scholarly journals Opportunistic Large Array Propagation Models: A Comprehensive Survey

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4206
Author(s):  
Farhan Nawaz ◽  
Hemant Kumar ◽  
Syed Ali Hassan ◽  
Haejoon Jung
Keyword(s):  
Energy Efficient ◽  
Large Scale ◽  
Performance Metrics ◽  
Experimental Studies ◽  
Cooperative Transmission ◽  
Analytical Models ◽  
Future Research ◽  
Large Array ◽  
Propagation Models ◽  
Comprehensive Survey

Enabled by the fifth-generation (5G) and beyond 5G communications, large-scale deployments of Internet-of-Things (IoT) networks are expected in various application fields to handle massive machine-type communication (mMTC) services. Device-to-device (D2D) communications can be an effective solution in massive IoT networks to overcome the inherent hardware limitations of small devices. In such D2D scenarios, given that a receiver can benefit from the signal-to-noise-ratio (SNR) advantage through diversity and array gains, cooperative transmission (CT) can be employed, so that multiple IoT nodes can create a virtual antenna array. In particular, Opportunistic Large Array (OLA), which is one type of CT technique, is known to provide fast, energy-efficient, and reliable broadcasting and unicasting without prior coordination, which can be exploited in future mMTC applications. However, OLA-based protocol design and operation are subject to network models to characterize the propagation behavior and evaluate the performance. Further, it has been shown through some experimental studies that the most widely-used model in prior studies on OLA is not accurate for networks with networks with low node density. Therefore, stochastic models using quasi-stationary Markov chain are introduced, which are more complex but more exact to estimate the key performance metrics of the OLA transmissions in practice. Considering the fact that such propagation models should be selected carefully depending on system parameters such as network topology and channel environments, we provide a comprehensive survey on the analytical models and framework of the OLA propagation in the literature, which is not available in the existing survey papers on OLA protocols. In addition, we introduce energy-efficient OLA techniques, which are of paramount importance in energy-limited IoT networks. Furthermore, we discuss future research directions to combine OLA with emerging technologies.

scholarly journals Large-Scale Object Monitoring in Internet-of-Things: Energy-Efficient Perspectives

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 461
Author(s):  
Yongbin Yim ◽  
Euisin Lee ◽  
Seungmin Oh
Keyword(s):  
Energy Efficient ◽  
Large Scale ◽  
Future Research ◽  
Technological Evolution ◽  
Node Density ◽  
Mobile Objects ◽  
Research Challenges ◽  
Energy Efficient Communication ◽  
Efficient Communication ◽  
Architectural Principles

Recently, the demand for monitoring a certain object covering large and dynamic scopes such as wildfires, glaciers, and radioactive contaminations, called large-scale fluid objects (LFOs), is coming to the fore due to disasters and catastrophes that lately happened. This article provides an analytic comparison of such LFOs and typical individual mobile objects (IMOs), namely animals, humans, vehicles, etc., to figure out inherent characteristics of LFOs. Since energy-efficient monitoring of IMOs has been intensively researched so far, but such inherent properties of LFOs hinder the direct adaptation of legacy technologies for IMOs, this article surveys technological evolution and advances of LFOs along with ones of IMOs. Based on the communication cost perspective correlated to energy efficiency, three technological phases, namely concentration, integration, and abbreviation, are defined in this article. By reviewing various methods and strategies employed by existing works with the three phases, this article concludes that LFO monitoring should achieve not only decoupling from node density and network structure but also trading off quantitative reduction against qualitative loss as architectural principles of energy-efficient communication to break through inherent properties of LFOs. Future research challenges related to this topic are also discussed.

scholarly journals Sex Differences in Biological Processes and Nitrergic Signaling in Mouse Brain

Biomedicines ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 124 ◽  
Author(s):  
Igor Khaliulin ◽  
Maryam Kartawy ◽  
Haitham Amal
Keyword(s):  
Sex Differences ◽  
Large Scale ◽  
Experimental Studies ◽  
Cysteine Residue ◽  
Future Research ◽  
Biological Processes ◽  
Biological Functions ◽  
Post Translational Modification ◽  
Female Brain ◽  
Spectrometry Technique

Nitric oxide (NO) represents an important signaling molecule which modulates the functions of different organs, including the brain. S-nitrosylation (SNO), a post-translational modification that involves the binding of the NO group to a cysteine residue, is a key mechanism of nitrergic signaling. Most of the experimental studies are carried out on male animals. However, significant differences exist between males and females in the signaling mechanisms. To investigate the sex differences in the SNO-based regulation of biological functions and signaling pathways in the cortices of 6–8-weeks-old mice, we used the mass spectrometry technique, to identify S-nitrosylated proteins, followed by large-scale computational biology. This work revealed significant sex differences in the NO and SNO-related biological functions in the cortices of mice for the first-time. The study showed significant SNO-induced enrichment of the synaptic processes in female mice, but enhanced SNO-related cytoskeletal processes in the male mice. Proteins, which were S-nitrosylated in the cortices of mice of both groups, were more abundant in the female brain. Finally, we investigated the shared molecular processes that were found in both sexes. This study presents a mechanistic insight into the role of S-nitrosylation in both sexes and provides strong evidence of sex difference in many biological processes and signalling pathways, which will open future research directions on sex differences in neurological disorders.

Evolutionary Large-Scale Multi-Objective Optimization: A Survey

2022 ◽  
Vol 54 (8) ◽  
pp. 1-34
Author(s):  
Ye Tian ◽  
Langchun Si ◽  
Xingyi Zhang ◽  
Ran Cheng ◽  
Cheng He ◽  
...  
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Benchmark Problems ◽  
Future Research ◽  
Decision Variable ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Space Reduction ◽  
Decision Variables ◽  
Comprehensive Survey

Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in solving various optimization problems, but their performance may deteriorate drastically when tackling problems containing a large number of decision variables. In recent years, much effort been devoted to addressing the challenges brought by large-scale multi-objective optimization problems. This article presents a comprehensive survey of stat-of-the-art MOEAs for solving large-scale multi-objective optimization problems. We start with a categorization of these MOEAs into decision variable grouping based, decision space reduction based, and novel search strategy based MOEAs, discussing their strengths and weaknesses. Then, we review the benchmark problems for performance assessment and a few important and emerging applications of MOEAs for large-scale multi-objective optimization. Last, we discuss some remaining challenges and future research directions of evolutionary large-scale multi-objective optimization.

scholarly journals A survey on evolutionary computation for complex continuous optimization

2021 ◽  
Author(s):  
Zhi-Hui Zhan ◽  
Lin Shi ◽  
Kay Chen Tan ◽  
Jun Zhang
Keyword(s):  
Evolutionary Computation ◽  
Large Scale ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Future Research ◽  
Practical Applications ◽  
Fast Development ◽  
Comprehensive Survey ◽  
Continuous Optimization Problems ◽  
Expensive Optimization

AbstractComplex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

Study and analysis of various sentiment classification strategies: A challenging overview

2021 ◽  
Author(s):  
Mandar Kundan Keakde ◽  
Akkalakshmi Muddana
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Performance Metrics ◽  
Research Area ◽  
Sentiment Classification ◽  
Future Research ◽  
Classification Schemes ◽  
Social Events ◽  
Election Prediction ◽  
Media Sentiment

In large-scale social media, sentiment classification is a significant one for connecting gaps among social media contents as well as real-world actions, including public emotional status monitoring, political election prediction, and so on. On the other hand, textual sentiment classification is well studied by various platforms, like Instagram, Twitter, etc. Sentiment classification has many advantages in various fields, like opinion polls, education, and e-commerce. Sentiment classification is an interesting and progressing research area due to its applications in several areas. The information is collected from various people about social, products, and social events by web in sentiment analysis. This review provides a detailed survey of 50 research papers presenting sentiment classification schemes such as active learning-based approach, aspect learning-based method, and machine learning-based approach. The analysis is presented based on the categorization of sentiment classification schemes, the dataset used, software tools utilized, published year, and the performance metrics. Finally, the issues of existing methods considering conventional sentiment classification strategies are elaborated to obtain improved contribution in devising significant sentiment classification strategies. Moreover, the probable future research directions in attaining efficient sentiment classification are provided.

scholarly journals Neuroscientific Insights for Improved Outcomes in Music-based Interventions

2020 ◽  
Vol 3 ◽  
pp. 205920432096506
Author(s):  
Psyche Loui
Keyword(s):  
Large Scale ◽  
Experimental Studies ◽  
Future Research ◽  
Active Ingredients ◽  
Policy Makers ◽  
Improved Outcomes ◽  
Standards Of Evidence ◽  
Current Article ◽  
Musical Elements ◽  
Selection Of

Music therapy is an evidence-based practice, but the needs and constraints of various stakeholders pose challenges towards providing the highest standards of evidence for each clinical application. First, what is the best path from clinical need to multi-site, widely adopted intervention for a given disease or disorder? Secondly, how can we inform policy makers that what we do matters for public health––what evidence do we have, and what evidence do we need? This article will review the multiple forms of evidence for music-based interventions in the context of neurological disorders, from large-scale randomized controlled trials (RCT) to smaller-scale experimental studies, and make the case that evidence at multiple levels continues to be necessary for informing the selection of active ingredients of interest in effective musical interventions. The current article reviews some of the existing literature on music-based interventions for neurodegenerative disorders, with particular focus on neural structures and networks that are targeted by specific therapies for disorders including Alzheimer’s Disease, Parkinson’s Disease, and aphasia. This is followed by a focused discussion of principles that are gleaned from studies in cognitive and clinical neuroscience, which may inform the active ingredients of music-based interventions. Therapies that are driven by a deeper understanding of the musical elements that target specific disease mechanisms are more likely to succeed, and to increase the chances of widespread adoption. The article closes with some recommendations for future research.

scholarly journals A Survey on Machine Reading Comprehension—Tasks, Evaluation Metrics and Benchmark Datasets

2020 ◽  
Vol 10 (21) ◽  
pp. 7640
Author(s):  
Changchang Zeng ◽  
Shaobo Li ◽  
Qin Li ◽  
Jie Hu ◽  
Jianjun Hu
Keyword(s):  
Reading Comprehension ◽  
Language Processing ◽  
Large Scale ◽  
Human Performance ◽  
Research Field ◽  
Evaluation Metrics ◽  
Future Research ◽  
Benchmark Datasets ◽  
Comprehensive Survey ◽  
Machine Reading

Machine Reading Comprehension (MRC) is a challenging Natural Language Processing (NLP) research field with wide real-world applications. The great progress of this field in recent years is mainly due to the emergence of large-scale datasets and deep learning. At present, a lot of MRC models have already surpassed human performance on various benchmark datasets despite the obvious giant gap between existing MRC models and genuine human-level reading comprehension. This shows the need for improving existing datasets, evaluation metrics, and models to move current MRC models toward “real” understanding. To address the current lack of comprehensive survey of existing MRC tasks, evaluation metrics, and datasets, herein, (1) we analyze 57 MRC tasks and datasets and propose a more precise classification method of MRC tasks with 4 different attributes; (2) we summarized 9 evaluation metrics of MRC tasks, 7 attributes and 10 characteristics of MRC datasets; (3) We also discuss key open issues in MRC research and highlighted future research directions. In addition, we have collected, organized, and published our data on the companion website where MRC researchers could directly access each MRC dataset, papers, baseline projects, and the leaderboard.

scholarly journals Designing a Validation Protocol for Remote Sensing Based Operational Forest Masks Applications. Comparison of Products Across Europe

2020 ◽  
Vol 12 (19) ◽  
pp. 3159
Author(s):  
Angel Fernandez-Carrillo ◽  
Antonio Franco-Nieto ◽  
Erika Pinto-Bañuls ◽  
Miguel Basarte-Mena ◽  
Beatriz Revilla-Romero
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Forest Cover ◽  
Performance Metrics ◽  
Temporal Dynamics ◽  
Forest Products ◽  
Future Research ◽  
Forest Types ◽  
Visual Interpretation ◽  
Sampling System

The spatial and temporal dynamics of the forest cover can be captured using remote sensing data. Forest masks are a valuable tool to monitor forest characteristics, such as biomass, deforestation, health condition and disturbances. This study was carried out under the umbrella of the EC H2020 MySustainableForest (MSF) project. A key achievement has been the development of supervised classification methods for delineating forest cover. The forest masks presented here are binary forest/non-forest classification maps obtained using Sentinel-2 data for 16 study areas across Europe with different forest types. Performance metrics can be selected to measure accuracy of forest mask. However, large-scale reference datasets are scarce and typically cannot be considered as ground truth. In this study, we implemented a stratified random sampling system and the generation of a reference dataset based on visual interpretation of satellite images. This dataset was used for validation of the forest masks, MSF and two other similar products: HRL by Copernicus and FNF by the DLR. MSF forest masks showed a good performance (OAMSF = 96.3%; DCMSF = 96.5), with high overall accuracy (88.7–99.5%) across all the areas, and omission and commission errors were low and balanced (OEMSF = 2.4%; CEMSF = 4.5%; relBMSF = 2%), while the other products showed on average lower accuracies (OAHRL = 89.2%; OAFNF = 76%). However, for all three products, the Mediterranean areas were challenging to model, where the complexity of forest structure led to relatively high omission errors (OEMSF = 9.5%; OEHRL = 59.5%; OEFNF = 71.4%). Comparing these results with the vision from external local stakeholders highlighted the need of establishing clear large-scale validation datasets and protocols for remote sensing-based forest products. Future research will be done to test the MSF mask in forest types not present in Europe and compare new outputs to available reference datasets.

scholarly journals A Comprehensive Survey on Exiting Solution Approaches towards Security and Privacy Requirements of IoT

2018 ◽  
Vol 8 (4) ◽  
pp. 2319 ◽  
Author(s):  
Rajani Chetan ◽  
Ramesh Shahabadkar
Keyword(s):  
Large Scale ◽  
Network Connectivity ◽  
Security And Privacy ◽  
Global Network ◽  
Future Research ◽  
Computing Systems ◽  
Research Track ◽  
Research Problems ◽  
Comprehensive Survey ◽  
Cloud Applications

<em>‘Internet of Things (IoT)’</em>emerged as an intelligent collaborative computation and communication between a set of objects capable of providing on-demand services to other objects anytime anywhere. A large-scale deployment of data-driven cloud applications as well as automated physical things such as embed electronics, software, sensors and network connectivity enables a joint ubiquitous and pervasive internet-based computing systems well capable of interacting with each other in an IoT. IoT, a well-known term and a growing trend in IT arena certainly bring a highly connected global network structure providing a lot of beneficial aspects to a user regarding business productivity, lifestyle improvement, government efficiency, etc. It also generates enormous heterogeneous and homogeneous data needed to be analyzed properly to get insight into valuable information. However, adoption of this new reality (i.e., IoT) by integrating it with the internet invites a certain challenges from security and privacy perspective. At present, a much effort has been put towards strengthening the security system in IoT still not yet found optimal solutions towards current security flaws. Therefore, the prime aim of this study is to investigate the qualitative aspects of the conventional security solution approaches in IoT. It also extracts some open research problems that could affect the future research track of IoT arena.

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