Energy Optimisation through Path Selection for Underwater Wireless Sensor Networks

Omeke, Kenechi G.; Mollel, Michael S.; Zhang, Lei; Abbasi, Qammer H.; Imran, Muhammad Ali

doi:10.1109/ucet51115.2020.9205429

Cited by 6 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The power consumed by the AUV is a function of the forces acting on it. The AUV energy usage can be analysed as follows [28]. The electrical power 𝑃 used for motion is given by…”

Section: Autonomous Underwater Vehicle Dynamicsmentioning

confidence: 99%

Toward a Sustainable Internet of Underwater Things Based on AUVs, SWIPT, and Reinforcement Learning

Omeke,

Mollel,

Shah

et al. 2024

IEEE Internet Things J.

Self Cite

View full text Add to dashboard Cite

Life on Earth depends on healthy oceans, which supply a large percentage of the planet's oxygen, food, and energy. However, the oceans are under threat from climate change, which is devastating the marine ecosystem and the economic and social systems that depend on it. The Internet-of-underwaterthings (IoUTs), a global interconnection of underwater objects, enables round-the-clock monitoring of the oceans. It provides high-resolution data for training machine learning (ML) algorithms for rapidly evaluating potential climate change solutions and speeding up decision-making. The sensors in conventional IoUTs are battery-powered, which limits their lifetime, and constitutes environmental hazards when they die. In this paper, we propose a sustainable scheme to improve the throughput and enable wireless charging of underwater networks, enabling them to potentially operate indefinitely. The scheme is based on simultaneous wireless information and power transfer (SWIPT) from an autonomous underwater vehicle (AUV) used for data collection. We model the problem of jointly maximising throughput and harvested power as a Markov Decision Process (MDP), and develop a model-free reinforcement learning (RL) solution. The model's reward function incentivises the AUV to find optimal trajectories that maximise throughput and power transfer to the underwater nodes while minimising its own energy consumption. To the best of our knowledge, this is the first attempt at using RL for this application. The scheme is implemented in an open 3D RL environment specifically developed in MATLAB for this study. The performance results show up 207% improvement in energy efficiency compared to those of a random trajectory scheme used as a baseline model.Index Terms-wireless underwater sensor networks, machine learning, reinforcement learning, internet-of-underwater-things, simultaneous wireless and information transfer, and wireless power transfer (SWIPT), autonomous underwater vehicles (auv).

show abstract

“…The power consumed by the AUV is a function of the forces acting on it. The AUV energy usage can be analysed as follows [28]. The electrical power 𝑃 used for motion is given by…”

Section: Autonomous Underwater Vehicle Dynamicsmentioning

confidence: 99%

Toward a Sustainable Internet of Underwater Things Based on AUVs, SWIPT, and Reinforcement Learning

Omeke,

Mollel,

Shah

et al. 2024

IEEE Internet Things J.

Self Cite

View full text Add to dashboard Cite

show abstract

“…This includes simplifying computational complexities, avoiding obstacles and hazardous areas that can cause unwanted errors, finding a shorter path to destinations, adapting to the speed of the current field [40] or taking advantage of the ocean current [41]. To optimize energy consumption, trajectory optimization algorithms have been explored in [42], [43], and [44].…”

Section: ) Energy Consumptionmentioning

confidence: 99%

An Overview of Machine Learning Techniques in Local Path Planning for Autonomous Underwater Vehicles

et al. 2023

View full text Add to dashboard Cite

Autonomous underwater vehicles (AUVs) have become attractive and essential for underwater search and exploration because of the advantages they offer over manned underwater vehicles. Hence the need to improve AUV technologies. One crucial area of AUV technology involves efficiently solving the path planning problem. Several approaches have been identified from the literature for AUV global and local path planning. The use of machine learning (ML) techniques in overcoming some of the challenges associated with AUV path planning problems such as safety and obstacle avoidance, energy consumption, and optimal time and distance travelled remains an active research area. While there is literature on global and local path planning that explores different techniques, there is still a lack of paper that provides an overview of the application of ML for local path planning. Hence the main objective of this paper is to present an overview of the state-of-the-art application of ML techniques on local path planning for AUVs. The ML algorithms are discussed under supervised, unsupervised, and reinforcement learning. The challenges faced in real-life deployment, simulated scenarios, computational issues, and application of ML algorithms are discussed, with future research directions presented.INDEX TERMS Machine learning, local path planning, autonomous underwater vehicle (AUV), real-time path planning, underwater.

show abstract

A joint optimized data collection algorithm based on dynamic cluster-head selection and value of information in UWSNs

Guang

Liu

et al. 2022

Vehicular Communications

View full text Add to dashboard Cite

Energy Optimisation through Path Selection for Underwater Wireless Sensor Networks

Cited by 6 publications

References 16 publications

Toward a Sustainable Internet of Underwater Things Based on AUVs, SWIPT, and Reinforcement Learning

Toward a Sustainable Internet of Underwater Things Based on AUVs, SWIPT, and Reinforcement Learning

An Overview of Machine Learning Techniques in Local Path Planning for Autonomous Underwater Vehicles

A joint optimized data collection algorithm based on dynamic cluster-head selection and value of information in UWSNs

Contact Info

Product

Resources

About