Trust-aware and energy-efficient data gathering in wireless sensor networks using PSO

Soltani, Keiwan; Farzinvash, Leili; Balafar, Mohammad Ali

doi:10.1007/s00500-023-07856-z

Cited by 2 publications

(5 citation statements)

References 49 publications

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“…The proposed formulation's optimal solution takes into account mean intra-cluster distance, sink proximity, remaining energy and the CH balancing factor. Keiwan Soltani [23] have introduced a trustaware and energy-efficient data gathering (TEDG) algorithm to collect data in an effective manner. The TEDG is comprised with three stages such as clustering, construction of tree and selection of watch dog.…”

Section: Literature Surveymentioning

confidence: 99%

“…Here, the cluster is created according to the residual energy and distance whereas the potential function is used in creating the cluster which is expressed in the Eq. (23).…”

Section: Cluster Formationmentioning

confidence: 99%

“…The TCRP's performance is measured using energy consumption, throughput, and packet delivery ratio. The performance of the proposed TCRP is evaluated with existing approaches such as SQEER [16], PFCS-ARP-HC [18], TEDG [23] and M-TCRSA [24]. The existing methodologies such as TEDG and M-TCRSA are implemented with the same simulation parameter in table 1 to evaluate the efficiency of TCRP.…”

Section: Performance Analysismentioning

confidence: 99%

“…This section provides a comparative examination of the TCRP with prior studies. The previous study SQEER [16], PFCS-ARP-HC [18], TEDG [23] and M-TCRSA [24] are utilized to assess the TCRP's efficiency. Table 2 shows a comparison of the SQEER [16], PFCS-ARP-HC [18], TEDG [23] and M-TCRSA [24].…”

Section: Comparative Analysismentioning

confidence: 99%

“…The previous study SQEER [16], PFCS-ARP-HC [18], TEDG [23] and M-TCRSA [24] are utilized to assess the TCRP's efficiency. Table 2 shows a comparison of the SQEER [16], PFCS-ARP-HC [18], TEDG [23] and M-TCRSA [24]. According to Table 2, the TCRP outperforms the existing approaches due to its effective balance between the exploration and exploitation phases.…”

Section: Comparative Analysismentioning

confidence: 99%

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TCRP: Trust-aware Clustering and Routing Protocol Based on Atom Search Optimization for WSNs

2023

IJIES

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Wireless sensor network (WSN) is one of the fast-growing technology which can be implemented in largescale applications due to their low-cost installation setup. But, the poor structural organization of the nodes present in WSN leads to various types of security threats and affects the overall performance. The ultimate goal of this paper is to provide a secured transmission of data with minimal consumption of energy. Here, a trust-aware clustering and routing protocol based on atom search optimization (TCRP) is proposed to select secured cluster heads (CHs) for largescale WSNs and ensure the data's freshness. In addition, the secured path for routing is created with node authentication, where the fitness parameter for a node is defined in terms of distance, trust, and energy. The proposed secured clustering and routing protocol based on Atom Search Optimization (ASO) provides resistance against malicious nodes on the selected routing path, with minimum energy consumption, the proposed TCRP identify an optimal path and it ensures the data integrity and confidentiality of the network. The proposed TCRP protocol is analyzed in terms of energy consumption, throughput, and packet delivery ratio. The proposed TCRP achieved better network throughput of 99% for 80 nodes which are comparatively higher than the existing services aware energy-efficient routing protocol (SQEER) with 98% and probabilistic fuzzy chain set, An authentication-based routing protocol, And a hybrid clustering algorithm PFCD-ARP-CH with (98.5%), trust-aware and energy-efficient data gathering (TEDG) with (97%) and multiobjective-trust centric reptile search algorithm with (96.25%).

show abstract

Section: Literature Surveymentioning

confidence: 99%

“…Here, the cluster is created according to the residual energy and distance whereas the potential function is used in creating the cluster which is expressed in the Eq. (23).…”

Section: Cluster Formationmentioning

confidence: 99%