MCH-EOR: Multi-objective Cluster Head Based Energy-aware Optimized Routing algorithm in Wireless Sensor Networks

Mehta, Deepak; Saxena, Sharad

doi:10.1016/j.suscom.2020.100406

Cited by 79 publications

(77 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rate mechanism method decreases end‐to‐end delay to advance lifetime of the network. Mehta et al 7 proposed a Multi‐Objective Based Clustering and Sailfish Optimizer (SFO) directed routing technique to endure power effectiveness in WSNs. Cluster Head (CH) is elected based on operative fitness function expressed from manifold objectives.…”

Section: Related Workmentioning

confidence: 99%

“…However, given the unique characteristics of a home network, such as limited performance, narrow bandwidth, and dynamic topology (including node failures, node downtime, or physical activity), the result was a high density network and challenges in the design and management of sensor networks 6,7 . These challenges require a solid knowledge of power and protocol design for all network protocols.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi‐objective lion optimization for energy‐efficient multi‐path routing protocol for wireless sensor networks

Singh

Rishiwal

Yadav

2021

Int J Communication

View full text Add to dashboard Cite

Summary Wireless Sensor Network (WSNs) is a collection of sensor nodes with a base station for data transmission. Mostly the network has various paths with the unbalanced nodes; therefore, energy consumption is always high. This causes the letdown of quality of service (QoS) routing metrics such as delay, inefficient throughput in WSNs. This paper concentrates on this problem and proposes an energy‐efficient multipath routing (EEMR) protocol for WSNs. EEMR protocol computes the optimal path using the multi‐objective lion optimization algorithm with less energy consumption. The main aim of EEMR protocol is to achieve an efficient path among multiple paths to satisfy quality requirements such as delay, throughput, energy consumption, delivery ratio, loss ratio, and latency. Thus, the proposed EEMR protocol is designed with a multi‐objective function using an optimization algorithm and implemented in MATLAB. The simulation results show that the performance of the proposed protocol is very efficient in comparison to state of the art.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi‐objective lion optimization for energy‐efficient multi‐path routing protocol for wireless sensor networks

Singh

Rishiwal

Yadav

2021

Int J Communication

View full text Add to dashboard Cite

show abstract

“…Sellers from third parties have legal access to vital infrastructure and confidential details of customers [12]. A third-party cyber-attack poses cyber protection, operational, conformity, and reputational threats for all the vendors' organizations [13,14]. The third-party data transfer generally consists of irritating behavioral or demographic data aggregated from several sources [15].…”

Section: Overview Of Managing Data Security Using Keyed-hash Message Authentication Code-based Encryption Algorithmmentioning

confidence: 99%

Code-based encryption techniques with distributed cluster head and energy consumption routing protocol

Jalasri

Lakshmanan

2021

Complex Intell. Syst.

View full text Add to dashboard Cite

Fog computing and the Internet of Things (IoT) played a crucial role in storing data in the third-party server. Fog computing provides various resources to collect data by managing data security. However, intermediate attacks and data sharing create enormous security challenges like data privacy, confidentiality, authentication, and integrity issues. Various researchers introduce several cryptographic techniques; security is still significant while sharing data in the distributed environment. Therefore, in this paper, Code-Based Encryption with the Energy Consumption Routing Protocol (CBE-ECR) has been proposed for managing data security and data transmission protocols using keyed-hash message authentication. Initially, the data have been analyzed, and the distributed cluster head is selected, and the stochastically distributed energy clustering protocol is utilized for making the data transmission. Code-driven cryptography relies on the severity of code theory issues such as disorder demodulation and vibration required to learn equivalence. These crypto-systems are based on error codes to build a single-way function. The encryption technique minimizes intermediate attacks, and the data have protected all means of transmission. In addition to data security management, the introduced CBE-ECR reduces unauthorized access and manages the network lifetime successfully, leading to the effective data management of 96.17% and less energy consumption of 21.11% than other popular methods.The effectiveness of the system is compared to the traditional clustering techniques.

show abstract

“…But the higher data delivery rate was not minimized. A multi-Objective Based Clustering and Sailfish Optimizer (SFO) was introduced in [16]. The delay was not reduced.…”

Section: Related Workmentioning

confidence: 99%

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

Suguna¹

2021

TURCOMAT

View full text Add to dashboard Cite

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.

show abstract

MCH-EOR: Multi-objective Cluster Head Based Energy-aware Optimized Routing algorithm in Wireless Sensor Networks

Cited by 79 publications

References 32 publications

Multi‐objective lion optimization for energy‐efficient multi‐path routing protocol for wireless sensor networks

Multi‐objective lion optimization for energy‐efficient multi‐path routing protocol for wireless sensor networks

Code-based encryption techniques with distributed cluster head and energy consumption routing protocol

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

Contact Info

Product

Resources

About