Design of An Energy Efficient Data Aggregation Method for Secured Routing Protocol in WSN

Pralhad, Kulkarni-Shinde Bharat; Singla, C Ram; Patil, Sanjay B.

doi:10.1109/asiancon55314.2022.9909403

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…The proposed technique is activated in NS2 using 2 GB RAM, Intel core processor, Windows10 OS. The efficacy of the proposed technique is analyzed with existing models, like Taylor‐spotted hyena optimization approach for dependable and energy‐efficient CH selection‐based secure data routing and fault tolerance in WSN (T‐SHO‐DAWSN), 36 multi‐objective CH utilizing SAPGAN for protected data aggregation (SAPGAN‐DAWSN), 37 and energy efficient data accumulation design for protected routing protocol in WSN (Table 1) (MDAS‐DAWSN) 38 …”

Section: Resultsmentioning

confidence: 99%

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Multi‐objective cluster head‐based energy aware routing using optimized auto‐metric graph neural network for secured data aggregation in Wireless Sensor Network

Janarthanan,

Srinivasan

2023

Int J Communication

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SummaryThe data are combined and transmitted through wireless sensor network (WSN) in internet of things applications. Multi‐Objective Cluster Head Based Energy Aware Routing using Auto‐Metric Graph Neural Network with Hybrid Balancing Composite Motion and Border Collie Optimization is proposed in this article for secured data aggregation (SDA) in WSN. The proposed method activates routing procedure via cluster head (CH). Hence, data aggregation basis Auto‐Metric Graph Neural Network selects CH depending upon multi‐objective fitness function. This fitness function deems factors, such as energy, delay, throughput, distance amid the nodes, capacity, collision, traffic rate, and cluster density. After CH selection, malicious node is present in the cluster. The optimum path is depending on three parameters: trust, connectivity, and quality of service (QoS). These three parameters have optimized under Hybrid Balancing Composite Motion and Border Collie Optimization method for ideal path selection. The ideal path is employed to transmit the gathered data to base station. The proposed technique is executed in network simulator, and its efficiency is evaluated under performance metrics, like network lifetime, packet delivery ratio, and delay. The proposed technique achieves 19.67%, 22.38%, and 27.45% better network lifetime; 28.92%, 25.83%, and 19.46% better packet delivery ratio; and 12.32%, 34.54%, and 22.33% lower delay while compared to the existing models: Taylor‐spotted hyena optimization for dependable and energy‐efficient CH selection‐based safe data routing and fault tolerance in WSN (T‐SHO‐DAWSN), multi‐objective cluster head utilizing SAPGAN for secured data aggregation (SAPGAN‐DAWSN), and energy efficient data accumulation design for protected routing protocol in WSN (MDAS‐DAWSN) methods, respectively.

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Section: Resultsmentioning

confidence: 99%

“…Tables 3–11 show the simulation result of network lifetime, delay, and packet delivery ratio (PDR) analyzed for BCMO‐BCO‐DAWSN method compared with existing T‐SHO‐DAWSN, 36 SA‐PGAN‐DAWSN, 37 and MDAS‐DAWSN 38 models.…”

Section: Resultsmentioning

confidence: 99%

Multi‐objective cluster head‐based energy aware routing using optimized auto‐metric graph neural network for secured data aggregation in Wireless Sensor Network

Janarthanan,

Srinivasan

2023

Int J Communication

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“…The probability of generating parameters for a trial vector from the mutant vector is represented by CR, a crossover control parameter or factor in the range [0, 1]. The index jrand is selected at random from the set [1,NP]. Then, as according to Eq.5, a greedy selection operation chooses vectors for the next generation either from the target vector (xi) or from corresponding trial vectors.…”

Section: Proposed Solutionmentioning

confidence: 99%

“…The small size of sensors, low cost, small power consumption and easy communications with their neighbors provide the functional benefits while deployment over hostile regions makes the situation nearly impossible to provide the power recharging. [1] Energy consumption in sensor nodes, long network life, fast data transmission, and stability are all important requirements for WSN implementations in order to conserve energy in sensor nodes and thereby increase network life. WSN is experiencing trouble with coverage range, network lifetime, convergence, and data aggregation due to different constraints in the sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Routing and Cluster Head Selection in Wsn Using Different Variant of Differential Evolution

SHANTHI¹,

PRASANNA²

2023

Preprint

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The primary goal of wireless sensor networks from a design point of view is to enhance the network's lifetime. Among the different options for reducing operational energy consumption, energy invested in routing and cluster head selection is considered to be very effective mechanisms. Both tasks have been considered as very challenging and difficult to obtain the efficient solution. Since it is difficult for traditional approaches to satisfy the requirements and difficulties, a heuristic solution focused on natural computation methods has provided a lot of naivety. The proposed work efforts to address these challenges using computational intelligence especially differential evolution and genetic algorithm. An energy efficient route discovery for dynamic network is designed with variations in DE, quick and adaptable routes were discovered for networks undergoing changes. A knowledge based DE has been designed depending on prior knowldge to redefine new routes for changing network. A hybrid mutation strategy under standard DE is designed for cluster head selection providing faster convergence characteristics. The proposed solutions were implemented under MATlab environment and the results have shown that the proposed solutions are performing better for different network configurations. Dynamic route discovery using KDE has achieved energy saving of 9.83 to 49.2 percentage as compared to RDE and 6.7 to 29.5 percentage as compared to PDE. Also energy saving attained in cluster head selection using proposed HMDE is 10 to 33 percentage better compared to dyPSO and 5 to 10 percentage better compared to SDE.

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Optimal and Suboptimal Routing Protocols for WSN

Bensaid

Mnaouer

Boujemâa

2023

Lecture Notes in Networks and Systems

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Design of An Energy Efficient Data Aggregation Method for Secured Routing Protocol in WSN

Cited by 5 publications

References 15 publications

Multi‐objective cluster head‐based energy aware routing using optimized auto‐metric graph neural network for secured data aggregation in Wireless Sensor Network

Multi‐objective cluster head‐based energy aware routing using optimized auto‐metric graph neural network for secured data aggregation in Wireless Sensor Network

Energy Efficient Routing and Cluster Head Selection in Wsn Using Different Variant of Differential Evolution

Optimal and Suboptimal Routing Protocols for WSN

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