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.
Background: The main objective of the Internet of Things (IoT) has significantly influenced and altered technology, such as interconnection, interoperability, and sensor devices. To ensure seamless healthcare faciliti...
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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
The Internet has been enhanced recently by blockchain and Internet of Things(IoT)*** Internet of Things is a network of various sensor-equipped *** gradually integrates the Internet,sensors,and cloud *** is based on e...
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The Internet has been enhanced recently by blockchain and Internet of Things(IoT)*** Internet of Things is a network of various sensor-equipped *** gradually integrates the Internet,sensors,and cloud *** is based on encryption algorithms,which are shared database technologies on the *** technology has grown significantly because of its features,such as flexibility,support for integration,anonymity,decentralization,and independent *** nodes in the blockchain network are used to verify online ***,this integration creates scalability,interoperability,and security *** the last decade,several advancements in blockchain technology have drawn attention fromresearch communities and *** technology helps IoT networks become more reliable and enhance security and *** also removes single points of failure and lowers the *** recent years,there has been an increasing amount of literature on IoT and blockchain technology *** paper extensively examines the current state of blockchain technologies,focusing specifically on their integration into the Internet of ***,it highlights the benefits,drawbacks,and opportunities of recent studies on security issues based on blockchain solutions into *** survey examined various research papers fromdifferent types of ***,a review of the other IoT applications has been included,focusing on the security requirements and challenges in IoT-based *** research directions are gathered for the effective integration of Blockchain and IoT.
Dynamic flexible job shop scheduling (DFJSS) aims to achieve the optimal efficiency for production planning in the face of dynamic events. In practice, deep Q-network (DQN) algorithms have been intensively studied for...
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Dynamic flexible job shop scheduling (DFJSS) aims to achieve the optimal efficiency for production planning in the face of dynamic events. In practice, deep Q-network (DQN) algorithms have been intensively studied for solving various DFJSS problems. However, these algorithms often cause moving targets for the given job-shop state. This will inevitably lead to unstable training and severe deterioration of the performance. In this paper, we propose a training algorithm based on genetic algorithm to efficiently and effectively address this critical issue. Specifically, a state feature extraction method is first developed, which can effectively represent different job shop scenarios. Furthermore, a genetic encoding strategy is designed, which can reduce the encoding length to enhance search ability. In addition, an evaluation strategy is proposed to calculate a fixed target for each job-shop state, which can avoid the parameter update of target networks. With the designs, the DQNs could be stably trained, thus their performance is greatly improved. Extensive experiments demonstrate that the proposed algorithm outperforms the state-of-the-art peer competitors in terms of both effectiveness and generalizability to multiple scheduling scenarios with different scales. In addition, the ablation study also reveals that the proposed algorithm can outperform the DQN algorithms with different updating frequencies of target networks. IEEE
With the development of the Internet, the use of social media has increased dramatically over time and has emerged as the most powerful networking tool of the twenty-first century. From youngsters of ten years to seni...
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Predictive maintenance is a crucial factor of commercial enterprise operations that ensures and minimizes the failure rate of machinery and equipment. however, the shortcomings of many machine-mastering fashions have ...
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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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In a cloud environment,graphics processing units(GPUs)are the primary devices used for high-performance *** exploit flexible resource utilization,a key advantage of cloud *** users share GPUs,which serve as coprocesso...
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In a cloud environment,graphics processing units(GPUs)are the primary devices used for high-performance *** exploit flexible resource utilization,a key advantage of cloud *** users share GPUs,which serve as coprocessors of central processing units(CPUs)and are activated only if tasks demand GPU *** a container environment,where resources can be shared among multiple users,GPU utilization can be increased by minimizing idle time because the tasks of many users run on a single ***,unlike CPUs and memory,GPUs cannot logically multiplex their ***,GPU memory does not support over-utilization:when it runs out,tasks will ***,it is necessary to regulate the order of execution of concurrently running GPU tasks to avoid such task failures and to ensure equitable GPU sharing among *** this paper,we propose a GPU task execution order management technique that controls GPU usage via time-based *** technique seeks to ensure equal GPU time among users in a container environment to prevent task *** the meantime,we use a deferred processing method to prevent GPU memory shortages when GPU tasks are executed simultaneously and to determine the execution order based on the GPU usage *** the order of GPU tasks cannot be externally adjusted arbitrarily once the task commences,the GPU task is indirectly paused by pausing the *** addition,as container pause/unpause status is based on the information about the available GPU memory capacity,overuse of GPU memory can be prevented at the *** a result,the strategy can prevent task failure and the GPU tasks can be experimentally processed in appropriate order.
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