Buffering_Slotted_ALOHA protocol for underwater acoustic sensor networks based on the slot status

Khater, Ehab; El-Fishawy, Nawal; Tolba, Maha; Ibrahim, Dina M.; Badawy, Mohammed

doi:10.1007/s11276-021-02639-6

Cited by 7 publications

(12 citation statements)

References 32 publications

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“…Furthermore, the fitness function has been created by considering the network’s energy usage, long delay, and bandwidth utilization. Khater et al 24 developed an efficient GA to maximize the data transmission route discovery by utilizing a straightforward encoding procedure, a unique crossover procedure, and an optimized mutation procedure. They also defined objective functions depending on the energy expenses and flow delay, which can reduce power consumption and expand the network’s average life span.…”

Section: Related Workmentioning

confidence: 99%

A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks

Lilhore¹,

Khalaf

Simaiya

et al. 2022

International Journal of Distributed Sensor Networks

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Underwater wireless sensor network attracted massive attention from researchers. In underwater wireless sensor network, many sensor nodes are distributed at different depths in the sea. Due to its complex nature, updating their location or adding new devices is pretty challenging. Due to the constraints on energy storage of underwater wireless sensor network end devices and the complexity of repairing or recharging the device underwater, this is highly significant to strengthen the energy performance of underwater wireless sensor network. An imbalance in power consumption can cause poor performance and a limited network lifetime. To overcome these issues, we propose a depth controlled with energy-balanced routing protocol, which will be able to adjust the depth of lower energy nodes and be able to swap the lower energy nodes with higher energy nodes to ensure consistent energy utilization. The proposed energy-efficient routing protocol is based on an enhanced genetic algorithm and data fusion technique. In the proposed energy-efficient routing protocol, an existing genetic algorithm is enhanced by adding an encoding strategy, a crossover procedure, and an improved mutation operation that helps determine the nodes. The proposed model also utilized an enhanced back propagation neural network for data fusion operation, which is based on multi-hop system and also operates a highly optimized momentum technique, which helps to choose only optimum energy nodes and avoid duplicate selections that help to improve the overall energy and further reduce the quantity of data transmission. In the proposed energy-efficient routing protocol, an enhanced cluster head node is used to select a strategy that can analyze the remaining energy and directions of each participating node. In the simulation, the proposed model achieves 86.7% packet delivery ratio, 12.6% energy consumption, and 10.5% packet drop ratio over existing depth-based routing and energy-efficient depth-based routing methods for underwater wireless sensor network.

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Section: Related Workmentioning

confidence: 99%

A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks

Lilhore¹,

Khalaf

Simaiya

et al. 2022

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…In the other direction, the waves of acoustic are less attenuated. Therefore, these waves can travel longer distances than optical signals and waves of radio [7,13,14]. So, waves of radio are utilized for communication in the TWSN with a constant propagation speed of about 3 * 10 8 m/s.…”

Section: Major Challenges Of the Uwsns Communicationmentioning

confidence: 99%

“…To provide higher throughput in the power efficient manner without additional cost, it is essential to design the MAC protocol for UWSN, because the MAC layer protocol coordinates the access of the nodes to shared wireless media. The MAC protocol lets nodes in the network to share public broadcast channels [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Review of ALOHA-based MAC Protocols for Underwater Wireless Sensor Networks. (Dept.E. Computer)

Khater

El-Fishawy

Tolba

et al. 2022

MEJ. Mansoura Engineering Journal

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the importance of these applications recently, there has been considerable interest in research analysis and improvement of UWSNs. The main reason for studying these networks is that they can enhance the ocean reconnoitering and meet the needs of multiple underwater applications [1][2][3][4]. UWSNs have protruded to be a master type of system for ocean exploration and vision where underwater sensor networks are used in the detection of underwater oil fields as well as identification of

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“…To this end, the terrestrial Medium Access Control (MAC) protocols that ignore the effects of propagation delays have poor performance in UWANs. It is important to design MAC protocols that are suitable for UWANs [7,8].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning Based Time-Domain Interference Alignment Scheduling for Underwater Acoustic Networks

Zhao

Yao

Gao

et al. 2022

JMSE

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Message conflicts caused by large propagation delays severely affect the performance of Underwater Acoustic Networks (UWANs). It is necessary to design an efficient transmission scheduling algorithm to improve the network performance. Therefore, we propose a Deep Reinforcement Learning (DRL) based Time-Domain Interference Alignment (TDIA) scheduling algorithm (called DRLSA-IA). The main objective of DRLSA-IA is to increase network throughput and reduce collisions. In DRLSA-IA, underwater nodes are regarded as agents of DRL. Nodes intelligently learn the scheduling by continuously interacting with the environment. Therefore, DRLSA-IA is suitable for the highly dynamic underwater environment. Moreover, we design a TDIA-based reward mechanism to improve the network throughput. With the TDIA-based reward mechanism, DRLSA-IA can achieve parallel transmissions and effectively reduce conflicts. Unlike other TDIA-based algorithms that require enumeration of the state space, nodes merely feed the current state to obtain the transmission decision. DRLSA-IA solves the problem of computational expense. Simulation results show that DRLSA-IA can greatly improve the network performance, especially in terms of throughput, packet delivery ratio and fairness under different network settings. Overall, DRLSA-IA can effectively improve network performance and is suitable for ever-changing underwater environments.

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Buffering_Slotted_ALOHA protocol for underwater acoustic sensor networks based on the slot status

Cited by 7 publications

References 32 publications

A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks

A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks

Review of ALOHA-based MAC Protocols for Underwater Wireless Sensor Networks. (Dept.E. Computer)

Deep Reinforcement Learning Based Time-Domain Interference Alignment Scheduling for Underwater Acoustic Networks

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