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1.
Rio de Janeiro (RJ); Fiocruz; 2005. 274 p. (Coleção Criança Mulher e Saúde).
Monography in Portuguese | IEC | ID: iec-9885
2.
Front Public Health ; 10: 1043184, 2022.
Article in English | MEDLINE | ID: mdl-36699901

ABSTRACT

Objective: This study investigated the impact of health resource enhancement on health and spatiotemporal variation characteristics from 2000 to 2010 at the county level. Methods: Multiscale Geographically Weighted Regression and curve fitting were used to explore the characteristics of spatiotemporal impact and divergence mechanism of health resource enhancement on population health. Results: From 2000 to 2010, China's population health continued to rise steadily, and health resource allocation improved. Population health demonstrated the significant spatial autocorrelation, and its spatial clustering patterns were relatively fixed. Health resource allocation was relatively equal. Health technicians per 1,000 persons had a significant positive effect on population health in 2000 and 2010. Meanwhile, its impact tends to be consistent across regions, and the impact scale has been continuously expanding. A quantitative relationship exists between population health and health resource inputs. When life expectancy ranged from 73.68 to 84.08 years, the death rate ranged from 6.27 to 9.00%, and the infant mortality rate ranged from 0.00 to 6.33%, investments in health resources, especially related to health technicians, were beneficial for population health. Conclusions: The government should improve the science and rationality of health resource planning. Planning meets regional realities by combining the impacts of economy and geography. The influence of health resources on population health depends on the overall allocation of health technicians. The number of health technicians needs to be further increased to improve the health resources' effective allocation between regions.


Subject(s)
Health Resources , Resource Allocation , Humans , Life Expectancy
3.
Front Public Health ; 10: 1035395, 2022.
Article in English | MEDLINE | ID: mdl-36684936

ABSTRACT

Although air pollution has been reduced in various industrial and crowded cities during the COVID-19 pandemic, curbing the high concentration of the crisis of air pollution in the megacity of Tehran is still a challenging issue. Thus, identifying the major factors that play significant roles in increasing contaminant concentration is vital. This study aimed to propose a mathematical model to reduce air pollution in a way that does not require citizen participation, limitation on energy usage, alternative energies, any policies on fuel-burn style, extra cost, or time to ensure that consumers have access to energy adequately. In this study, we proposed a novel framework, denoted as the Energy Resources Allocation Management (ERAM) model, to reduce air pollution. The ERAM is designed to optimize the allocation of various energies to the recipients. To do so, the ERAM model is simulated based on the magnitude of fuel demand consumption, the rate of air pollution emission generated by each energy per unit per consumer, and the air pollution contribution produced by each user. To evaluate the reflectiveness and illustrate the feasibility of the model, a real-world case study, i.e., Tehran, was employed. The air pollution emission factors in Tehran territory were identified by considering both mobile sources, e.g., motorcycles, cars, and heavy-duty vehicles, and stationary sources, e.g., energy conversion stations, industries, and household and commercial sectors, which are the main contributors to particulate matter and nitrogen dioxide. An elaborate view of the results indicates that the ERAM model on fuel distribution could remarkably reduce Tehran's air pollution concentration by up to 14%.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Humans , Air Pollutants/analysis , Pandemics , COVID-19/epidemiology , COVID-19/prevention & control , Iran , Air Pollution/analysis , Resource Allocation
4.
Sensors (Basel) ; 23(2)2023 Jan 06.
Article in English | MEDLINE | ID: mdl-36679460

ABSTRACT

Mobile edge computing (MEC)-enabled satellite-terrestrial networks (STNs) can provide task computing services for Internet of Things (IoT) devices. However, since some applications' tasks require huge amounts of computing resources, sometimes the computing resources of a local satellite's MEC server are insufficient, but the computing resources of neighboring satellites' MEC servers are redundant. Therefore, we investigated inter-satellite cooperation in MEC-enabled STNs. First, we designed a system model of the MEC-enabled STN architecture, where the local satellite and the neighboring satellites assist IoT devices in computing tasks through inter-satellite cooperation. The local satellite migrates some tasks to the neighboring satellites to utilize their idle resources. Next, the task completion delay minimization problem for all IoT devices is formulated and decomposed. Then, we propose an inter-satellite cooperative joint offloading decision and resource allocation optimization scheme, which consists of a task offloading decision algorithm based on the Grey Wolf Optimizer (GWO) algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method. The optimal solution is obtained by continuous iterations. Finally, simulation results demonstrate that the proposed scheme achieves relatively better performance than other baseline schemes.


Subject(s)
Algorithms , Internet of Things , Computer Simulation , Resource Allocation
5.
Sensors (Basel) ; 23(2)2023 Jan 10.
Article in English | MEDLINE | ID: mdl-36679601

ABSTRACT

The paper studies the secrecy communication threatened by a single eavesdropper in Energy Harvesting (EH)-based cognitive radio networks, where both the Secure User (SU) and the jammer harvest, store, and utilize RF energy from the Primary Transmitter (PT). Our main goal is to optimize the time slots for energy harvesting and wireless communication for both the secure user as well as the jammer to maximize the long-term performance of secrecy communication. A multi-agent Deep Reinforcement Learning (DRL) method is proposed for solving the optimization of resource allocation and performance. Specifically, each sub-channel from the Secure Transmitter (ST) to the Secure Receiver (SR) link, along with the jammer to the eavesdropper link, is regarded as an agent, which is responsible for exploring optimal power allocation strategy while a time allocation network is established to obtain optimal EH time allocation strategy. Every agent dynamically interacts with the wireless communication environment. Simulation results demonstrate that the proposed DRL-based resource allocation method outperforms the existing schemes in terms of secrecy rate, convergence speed, and the average number of transition steps.


Subject(s)
Communication , Resource Allocation , Physical Phenomena , Computer Simulation , Cognition
6.
Sensors (Basel) ; 23(2)2023 Jan 13.
Article in English | MEDLINE | ID: mdl-36679748

ABSTRACT

The high-density Industrial Internet of Things needs to meet the requirements of high-density device access and massive data transmission, which requires the support of multiple-input multiple-output (MIMO) antenna cognitive systems to keep high throughput. In such a system, spectral efficiency (SE) optimization based on dynamic power allocation is an effective way to enhance the network throughput as the channel quality variations significantly affect the spectral efficiency performance. Deep learning methods have illustrated the ability to efficiently solve the non-convexity of resource allocation problems induced by the channel multi-path and inter-user interference effects. However, current real-valued deep-learning-based power allocation methods have failed to utilize the representational capacity of complex-valued data as they regard the complex-valued channel data as two parts: real and imaginary data. In this paper, we propose a complex-valued power allocation network (AttCVNN) with cross-channel and in-channel attention mechanisms to improve the model performance where the former considers the relationship between cognitive users and the primary user, i.e., inter-network users, while the latter focuses on the relationship among cognitive users, i.e., intra-network users. Comparison experiments indicate that the proposed AttCVNN notably outperforms both the equal power allocation method (EPM) and the real-valued and the complex-valued fully connected network (FNN, CVFNN) and shows a better convergence rate in the training phase than the real-valued convolutional neural network (AttCNN).


Subject(s)
Internet of Things , Industry , Internet , Neural Networks, Computer , Resource Allocation
7.
Int J Environ Res Public Health ; 20(2)2023 Jan 12.
Article in English | MEDLINE | ID: mdl-36674198

ABSTRACT

COVID-19 accelerated the growth of the digital economy and digital transformation across the globe. Meanwhile, it also created a higher demand for productivity in the real economy. Hence, the correlation between the digital economy and green productivity is worth studying as COVID-19 prevention becomes the norm. The digital economy overcomes the limitations imposed by traditional factors of production on economic growth and empowers innovative R&D and resource allocation in all aspects. This study delved into the digital economy by focusing on its green value at different levels of development. The study gathered the green-productivity indices and the principal components of the digital economy for each prefecture-level city in China from 2011 to 2019 and meticulously portrayed their trends in spatial and temporal figures. Meanwhile, regression models were used to verify the mechanism through which digital-economy development influences the changes in green productivity. The results showed that: (1) a higher level of digital economy helps to increase urban green total-factor productivity (GTFP) and that the conclusions of this paper still held after potential endogeneity problems were solved through the instrumental-variables approach; (2) the digital economy will drive an increase in urban GTFP by upgrading firms' production technologies and that digital-economy development encourages green patent applications from firms; and (3) as the digital economy develops, it will also drive urban GTFP increases by removing polluting enterprises from the market and that the higher the level of digital-economy development, the greater the number and probability of polluting enterprises exiting the market. In view of this study's results, the government should increase the importance of the digital economy, strengthen the role of the digital economy in promoting urban green development, and provide more guidance on regional green development with the help of the digital economy.


Subject(s)
COVID-19 , Humans , Cities , COVID-19/epidemiology , Economic Development , Resource Allocation , China , Efficiency
8.
Rev Saude Publica ; 56: 123, 2023.
Article in English, Portuguese | MEDLINE | ID: mdl-36629714

ABSTRACT

OBJECTIVE: Analyze the implications of parliamentary amendments (EP) for the model of equitable allocation of resources from the Fixed Primary Care Minimum (PAB-Fixo) to municipalities in the period from 2015 to 2019. METHODS: A descriptive and exploratory study was conducted on allocating federal resources to the PAB-Fixo and on the increment in the PAB by parliamentary amendment. The municipalities were classified into four groups according to degrees of socioeconomic vulnerability defined by the Ministry of Health for the allocation of PAB-Fixo resources. The transfers from the Ministry by parliamentary amendment were identified. The proportions of municipalities benefiting per group were analyzed by resources allocated from the PAB-Fixo and increment to the minimum by EP. RESULTS: There were reduced resources allocated to the PAB-Fixo (from R$ 6.04 billion to R$ 5.51 billion, -8.8%) and increased increment to PAB by parliamentary amendment (from R$ 95.06 million to R$ 5.58 billion, 5.767%) between 2015 and 2019. The participation of municipalities by the group of those favored by EP was similar to that in the PAB-Fixo. In the proportion of resources for amendments, the municipalities of group I (most vulnerable) had more participation, and those of group IV had less participation if compared to the allocation of the PAB-Fixo. The distribution of resources by the parliamentary amendment did not cover all municipalities, even the most vulnerable ones, i.e., belonging to groups I and II. There was great inequality of resources per capita according to the groups of municipalities. CONCLUSION: The EP distorted the model of equitable allocation of resources proposed by the Ministry of Health for the PAB-Fixo, by allocating resources in a much more significant proportion to the municipalities of group I and much less to those of group IV, which is in disagreement with this model. Furthermore, this distribution by amendments does not benefit all municipalities, not even the most vulnerable.


Subject(s)
Health Resources , Primary Health Care , Resource Allocation , Humans , Brazil , Cities , Primary Health Care/organization & administration , Health Resources/organization & administration
9.
Sensors (Basel) ; 23(1)2022 Dec 24.
Article in English | MEDLINE | ID: mdl-36616788

ABSTRACT

The fifth-generation (5G) wireless network is visualized to offer many types of services with low latency requirements in Internet of Things (IoT) networks. However, the computational capabilities of IoT nodes are not enough to process complex tasks in real time. To solve this problem, multi-access edge computing (MEC) has emerged as an effective solution that will allow IoT nodes to completely or partially offload their computational tasks to MEC servers. However, the large communication delay at a low transmission rate for nodes far from the access point (AP) makes this offloading less meaningful. This paper studies joint multi-task partial offloading from multiple IoT nodes to a common MEC server collocated with an AP, and it uses relay selection to help nodes far from the AP. The computation time of all tasks is minimized by adaptive task division and resource allocation (bandwidth and computation resource), and it is solved with an evolutionary algorithm. The simulation results confirm that the proposed method with both relay selection and adaptive bandwidth allocation outperforms the methods with neither or only one function.


Subject(s)
Internet of Things , Algorithms , Biological Evolution , Computer Simulation , Resource Allocation
10.
Int J Environ Res Public Health ; 20(1)2022 Dec 28.
Article in English | MEDLINE | ID: mdl-36612817

ABSTRACT

Land-use optimization, as an important resource-allocation method, can be defined as the process of allocating various activities to different geographic units. How to manage and control land expansion has become an urgent issue, leading a series of problems such as environmental damage and a sharp decrease in cultivated land, leading to unfavorable phenomena such as excessive urban expansion, occupation of cultivated land and important ecological spaces, and overheating of real estate development. Based on the land-use data of Wuhan city in 2020, a coupling MOP (Multi-Objective Programming) and FLUS (Future Land-Use Simulation) model was used to examine the national spatial structure and the optimization of the spatial layout. Our results showed that (1) in terms of quantitative optimal allocation, the ecological space and urban space increased, while the agricultural space greatly decreased under the three development scenarios. (2) In the simulation of spatial layout, the urban space mainly expanded vertically in the north-south direction. In the ecological space scenario, the ecological space occupied part of the cultivated land in the northeast of the city, resulting in a high degree of landscape fragmentation, which is not conducive to large-scale agricultural management. However, under optimal comprehensive benefit, part of the fragmented ecological space in the western part of Wuhan was transformed into an agricultural space. (3) A combination of the MOP and FLUS models could effectively determine land-use structure and address spatial layout optimization problems and can project space in the future urban resource configuration mode. This finding can provide a reference for the optimization of the spatial structure and layout of similar cities.


Subject(s)
Agriculture , Resource Allocation , Cities , Forecasting , China , Conservation of Natural Resources , Ecosystem
11.
Sci Rep ; 13(1): 299, 2023 Jan 06.
Article in English | MEDLINE | ID: mdl-36609446

ABSTRACT

How to allocate the existing medical resources reasonably, alleviate hospital congestion and improve the patient experience are problems faced by all hospitals. At present, the combination of artificial intelligence and the medical field is mainly in the field of disease diagnosis, but lacks successful application in medical management. We distinguish each area of the emergency department by the division of medical links. In the spatial dimension, in this study, the waitlist number in real-time is got by processing videos using image recognition via a convolutional neural network. The congestion rate based on psychology and architecture is defined for measuring crowdedness. In the time dimension, diagnosis time and time-consuming after diagnosis are calculated from visit records. Factors related to congestion are analyzed. A total of 4717 visit records from the emergency department and 1130 videos from five areas are collected in the study. Of these, the waiting list of the pediatric waiting area is the largest, including 10,436 (person-time) people, and its average congestion rate is 2.75, which is the highest in all areas. The utilization rate of pharmacy is low, with an average of only 3.8 people using it at the one time. Its average congestion rate is only 0.16, and there is obvious space waste. It has been found that the length of diagnosis time and the length of time after diagnosis are related to age, the number of diagnoses and disease type. The most common disease type comes from respiratory problems, accounting for 54.3%. This emergency department has congestion and waste of medical resources. People can use artificial intelligence to investigate the congestion in hospitals effectively. Using artificial intelligence methods and traditional statistics methods can lead to better research on healthcare resource allocation issues in hospitals.


Subject(s)
Artificial Intelligence , Delivery of Health Care , Humans , Child , Resource Allocation , Emergency Service, Hospital , Hospitals
12.
BMC Plant Biol ; 23(1): 10, 2023 Jan 05.
Article in English | MEDLINE | ID: mdl-36604618

ABSTRACT

BACKGROUND: Success in any genomic prediction platform is directly dependent on establishing a representative training set. This is a complex task, even in single-trait single-environment conditions and tends to be even more intricated wherein additional information from envirotyping and correlated traits are considered. Here, we aimed to design optimized training sets focused on genomic prediction, considering multi-trait multi-environment trials, and how those methods may increase accuracy reducing phenotyping costs. For that, we considered single-trait multi-environment trials and multi-trait multi-environment trials for three traits: grain yield, plant height, and ear height, two datasets, and two cross-validation schemes. Next, two strategies for designing optimized training sets were conceived, first considering only the genomic by environment by trait interaction (GET), while a second including large-scale environmental data (W, enviromics) as genomic by enviromic by trait interaction (GWT). The effective number of individuals (genotypes × environments × traits) was assumed as those that represent at least 98% of each kernel (GET or GWT) variation, in which those individuals were then selected by a genetic algorithm based on prediction error variance criteria to compose an optimized training set for genomic prediction purposes. RESULTS: The combined use of genomic and enviromic data efficiently designs optimized training sets for genomic prediction, improving the response to selection per dollar invested by up to 145% when compared to the model without enviromic data, and even more when compared to cross validation scheme with 70% of training set or pure phenotypic selection. Prediction models that include G × E or enviromic data + G × E yielded better prediction ability. CONCLUSIONS: Our findings indicate that a genomic by enviromic by trait interaction kernel associated with genetic algorithms is efficient and can be proposed as a promising approach to designing optimized training sets for genomic prediction when the variance-covariance matrix of traits is available. Additionally, great improvements in the genetic gains per dollar invested were observed, suggesting that a good allocation of resources can be deployed by using the proposed approach.


Subject(s)
Gene-Environment Interaction , Zea mays , Zea mays/genetics , Genome, Plant/genetics , Models, Genetic , Selection, Genetic , Phenotype , Genotype , Genomics/methods , Resource Allocation
13.
PLoS One ; 18(1): e0279886, 2023.
Article in English | MEDLINE | ID: mdl-36602985

ABSTRACT

This paper proposes an optimal resource allocation method. The method is to maximize the Energy Efficiency (EE) for an Energy Harvesting (EH) enabled underlay Cognitive Radio (CR) network. First, we assumed the Secondary Users (SUs) can harvest energy from the surrounding Radio Frequency (RF) signals. Then, we modelled the EE maximisation problem as a joint time and power optimization model. Next, the optimal EH time allocation factor can be calculated. After that the optimal power allocation strategy can be obtain by the fractional programming and Lagrange multiplier method. Finally simulation results show that the proposed iterative method can be better performance advantages compared with the exhaustive method and genetic algorithm. And the EE of this system model is significantly improved compared to the EE model without considering EH.


Subject(s)
Radio Waves , Resource Allocation , Physical Phenomena , Computer Simulation , Cognition
14.
BMC Health Serv Res ; 22(1): 1571, 2022 Dec 22.
Article in English | MEDLINE | ID: mdl-36550580

ABSTRACT

BACKGROUND: To understand the trend of equalization in maternal services and to guide policy-makers regarding resource allocation and public health policy in China. METHODS: Twelve indicators, including maternal services needs, utilization, and resource allocation, were collected from China Health Statistical Year Book 2010 and 2020. WHO's comprehensive evaluation model and the non-integral Rank Sum Ratio (RSR) method were used to analyze, rank, and categorize maternal services of 31 provinces (cities, autonomous regions) in China. RESULTS: All provinces (cities, autonomous regions) are grouped into relative balance areas, low input areas, resource shortage areas, overutilization areas, and resource waste areas. In 2019, there were 18 provinces (cities, autonomous regions) in the relative balanced area, and more than one-half had achieved equal development. Compared to 2009, the resource shortage area decreased from three to zero, and the resource waste area increased from four to six. Among the provinces (cities, autonomous regions) with a type change compared with 2009, eight changed to a relative balance areas, and four showed an improvement. CONCLUSION: Under the policy guidance of promoting the equalization of public health services, maternal services are gradually realized. However, several provinces (cities, autonomous regions) still have problems such as the mismatch between resource input and health needs, resource waste, over-utilization, etc. Therefore, specific policies should be formulated according to the actual types to promote the transformation into equalization regions.


Subject(s)
Family , Resource Allocation , Humans , China , Cities , World Health Organization
15.
Psychol Addict Behav ; 37(1): 144-155, 2023 Feb.
Article in English | MEDLINE | ID: mdl-36521143

ABSTRACT

OBJECTIVE: Relative spending on substances (vs. alternatives) is predictive of several substance use outcomes, but it can be challenging to assess. We examined a novel method of assessing relative resource allocation through the use of a hypothetical lottery task wherein participants assume they collected $100,000 United States dollars in lottery winnings and were tasked with allocating their winnings across spending categories (e.g., savings, leisure, alcohol, cannabis). We hypothesized relative allocation of funds toward alcohol and cannabis would be positively associated with more use and problems of each substance. METHOD: College students (N = 479; Mage = 19.9 [SD = 2.2]) reported on their substance use and problems, alcohol and cannabis demand, and the hypothetical lottery task. RESULTS: Relative resource allocation toward alcohol and cannabis on the lottery task positively correlated with alcohol and cannabis demand indices (intensity, breakpoint, Omax, and elasticity [negatively]), respectively. Using zero-inflated modeling, greater relative allocation toward alcohol positively related to alcohol use and problems in models that controlled for alcohol demand indices. For cannabis, relative resource allocation was also positively associated with cannabis use, but not problems, independently from cannabis demand indices. CONCLUSIONS: Results provide initial support for the hypothetical lottery task as an indicator of relative resource allocation toward substances. Generally, these results extend previous behavioral economic research demonstrating the utility of relative resource allocation as a unique predictor of clinically relevant outcomes. (PsycInfo Database Record (c) 2023 APA, all rights reserved).


Subject(s)
Cannabis , Substance-Related Disorders , Humans , Young Adult , Adult , Alcohol Drinking/epidemiology , Ethanol , Resource Allocation
16.
Sensors (Basel) ; 22(23)2022 Nov 29.
Article in English | MEDLINE | ID: mdl-36502006

ABSTRACT

Low Earth Orbit (LEO) satellite communication networks have become an important means to provide internet access services for areas with limited infrastructure. Compared with the Geostationary Earth Orbit (GEO) satellites, the LEO satellites have limited on-board communication caching and calculating resources. Furthermore, the distribution of traffic requests is dynamically changing and uneven due to the relative movement between the LEO satellites and the ground. Therefore, how to schedule the multi-dimensional resources is an important issue for the LEO satellite communication networks. Beam-hopping is an efficient approach to improve the resource utilization by dynamically allocating time, power, and frequency according to the traffic requests. This paper proposes an efficient multi-dimensional resource allocation mechanism for beam-hopping in LEO satellite networks, which simultaneously satisfies the GEO interference avoidance. First, we construct the beam-hopping model of LEO satellites, and formulate the resource optimization problem. Second, we provide the weighted greedy strategy to determine the illumination pattern. In order to reduce the search space, the cells are clustered to non-interference clusters. Then, an improved genetic algorithm is provided to jointly allocate the communication resources. Finally, we construct various simulations to evaluate our proposed mechanism. Compared with the random-BH, polling-BH and traditional genetic algorithm, our algorithm achieves better performance in terms of both system throughput, access success rate, average delay and fairness between cells. The performance improvement is more significant in scenarios where traffic demand is unevenly distributed.


Subject(s)
Earth, Planet , Movement , Resource Allocation , Satellite Communications , Algorithms
17.
Sensors (Basel) ; 22(23)2022 Dec 01.
Article in English | MEDLINE | ID: mdl-36502061

ABSTRACT

In this study, we investigate the proportional fair trajectory design and resource allocation for an unmanned-aerial-vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) system, where multiple ground nodes (GNs) receive information and harvest energy from the signal transmitted by the UAV using a power-splitting (PS) policy. With this system, we aim to maximize the sum of the logarithmic average spectral efficiency (SE) of the GNs while guaranteeing the average harvested energy requirement to improve the average SE and user fairness simultaneously. To deal with the nonconvexity of the optimization problem, we adopt the quadratic transform and first-order Taylor expansion, proposing an iterative algorithm to find the optimal trajectory and transmit the power of the UAV and the PS ratio of the GNs. Through simulations, we confirm that the proposed scheme achieves a higher average SE compared with the conventional baseline schemes and ensures a level of user fairness similar to that of the state-of-the-art baseline scheme.


Subject(s)
Algorithms , Resource Allocation , Policy
18.
Sensors (Basel) ; 22(23)2022 Dec 02.
Article in English | MEDLINE | ID: mdl-36502109

ABSTRACT

Industry 4.0 requires high-speed data exchange that includes fast, reliable, low-latency, and cost-effective data transmissions. As visible light communication (VLC) can provide reliable, low-latency, and secure connections that do not penetrate walls and are immune to electromagnetic interference; it can be considered a solution for Industry 4.0. The non-orthogonal multiple access (NOMA) technique can achieve high spectral efficiency using the same frequency and time resources for multiple users. It means that smaller amounts of resources will be used compared with orthogonal multiple access (OMA). Therefore, handling multiple data transmissions with VLC-NOMA can be easier for factory automation than OMA. However, as the transmit power is split, the reliability is reduced. Therefore, this study proposed a deep neural network (DNN)-based power-allocation algorithm (DBPA) to improve the reliability of the system. Further, to schedule multiple nodes in VLC-NOMA system, a priority-based user-pairing (PBUP) scheme is proposed. The proposed techniques in VLC-NOMA system were evaluated in terms of the factory automation scenario and showed that it improves reliability and reduces missed deadlines.


Subject(s)
Light , Resource Allocation , Reproducibility of Results , Automation , Algorithms
19.
Sensors (Basel) ; 22(23)2022 Dec 05.
Article in English | MEDLINE | ID: mdl-36502211

ABSTRACT

IEEE 802.11ah, known as Wi-Fi HaLow, is envisioned for long-range and low-power communication. It is sub-1 GHz technology designed for massive Internet of Things (IoT) and machine-to-machine devices. It aims to overcome the IoT challenges, such as providing connectivity to massive power-constrained devices distributed over a large geographical area. To accomplish this objective, IEEE 802.11ah introduces several unique physical and medium access control layer (MAC) features. In recent years, the MAC features of IEEE 802.11ah, including restricted access window, authentication (e.g., centralized and distributed) and association, relay and sectorization, target wake-up time, and traffic indication map, have been intensively investigated from various aspects to improve resource allocation and enhance the network performance in terms of device association time, throughput, delay, and energy consumption. This survey paper presents an in-depth assessment and analysis of these MAC features along with current solutions, their potentials, and key challenges, exposing how to use these novel features to meet the rigorous IoT standards.


Subject(s)
Internet of Things , Caffeine , Communication , Resource Allocation , Technology
20.
Sensors (Basel) ; 22(23)2022 Nov 30.
Article in English | MEDLINE | ID: mdl-36502043

ABSTRACT

Vehicular edge computing (VEC) has emerged in the Internet of Vehicles (IoV) as a new paradigm that offloads computation tasks to Road Side Units (RSU), aiming to thereby reduce the processing delay and resource consumption of vehicles. Ideal computation offloading policies for VEC are expected to achieve both low latency and low energy consumption. Although existing works have made great contributions, they rarely consider the coordination of multiple RSUs and the individual Quality of Service (QoS) requirements of different applications, resulting in suboptimal offloading policies. In this paper we present FEVEC, a Fast and Energy-efficient VEC framework, with the objective of realizing an optimal offloading strategy that minimizes both delay and energy consumption. FEVEC coordinates multiple RSUs and considers the application-specific QoS requirements. We formalize the computation offloading problem as a multi-objective optimization problem by jointly optimizing offloading decisions and resource allocation, which is a mixed-integer nonlinear programming (MINLP) problem and NP-hard. We propose MOV, a Multi-Objective computing offloading method for VEC. First, vehicle prejudgment is proposed to meet the requirements of different applications by considering the maximum tolerance delay related to the current vehicle speed. Second, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to obtain the Pareto-optimal solutions with low complexity. Finally, the optimal offloading strategy is selected for QoS maximization. Extensive evaluation results based on real and simulated vehicle trajectories verify that the average QoS value of MOV is improved by 20% compared with the state-of-the-art VEC mechanism.


Subject(s)
Awareness , Resource Allocation , Cell Movement , Internet , Policy
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