Energy-Efficient Clustering Algorithm for Magnetic Induction-Based Underwater Wireless Sensor Networks

Wang, Sai; Nguyen, Thu L. N.; Shin, Yoan

doi:10.1109/access.2018.2889910

Cited by 27 publications

(13 citation statements)

References 17 publications

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“…However, CBEER does not focus on coverage preservation and data gathering reliability. HENPC [23] was an energyefficient clustering algorithm for magnetic induction-based underwater wireless sensor networks. e clustering protocol included two main parts.…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Hierarchical Clustering Data Gathering Algorithm Based on Multiple Criteria Decision Making for 3D Underwater Sensor Networks

2020

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Data gathering is the basis of monitoring applications in an underwater sensor network, and excellent network coverage and data transmission reliability are the guarantees for the quality of monitoring tasks. However, the energy consumption of the nodes is too fast due to the heavy load of the cluster heads closer to the sink when data is transmitted between cluster heads (CHs) and the sink by multihop, which leads to an energy hole problem in an underwater sensor network of clustering technology. Aiming to address this problem, we propose a dynamic hierarchical clustering data gathering algorithm based on multiple criteria decision making (DHCDGA) in a 3D underwater sensor network. Firstly, the entire monitoring network is divided into many layers. For selecting a cluster head in each layer, multiple criteria decision making of an intuitionistic fuzzy Analytic Hierarchy Process (AHP) and hierarchical fuzzy integration is adopted. Furthermore, a sorting algorithm is used to form a clustering topology algorithm to solve the problem that there is the only node in one cluster. Then, an energy-balanced routing algorithm between clusters is proposed according to the residual energy of the node, the depth, and the number of neighbor nodes. Finally, the simulation results show that DHCDGA can not only effectively balance the energy consumption of the network and prolong the network lifetime but also improve network coverage and data gathering reliability.

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Section: Related Workmentioning

confidence: 99%

A Dynamic Hierarchical Clustering Data Gathering Algorithm Based on Multiple Criteria Decision Making for 3D Underwater Sensor Networks

2020

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“…However, the multi-hop routing mechanism and the data fusion method are not considered by the authors. Wang et al put forward an underwater clustering scheme based on the magnetic induction for UWSNs [33], where the Voronoi diagram is employed to form clusters and the jellyfish breathing process is used for CHN selection. This scheme can achieve the high energy-efficiency and prolong the network lifetime.…”

Section: The Clustering Routing Protocolmentioning

confidence: 99%

“…This scheme can achieve the high energy-efficiency and prolong the network lifetime. However, the multi-hop routing path has not been optimized in [33]. Ahmed et al introduced an underwater clustering protocol according to redundant transmission control (RTC), which eliminates the data redundancy at the CHN level and at the region head level [6].…”

Section: The Clustering Routing Protocolmentioning

confidence: 99%

Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms

2020

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Due to the limited battery energy of underwater wireless sensor nodes and the difficulty in replacing or recharging the battery underwater, it is of great significance to improve the energy efficiency of underwater wireless sensor networks (UWSNs). We propose a novel energy-efficient clustering routing protocol based on data fusion and genetic algorithms (GAs) for UWSNs. In the clustering routing protocol, the cluster head node (CHN) gathers the data from cluster member nodes (CMNs), aggregates the data through an improved back propagation neural network (BPNN), and transmits the aggregated data to a sink node (SN) through a multi-hop scheme. The effective multi-hop transmission path between the CHN and the SN is determined through the enhanced GA, thereby improving transmission efficiency and reducing energy consumption. This paper presents the GA based on a specific encoding scheme, a particular crossover operation, and an enhanced mutation operation. Additionally, the BPNN employed for data fusion is improved by adopting an optimized momentum method, which can reduce energy consumption through the elimination of data redundancy and the decrease of the amount of transferred data. Moreover, we introduce an optimized CHN selecting scheme considering residual energy and positions of nodes. The experiments demonstrate that our proposed protocol outperforms its competitors in terms of the energy expenditure, the network lifespan, and the packet loss rate.

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“…To avoid the short signal overloading, we set the maximum input threshold to choose a suitable wireless transmitter gain. For those net- work layers that sense or monitor environmental information, the improved high energy node priority clustering (IHENPC) algorithm [18] is utilized to divide the network into several clusters. Because results show the IHENPC algorithm has a good performance in balancing the remaining energy of node and avoiding premature appearance of energy holes.…”

Section: A Network Architecturementioning

confidence: 99%

Efficient Routing Protocol Based on Reinforcement Learning for Magnetic Induction Underwater Sensor Networks

Wang

Shin

2019

IEEE Access

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In recent research on 3D underwater wireless sensor network (UWSN), magnetic induction communication is a promising candidate, thanks to several unique features, such as small transmission delay, constant channel behavior, and adequate long communication range. However, designing a routing protocol that prolongs the network lifetime and reduces the transmission delay has been still a challenge for a 3D UWSN. In this paper, we propose an efficient routing protocol based on reinforcement learning, in particular, the Q-learning that aims to investigate the resource management in the hierarchical networks. Through defining the single hopping bonus metrics of distance and energy, we deduce the updating formula of the routing algorithm and derive the relationship between energy priority and distance priority. In addition, we set up a regulatory factor to adjust the proportion between energy saving and low delay, and thus, it can meet different needs. The simulation results show that the proposed routing approach outperforms the conventional protocol in extending the network lifetime and reducing the transmission delay.INDEX TERMS Underwater wireless sensor network, magnetic induction, routing protocol, reinforcement learning.

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Energy-Efficient Clustering Algorithm for Magnetic Induction-Based Underwater Wireless Sensor Networks

Cited by 27 publications

References 17 publications

A Dynamic Hierarchical Clustering Data Gathering Algorithm Based on Multiple Criteria Decision Making for 3D Underwater Sensor Networks

A Dynamic Hierarchical Clustering Data Gathering Algorithm Based on Multiple Criteria Decision Making for 3D Underwater Sensor Networks

Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms

Efficient Routing Protocol Based on Reinforcement Learning for Magnetic Induction Underwater Sensor Networks

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