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• Hierarchical RL integrates optimisation of cutting parameters and tool path.
• A novel energy-efficient dynamic optimisation model accounts for deformation.
• Dual-layer Markov decision processes decouple the model in macro-micro ...

Five-axis machining, especially flank milling, is popular in machining thin-walled freeform surface parts with high energy consumption. Reducing the machining energy consumption is paramount for advancing green manufacturing. Therefore, this ...

Wireless acoustic sensor network (WASN) has shown great potential for speech enhancement compared with traditional microphone arrays. As dealing with all sensor measurements demands a powerful centralized processor, a distributed framework for ...

• A distributed frame-work for speech enhancement via temporal and spatial data fusion.
• Sensor selection strategy to reduce power consumption in system.
• A near-optimal greedy searching method for sensor selection.

Within wireless sensor networks (WSNs), a multitude of vulnerabilities can arise, particularly those originating from malicious nodes (MNs), which lead to compromised data integrity, network stability, and critical application reliability. ...

Non-orthogonal multiple access (NOMA) is considered as a promising technique for improving energy efficiency (EE), spectral efficiency (SE), and data rate in 5 G and beyond wireless systems. Although power allocation plays a crucial role in ...

Wireless Sensor Networks (WSNs), key components in the Internet of Things (IoT), are widely used in fields such as environmental monitoring, healthcare, and facility management. These networks consist of resource-constrained sensors that collect, ...

Underwater wireless sensor networks (UWSNs) face significant challenges, such as limited energy resources, high propagation delays, and harsh underwater environments. Efficient clustering can help address these challenges by grouping nearby nodes ...

In this study, we present an edge-based intelligent IoT control system designed to optimize energy efficiency while ensuring secure and real-time incident detection. The system leverages deep reinforcement learning (DRL) to dynamically adjust the ...

Incorporating human natural features into the algorithmic functions of robots can enhance performance efficiency. One of the most popular features of human movements is the power law that defines the connection between movement speed and path ...

Spiking Neural Networks (SNNs) are naturally suited to process sequence tasks such as NLP with low power, due to its brain-inspired spatio-temporal dynamics and spike-driven nature. Current SNNs employ ”repeat coding” that re-enter all input ...

A significant part of CO₂ emissions is due to high electricity consumption in residential buildings. Using load shifting can help to improve the households’ energy efficiency. To nudge changes in energy consumption behavior, simple but powerful ...

• We propose a recommendation algorithm for generating device usage recommendations.
• Multi-agent architecture supports seamless functionality expansion.
• We suggest the performance evaluation framework for our recommendation system.

Achieving climate change mitigation while maintaining economic growth, requires a significant reduction in the energy consumption of the industrial and commercial sectors. The evaluation of energy efficiency projects (EEPs) in line with ISO 50001 ...

Edge computing is a novel and potential computing model which moves storage and computing capabilities to the network edge, substantially decreasing service latency and network traffic. The existing Internet of Things (IoT) network offloading ...

Zero-outage secrecy in short packet is critical in many applications ranging from civilians to military as it guarantees no information leakage to eavesdropper with one hundred percent for any circumstance. This paper aims to consider a novel ...

The dramatic recent increase of the smart Internet of Everything (IoE) in Industry 4.0 has significantly increased energy consumption, carbon emissions, and global warming. IoE applications in Industry 4.0 face many challenges, including energy ...

The COVID-19 pandemic has accelerated the demand for more efficient Smart Health Monitoring Systems (SHMS) in healthcare. While clustering techniques are widely used to improve energy efficiency and reduce data latency in smart hospitals, existing ...

The end of Moore’s law and Dennard scaling emphasizes the need for application-specific computing architectures to achieve high resource and energy efficiency and real-time performance. The concept of a silicon compiler remains an enduring aspiration for ...

Wireless networks are a critical component of modern communication infrastructures, with Wi-Fi being one of the most widespread protocols of this kind. Despite huge advances in performance and features, researchers still face significant ...

To improve the energy efficiency of computation, accelerators trade off performance and energy consumption for flexibility. Fixed-function accelerators reach high energy efficiency, but are inflexible. Adding programmability via an instruction set ...

Turbid media such as fog and haze seriously affects the quality of imaging for systems such as urban surveillance and satellite remote sensing. Image dehazing has become a research hotspot in the field of computer vision. Neural-network-based ...$_{}^{\mathrm{}}$$_{}^{\mathrm{}}$$_{}^{\mathrm{}}$

In the era of exponential data growth, data centers face a pressing need to manage energy consumption while maintaining performance. Existing methods for optimizing energy efficiency, particularly in the context of virtual machine (VM) migrations, ...