A novel data aggregation using multi objective based male lion optimization algorithm (DA-MOMLOA) in wireless sensor network

Saranraj, G.; Selvamani, K.; Malathi, P.

doi:10.1007/s12652-021-03230-9

Cited by 17 publications

(20 citation statements)

References 20 publications

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“…Regrettably, most of them are dedicated to only one sink [20]. In this IoT era, many of these single-sink oriented solutions are still proposed despite their low applicability [21] - [23].…”

Section: Related Workmentioning

confidence: 99%

Untitled

2023

IJWMN

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Convergecast is one of the most challenging tasks in Wireless Sensor Networks (WSNs). Indeed, this data collection process must be conducted while copying with packet collisions, nodes' congestion or data redundancy. These issues always result in energy waste which is detrimental to network efficiency and lifetime. This paper is aimed to address these problems in large-scale multi-sink WSNs. Inspired by our previous work MSCP, we designed a lightweight protocol stack that seamlessly combines clustering, pathvector routing, sinks' duty cycling, data aggregation and transmission scheduling in order to minimise message overhead and packet losses. Simulation results show that this solution can mitigate delay while significantly increasing packet delivery and network lifetime.

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Section: Related Workmentioning

confidence: 99%

Untitled

2023

IJWMN

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“…Besides, in F-LEACH, the FIS membership functions are optimized, and the average of several executions are selected as the optimal parameters by modifying the network scenario. Cluster-based data aggregation method using multi-objective male lion optimization algorithm (DA-MOMLOA) for evaluating the network based on energy, delay, density and distance is presented in [93]. In DA-MOMLOA, the data aggregation method is employed with the help of cluster head wherein the data aggregated from similar clusters are forwarded to the sink node.…”

Section: Ai Based Solutions To Data Collection Aggregation and Dissem...mentioning

confidence: 99%

Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review

et al. 2022

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The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in numerous sectors such as manufacturing, smart cities, transportation and Internet of Things by providing pervasive real-time applications. In this survey, we analyze the existing research trends with respect to Artificial Intelligence (AI) methods in WSN and the possible use of these methods for WSN enhancement. The main goal of data collection, aggregation and dissemination algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. In this paper, we highlight data collection, aggregation and dissemination challenges in WSN and present a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, during the span of 2010 to 2021. We compare and contrast different algorithms on the basis of optimization criteria, simulation/real deployment, centralized/distributed kind, mobility and performance parameters. We conclude with possible future research directions. This would guide the reader towards an understanding of up-to-date applications of AI methods with respect to data collection, aggregation and dissemination challenges in WSN. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for the research community in identifying the mostly adapted methods and the benefits of using various AI methods for solving the challenges related to WSNs. Finally, we conclude the paper stating the open research issues and new possibilities for future studies.

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“…Recently, deep learning-base optimization approaches have opened up new ways for addressing data aggregation and routing problems. 1,24,25,[27][28][29][30][31] This motivated to do further research in this sector.…”

Section: Introductionmentioning

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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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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A novel data aggregation using multi objective based male lion optimization algorithm (DA-MOMLOA) in wireless sensor network

Cited by 17 publications

References 20 publications

Untitled

Untitled

Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review

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

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