues and difficulties of massive MTC (mMTC) access in large density sensor network‐enabled IoT communication environment. Recently, data aggregation with efficient route selection has attracted a lot of research attention owing to its robust ability to resolve the above‐mentioned challenges. Existing data routing and aggregation methodologies are not efficient when adopted to heterogeneous IoT applications as they fail to bring trade‐off between energy efficiency and latency minimization. This research work proposes reliable and energy‐efficient route selection (REERS) scheme to reduce energy dissipation and latency for catering both real‐time and non‐real‐time applications. Experiment outcomes shows the REERS schemes achieves much superior performance than existing routing and aggregation methodologies over latency reduction and energy efficiency.

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“…data packets are successfully delivered at destination to total number of data packets. Packet delivery ratio is formulated as below, = ( ) * 100 (7) In (7), indicates packet delivery ratio, represents data packet received, indicates data packet sent. Packet delivery ratio is calculated in percentage (%).…”

Section: A Impact Of Packet Delivery Ratiomentioning

confidence: 99%

“…However, delay was not minimized. The dynamic clustering-based methodology was proposed in [7] to enhance the data packet transmission. Designed method increases the throughput but the loss rate was not minimized.…”

Section: Introductionmentioning

confidence: 99%

Resource Aware Gaussian Regressive Jarvis Patrick Clustering for Reliable Data Transmission in E-Health using WSN-IoT

Suguna¹

2021

TURCOMAT

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Wireless sensor network (WSN) includes numerous sensor nodes (SN) integrated with Internet of Things (IoT) play a crucial role in numerous applications. IoT links physical devices as sensor and forms whole network for sharing information. The IoT has been used in different domains. In this scenario, patients utilize wearable medical sensors to monitor medical parameters. This medical sensor is equipped with batteries and has limited energy. Therefore, the network lifetime enhancement is a major challenging issue. To prolong network lifetime, novel technique called Resource-efficient Gaussian process regressive Jarvis Patrick clustering (REGPRJPC) is introduced. At first, the IoT devices are used in SN for sensing and collecting the patient data. After data collection process, SN is grouped into diverse clusters using Jarvis Patrick clustering technique. Jarvis Patrick clustering is graph-based clustering to partition SN with help of Gaussian process regression function. The regression function analyzes the SN and performs the clustering process based on the estimated energy and bandwidth. After clustering process, cluster head (CH) is selected to enhance data transmission and minimizes delay. Source node transmits gathered data to their CH. Then CH finds nearest CH using time of flight method. Followed by, data transmission is performed from source to sink node via the cluster head. In this way, resource-efficient data transmission is performed in WSN. Numerical analysis indicates that the REGPRJPC technique efficiently improves the reliable patient data packet delivery and minimizes the loss rate, delay.

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Dynamic clustering approach based on wireless sensor networks genetic algorithm for IoT applications

Cited by 132 publications

References 19 publications

Autonomous Operation Control of IoT Blockchain Networks

Autonomous Operation Control of IoT Blockchain Networks

REERS: Reliable and energy‐efficient route selection algorithm for heterogeneous Internet of things applications

Resource Aware Gaussian Regressive Jarvis Patrick Clustering for Reliable Data Transmission in E-Health using WSN-IoT

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