Cuckoo search algorithm for target coverage and sensor scheduling with adjustable sensing range in wireless sensor network

Arivudainambi, D.; Pavithra, R.; Kalyani, P.

doi:10.1080/09720529.2020.1753301

Cited by 15 publications

(8 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unfortunately, this approach is not suitable for homogeneous networks. Authors in [10] suggested an improved cuckoo search algorithm that partitions sensors into a maximum number of NDSC with a sensing range that can be adjustable. Each cover provides k-target coverage to maximize the lifetime of the WSN.…”

Section: Related Workmentioning

confidence: 99%

Particle Swarm Optimization for Wireless Sensor Network Lifespan Maximization

Kamel,

Al Qahtani,

Al-Shahrani

2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

Despite the deployment of wireless sensor networks in diverse fields (health, environment, military applications, etc.) for tracking or monitoring, several challenges, such as extending the lifetime of the network under energy constraints, still need to be resolved. Lifetime is the operational time of the network during which it can perform dedicated tasks and satisfy the application requirements. The energy constraints dictate that the energy consumption of sensors should be minimized since in most cases the sensors are battery-powered. Various methods have been proposed to work around this problem using scheduling approaches. In this paper, particle swarm optimization-based scheduling was designed and implemented to maximize the lifetime of wireless sensor networks formulated as a Non-Disjoint Sets Cover (NDSC) problem. The experimental findings show that the proposed approach is extremely competitive to the state-of-the-art algorithms, as it is able to find the optimal and best-known solutions in the instances investigated.

show abstract

Section: Related Workmentioning

confidence: 99%

Particle Swarm Optimization for Wireless Sensor Network Lifespan Maximization

Kamel,

Al Qahtani,

Al-Shahrani

2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

show abstract

“…Improved CSO algorithm proposed in [73] determines the sensor covers by optimizing the sensing range of the sensors to reduce the redundancy in the network. The proposed algorithm partitions the sensor set into non-disjoint subsets with varying sensing range such that each cover provides kcoverage in the network.…”

Section: Coverage Solutions Based Ai Techniques In Wsnsmentioning

confidence: 99%

Coverage, Deployment and Localization Challenges in Wireless Sensor Networks Based on Artificial Intelligence Techniques: A Review

et al. 2022

View full text Add to dashboard Cite

The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in various fields such as manufacturing, smart city, transport, health and the Internet of Things, by providing pervasive real-time applications. In this paper, we analyze the existing research trends of Coverage, Deployment and Localization challenges in WSN with respect to Artificial Intelligence (AI) methods for WSN enhancement. We 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. This would guide the reader towards an understanding on upto-date applications of AI methods with respect to different WSN challenges. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for research community in identifying the mostly adapted methods and the benefits of using various AI methods for solving the Coverage, Deployment and Localization challenges related to WSNs. Finally, we conclude the paper stating the open research issues and new directions for future research.

show abstract

“…According to [101], the CS algorithm is based on three idealized rules: 1) Each cuckoo lays one egg at a time and deposits it in a nest that is picked at random; 2) The best nests with the highest quality eggs will be passed down to future generations; 3) The number of possible host nests is predetermined, and the host bird discovers the cuckoo's egg with a probability p a ∈ [0, 1]. The CS algorithm has been used in several works [84], [87], [88]. In [84], the authors proposed a two-stage mobile sensor deployment approach based on the CS algorithm.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

“…According to the experimental results, the improved CS algorithm provides a good solution in a short time compared to other metaheuristics. In [88], authors developed an improved CS algorithm to tackle the k-target coverage problem of randomly deployed sensor nodes with adjustable sensing radius. The problem was formulated as a non-linear integer programming problem to optimize both coverage and network lifetime.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling

Zaimen¹,

Brahmia²,

Moalic

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Recent advances in hardware and communication technologies have accelerated the deployment of billions of wireless sensors. This transformation has created a wide range of applications adapted to the evolving trends of our daily life requirements. Wireless sensor networks (WSNs) could be deployed in several target areas including buildings, forests, oceans, and smart cities. Nevertheless, finding the optimal location for each sensor node is a challenging task, typically when the environment involves heterogeneous obstacles. Many approaches and methods have been proposed to deal with the problem of WSN deployment, each addressing one or more objectives and constraints, such as network coverage, lifetime, connectivity, and energy consumption. The purpose of this survey paper is to provide the needed background to understand and study the WSNs deployment problem with a focus on its two key aspects: the optimization model and the solving methods based on artificial intelligence (AI). Additionally, it covers recent works on WSNs deployment and identifies their advantages and limitations. Furthermore, simulation experiments were carried out to compare the performance of widely used algorithms in the context of WSNs deployment problem, primarily genetic algorithm, particle swarm optimization, flower pollination, and ant colony optimization. Finally, this paper discusses and highlights several open challenges and research issues that must be explored in the future.

show abstract

Cuckoo search algorithm for target coverage and sensor scheduling with adjustable sensing range in wireless sensor network

Cited by 15 publications

References 15 publications

Particle Swarm Optimization for Wireless Sensor Network Lifespan Maximization

Particle Swarm Optimization for Wireless Sensor Network Lifespan Maximization

Coverage, Deployment and Localization Challenges in Wireless Sensor Networks Based on Artificial Intelligence Techniques: A Review

A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling

Contact Info

Product

Resources

About