Results 181 to 190 of about 89,828 (281)

Redefining Optimal Coverage Path Planning for FLS‐Equipped AUVs With Deep Reinforcement Learning

Journal of Field Robotics, EarlyView.
ABSTRACT Autonomous Underwater Vehicles (AUVs) have emerged as indispensable tools for a variety of subsea tasks, from habitat monitoring and seabed mapping to infrastructure inspection and mine countermeasures. A fundamental challenge in this field is Coverage Path Planning (CPP), the problem of ensuring complete and efficient area coverage.
Lorenzo Cecchi, Alberto Topini, Alessandro Bucci, Alessandro Ridolfi   +3 more
wiley   +1 more source

Quantum-secured routing in drone communication for 6G-enabled smart mobility. [PDF]

Sci Rep
Hafeez S, Abro GEM, Memon SA, Khan TA, Memon I, Nasir H.   +5 more
europepmc   +1 more source

Machine learning‐driven advances in carbon‐based quantum dots: Opportunities accompanied by challenges

Responsive Materials, EarlyView.
Machine learning provides a unifying framework to connect structure, fluorescence properties, and applications of carbon‐based quantum dots. This review highlights how data‐driven strategies enable fluorescence regulation, reveal underlying mechanisms, and accelerate the rational design of functional carbon dots.
Liangfeng Chen, Siwei Yang, Xiangqian Cui, Ziheng Liu, Hang Wang, Yongqiang Li, Peng He, Miao Wang, Guqiao Ding   +8 more
wiley   +1 more source

Routing Protocols for Wireless Body Area Networks: Recent Advances and Open Challenges. [PDF]

Sensors (Basel)
Qin H, Su H, Niu X, Chen H.
europepmc   +1 more source

River‐Scale Evaluation of a Floating LED Light Array for Behavioral Guidance of American Eel

River Research and Applications, EarlyView.
ABSTRACT Exploiting the sensory physiology of migratory fishes to guide them away from hazards like hydroelectric turbines is of interest to regulatory agencies and hydropower operators to achieve conservation and management goals. Here we describe a river‐scale experiment where out‐migrating adult American eel (Anguilla rostrata) were tracked ...
Chris K. Elvidge, M. E. Cole MacLeod, Daniel Hatin, Alexandre Guindon, René Dion, Scott Schlueter, Justin Ecret, Jana R. Lantry, Thomas C. Pratt, David Stanley, Jake K. L. La Rose, Paul T. Jacobson, Douglas Bradley, Steven J. Cooke   +13 more
wiley   +1 more source

AI-Driven Energy-Efficient Routing in IoT-Based Wireless Sensor Networks: A Comprehensive Review. [PDF]

Sensors (Basel)
Thakur S, Sarkar NI, Yongchareon S.
europepmc   +1 more source

An autonomous network of acoustic detectors to map tiger risk by eavesdropping on prey alarm calls

Remote Sensing in Ecology and Conservation, EarlyView.
Tiger population recovery brings with it increased fatalities from human‐tiger conflict. We describe a network of autonomous intelligent passive acoustic sensors that monitor the forest for deer alarm calls as a proxy for tiger risk and provide a risk map to local communities in real‐time.
Arik Kershenbaum, Andrew Markham, Holly Root‐Gutteridge, Bethany Smith, Casey Anderson, Riley McClaughry, Ramjan Chaudhary, Amogh Vishwakarma, Stephen Cummins, Angela Dassow   +9 more
wiley   +1 more source

Federated learning-based trust and energy-aware routing in Fog-Cloud computing environments for the Internet of Things. [PDF]

Sci Rep
Wang F, Wang K.
europepmc   +1 more source

Digital Twin‐Based Optimization of Service Availability in LEO Mega Constellations Considering Handover Delays in Open RAN

International Journal of Satellite Communications and Networking, EarlyView.
ABSTRACT As non‐terrestrial networks (NTNs) become integral to future 6G systems, ensuring seamless connectivity and service continuity over low Earth orbit (LEO) satellite constellations is essential. This work investigates the impact of open radio access network (RAN) functional splits on handover performance in NTNs, focusing on minimizing service ...
Siva Satya Sri Ganesh Seeram, Luca Feltrin, Mustafa Ozger, Cicek Cavdar   +3 more
wiley   +1 more source

Improved energy efficient load balanced mobility management RPL protocol for mobile internet of things networks. [PDF]

Sci Rep
Diniesh VC, Palaniswamy B, Murali L, Manikandan N, Ramaswamy K.   +4 more
europepmc   +1 more source