A New Technique for Underwater Acoustic Wireless Sensor Network

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

doi:10.1109/icces51560.2020.9334682

Cited by 8 publications

(9 citation statements)

References 11 publications

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“…The Slotted-Buffering-ALOHA protocol has been widely utilized for saving energy leading to extended network lifetime [87]. The efficiency of energy has an attendant trading-off with delay.…”

Section: K Slotted-buffering-alohamentioning

confidence: 99%

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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Section: K Slotted-buffering-alohamentioning

confidence: 99%

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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“…Modified-Slotted-Aloha [37] Introduces a new backoff technique and a buffer mechanism to minimize energy consumption.…”

Section: Uw-seedex [24]mentioning

confidence: 99%

“…Modified-Slotted-Aloha [37] DAP-MAC [52] TARS [29] RC-SFAMA [44] MACA-PC [35] OPMAC [47] ARQ-MAC [48] ACP-CA [53] PC-MAC [54] CPOR [55] Hybrid MAC Protocols…”

Section: Contention-based Mac Protocolsmentioning

confidence: 99%

“…In this paper, we aim to enhance the performance of underwater communication by designing an efficient MAC protocol that can meet the Quality of Service (QoS) requirements of time-sensitive applications. To achieve this, we must analyze different MAC protocol approaches, which are classified into the categories of contention-free, contention-based, and hybrid [ 35 , 36 , 37 , 38 ]. Figure 1 illustrates the classification of the MAC protocol approaches.…”

Section: Introductionmentioning

confidence: 99%

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DAMAC: A Delay-Aware MAC Protocol for Ad Hoc Underwater Acoustic Sensor Networks

Guqhaiman

Akanbi

Aljaedi

et al. 2021

Sensors

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In a channel shared by several nodes, the scheduling algorithm is a key factor to avoiding collisions in the random access-based approach. Commonly, scheduling algorithms can be used to enhance network performance to meet certain requirements. Therefore, in this paper we propose a Delay-Aware Media Access Control (DAMAC) protocol for monitoring time-sensitive applications over multi-hop in Underwater Acoustic Sensor Networks (UASNs), which relies on the random access-based approach where each node uses Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) to determine channel status, switches nodes on and off to conserve energy, and allows concurrent transmissions to improve the underwater communication in the UASNs. In addition, DAMAC does not require any handshaking packets prior to data transmission, which helps to improve network performance in several metrics. The proposed protocol considers the long propagation delay to allow concurrent transmissions, meaning nodes are scheduled to transmit their data packets concurrently to exploit the long propagation delay between underwater nodes. The simulation results show that DAMAC protocol outperforms Aloha, BroadcastMAC, RMAC, Tu-MAC, and OPMAC protocols under varying network loads in terms of energy efficiency, communication overhead, and fairness of the network by up to 65%, 45%, and 726%, respectively.

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“…Contrarily, the UASN is generally accompanied by inherent narrow bandwidth [10] and non-negligible long propagation delay caused by the slow acoustic speed of 1500 m/s, resulting in low network throughput and channel utilization. Therefore, it is critical and urgent for UASNs to improve the network throughput and channel utilization [11].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning Based Optical and Acoustic Dual Channel Multiple Access in Heterogeneous Underwater Sensor Networks

Liu

Chen

2022

Sensors

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In this paper, we investigate how to efficiently utilize channel bandwidth in heterogeneous hybrid optical and acoustic underwater sensor networks, where sensor nodes adopt different Media Access Control (MAC) protocols to transmit data packets to a common relay node on optical or acoustic channels. We propose a new MAC protocol based on deep reinforcement learning (DRL), referred to as optical and acoustic dual-channel deep-reinforcement learning multiple access (OA-DLMA), in which the sensor nodes utilizing the OA-DLMA protocol are called agents, and the remainder are non-agents. The agents can learn the transmission patterns of coexisting non-agents and find an optimal channel access strategy without any prior information. Moreover, in order to further enhance network performance, we develop a differentiated reward policy that rewards specific actions over optical and acoustic channels differently, with priority compensation being given to the optical channel to achieve greater data transmission. Furthermore, we have derived the optimal short-term sum throughput and channel utilization analytically and conducted extensive simulations to evaluate the OA-DLMA protocol. Simulation results show that our protocol performs with near-optimal performance and significantly outperforms other existing protocols in terms of short-term sum throughput and channel utilization.

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A New Technique for Underwater Acoustic Wireless Sensor Network

Cited by 8 publications

References 11 publications

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

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

DAMAC: A Delay-Aware MAC Protocol for Ad Hoc Underwater Acoustic Sensor Networks

Deep Reinforcement Learning Based Optical and Acoustic Dual Channel Multiple Access in Heterogeneous Underwater Sensor Networks

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