A novel energy-efficient clustering-based congestion-awareness routing mechanism has been developed for wireless sensor network (WSN). In the first stage, some set of sensor nodes are initialised in the WSN environmen...
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With the development of the sixth-generation network, Digital Twin (DT) is driving the explosive growth of Internet-of-Vehicles (IoVs). The rapid proliferation of highly mobile IoVs, coupled with advanced applications...
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With the development of the sixth-generation network, Digital Twin (DT) is driving the explosive growth of Internet-of-Vehicles (IoVs). The rapid proliferation of highly mobile IoVs, coupled with advanced applications, resulted in rigorous demands for quality of experience (QoE) and intricate task caching. The diverse requirements of on-vehicle applications, as well as the freshness of dynamic cached information, provide significant challenges for edge servers in efficiently fulfilling energy and latency demands. This work studies a freshness-aware caching-aided offloading-based task allocation problem (FCAOP) in DT-enabled IoV (DTIoV) with Intelligent Reflective Surfaces (IRS) and edge computing. DT is used to accumulate real-time data and digitally depict the physical objects of the IoV to enhance decision-making. A quantum-inspired differential evolution (QDE) algorithm is proposed to reduce the overall delay and energy consumption in DTIoV (QDE-DTIoV). The quantum vector (QV) is encoded to represent a complete solution to the FCAOP. The decoding of the QVs is done using a one-time hashing algorithm. The fitness function is derived by considering delay, energy consumption, and freshness of the tasks. Extensive simulations demonstrate the superiority of QDE-DTIoV over other benchmark algorithms, showing an average latency improvement of 23%-26% and a reduction in energy consumption ranging from 22% to 33%. IEEE
Airplanes play a critical role in global transportation, ensuring the efficient movement of people and goods. Although generally safe, aviation systems occasionally encounter incidents and accidents that underscore th...
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Millimeter-wave is the core technology to enable multi-Gbps throughput and ultra-low latency *** the devices need to operate at very high frequency and ultra-wide bandwidth:They consume more energy,dissipate more powe...
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Millimeter-wave is the core technology to enable multi-Gbps throughput and ultra-low latency *** the devices need to operate at very high frequency and ultra-wide bandwidth:They consume more energy,dissipate more power,and subsequently heat up *** overheating is a common concern of many users,and millimeter-wave would exacerbate the *** this work,we first thermally characterize millimeter-wave *** measurements reveal that after only 10 s of data transfer at 1.9 Gbps bit-rate,the millimeter-wave antenna temperature reaches 68◦C;it reduces the link throughput by 21%,increases the standard deviation of throughput by 6×,and takes 130 s to dissipate the heat *** degrading the user experience,exposure to high device temperature also creates *** on the measurement insights,we propose Aquilo,a temperature-aware,multi-antenna network *** maintains relatively high throughput performance but cools down the devices *** testbed experiments under both static and mobile conditions demonstrate that Aquilo achieves a median peak temperature only 0.5◦C to 2◦C above the optimal while sacrificing less than 10%of throughput.
Load forecasting plays a crucial role in mitigating risks for utilities by predicting future usage of commodity markets transmission or supplied by the utility. To achieve this, various techniques such as price elasti...
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Load forecasting plays a crucial role in mitigating risks for utilities by predicting future usage of commodity markets transmission or supplied by the utility. To achieve this, various techniques such as price elastic demand, climate and consumer response, load analysis, and sustainable energy generation predictive modelling are used. As both supply and demand fluctuate, and weather and power prices can rise significantly during peak periods, accurate load forecasting becomes critical for utilities. By providing brief demand forecasts, load forecasting can assist in estimating load flows and making decisions that prevent overloading. Therefore, load forecasting is crucial in helping electric utilities make informed decisions related to power, load switching, voltage regulation, switching, and infrastructure development. Forecasting is a methodology used by electricity companies to forecast the amount of electricity or power production needed to maintain constant supply as well as load demand balance. It is required for the electrical industry to function properly. The smart grid is a new system that enables electricity providers and customers to communicate in real-time. The precise energy consumption sequence of the consumers is required to enhance the demand schedule. This is where predicting the future comes into play. Forecasting future power system load (electricity consumption) is a critical task in providing intelligence to the power grid. Accurate forecasting allows utility companies to allocate resources and assume system control in order to balance the same demand and availability for electricity. In this article, a study on load forecasting algorithms based on deep learning, machine learning, hybrid methods, bio-inspired techniques, and other techniques is carried out. Many other algorithms based on load forecasting are discussed in this study. Different methods of load forecasting were compared using three performance indices: RMSE (Root Mean Square Err
In supervised learning algorithms, the class imbalance problem often leads to generating results biased towards the majority classes. Present methods used to deal with the class imbalance problem ignore a principal as...
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The popular East Asian card game Big2 involves rules that do not allow players to view each other's hand cards, making artificial intelligence face challenges in performing well in this game. Based on Markov Decis...
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The popular East Asian card game Big2 involves rules that do not allow players to view each other's hand cards, making artificial intelligence face challenges in performing well in this game. Based on Markov Decision Processes (MDPs) that can handle partially observable and stochastic information, we design the Big2MDP framework to explore card-playing strategies that minimize losing risks while maximizing scoring opportunities for the Big2 game. According to our review of relevant research, this is the first Big2 artificial intelligence framework with the following features: (1) the ability to simultaneously consider scoring and losing points to make the best winning decisions with minimal losing risk, (2) the capability to predict multiple opponents' actions to optimize the decision-making, and (3) the adaptability to compete for the free-playing right to change card combinations at the essential moment. We implement a system of four-player card game Big2 on the Android platform to validate the feasibility and effectiveness of Big2MDP. Experimental results show that Big2MDP outperforms existing artificial intelligence methods, achieving the highest win rate and the least number of losing points as competing against both computer and human players in Big2 games. IEEE
Perceptually-inspired objective functions such as the perceptual evaluation of speech quality (PESQ), signal-to-distortion ratio (SDR), and short-time objective intelligibility (STOI), have recently been used to optim...
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Classification of brain haemorrhage is a challenging task that needs to be solved to help advance medical treatment. Recently, it has been observed that efficient deep learning architectures have been developed to det...
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Developing manufacturing methods for flexible electronics will enable and improve the large-scale production of flexible, spatially efficient, and lightweight devices. Laser sintering is a promising postprocessing met...
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