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

Osamy, Walid; Khedr, Ahmed M.; Salim, Ahmed; Ali, Amal Ibrahim Al; El‐Sawy, A. A.

doi:10.1109/access.2022.3156729

Cited by 60 publications

(26 citation statements)

References 111 publications

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“…In this section, we follow the same research methodology presented by Osamy et al (2022a , 2022b) . As indicated in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…The research in the area of WSN has become more active over the years, and a wide range of works has been executed to enhance WSN. To address the challenges of WSN, different surveys have been conducted based on different factors ( e.g ., Osamy et al (2022a) , Majid et al (2022) , Sridhar et al (2022) and Osamy et al (2022b) ). A survey for solving various WSN challenges using computational intelligence (CI) techniques is proposed by Kulkarni, Forster & Venayagamoorthy (2010) .…”

Section: Related Workmentioning

confidence: 99%

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A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks

Osamy

Khedr

Salim

et al. 2022

PeerJ Computer Science

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Wireless sensor networks (WSNs) are becoming increasingly important, providing pervasive real-time applications that have been used to enhance smart environments in various fields such as smart cities, manufacturing, and the Internet of Things (IoT). This survey reviews and analyzes the research trends related to the utilized Artificial Intelligence (AI) methods for WSN and the potential enhancement of WSNs using these methods. We highlight the routing challenge in WSN and present a comprehensive discussion on the recent studies that utilized various AI methods in addressing the routing challenge to meet specific objectives of WSN, during the span of 2010 to 2020. This would guide the reader towards an understanding of up-to-date applications of AI methods with respect to routing challenge in WSN. In addition, a general evaluation is provided along with a comparison of utilized AI methods in WSNs, which guides the reader in identifying the most appropriate AI methods that can be utilized for solving the routing challenge. Finally, we conclude the paper by stating the open research issues and new directions for future research.

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“…In this section, we follow the same research methodology presented by Osamy et al (2022a , 2022b) . As indicated in Fig.…”

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks

Osamy

Khedr

Salim

et al. 2022

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…This article addressed the optimization of some parameters like successful packet delivery, received signal strength, packet arrival time, and delay. In 2022, Osamy et al 52 reviewed some work related to artificial intelligence (AI) techniques. The proposed work presented the AI techniques for coverage, deployment, and localization for WSN.…”

Section: Literature Review Of the Current Status Of The Survey In Wsnmentioning

confidence: 99%

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

Jain

Kumar

Singh

2022

Trans Emerging Tel Tech

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Wireless sensor networks (WSNs) have now become a substantial part of our life as it provides an incredible setup for monitoring environmental conditions such as glaciers melting, earthquakes, climate change, volcanic eruption, agriculture, and other natural calamities. One of the most important challenges in data gathering and examining accurate data is "energy consumption." However, continuously changing environmental conditions, mobility in devices and extreme resource usage, such as battery capacity in WSNs collectively contribute to a high possibility of redundant data transmission thus making it tremendously hard to achieve a satisfactory network lifetime. Additionally, these environmental monitoring applications periodically senses and aggregate data which usually exhibits high temporal and spatial redundancies. As a result, exploiting these redundancies is essential for both resource utilization, as well as accurate data transmission. Since a huge amount of energy is exhausted in transmitting the redundant data, which has become a bottleneck in scaling such applications. In this context, this article provides a thorough examination of existing energy-efficient redundant data transmission reduction techniques, as well as their strengths and drawbacks. The idea of redundant data transmission reduction concept is divided into three stages: the sensor nodes (SN) at the first stage, the cluster heads (CHs) at the second stage, and the base station (BS) at the third stage. Furthermore, this survey focuses on the current issues and challenges, as well as the future directions in reducing redundant data transmission for future investigation.

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“…It uses previously observed samples for the current day and the average values of the past days to predict the harvested energy. The predicted energy ( ) for time slot of the current day is expressed by (3).…”

Section: Letmentioning

confidence: 99%

“…The limited battery power capacity is one of the major constraints in the application of traditional wireless sensor networks [1], [2], [3]. Energy harvesting (EH) technology has become a promising solution to this problem [4], [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

An Opportunistic Routing for Energy-Harvesting Wireless Sensor Networks With Dynamic Transmission Power and Duty Cycle

Ren

Yao

2022

IEEE Access

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Opportunistic routing (OR) is widely used in energy-harvesting wireless sensor networks (EH-WSNs). The transmission power of sensor nodes in EH-WSNs is usually adjusted dynamically to make full use of the harvested energy. Many OR algorithms with dynamic transmission power adjustment have been proposed for EH-WSNs. However, fewer studies consider the non-bidirectional communication and the increase in packet retransmission and delay caused by the heterogeneous transmission power/radii. To this end, we propose an opportunistic routing algorithm for EH-WSNs with dynamic transmission power and duty cycle (ORDPD). It adjusts the transmission power of the sensor nodes at the end of each time slot, according to the predicted available energy. It also adjusts the transmission power and duty cycle of the sensor nodes in time slots if the nodes receive packets from other nodes outside their current transmission range. ORDPD also adopts an improved transmission model and information exchange mechanism to dynamically update relay sets and forwarding paths. A series of experiments were conducted to verify the effectiveness of the proposed algorithm. The experimental results demonstrate that the proposed ORDPD reduces non-bidirectional communication and retransmission significantly and performs better than its corresponding competitors.

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

Cited by 60 publications

References 111 publications

A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks

A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

An Opportunistic Routing for Energy-Harvesting Wireless Sensor Networks With Dynamic Transmission Power and Duty Cycle

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