Intelligent Resource Research Articles

Intelligent Resource Allocation for IRS-Enhanced OFDM Communication Systems: A Hybrid Deep Reinforcement Learning Approach

Orthogonal frequency division multiplexing (OFDM) systems have been widely applied in practice since OFDM has diverse outstanding advantages. However, their performance improvement is confronted with bottlenecks. In this paper, in order to tackle this issue, intelligent resource allocation driven by reinforcement learning is studied in intelligent reflecting surface (IRS) enhanced OFDM systems. The system sum rate is maximized by jointly optimizing the subcarrier allocation, the transmit beamforming of the base station and the phase shift of the IRS. An intelligent resource allocation scheme based on combining deep Q networks (DQN) and deep deterministic policy-gradient (DDPG) is proposed to tackle the formulated challenging non-convex problems. In order to further improve the spectrum efficiency, spectrum sharing is considered in the IRS-enhanced OFDM system. The secondary users sum rate maximization framework is formulated by jointly optimizing the channel allocation, the transmit beamforming of the secondary base station (SBS) and the phase shift of the IRS. Dueling double deep Q networks (D3QN) and twin delayed deep deterministic policy gradient (TD3) are exploited to tackle the hybrid action space issue under interference. Simulation results demonstrate that our proposed schemes can significantly improve the transmission rate compared to the benchmark schemes.

Toward Secure Transmission in Fog Internet of Things Using Intelligent Resource Allocation

Thanks to the full play of the synergy of local node sets, fog Internet of things technology has become the focus of many researchers. Unfortunately, the data transmission of the sensing terminal therein has the risk of being eavesdropped, due to the openness of the wireless channel and the large-scale deployment of different types of terminals. Meanwhile, the encryption method based on cryptography is difficult to apply to the Internet of things terminals with limited capacity because of its high computational complexity. This paper considers the use of physical layer security technology where the fog node improves the secrecy capacity of the desired channel by sending artificial noise to the untrusted third party to ensure the reliable transmission of data. Specifically, an intelligent resource allocation method based on deep reinforcement learning is proposed to realize the rapid allocation of link resources and interference noise power in the scene, where the relevant elements, such as state, action and reward, are reasonably designed. Simulation results demonstrate that the presented algorithm could effectively resolves the allocation problem, and its transmission delay obviously outperforms that of multiple comparison methods.

Toward Intelligent Resource Allocation on Task-Oriented Semantic Communication

Toward Intelligent Resource Allocation on Task-Oriented Semantic Communication

Intelligent Mobile User Profiling for Maximum Performance

Abstract The use of smartphones and their applications is expanding rapidly, thereby increasing the demand of computational power and other hardware resources of the smartphones. On the other hand, these small devices can have limited resources of computation power, battery backup, RAM memory, and storage space due to their small size. These devices need to reconcile resource hungry applications. This research focuses on solving issues of power and efficiency of smart devices by adapting intelligently to mobile usage by profiling the user intelligently. Our designed architecture makes a smartphone smarter by intelligently utilizing its resources to increase the battery life. Our developed application makes profiles of the applications usage at different time intervals. These stored usage profiles are utilized to make intelligent resource allocation for next time interval. We implemented and evaluated the profiling scheme for different brands of android smartphone. We implemented our approach with Naive Bayes and Decision Tree for performance and compared it with conventional approach. The results show that the proposed approach based on decision trees saves 31 % CPU and 60 % of RAM usage as compared to the conventional approach.

Intelligent resource allocation scheme for cloud-edge-end framework aided multi-source data stream

To support multi-source data stream generated from Internet of Things devices, edge computing emerges as a promising computing pattern with low latency and high bandwidth compared to cloud computing. To enhance the performance of edge computing within limited communication and computation resources, we study a cloud-edge-end computing architecture, where one cloud server and multiple computational access points can collaboratively process the compute-intensive data streams that come from multiple sources. Moreover, a multi-source environment is considered, in which the wireless channel and the characteristic of the data stream are time-varying. To adapt to the dynamic network environment, we first formulate the optimization problem as a markov decision process and then decompose it into a data stream offloading ratio assignment sub-problem and a resource allocation sub-problem. Meanwhile, in order to reduce the action space, we further design a novel approach that combines the proximal policy optimization (PPO) scheme with convex optimization, where the PPO is used for the data stream offloading assignment, while the convex optimization is employed for the resource allocation. The simulated outcomes in this work can help the development of the application of the multi-source data stream.

Intelligent resource allocation decision using deep learning and optimization techniques for HetNets

Intelligent resource allocation decision using deep learning and optimization techniques for HetNets

Clinical application of intelligent technologies and integration in medical laboratories

AbstractWith the development of scientific technology, the transition to the intelligent era of digitalization and automation is an irresistible trend for medical laboratories. Medical diagnosis systems have undergone significant changes as a result of intelligent technologies, such as machine learning, artificial intelligence, and the Internet of Things, from the collection, transmission, and detection of test samples to the review of reports and the provision of clinical feedback. In addition to significantly enhancing the efficiency, consistency, and accuracy of medical laboratory testing, these technologies also assist the improvement of individualized healthcare and medical expert systems, as well as the early detection and treatment of diseases. The future development of medical laboratories will focus on integrating big data and diverse intelligent resources, cooperating more closely with clinical departments, and realizing the effective pathway of patient‐centered care. The purpose of this review is to illustrate the current state of intelligent technology integration in medical laboratories and provide a preliminary discussion about the potential future influences of intelligent technology development on the evolution of medical laboratories.

Dynamic Student Data Management Using Resource Optimization Technology in Higher Education Platforms

This study combines intelligent resource optimization technology to build a dynamic student data management model and suggests a fuzzy hierarchical network representation model based on isomorphism and homogeneity in order to increase the effectiveness of dynamic student data management in colleges and universities. The important semantics of nodes in the network are also captured in this study using fuzzy k-kernel decomposition as a technique for multigranularity partitioning. Based on the SIR model, FHNE compares the production of the sequence to the process of information transmission in the random walk stage, which increases the node sequence’s accuracy. According to the research, the dynamic student data management system that is used in the higher education platform that is suggested in this study can significantly increase the effectiveness of managing student data.

WISe: Wireless Intelligent Sensing for Human-Centric Applications

Contactless sensors based on electromagnet-wave (EM-wave) have great potential for human centric applications due to privacy, convenience and affordability advantages. Traditional researches usually focus on specific topics, such as hardware designs or software algorithms. However, a practical wireless sensing system covers multiple domains and technologies. Hence, a systematic framework of Wireless Intelligent Sensing (WISe) is proposed, for the first time, and a low-noise EM-wave system architecture is presented in this paper. The proposed framework and architecture make use of intelligent resources to minimize the noise in hardware and digital signal processing for various human-centric practical applications. WISe empowered healthcare applications and in-vehicle safety monitoring are shown to demonstrate the effectiveness of the proposed framework and architecture.

Chat Generative Pre-trained Transformer: why we should embrace this technology

With the advent of artificial intelligence that not only can learn from us but also can communicate with us in plain language, humans are embarking on a brave new future. The interaction between humans and artificial intelligence has never been so widespread. Chat Generative Pre-trained Transformer is an artificial intelligence resource that has potential uses in the practice of medicine. As clinicians, we have the opportunity to help guide and develop new ways to use this powerful tool. Optimal use of any tool requires a certain level of comfort. This is best achieved by appreciating its power and limitations. Being part of the process is crucial in maximizing its use in our field. This clinical opinion demonstrates the potential uses of Chat Generative Pre-trained Transformer for obstetrician-gynecologists and encourages readers to serve as the driving force behind this resource.

Blockchain-Based Resource Trading in Multi-UAV-Assisted Industrial IoT Networks: A Multi-Agent DRL Approach

With the Industrial Internet of Things (IIoT), mobile devices (MDs) and their demands for low-latency data communication are increasing. Due to the limited resources of MDs, such as energy, computation, storage, and bandwidth, IIoT systems cannot meet MDs’ quality of service (QoS) and security requirements. Recently, UAVs have been deployed as aerial base stations in the IIoT network to provide connectivity and share resources with MDs. We consider a resource trading environment where multiple resource providers compete to sell their resources to MDs and maximize their profit by continually adjusting their pricing strategies. Multiple MDs, on the other hand, interact with the environment to make purchasing decisions based on the prices set by resource providers to reduce costs and improve QoS. We propose a novel intelligent resource trading framework that integrates multi-agent deep reinforcement Learning (MADRL), blockchain, and game theory to manage dynamic resource trading environments. A consortium blockchain with a smart contract is deployed to ensure the security and privacy of the resource transactions. We formulated the optimization problem using a Stackelberg game. However, the formulated optimization problem in the multi-agent IIoT environment is complex and dynamic, making it difficult to solve directly. Thus, we transform it into a stochastic game to solve the dynamics of the optimization problem. We propose a dynamic pricing algorithm that combines the Stackelberg game with the MADRL algorithm to solve the formulated stochastic game. The simulation results show that our proposed scheme outperforms others to improve resource trading in UAV-assisted IIoT networks.

Decentralized Renewable Resource Redistribution and Optimization for Beyond 5G Small Cell Base Stations: A Machine Learning Approach

Optimal resource provisioning and management of the next generation communication networks are crucial for attaining a seamless quality of service with reduced environmental impact. Considering the ecological assessment, urban and rural telecommunication infrastructure is moving toward deploying green cellular base stations to cater to the needs of the ever-growing traffic demands of heterogeneous networks. In such scenarios, the existing learning-based renewable resource provisioning methods lack intelligent and optimal resource management at the small cell base stations (SCBS). Therefore, in this article, we present a novel machine learning-based framework for intelligent resource provisioning mechanisms for micro-grid connected green SCBSs with a completely modified ring parametric distribution method. In addition, an algorithmic implementation is proposed for prediction-based renewable resource re-distribution with energy flow control unit mechanism for grid-connected SCBS, eliminating the need for centralized hardware. Moreover, this modeling enables the prediction mechanism to estimate the future on-demand traffic provisioning capability of SCBS. Furthermore, we present the numerical analysis of the proposed framework showcasing the systems’ ability to attain a balanced energy convergence level of all the SCBS at the end of the periodic cycle, signifying our model’s merits.

Intelligent resource allocation for transmission security on IRS-assisted spectrum sharing systems with OFDM

In this paper, intelligent reflecting surface (IRS) technology is employed to enhance physical layer security (PLS) for spectrum sharing communication systems with orthogonal frequency division multiplexing (OFDM). Aiming to improve the secondary users’ secrecy rates, a design problem for jointly optimizing the transmission beamforming of secondary base station (SBS), the IRS’s reflecting coefficient and the channel allocation is formulated under the constraints of the requirements of minimum data rates of primary users and the interference between users. As the scenario is highly complex, it is quite challenging to address the non-convexity of the optimization problem. Thus, a deep reinforcement learning (DRL) based approach is taken into consideration. Specifically, we use dueling double deep Q networks (D3QN) and soft Actor–Critic (SAC) to solve the discrete and continuous action space optimization problems, respectively, taking full advantage of the maximum entropy RL algorithm to explore all possible optimal paths. Finally, simulation results show that our proposed approach has a great improvement in security transmission rate compared with the scheme without IRS and OFDM, and our proposed D3QN-SAC approach is more effective than other approaches in terms of maximum security transmission rate.

Attitude Control of Vehicle Based on Series Active Suspensions

When vehicles with traditional passive suspension systems are driving in complex terrain, large swing and vibrations of the car body make passengers and goods uncomfortable and unstable, even at very low-speed conditions. Considering the actual need for intelligent resource exploration in the sustainable economy, visual-based perception and localization systems of unmanned vehicles still cannot handle the sensor noise coursed by large body motions. In order to improve the stability and safety of vehicles in complex terrain, an attitude control system is proposed for mainly eliminating the external body motions of the vehicle by using series active suspensions. A model predictive control method considered the differences between the simulated and real vehicle, and the performance restrictions of actuators are used to design the attitude controller for reducing the heaving, pitching, and rolling motions of the vehicle. After simulations and real car tests, the results show that the proposed attitude controller can significantly improve the attitude stability of vehicles in harsh terrain.

Collaborative and Intelligent Resource Optimization for Computing and Caching in IoV With Blockchain and MEC Using A3C Approach

Recently, the rise of the Internet of Vehicles (IoV) has driven the broad development of intelligent transportation and smart cities. In order to promote the computing power of mobile vehicles and decrease the content delivery latency of suppliers, mobile edge computing (MEC) is recognized as a promising computational paradigm and used in vehicular networks. However, there are some essential issues to be considered: 1) privacy and authenticity of data transmission in IoV, and 2) reasonable resource allocation for collaborative computing and caching. In this paper, to solve above issues, blockchain technology is introduced and adopted to ensure the accuracy and reliability of data transmission and interaction. Meanwhile, we develop an intelligent framework of resource allocation about computing and caching for blockchain-enabled MEC systems in IoV. Through jointly considering and optimizing offloading decision of computation task carried by vehicle, caching decision, the number of offloaded consensus nodes, block interval and block size, the weighted consumption costs of energy consumption and computation overheads can be decreased, and the transactional throughput of the blockchain can be increased. Moreover, due to the continuity and dynamic of the available resources of mobile vehicles and computing servers, the optimization problem is modeled as a Markov decision process (MDP). Facing the large-scale and dynamic characteristics of the system, the asynchronous advantage actor-critic (A3C) approach is introduced to deal with the optimization problem. Simulation results show that our proposed scheme achieves significant advantages over other comparison schemes, such as the total reward of the proposed scheme is about 14% higher than that of the deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -network based scheme.

Identifying consensus on activities that underpin value-based healthcare in outpatient specialty consultations, among clinicians

ObjectiveTo find a consensus on clinicians’ and patients’ activities that underpin an ideal value-based outpatient specialty consultation, among clinicians. MethodsA three-round online Delphi study was conducted. A purposive sample of nineteen clinicians from a Dutch university hospital judged activities on importance. Consensus was defined at 80% agreement. Activities were thematically analyzed to derive conceptual themes. ResultsThe expert panel agreed on 63 activities as being important for an ideal value-based outpatient specialty consultation and two activities as being unimportant. They failed to reach a consensus on 11 activities. Conceptual themes for activities that were considered important regard: 1) empowerment, 2) patient-reported biopsychosocial outcomes, 3) the patient as a person, 4) the patient’s kin, 5) shared power and responsibility, 6) optimization, 7) coordination, 8) therapeutic relationships, and 9) resource-consciousness. ConclusionA value-based outpatient specialty consultation requires contextual decision-making, is person-centered, and focusses attention on care optimization and intelligent resource allocation. No importance is attributed to healthcare’s societal burden and climate footprint. Disparities existed in various areas including the role of patient reported experience measures, “patient-like-me” data, and healthcare costs. Practice implicationsThis study contributes a toolbox to guide and evaluate clinicians’ and patients’ behaviors in value-based outpatient specialty consultations and reveals opportunities to enhance facilitation.

An intelligent resource management method in SDN based fog computing using reinforcement learning

An intelligent resource management method in SDN based fog computing using reinforcement learning

Construction and application of Chinese breast cancer knowledge graph based on multi-source heterogeneous data.

The knowledge graph is a critical resource for medical intelligence. The general medical knowledge graph tries to include all diseases and contains much medical knowledge. However, it is challenging to review all the triples manually. Therefore the quality of the knowledge graph can not support intelligence medical applications. Breast cancer is one of the highest incidences of cancer at present. It is urgent to improve the efficiency of breast cancer diagnosis and treatment through artificial intelligence technology and improve the postoperative health status of breast cancer patients. This paper proposes a framework to construct a breast cancer knowledge graph from heterogeneous data resources in response to this demand. Specifically, this paper extracts knowledge triple from clinical guidelines, medical encyclopedias and electronic medical records. Furthermore, the triples from different data resources are fused to build a breast cancer knowledge graph (BCKG). Experimental results demonstrate that BCKG can support knowledge-based question answering, breast cancer postoperative follow-up and healthcare, and improve the quality and efficiency of breast cancer diagnosis, treatment and management.

Modern Industrial Productivity Development Challenges and Its Big Data Solutions

Since the establishment of the modern industrial production mode, it is faced with the dilemma, shortages of resources and market, and the detail labourer, which limiting its productivity development. These problems are further deepened after three industrial revolutions and manifested in the present day as follows: the inefficiency and poor resilience of the production system; the structural contradiction between supply and demand structure triggering a decline in the vitality of the production organism; and the inability of the efficiency of production and utilization of knowledge to meet the needs of productivity development. The root cause of the problem is the division of labour in modern industrial, which destroys the internal links of the production organism. Big Data technology provides new solutions to solve those dilemmas: firstly, it overcomes the impact of uncertainties on the production system and increases total factor productivity; secondly, the demand-oriented production model generates new demand and stimulates the development of productivity; finally, it imbues things with human intelligence, provides a platform for innovation for the people and fully releases human intelligence resources.

Deep-Deterministic Policy Gradient Based Multi-Resource Allocation in Edge-Cloud System: A Distributed Approach

Edge Cloud (EC) empowers the beyond 5G (B5G) wireless networks to cope with large-scale and real-time traffics of Internet-of-Things (IoT) by minimizing the latency and providing compute power at the edge of the network. Due to a limited amount of resources at the EC compared to the back-end cloud (BC), intelligent resource management techniques become imperative. This paper studies the problem of multi-resource allocation (MRA) in terms of compute and wireless resources in an integrated EC and BC environment. Machine learning-based approaches are emerging to solve such optimization problems. However, it is challenging to adopt traditional discrete action space-based methods due to their high dimensionality issue. To this end, we propose a deep-deterministic policy gradient (DDPG) based temporal feature learning attentional network (TFLAN) model to address the MRA problem. TFLAN combines convolution, gated recurrent unit and attention layers together to mine local and long term temporal information from the task sequences for excellent function approximation. A novel heuristic-based priority experience replay (hPER) method is formulated to accelerate the convergence speed. Further, a pruning principle helps the TFLAN agent to significantly reduce the computational complexity and balance the load among base stations and servers to minimize the rejection-rate. Lastly, data parallelism technique is adopted for distributed training to meet the needs of a high-volume of IoT traffic in the EC environment. Experimental results demonstrate that the distributed training approach suites well to the problem scale and can magnify the speed of the learning process. We validate the proposed framework by comparing with five state-of-the-art RL agents. Our proposed agent converges fast and achieves up to 28% and 72% reduction in operational cost and rejection-rate, and achieves up to 32% gain in the quality of experience on average, compared to the most advanced DDPG agent.