Efficient Deployment of Wireless Sensor Nodes with Evolutionary Approaches

Akar, Sibel Birtane; Korkmaz, Hayriye; Şahingöz, Özgür Koray

doi:10.1109/hora55278.2022.9800045

Cited by 1 publication

(5 citation statements)

References 18 publications

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“…Both methods have unique advantages and disadvantages. [1], [13]- [15]. Deterministic node deployment strategies are often used to achieve specific goals such as balancing the energy use of the network, optimizing coverage, minimizing communication costs or optimizing against certain criteria.…”

Section: A Coverage In Wsnsmentioning

confidence: 99%

“…GAs try to find the best solution in a search space using mechanisms such as natural selection, crossover, and mutation. GAs are commonly used to solve complex optimization problems such as node placement, mission scheduling, route optimization, and resource allocation [1], [16], [18], [20], [21] The flowchart of GAs consists of the following steps:…”

Section: The Genetic Algorithmmentioning

confidence: 99%

“…Birtane et al utilized a GA to maximize coverage area in a uniform space with homogeneous sensors. The proposed method to calculate the coverage area in the GA's fitness function showed high accuracy and reduced calculation time, providing a novel contribution to the literature [1]. Egwuche et al conducted a comprehensive review, emphasizing WSN coverage optimization using machine learning and nature-inspired algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Consequently, this research is focused on gaining a better un- with some excluding parts derstanding of how WSNs function in such irregular environments and developing models that are specifically tailored to these conditions. The research introduces a novel approach to determining the coverage area, known as the Point Counting Method (PCM), which is thoroughly explained in the paper [1]. PCM is used as a fitness function, which evaluates the effectiveness of a potential solution to assess each chromosome in the population.…”

Section: A Measuring Wsn Coveragementioning

confidence: 99%

“…As such, there is a growing need for research and development in this field to further improve the capabilities and performance of WSNs. [1]- [7].…”

Section: Introductionmentioning

confidence: 99%

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Vibrational Genetic Algorithm-Based Deployment of Wireless Sensor Networks With Heterogeneous Nodes in Irregularly Shaped Areas

Birtane,

Sahingoz,

Korkmaz

2024

IEEE Access

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Over the past few years, there has been a significant emphasis on improving the capabilities of Wireless Sensor Networks (WSNs) by making advancements in communication protocols, energy efficiency, optimal deployment, data analytics, and integration with emerging technologies such as the Internet of Things and artificial intelligence. The deployment of WSN nodes can greatly enhance the effectiveness, scalability, and capability of different systems, resulting in cost reductions, enhanced performance, and improved safety in which the deployment of WSN involves determining the best positioning of sensor nodes to attain maximum coverage and connectivity while minimizing the number of nodes needed. WSNs often face challenges in deploying nodes effectively and Genetic Algorithms (GAs) offer a valuable approach for tackling this problem due to their ability to efficiently search large and complex solution spaces, such as those of complex network design, taking into account various constraints and objectives, which are common characteristics of real-world WSN deployment scenarios. The objective of this study is to use a new method, called the vibrational genetic algorithm, which can be used to optimize the placement of sensor nodes more efficiently. Apart from the other research, it is preferred to use heterogeneous sensor nodes to increase the coverage rate in an irregularly shaped area. The results of the experiments demonstrate that the proposed model offers an effective solution for achieving maximum coverage in application theaters that are more realistic and complex.

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Section: A Coverage In Wsnsmentioning

confidence: 99%

Section: The Genetic Algorithmmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: A Measuring Wsn Coveragementioning

confidence: 99%

“…As such, there is a growing need for research and development in this field to further improve the capabilities and performance of WSNs. [1]- [7].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations