Routing strategy of reducing energy consumption for underwater data collection

Wu, Jiehong; Sun, Xichun; Wu, Jianxin; Han, Guangjie

doi:10.23919/icn.2021.0012

Cited by 14 publications

(5 citation statements)

References 18 publications

(18 reference statements)

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“…Mourade et al [48] propose an enhanced authentication protocol for IoT. Jiehong et al [49] proposes a Hierarchical Adaptive Energyefficient Clustering Routing strategy to reduce and balance network energy consumption.…”

Section: Related Workmentioning

confidence: 99%

AlignTrack: Push the SNR Limit of LoRa Collision Decoding

Chen

Wang

2023

IEEE/ACM Trans. Networking

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LoRa has been shown as a promising Low-Power Wide Area Network (LPWAN) technology to connect millions of devices for the Internet of Things by providing long-distance low-power communication when the SNR is very low. Real LoRa networks, however, suffer from severe packet collisions. Existing collision resolution approaches introduce a high SNR loss, i.e., require a much higher SNR than LoRa. To push the limit of LoRa collision decoding, we present AlignTrack, the first LoRa collision decoding approach that can work in the SNR limit of the original LoRa. Our key finding is that a LoRa chirp aligned with a decoding window should lead to the highest peak in the frequency domain and thus has the least SNR loss. By aligning a moving window with different packets, we separate packets by identifying the aligned chirp in each window. We theoretically prove this leads to the minimal SNR loss. In practical implementation, we address two key challenges: (1) accurately detecting the start of each packet, and (2) separating collided packets in each window in the presence of CFO and inter-packet interference. We implement AlignTrack on HackRF One and compare its performance with the state-of-the-arts. The evaluation results show that AlignTrack improves network throughput by 1.68× compared with NScale and 3× compared with CoLoRa.

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Section: Related Workmentioning

confidence: 99%

AlignTrack: Push the SNR Limit of LoRa Collision Decoding

Chen

Wang

2023

IEEE/ACM Trans. Networking

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“…Where, 𝑖 th jellyfish's step length with the direction of motion is denoted as 𝑠𝑡𝑒𝑝 ⃗⃗⃗⃗⃗⃗⃗⃗ which is expressed in Eq. (8).…”

Section: Group Movementsmentioning

confidence: 99%

“…The battery energy of underwater sensors is restricted whereas these batteries cannot be simply replaced or charged in the ocean. The energy consumption of the UWSN is high because of the higher communication power, restricted bandwidth capacity, higher and variable time delay and higher bit error rate [8][9][10]. The limited battery source of the node creates the failure nodes over the UWSN [11].…”

Section: Introductionmentioning

confidence: 99%

Multi Objective Energy Based Improved Jellyfish Swarm Optimization for Effective Cluster Head Discovery in UWSN

2023

IJIES

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Underwater wireless sensor networks (UWSNs) have a huge amount of sensors located underwater to collect data from the underwater scenario. UWSN is considered a promising method for monitoring and exploring an underwater scenario. Energy-efficient and reliable data broadcasting are considered challenging tasks, because of the limited energy source of sensors. To address this issue, an energy-efficient cluster head (CH) selection and multi-hop routing are developed in UWSN. The multi objective energy based improved jellyfish swarm optimization (MOEIJSO) is proposed to select optimal CHs from normal sensors. The input parameters of MOEIJSO are residual energy, neighbor node distance, sink distance, and CH balancing factor. Next, multi-hop routing is developed by using ant colony optimization (ACO) for delivering the data packets. Therefore, the proposed MOEIJSO-ACO method is used to improve energy efficiency while increasing data delivery. The proposed MOEIJSO-ACO method is evaluated by using the alive and dead nodes, total energy consumption, data received in base station (BS), and life expectancy. The existing researches such as metaheuristics-based clustering with a routing (MCR) for UWSN, modified low energy adaptive clustering hierarchy (MLEACH) and cooperative energy-efficient routing (CEER) approach are used to compare the MOEIJSO-ACO method. The alive nodes of the MOEIJSO-ACO are 399 for 800 rounds, which is high when compared to the MCR-UWSN.

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“…In [ 23 ], the authors implemented a hierarchical adaptive energy efficient cluster routing (HAECR) algorithm. With its hierarchical region division based on node depth in 3D space and cluster head selection based on competition radii and remaining energy, the algorithm is carried out to form clusters of different sizes.…”

Section: Related Workmentioning

confidence: 99%

Non-Uniform Clustering Algorithm for UWSNs Based on Energy Equalization

Tang

Yuan

et al. 2023

Sensors

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Underwater sensor nodes are usually deployed by ships, aircraft, etc., in random drops, and there is current movement in the underwater environment, which results in an uneven distribution of sensor nodes and thus, different energy consumption in each area of the network. In addition, the underwater sensor network also has a “hot zone” problem. To address the uneven energy consumption of the network caused by the above problem, the non-uniform clustering algorithm for energy equalization is put forward. Considering the residual energy, density and coverage redundancy of nodes, this algorithm selects the cluster heads and makes them more reasonably distributed. Additionally, according to the selected cluster heads, the size of each cluster is designed to equalize the energy consumption of the network during multi-hop routing. In this process, the residual energy of cluster heads and the mobility of nodes are considered, and real-time maintenance is performed for each cluster. The simulation results demonstrate that the proposed algorithm is effective in prolonging the network lifetime and balancing the network energy consumption; moreover, the network coverage maintenance is better than that of other algorithms.

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Routing strategy of reducing energy consumption for underwater data collection

Cited by 14 publications

References 18 publications

AlignTrack: Push the SNR Limit of LoRa Collision Decoding

AlignTrack: Push the SNR Limit of LoRa Collision Decoding

Multi Objective Energy Based Improved Jellyfish Swarm Optimization for Effective Cluster Head Discovery in UWSN

Non-Uniform Clustering Algorithm for UWSNs Based on Energy Equalization

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