| 1. |
- Abbafati, Cristiana, et al.
(författare)
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- 2020
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Tidskriftsartikel (refereegranskat)
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| 2. |
- Micah, Angela E., et al.
(författare)
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Tracking development assistance for health and for COVID-19 : a review of development assistance, government, out-of-pocket, and other private spending on health for 204 countries and territories, 1990-2050
- 2021
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Ingår i: The Lancet. - : Elsevier. - 0140-6736 .- 1474-547X. ; 398:10308, s. 1317-1343
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Forskningsöversikt (refereegranskat)abstract
- Background The rapid spread of COVID-19 renewed the focus on how health systems across the globe are financed, especially during public health emergencies. Development assistance is an important source of health financing in many low-income countries, yet little is known about how much of this funding was disbursed for COVID-19. We aimed to put development assistance for health for COVID-19 in the context of broader trends in global health financing, and to estimate total health spending from 1995 to 2050 and development assistance for COVID-19 in 2020. Methods We estimated domestic health spending and development assistance for health to generate total health-sector spending estimates for 204 countries and territories. We leveraged data from the WHO Global Health Expenditure Database to produce estimates of domestic health spending. To generate estimates for development assistance for health, we relied on project-level disbursement data from the major international development agencies' online databases and annual financial statements and reports for information on income sources. To adjust our estimates for 2020 to include disbursements related to COVID-19, we extracted project data on commitments and disbursements from a broader set of databases (because not all of the data sources used to estimate the historical series extend to 2020), including the UN Office of Humanitarian Assistance Financial Tracking Service and the International Aid Transparency Initiative. We reported all the historic and future spending estimates in inflation-adjusted 2020 US$, 2020 US$ per capita, purchasing-power parity-adjusted US$ per capita, and as a proportion of gross domestic product. We used various models to generate future health spending to 2050. Findings In 2019, health spending globally reached $8. 8 trillion (95% uncertainty interval [UI] 8.7-8.8) or $1132 (1119-1143) per person. Spending on health varied within and across income groups and geographical regions. Of this total, $40.4 billion (0.5%, 95% UI 0.5-0.5) was development assistance for health provided to low-income and middle-income countries, which made up 24.6% (UI 24.0-25.1) of total spending in low-income countries. We estimate that $54.8 billion in development assistance for health was disbursed in 2020. Of this, $13.7 billion was targeted toward the COVID-19 health response. $12.3 billion was newly committed and $1.4 billion was repurposed from existing health projects. $3.1 billion (22.4%) of the funds focused on country-level coordination and $2.4 billion (17.9%) was for supply chain and logistics. Only $714.4 million (7.7%) of COVID-19 development assistance for health went to Latin America, despite this region reporting 34.3% of total recorded COVID-19 deaths in low-income or middle-income countries in 2020. Spending on health is expected to rise to $1519 (1448-1591) per person in 2050, although spending across countries is expected to remain varied. Interpretation Global health spending is expected to continue to grow, but remain unequally distributed between countries. We estimate that development organisations substantially increased the amount of development assistance for health provided in 2020. Continued efforts are needed to raise sufficient resources to mitigate the pandemic for the most vulnerable, and to help curtail the pandemic for all. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.
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| 3. |
- Azari, Amin, et al.
(författare)
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Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence
- 2016
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Ingår i: 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509013289
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Konferensbidrag (refereegranskat)abstract
- Fundamental tradeoffs in green cellular networkswith coexistence of machine-oriented and human-oriented trafficsare investigated. First, we present a queuing system to modelthe uplink transmission of a green base station which servestwo types of distinct traffics with strict requirements on delayand battery lifetime. Then, the energy-lifetime and energydelaytradeoffs are introduced, and closed-form expressions forenergy consumption of the base station, average experienceddelay in data transmission, and expected battery lifetime ofmachine devices are derived. Furthermore, we extend the derivedresults to the multi-cell scenario, and investigate the impacts ofsystem and traffic parameters on the energy-lifetime and energydelaytradeoffs using analytical and numerical results. Numericalresults show the impact of energy saving for the access network onthe introduced tradeoffs, and figure out the ways in which energycould be saved by compromising on the level of performance.
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| 4. |
- Azari, Amin
(författare)
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Bitcoin Price Prediction : An ARIMA Approach
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Bitcoin is considered as the most valuable currency in the world. Besides being highly valuable, its value has also experienced a steep increase, from around 1 dollar in 2010 to around 18000 in 2017. Then, in recent years, it has attracted considerable attention in a diverse set of fields, including economics and computer science. The former mainly focuses on studying how it affects the market, determining reasons behinds its price fluctuations, and predicting its future prices. The latter mainly focuses on its vulnerabilities, scalability, and other techno-cryptoeconomic issues. Here, we aim at revealing the usefulness of traditional autoregressive integrative moving average (ARIMA)model in predicting the future value of bitcoin by analyzing the price time series in a 3-years-long time period. On the one hand, our empirical studies reveal that this simple scheme is efficient in sub-periods in which the behavior of the time-series is almost unchanged, especially when it is used for short-term prediction,e.g. 1-day. On the other hand, when we try to train the Arima model to a 3-years-long period, during which the bitcoin price has experienced different behaviors, or when we try to use it for a long-term prediction, we observe that it introduces large prediction errors. Especially, the ARIMA model is unable to capture the sharp fluctuations in the price, e.g. the volatility at the end of 2017. Then, it calls for more features to be extracted and used along with the price for a more accurate prediction of the price. We have further investigated the bitcoin price prediction using an ARIMA model trained over the whole dataset, as well as a limited part of the history of the bitcoin price, with length of w, as inputs. Our study sheds lights on the interaction of the prediction accuracy, choice of (p; q; d), and window size w.
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| 5. |
- Azari, Amin, et al.
(författare)
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Cellular Traffic Prediction and Classification : A Comparative Evaluation of LSTM and ARIMA
- 2019
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Ingår i: Discovery Science. - Cham : Springer. - 9783030337773 - 9783030337780 ; , s. 129-144
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Konferensbidrag (refereegranskat)abstract
- Prediction of user traffic in cellular networks has attracted profound attention for improving the reliability and efficiency of network resource utilization. In this paper, we study the problem of cellular network traffic prediction and classification by employing standard machine learning and statistical learning time series prediction methods, including long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA), respectively. We present an extensive experimental evaluation of the designed tools over a real network traffic dataset. Within this analysis, we explore the impact of different parameters on the effectiveness of the predictions. We further extend our analysis to the problem of network traffic classification and prediction of traffic bursts. The results, on the one hand, demonstrate the superior performance of LSTM over ARIMA in general, especially when the length of the training dataset is large enough and its granularity is fine enough. On the other hand, the results shed light onto the circumstances in which, ARIMA performs close to the optimal with lower complexity.
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| 6. |
- Azari, Amin, 1988-
(författare)
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Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G
- 2022
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Ingår i: IEEE Communications Surveys and Tutorials. - IEEE. - 1553-877X.
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Tidskriftsartikel (refereegranskat)abstract
- —The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in fifth-generation (5G)and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, nonorthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. In particular, the potential of using emerging deep learning and federated learning techniques for enhancing the efficiency and security of IoT communication are discussed, and their promises and challenges are introduced. Finally, future research directions toward beyond 5G IoT networks are pointed out.
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| 7. |
- Azari, Amin, 1988-, et al.
(författare)
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Energy and Resource Efficiency by User Traffic Prediction and Classification in Cellular Networks
- 2022
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Ingår i: IEEE Transactions on Green Communications and Networking. - : Institute of Electrical and Electronics Engineers (IEEE). - 2473-2400. ; 6:2, s. 1082-1095
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Tidskriftsartikel (refereegranskat)abstract
- There is a lack of research on the analysis of peruser traffic in cellular networks, for deriving and following traffic-aware network management. In fact, the legacy design approach, in which resource provisioning and operation control are performed based on the cell-aggregated traffic scenarios, are not so energy- and cost-efficient and need to be substituted with user-centric predictive analysis of mobile network traffic and proactive network resource management. Here, we shed light on this problem by designing traffic prediction tools that utilize standard machine learning (ML) tools, including long shortterm memory (LSTM) and autoregressive integrated moving average (ARIMA) on top of per-user data. We present an expansive empirical evaluation of the designed solutions over a real network traffic dataset. Within this analysis, the impact of different parameters, such as the time granularity, the length of future predictions, and feature selection are investigated. As a potential application of these solutions, we present an ML-powered Discontinuous reception (DRX) scheme for energy saving. Towards this end, we leverage the derived ML models for dynamic DRX parameter adaptation to user traffic. The performance evaluation results demonstrate the superiority of LSTM over ARIMA in general, especially when the length of the training time series is high enough, and it is augmented by a wisely-selected set of features. Furthermore, the results show that adaptation of DRX parameters by online prediction of future traffic provides much more energy-saving at low latency cost in comparison with the legacy cell-wide DRX parameter adaptation.
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| 8. |
- Azari, Amin, et al.
(författare)
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Energy-Efficient and Reliable IoT Access Without Radio Resource Reservation
- 2021
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Ingår i: IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING. - : Institute of Electrical and Electronics Engineers (IEEE). - 2473-2400. ; 5:2, s. 908-920
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Tidskriftsartikel (refereegranskat)abstract
- One of the major challenges for Internet-of-Things applications is that the existing cellular technologies do not support the uplink IoT traffic in an energy-efficient manner. There are two principal ways for serving the uplink IoT traffic: grant-based (i.e., scheduled) and grant-free (i.e., random access). Grant-based access provides fine-grained control of reliability and latency at the cost of energy consumption required for signaling. Grant-free access removes the signaling overhead at the cost of looser control of performance in terms of reliability and latency. However, a precise analysis of reliability, latency and energy performance of grant-free access (GFA) is largely missing. This article focuses on a GFA-type protocol, in which a device transmits several packet replicas, asynchronously with respect to the other devices. Using stochastic geometry, we derive closed-form expressions for reliability, delay, and energy consumption, which can be used to identify the tradeoffs among these performance parameters. In order to improve the performance of the protocol, we develop a receiver that leverages the random timing and frequency offsets among the devices in order to facilitate resolution of collisions. This is complemented by a per-device adaptive scheme that controls the number of transmitted replicas. The evaluation confirms the validity of the analysis and the potential of the proposed solution, identifying operating regions in which GFA outperforms the grant-based access.
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| 9. |
- Azari, Amin, 1988-, et al.
(författare)
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Energy Efficient MAC for Cellular-Based M2M Communications
- 2014
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Ingår i: Signal and Information Processing (GlobalSIP), 2014 IEEE Global Conference on. - : IEEE conference proceedings. ; , s. 128-132
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Konferensbidrag (refereegranskat)abstract
- In Machine-to-Machine (M2M) networks, an energyefficient scalable medium access control (MAC) is crucial forserving massive battery-driven machine-type devices. In thispaper, we investigate the energy efficient MAC design to minimizebattery power consumption in cellular-based M2M communications.We present an energy efficient MAC protocol that notonly adapts contention and reservation-based protocols for M2Mcommunications in cellular networks, but also benefits frompartial clustering to handle the massive access problem. Then weinvestigate the energy efficiency and access capacity of contentionbasedprotocols and present an energy efficient contention-basedprotocol for intra-cluster communication of the proposed MAC,which results in huge power saving. The simulation results showthat the proposed MAC protocol outperforms the others in energysaving without sacrificing much delay or throughput. Also, thelifetimes of both individual nodes and the whole M2M networkare significantly extended.
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| 10. |
- Azari, Amin, 1988-
(författare)
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Energy Efficient Machine-Type Communications over Cellular Networks : A Battery Lifetime-Aware Cellular Network Design Framework
- 2016
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Internet of Things (IoT) refers to the interconnection of uniquely identifiable smart devices which enables them to participate more actively in everyday life. Among large-scale applications, machine-type communications (MTC) supported by cellular networks will be one of the most important enablers for the success of IoT. The existing cellular infrastructure has been optimized for serving a small number of long-lived human-oriented communications (HoC) sessions, originated from smartphones whose batteries are charged in a daily basis. As a consequence, serving a massive number of non-rechargeable machine-type devices demanding a long battery lifetime is a big challenge for cellular networks.The present work is devoted to energy consumption modeling, battery lifetime analysis, and lifetime-aware network design for massive MTC services over cellular networks. At first, we present a realistic model for energy consumption of machine devices in cellular connectivity, which is employed subsequently in deriving the key performance indicator, i.e. network battery lifetime. Then, we develop an efficient mathematical foundation and algorithmic framework for lifetime-aware clustering design for serving a massive number of machine devices. Also, by extending the developed framework to non-clustered MTC, lifetime-aware uplink scheduling and power control solutions are derived. Finally, by investigating the delay, energy consumption, spectral efficiency, and battery lifetime tradeoffs in serving coexistence of HoC and MTC traffic, we explore the ways in which energy saving for the access network and quality of service for HoC traffic can be traded to prolong battery lifetime for machine devices.The numerical and simulation results show that the proposed solutions can provide substantial network lifetime improvement and network maintenance cost reduction in comparison with the existing approaches.
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