Energy Aware Approach For Underwater Wireless Sensor Networks Scheduling: UMOD_LEACH

Alhazmi, Abrar S.; Moustafa, Abdellatif I.; Aldosari, Fahd

doi:10.1109/ncg.2018.8593112

Cited by 14 publications

(10 citation statements)

References 8 publications

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“…Contrarily, computing and selecting CHs at each node consumes much more power than that. Known as UMOD-LEACH (Alhazmi et al, 2018), it is a highly-improved variant of the Leach protocol. When applied to water, this technique outperforms LEACH by 30%.…”

Section: Related Workmentioning

confidence: 99%

“…The unique characteristics of UWSNs have attracted the attention of researchers interested in exploring their designs and networking protocols. Grounded on this interest, we developed an energyefficient technique for clustering UWSNs that outperformed LEACH and other bio-inspired clustering algorithms (Heinzelman et al, 2002;Singh and Lobiyal, 2012;Rao et al, 2017;Alhazmi et al, 2018;Wan et al, 2018). Among the most well-known and energyefficient clustering algorithms, those employed in terrestrial wireless sensor networks cannot deploy in the UWSN network.…”

Section: Introductionmentioning

confidence: 99%

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Energy-efficient clustering protocol for underwater wireless sensor networks using optimized glowworm swarm optimization

et al. 2023

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In the past few decades, cutting-edge information and communication technology has been used in several ways to keep an eye on the marine environment. Underwater wireless sensor networks (UWSNs) can measure the amount of water and soil conditions, such as soil salinity, moisture, and movements, to predict landslides. UWSNs are made up of many wireless underwater sensor nodes (WSNs) that are spread out across the thalassic environment. These networks have several uses, including data collection, navigation, resource analysis, surveillance, disaster prediction, etc. Nowadays, energy efficiency becomes a complex issue to handle in the design of the UWSN due to the limited battery capacity and the challenges associated with changing or charging the integrated batteries. According to previous research, clustering and routing have already been effective methods of improving energy efficiency in the UWSN, as unreplaceable batteries and long-distance communication delays are particularly vulnerable. As a result, one of the UWSN’s critical issues is determining how to extend the network’s lifespan while balancing its energy consumption and shortening transmission distances. In UWSN clustering, the most important considerations are acquiring a suitable count of clusters, constituting the clusters, and picking the most satisfactory cluster head (CH) for each cluster. Based on several factors, such as residuary energy, total energy consumption, and other considerations, our proposed approach picks CHs and arranges them into clusters. Also, the proposed SS-GSO method constructs a fitness function by including various sources of information, like total energy, residual energy, and luciferin value. Several simulation runs were executed to test how much better the SS-GSO approach worked. The comparison results showed that while evaluating clustering time, our proposed SS-GSO technique performs 22.91%, 50.03%, 42.42%, 58.06% better, in case of Total energy consumption 27.02%,14%,33.76%,41.97% more energy efficient, in Cluster lifetime 9.2%,19.88%,35.91%,40.54% less and in Packet delivery rate 8.29%,14.05%,17.67%,23.97% better as compared with other heuristic techniques, such as ACO, GWO, MFO and LEACH.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy-efficient clustering protocol for underwater wireless sensor networks using optimized glowworm swarm optimization

et al. 2023

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“…When a member node selects a cluster to join, it consider the position of the cluster head and energy ingestion between member nodes and cluster head, also energy consumption between cluster head and sink. UMOD-LEACH [ 30 ] protocol is modification of Leach protocol and modified algorithm shows 30% better results than (LEACH) in underwater environment. It uses localization concept and time division multiple access (TDMA).…”

Section: Related Workmentioning

confidence: 99%

Adaptive Node Clustering for Underwater Sensor Networks

Khan

Bibi

Aadil

et al. 2021

Sensors

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Monitoring of an underwater environment and communication is essential for many applications, such as sea habitat monitoring, offshore investigation and mineral exploration, but due to underwater current, low bandwidth, high water pressure, propagation delay and error probability, underwater communication is challenging. In this paper, we proposed a sensor node clustering technique for UWSNs named as adaptive node clustering technique (ANC-UWSNs). It uses a dragonfly optimization (DFO) algorithm for selecting ideal measure of clusters needed for routing. The DFO algorithm is inspired by the swarming behavior of dragons. The proposed methodology correlates with other algorithms, for example the ant colony optimizer (ACO), comprehensive learning particle swarm optimizer (CLPSO), gray wolf optimizer (GWO) and moth flame optimizer (MFO). Grid size, transmission range and nodes density are used in a performance matrix, which varies during simulation. Results show that DFO outperform the other algorithms. It produces a higher optimized number of clusters as compared to other algorithms and hence optimizes overall routing and increases the life span of a network.

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“…Another well-known self-organizing, adaptive clustering and scheduling algorithm known as low energyadaptive clustering hierarchy (LEACH), uses randomization to evenly distribute the energy load among the network's sensor nodes. Alhazmi et al [21] proposed a novel solution named underwater modified LEACH (UMOD_LEACH) to minimize energy consumption. Li et al [22] proposed the LEACH-L, which consists of two phases: an initialization phase and an update phase.…”

Section: Related Workmentioning

confidence: 99%

Research on Hybrid Cooperative Charging Scheduling Schemes in Underwater Sensor Networks

2019

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The lifetime of underwater sensor networks (USNs) can be prolonged significantly thanks to wireless power transfer technology. In this paper, we first proposed a Shortest Path Partial Charging based on Charging Curve Scheme (SPBS) to increase the survival rate of nodes in 3D underwater networks, and then we proposed a concept of secondary charging stations for mobile charging ships to reduce the traveling cost and improve charging efficiency. We first use k-means clustering algorithm to divide our network with k clusters, and then we place our secondary stations at k clustering centers, in this way, mobile charging ships can be charged at secondary stations quickly. Based on secondary stations, we proposed Hamilton Charging Scheme (HCS) using the Hamilton ring, and then we proposed a temporal and spatial collaborative charging algorithm (mCS-TS) for USNs with multiple mobile charging ships and secondary charging stations, which also takes the cluster factor and deadline time into consideration. Simulation results show the effectiveness of our proposed algorithms. INDEX TERMS Underwater sensor networks, survival rate, SPBS, secondary charging stations, mobile charging ships, cluster factor.

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Energy Aware Approach For Underwater Wireless Sensor Networks Scheduling: UMOD_LEACH

Cited by 14 publications

References 8 publications

Energy-efficient clustering protocol for underwater wireless sensor networks using optimized glowworm swarm optimization

Energy-efficient clustering protocol for underwater wireless sensor networks using optimized glowworm swarm optimization

Adaptive Node Clustering for Underwater Sensor Networks

Research on Hybrid Cooperative Charging Scheduling Schemes in Underwater Sensor Networks

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