Energy-Effective Cooperative and Reliable Delivery Routing Protocols for Underwater Wireless Sensor Networks

Ullah, Ubaid; Khan, Abdul Hakim; Zareei, Mahdi; Ali, Ihsan; Khattak, Hasan Ali; Din, Ikram Ud

doi:10.3390/en12132630

Cited by 38 publications

(29 citation statements)

References 34 publications

(37 reference statements)

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“…In Reference [36], Ullah et al propose two routing mechanisms of effective energy and reliable delivery (EERD) and cooperative effective energy and reliable delivery (CoEERD) for UWSNs to improve the energy efficiency and reliability of packet transmissions. EERD is a single path routing mechanism, in which a certain weight function based on residual energy, bit error rate and shortest distance is designed to prolong the network lifetime and reduce the energy consumption and delay.…”

Section: Related Workmentioning

confidence: 99%

“…The above-mentioned OR algorithms can adapt to the UWSNs well but they do not have void-handling techniques to deal with the routing void problem. In Reference [38], the authors propose a void-aware pressure routing (VAPR) algorithm to enhance the performance of In Reference [36], Ullah et al propose two routing mechanisms of effective energy and reliable delivery (EERD) and cooperative effective energy and reliable delivery (CoEERD) for UWSNs to improve the energy efficiency and reliability of packet transmissions. EERD is a single path routing mechanism, in which a certain weight function based on residual energy, bit error rate and shortest distance is designed to prolong the network lifetime and reduce the energy consumption and delay.…”

Section: Related Workmentioning

confidence: 99%

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Energy-Efficient Depth-Based Opportunistic Routing with Q-Learning for Underwater Wireless Sensor Networks

Yongjie

Chen

et al. 2020

Sensors

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Underwater Wireless Sensor Networks (UWSNs) have aroused increasing interest of many researchers in industry, military, commerce and academe recently. Due to the harsh underwater environment, energy efficiency is a significant theme should be considered for routing in UWSNs. Underwater positioning is also a particularly tricky task since the high attenuation of radio-frequency signals in UWSNs. In this paper, we propose an energy-efficient depth-based opportunistic routing algorithm with Q-learning (EDORQ) for UWSNs to guarantee the energy-saving and reliable data transmission. It combines the respective advantages of Q-learning technique and opportunistic routing (OR) algorithm without the full-dimensional location information to improve the network performance in terms of energy consumption, average network overhead and packet delivery ratio. In EDORQ, the void detection factor, residual energy and depth information of candidate nodes are jointly considered when defining the Q-value function, which contributes to proactively detecting void nodes in advance, meanwhile, reducing energy consumption. In addition, a simple and scalable void node recovery mode is proposed for the selection of candidate set so as to rescue packets that are stuck in void nodes unfortunately. Furthermore, we design a novel method to set the holding time for the schedule of packet forwarding base on Q-value so as to alleviate the packet collision and redundant transmission. We conduct extensive simulations to evaluate the performance of our proposed algorithm and compare it with other three routing algorithms on Aqua-sim platform (NS2). The results show that the proposed algorithm significantly improve the performance in terms of energy efficiency, packet delivery ratio and average network overhead without sacrificing too much average packet delay.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Depth-Based Opportunistic Routing with Q-Learning for Underwater Wireless Sensor Networks

Yongjie

Chen

et al. 2020

Sensors

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“…The result of the study demonstrates that the approach is effective in minimizing data collection cost. In a recent study by Ullah et al, two routing protocols for underwater WSNs named effective energy and reliable delivery (EERD) and cooperative effective energy and reliable delivery (CoEERD) where proposed [59]. EERD is aimed at minimizing energy consumption while CoEERD enhances network reliability.…”

Section: A Data Collection In Wireless Sensor Network (Wsns)mentioning

confidence: 99%

Data Collection in Sensor Cloud: Recent Advances, Taxonomy, Use Cases and Open Challenges

Ali¹

2020

Preprint

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<div>In recent years, Wireless Sensor Networks (WSNs) technologies are used in many important</div><div>areas, including weather forecasting, security, environmental monitoring, health care, and industry.</div><div>Nonetheless, due to the constraints of WSNs in terms of energy, processing, connectivity, computation and</div><div>data integrity. An important issue in this research field is the efficient management of the large number</div><div>of WSN’s data. Therefore, there is a need for strong and scalable high-performance computing and large</div><div>storage infrastructure for real-time processing and storage of WSN’s data. This article attempts to discuss</div><div>data collection methods in WSNs, sensor cloud, and IoT. Also, in this context, a general overview of</div><div>WSNs and sensor cloud platforms, including their definitions, architectures, and applications are given.</div><div>We investigate, highlight and report recent advances in data collection technology by categorizing and</div><div>classifying data collection methods in WSNs, sensor cloud, IoT and developing a taxonomy based on</div><div>these classifications. Furthermore, the analysis and synthesis of existing works in this research domain</div><div>is highlighted. The identification of key research challenges for future work and use case in this research</div><div>domain were also highlighted and discussed.</div>

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“…The destination finds the bit error probability P e or bit error rate (BER) in the combined signal. The fading environment of underwater communication can be modeled by Rayleigh fading [18], for which the BER with binary phase shift keying (BPSK) modulation can be computed as [19]:…”

Section: Operation Of Co-esrvrmentioning

confidence: 99%

Energy-Aware Scalable Reliable and Void-Hole Mitigation Routing for Sparsely Deployed Underwater Acoustic Networks

et al. 2019

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In wireless underwater sensor networks (WUSNs), network protocols for information routing are usually designed when a significant number of nodes are present in the network. Therefore, for sparse conditions, when a noticeable reduction in the number of nodes occurs, the performance of such protocols exhibits a degraded behavior pattern. To cope with routing issues when sparse conditions prevail, two routing algorithms for WUSNs are proposed in this paper. They are energy-aware scalable reliable and void-hole mitigation routing (ESRVR) and cooperative energy-aware scalable reliable and void-hole mitigation routing (Co-ESRVR). The ESRVR uses a number of strategies. Firstly, it uses two hop neighbors’ information to develop routing trajectories for information advancement, as one hop information cannot avoid a void-hole, a condition when a node is not able to find neighbors towards the sea surface, and more than two hop information is difficult to obtain when sparse conditions prevail. Secondly, when a void-hole still exists, the protocol uses a backward transmission mechanism to find other routing paths to deliver packets to the end target. Thirdly, the time by which a packet is held by a node prior to transmission is short for the nodes with low energy, depth and high count of neighbors. This reduces packets loss and avoids congestion of the channel. It also helps the nodes with no or few neighbors to hold the packet for a significant chunk of time until they find suitable neighbors, due to sea tides and currents, for further packets’ advancement. The Co-ESRVR further adds reliability to information advancement by adding routing in a cooperative fashion to ESRVR, which involves packets advancement to destination along two paths: directly from source and via a relayed path. This provides multiple paths for data advancement to the sea surface, so that if one path is badly affected by the harsh sea characteristics, others may not be. Unlike the counterpart protocol, the proposed algorithms are not dependent on a node’s geographical location or the distance from the sea surface, which increases their scalability and reduces the computational complexity. Performance analysis displays superior behavior patterns of the proposed algorithms over the counterpart in terms of the compared characteristics.

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Energy-Effective Cooperative and Reliable Delivery Routing Protocols for Underwater Wireless Sensor Networks

Cited by 38 publications

References 34 publications

Energy-Efficient Depth-Based Opportunistic Routing with Q-Learning for Underwater Wireless Sensor Networks

Energy-Efficient Depth-Based Opportunistic Routing with Q-Learning for Underwater Wireless Sensor Networks

Data Collection in Sensor Cloud: Recent Advances, Taxonomy, Use Cases and Open Challenges

Energy-Aware Scalable Reliable and Void-Hole Mitigation Routing for Sparsely Deployed Underwater Acoustic Networks

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