Swarm intelligence based centralized clustering: a novel solution

Mahapatra, Cosmena; Payal, Ashish; Chopra, Madhu

doi:10.1007/s10845-020-01542-9

Cited by 17 publications

(5 citation statements)

References 15 publications

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“…4, this method can be classified as evolution-based [15]. nature-based swarm intelligence [16] and logically based. Darwinian evolution inspires evolutionary methods.…”

Section: Optimization Techniquesmentioning

confidence: 99%

A Survey on Bio-inspired Algorithms of Cybersecurity

Hussein

2024

AJEST

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Cyberspace has evolved into a vital resource and dynamic hub for modern living as a result of a rising reliance on information, technology, and communication. As a result, cyber security concerns are a worldwide phenomenon with disastrous effects. Because of its worldwide connectivity and data explosion, cyberspace is complicated and unpredictable, and as cyber infrastructures get more intricate and dynamic, so does the threat surface. Cyberspace management, or securing and assuring security, calls for the use of sophisticated complex systems. Natural tendencies that facilitate adaptability for survival have been generated by nature through evolution in complex systems, including animal and plant life. By using them in cyber security, predation-avoidance and anti-predation strategies used by non-extinct prey could be exploited or adopted as mechanisms for adaptability. An overview of the existing situation of is provided in this chapter. With the explosion of data generation, finding optimal solutions to data-driven issues is becoming increasingly difficult, if not impossible. It is becoming increasingly clear that the use of intelligent bio-inspired algorithms is required for addressing highly complex problems and providing working solutions in a timely manner, especially when dealing with dynamic problem definitions, fluctuating constraints, incomplete or imperfect information, and limited computation capacity. As a result, more and more intelligent algorithms are being investigated for solving various complex issues.

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“…4, this method can be classified as evolution-based [15]. nature-based swarm intelligence [16] and logically based. Darwinian evolution inspires evolutionary methods.…”

Section: Optimization Techniquesmentioning

confidence: 99%

A Survey on Bio-inspired Algorithms of Cybersecurity

Hussein

2024

AJEST

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“…This technique involves in calculating the shortest paths for the entire pairs of nodes. When the shortest distance is determined and the MDS matrix is constructed [15][16][17].…”

Section: Centralized Techniquementioning

confidence: 99%

Systematic Review on WSN Localization using Metaheuristic and Learning Methods Based on AI Techniques

Mehta,

Mahajan

2023

ijmst

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The localization process in Wireless Sensor Networks (WSNs) is carried out for finding the exact position of the sensor node. WSN are applied in various number of applications like monitoring, humidity, hospitality and so on. Hundreds to thousands of nodes in the sensor sense the data to the base station. Thousands of nodes in the WSN makes the installing of Global processing System (GPS). It is more costly and the GPS not provide the exact location in the indoor environment. In spite of dense network, manual configuration is not possible in the indoor environment. This cause a raise in problem to identify the exact position without GPS or any manual configuration methods. This paper presents the methods to overcome the above issues in WSN localization, by path planning, metaheuristic and deep learning are the techniques having high scopes in the industries, mining etc. Many times sensing of data are restricted by accuracy, energy, power and efficiency in the nodes. This paper discuss the WSN localization process classifications and the methodologies in localization. Localization issues are minimized by various techniques like ToA, TDoA and RSSI. The types of localization, its applications are also discussed.

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“…The robustness of the system lies in the integration of the three phases, which invariably forms the major aspect of the protocol. The authors in [28] presented an SI-based centralized clustering solution for large WSNs and compared to other state-of-the-art algorithms using some evaluation parameters (the scalability, the energy consumption, and the data delivery rate). The result of this comparison showed that the SI-based centralized clustering approach outperformed the other algorithms in terms of the performance metrics.…”

Section: Swarm Intelligence (Si)-based Techniquesmentioning

confidence: 99%

Swarm Intelligence Techniques for Mobile Wireless Charging

2022

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This paper proposes energy-efficient swarm intelligence (SI)-based approaches for efficient mobile wireless charging in a distributed large-scale wireless sensor network (LS-WSN). This approach considers the use of special multiple mobile elements, which traverse the network for the purpose of energy replenishment. Recent techniques have shown the advantages inherent to the use of a single mobile charger (MC) which periodically visits the network to replenish the sensor-nodes. However, the single MC technique is currently limited and is not feasible for LS-WSN scenarios. Other approaches have overlooked the need to comprehensively discuss some critical tradeoffs associated with mobile wireless charging, which include: (1) determining the efficient coordination and charging strategies for the MCs, and (2) determining the optimal amount of energy available for the MCs, given the overall available network energy. These important tradeoffs are investigated in this study. Thus, this paper aims to investigate some of the critical issues affecting efficient mobile wireless charging for large-scale WSN scenarios; consequently, the network can then be operated without limitations. We first formulate the multiple charger recharge optimization problem (MCROP) and show that it is N-P hard. To solve the complex problem of scheduling multiple MCs in LS-WSN scenarios, we propose the node-partition algorithm based on cluster centroids, which adaptively partitions the whole network into several clusters and regions and distributes an MC to each region. Finally, we provide detailed simulation experiments using SI-based routing protocols. The results show the performance of the proposed scheme in terms of different evaluation metrics, where SI-based techniques are presented as a veritable state-of-the-art approach for improved energy-efficient mobile wireless charging to extend the network operational lifetime. The investigation also reveals the efficacy of the partial charging, over the full charging, strategies of the MCs.

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Swarm intelligence based centralized clustering: a novel solution

Cited by 17 publications

References 15 publications

A Survey on Bio-inspired Algorithms of Cybersecurity

A Survey on Bio-inspired Algorithms of Cybersecurity

Systematic Review on WSN Localization using Metaheuristic and Learning Methods Based on AI Techniques

Swarm Intelligence Techniques for Mobile Wireless Charging

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