Energy-efficient routing protocols for UWSNs: A comprehensive review of taxonomy, challenges, opportunities, future research directions, and machine learning perspectives

Journal of King Saud University - Computer and Information Sciences, Год журнала: 2024, Номер 36(7), С. 102128 - 102128

Опубликована: Июль 24, 2024

Underwater Wireless Sensor Networks (UWSNs) are essential for a number of environmental and oceanographic monitoring applications. However, they face different more complex challenges than terrestrial wireless sensor networks (TWSNs). The main faced by UWSNs limited include high propagation delays, poor bandwidth, low throughput, energy consumption. Replacing batteries in such becomes extremely difficult as usually deployed remote areas where human interaction is possible. unbalanced inefficient usage various network nodes poses another issue, it may reduce the applicability feasibility network. Therefore, proposing Energy-Efficient Routing Protocols (E-ER-Ps) crucial to improve performance lifespan these networks. Due mentioned earlier, this research presents an extensive analysis several E-ER-Ps intended UWSNs. We compare contemporary approaches that use machine learning (ML) with conventional protocols, ML-based have shown significant potential resolving intricate This paper aims present critical review from prospects To better comprehend structure uses we provide innovative taxonomy their classification. While protocols evaluated flexibility, predictive power, overall efficiency advancements, traditional based on routing tactics energy-efficiency improvements. A thorough comparative highlights advantages, disadvantages, possible protocols. Furthermore, ML's function, incorporating intelligent adaptive approaches, presented, highlighting technology's completely alter UWSN management. formulate implement UWSNs, article concludes obstacles, including need real-time resilience alters, pre-existing infrastructures. development hybrid combine methodologies, design can adapt dynamically changing circumstances underwater habitats highlighted future objectives. provides foundation advancements field presenting comprehensive overview state-of-the-art E-ER-Ps.

Язык: Английский

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Energy-efficient deep Q-network: reinforcement learning for efficient routing protocol in wireless internet of things

Indonesian Journal of Electrical Engineering and Computer Science, Год журнала: 2024, Номер 33(2), С. 971 - 971

Опубликована: Янв. 19, 2024

<div align="center"><span>The internet of things (IoT) underscores pivotal real-world applications ranging from security systems to smart infrastructure and traffic management. However, contemporary IoT devices grapple with significant challenges pertaining battery longevity energy efficiency, constraining the assurance prolonged network lifetimes expansive sensor coverage. Many existing solutions, although promising on paper, are intricate often impractical for implementations. Addressing this gap, we introduce an energy-efficient routing protocol leveraging reinforcement learning (RL) tailored wireless networks (WSNs). This harnesses RL discern optimal transmission route source sink node, factoring in profile each intermediary node. Training algorithm is facilitated through a reward function that includes outflow data efficacy. The model was compared against two prevalent protocols, LEACH fuzzy C-means (FCM), comprehensive assessment. Simulation results highlight our protocol’s superiority respect active node count, conservation, longevity, delivery efficiency.</span></div>

Язык: Английский

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Enhancing underwater target localization through proximity-driven recurrent neural networks

Heliyon, Год журнала: 2024, Номер 10(7), С. e28725 - e28725

Опубликована: Март 30, 2024

Environmental monitoring, ocean research, and underwater exploration are just a few of the marine applications that require precise target localization. This study goes into field localization using Recurrent Neural Networks (RNNs) enhanced with proximity-based approaches, focus on mean estimation error as performance metric. In complex dynamic environments, conventional systems frequently face challenges such signal degradation, noise interference, unstable hydrodynamic conditions. paper presents novel approach to employing RNNs increase accuracy by exploiting temporal dynamics proximity-informed data. method uses an RNN architecture track changes in audio emissions from targets sensed microphone network. Using correlations represented data, learns patterns indicative quickly correctly. Furthermore, addition features increases model's ability understand relative distances between hydrophone nodes target, resulting more accurate estimates. To evaluate suggested methodology, thorough simulations practical experiments were carried out variety environments. The results show RNN-based strategy beats methods works effectively even difficult settings. utility proximity-aware model is demonstrated, particular, considerable reductions estimate (MEE), important measure.

Язык: Английский

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Hybrid Termite Queen and Walrus Optimization Algorithm-based energy efficient cluster-based routing with static and mobile sink node in WSNs

Peer-to-Peer Networking and Applications, Год журнала: 2025, Номер 18(2)

Опубликована: Янв. 3, 2025

Язык: Английский

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A Hybrid Bio-Inspired Approach for Clustering and Routing in UWSNs Using MPA and HGS

Sustainable Computing Informatics and Systems, Год журнала: 2025, Номер unknown, С. 101108 - 101108

Опубликована: Март 1, 2025

Язык: Английский

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Acoustic Internet of Things Network Life Optimization by BAT Clustering Algorithm

Lecture notes in networks and systems, Год журнала: 2025, Номер unknown, С. 545 - 556

Опубликована: Янв. 1, 2025

Язык: Английский

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Evolutionary Cost Analysis and Computational Intelligence for Energy Efficiency in Internet of Things-Enabled Smart Cities: Multi-Sensor Data Fusion and Resilience to Link and Device Failures

Smart Cities, Год журнала: 2025, Номер 8(2), С. 64 - 64

Опубликована: Апрель 9, 2025

This work presents an innovative, energy-efficient IoT routing protocol that combines advanced data fusion grouping and strategies to effectively tackle the challenges of management in smart cities. Our employs hierarchical Data Fusion Head (DFH), relay DFHs, marine predators algorithm, latter which is a reliable metaheuristic algorithm incorporates fitness function optimizes parameters such as how closely Sensor Nodes (SNs) group (DFG) are gathered together, distance sink node, proximity SNs within group, remaining energy (RE), Average Scale Building Occlusions (ASBO), Primary DFH (PDFH) rotation frequency. A key innovation our approach introduction techniques minimize redundant transmissions enhance quality DFG. By consolidating from multiple using algorithms, reduces volume transmitted information, leading significant savings. supports both direct routing, where fused flow straight multi-hop PDF chosen based on influential cost considers RE, ASBO. Given proposed efficient failure recovery strategies, redundancy management, techniques, it enhances overall system resilience, thereby ensuring high performance even unforeseen circumstances. Thorough simulations comparative analysis reveal protocol’s superior across metrics, namely, network lifespan, consumption, throughput, average delay. When compared most recent relevant protocols, including Particle Swarm Optimization-based clustering (PSO-EEC), linearly decreasing inertia weight PSO (LDIWPSO), Optimized Fuzzy Clustering Algorithm (OFCA), Novel PSO-based Protocol (NPSOP), achieves very promising results. Specifically, extends lifespan by 299% over PSO-EEC, 264% LDIWPSO, 306% OFCA, 249% NPSOP. It also consumption 254% relative 247% against 253% The throughput improvements reach 67% 59% 53% 50% fusing optimizing sets new benchmark for DFG, offering robust solution diverse deployments.

Язык: Английский

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Application of Deep Neural Networks in Multi-Hop Wireless Sensor Network (WSN) Channel Optimization

Applied Mathematics and Nonlinear Sciences, Год журнала: 2025, Номер 10(1)

Опубликована: Янв. 1, 2025

Abstract Optimizing communication channels in multi-hop wireless sensor networks (WSNs) is critical for improving network efficiency, energy consumption, and data transmission reliability. Traditional optimization methods often rely on heuristic algorithms, which may struggle with dynamic conditions high-dimensional feature spaces. This paper explores the application of deep neural (DNNs) to optimize WSN channel allocation routing strategies. By leveraging learning, model learns adaptive policies that minimize interference, reduce latency, enhance overall performance. The proposed framework integrates reinforcement learning techniques convolutional recurrent architectures capture spatial-temporal variations quality. Experimental results demonstrate DNN-based approach outperforms conventional terms throughput, stability under varying traffic loads environmental conditions. These findings highlight potential real-time, intelligent optimization.

Язык: Английский

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Node Load and Location-Based Clustering Protocol for Underwater Acoustic Sensor Networks

Journal of Marine Science and Engineering, Год журнала: 2024, Номер 12(6), С. 982 - 982

Опубликована: Июнь 11, 2024

Clustering protocols for underwater acoustic sensor networks (UASNs) have gained widespread attention due to their importance in reducing network complexity. Congestion occurs when the intra-cluster load is greater than upper limit of information transmission capacity, which leads a dramatic deterioration performance despite reduction To avoid congestion, we propose node and location-based clustering protocol UASNs (LLCP). First, optimization mechanism proposed. The number cluster members optimized based on location maximize while avoiding congestion. Then, degree member selection proposed select optimal members. Finally, priority-based order adjusted priority complexity by increasing average Simulation results show that our LLCP minimizes

Язык: Английский

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Improving wireless sensor network lifespan with optimized clustering probabilities, improved residual energy LEACH and energy efficient LEACH for corner-positioned base stations

Heliyon, Год журнала: 2024, Номер 10(14), С. e34382 - e34382

Опубликована: Июль 1, 2024

The goal of this paper's novel energy-conscious routing method is to optimize energy usage and extend network lifespans using a new clustering probability. Versatile arrangements longer lifespan (until the last node dies) are achieved through cluster-based strategies. Existing algorithms, such as low adaptive hierarchy (LEACH), residual LEACH (RES-EL), distributed (DIS-RES-EL), have been compared newly proposed algorithms: improved (IMP-RES-EL) efficient (EEL). IMP-RES-EL EEL outperform all other stated algorithms by extending lifespan, enhancing stability, increasing number aggregated data packets transmitted from cluster heads base station (BS), selecting with efficiency optimal within network. approaches existing particularly when every corner-located BS considered in wireless sensor (WSN). rounds increased 36 %, 44 BSs 20 %. Extensive simulations on five distinct topologies were reviewed three techniques listed above, demonstrating superiority algorithms.

Язык: Английский

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