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Kozlov S, Spirina E. Novel Modification of Integrated Optimization Method for Sensor's Communication in Wi-Fi Public Networks. Sensors (Basel) 2024; 24:1395. [PMID: 38474931 DOI: 10.3390/s24051395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/17/2024] [Accepted: 02/19/2024] [Indexed: 03/14/2024]
Abstract
A novel modification of IP networks integrated optimization method for heterogeneous networks, for example, the seamless Wi-Fi network serving simultaneously mobile users and wireless sensors, has been developed in this article. The mutual influence of signal reception, frequency-territorial planning, and routing procedures in heterogeneous networks have been analyzed in the case of simultaneous data transmission by both mobile users and wireless sensors. New principles for the listed procedures interaction and the basic functions for their describing are formulated. A novel modification of the integrated optimization method and its algorithm have been developed. The developed method's effectiveness has been analyzed for the IEEE 802.11ax network segment. Its result showed that the network load was decreased by an average of 20%, the data rate over the network as a whole increased for users and sensors by an average of 25% and 40%, respectively, and the sensors' lifetime increased by an average of 20% compared to the novel modification of the Collective Dynamic Routing method.
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Affiliation(s)
- Sergey Kozlov
- Radioelectronic and Telecommunication Systems Department, Kazan National Research Technical University Named after A. N. Tupolev-KAI, Kazan 420111, Russia
| | - Elena Spirina
- Radioelectronic and Telecommunication Systems Department, Kazan National Research Technical University Named after A. N. Tupolev-KAI, Kazan 420111, Russia
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2
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Shaska J, Mitra U. Joint Detection and Communication over Type-Sensitive Networks. Entropy (Basel) 2023; 25:1313. [PMID: 37761612 PMCID: PMC10527969 DOI: 10.3390/e25091313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 08/07/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023]
Abstract
Due to the difficulty of decentralized inference with conditional dependent observations, and motivated by large-scale heterogeneous networks, we formulate a framework for decentralized detection with coupled observations. Each agent has a state, and the empirical distribution of all agents' states or the type of network dictates the individual agents' behavior. In particular, agents' observations depend on both the underlying hypothesis as well as the empirical distribution of the agents' states. Hence, our framework captures a high degree of coupling, in that an individual agent's behavior depends on both the underlying hypothesis and the behavior of all other agents in the network. Considering this framework, the method of types, and a series of equicontinuity arguments, we derive the error exponent for the case in which all agents are identical and show that this error exponent depends on only a single empirical distribution. The analysis is extended to the multi-class case, and numerical results with state-dependent agent signaling and state-dependent channels highlight the utility of the proposed framework for analysis of highly coupled environments.
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Affiliation(s)
- Joni Shaska
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA;
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3
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Zhong J, Cui P, Zhu Y, Xiao Q, Qu Z. DAHNGC: A Graph Convolution Model for Drug-Disease Association Prediction by Using Heterogeneous Network. J Comput Biol 2023; 30:1019-1033. [PMID: 37702623 DOI: 10.1089/cmb.2023.0135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023] Open
Abstract
In the field of drug development and repositioning, the prediction of drug-disease associations is a critical task. A recently proposed method for predicting drug-disease associations based on graph convolution relies heavily on the features of adjacent nodes within the homogeneous network for characterizing information. However, this method lacks node attribute information from heterogeneous networks, which could hardly provide valuable insights for predicting drug-disease associations. In this study, a novel drug-disease association prediction model called DAHNGC is proposed, which is based on a graph convolutional neural network. This model includes two feature extraction methods that are specifically designed to extract the attribute characteristics of drugs and diseases from both homogeneous and heterogeneous networks. First, the DropEdge technique is added to the graph convolutional neural network to alleviate the oversmoothing problem and obtain the characteristics of the same nodes of drugs or diseases in the homogeneous network. Then, an automatic feature extraction method in the heterogeneous network is designed to obtain the features of drugs or diseases at different nodes. Finally, the obtained features are put into the fully connected network for nonlinear transformation, and the potential drug-disease pairs are obtained by bilinear decoding. Experimental results demonstrate that the DAHNGC model exhibits good predictive performance for drug-disease associations.
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Affiliation(s)
- Jiancheng Zhong
- School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Pan Cui
- School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Yihong Zhu
- School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Qiu Xiao
- School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Zuohang Qu
- School of Information Science and Engineering, Hunan Normal University, Changsha, China
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4
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Kim Y, Cho YR. Predicting Drug-Gene-Disease Associations by Tensor Decomposition for Network-Based Computational Drug Repositioning. Biomedicines 2023; 11:1998. [PMID: 37509637 PMCID: PMC10377142 DOI: 10.3390/biomedicines11071998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/07/2023] [Accepted: 07/12/2023] [Indexed: 07/30/2023] Open
Abstract
Drug repositioning offers the significant advantage of greatly reducing the cost and time of drug discovery by identifying new therapeutic indications for existing drugs. In particular, computational approaches using networks in drug repositioning have attracted attention for inferring potential associations between drugs and diseases efficiently based on the network connectivity. In this article, we proposed a network-based drug repositioning method to construct a drug-gene-disease tensor by integrating drug-disease, drug-gene, and disease-gene associations and predict drug-gene-disease triple associations through tensor decomposition. The proposed method, which ensembles generalized tensor decomposition (GTD) and multi-layer perceptron (MLP), models drug-gene-disease associations through GTD and learns the features of drugs, genes, and diseases through MLP, providing more flexibility and non-linearity than conventional tensor decomposition. We experimented with drug-gene-disease association prediction using two distinct networks created by chemical structures and ATC codes as drug features. Moreover, we leveraged drug, gene, and disease latent vectors obtained from the predicted triple associations to predict drug-disease, drug-gene, and disease-gene pairwise associations. Our experimental results revealed that the proposed ensemble method was superior for triple association prediction. The ensemble model achieved an AUC of 0.96 in predicting triple associations for new drugs, resulting in an approximately 7% improvement over the performance of existing models. It also showed competitive accuracy for pairwise association prediction compared with previous methods. This study demonstrated that incorporating genetic information leads to notable advancements in drug repositioning.
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Affiliation(s)
- Yoonbee Kim
- Division of Software, Yonsei University Mirae Campus, Wonju-si 26493, Gangwon-do, Republic of Korea
| | - Young-Rae Cho
- Division of Software, Yonsei University Mirae Campus, Wonju-si 26493, Gangwon-do, Republic of Korea
- Division of Digital Healthcare, Yonsei University Mirae Campus, Wonju-si 26493, Gangwon-do, Republic of Korea
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5
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Sun Z, Chen G. Contract-Optimization Approach (COA): A New Approach for Optimizing Service Caching, Computation Offloading, and Resource Allocation in Mobile Edge Computing Network. Sensors (Basel) 2023; 23:4806. [PMID: 37430721 DOI: 10.3390/s23104806] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/25/2023] [Accepted: 05/09/2023] [Indexed: 07/12/2023]
Abstract
An optimal method for resource allocation based on contract theory is proposed to improve energy utilization. In heterogeneous networks (HetNets), distributed heterogeneous network architectures are designed to balance different computing capacities, and MEC server gains are designed based on the amount of allocated computing tasks. An optimal function based on contract theory is developed to optimize the revenue gain of MEC servers while considering constraints such as service caching, computation offloading, and the number of resources allocated. As the objective function is a complex problem, it is solved utilizing equivalent transformations and variations of the reduced constraints. A greedy algorithm is applied to solve the optimal function. A comparative experiment on resource allocation is conducted, and energy utilization parameters are calculated to compare the effectiveness of the proposed algorithm and the main algorithm. The results show that the proposed incentive mechanism has a significant advantage in improving the utility of the MEC server.
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Affiliation(s)
- Zhiyao Sun
- School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130000, China
| | - Guifen Chen
- School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130000, China
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6
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Galvis D, Hodson DJ, Wedgwood KC. Spatial distribution of heterogeneity as a modulator of collective dynamics in pancreatic beta-cell networks and beyond. Front Netw Physiol 2023; 3:fnetp.2023.1170930. [PMID: 36987428 PMCID: PMC7614376 DOI: 10.3389/fnetp.2023.1170930] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
We study the impact of spatial distribution of heterogeneity on collective dynamics in gap-junction coupled beta-cell networks comprised on cells from two populations that differ in their intrinsic excitability. Initially, these populations are uniformly and randomly distributed throughout the networks. We develop and apply an iterative algorithm for perturbing the arrangement of the network such that cells from the same population are increasingly likely to be adjacent to one another. We find that the global input strength, or network drive, necessary to transition the network from a state of quiescence to a state of synchronised and oscillatory activity decreases as network sortedness increases. Moreover, for weak coupling, we find that regimes of partial synchronisation and wave propagation arise, which depend both on network drive and network sortedness. We then demonstrate the utility of this algorithm for studying the distribution of heterogeneity in general networks, for which we use Watts-Strogatz networks as a case study. This work highlights the importance of heterogeneity in node dynamics in establishing collective rhythms in complex, excitable networks and has implications for a wide range of real-world systems that exhibit such heterogeneity.
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Affiliation(s)
- Daniel Galvis
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, UK
- Institute of Metabolism and Systems Research (IMSR), University of Birmingham, Birmingham, UK
- Correspondence: Daniel Galvis,
| | - David J. Hodson
- Institute of Metabolism and Systems Research (IMSR), University of Birmingham, Birmingham, UK
- Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK
- Oxford Centre for Diabetes, Endocrinology, and Metabolism (OCDEM), Churchill Hospital, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Churchill Hospital, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Kyle C.A. Wedgwood
- Living Systems Institute, University of Exeter, Exeter, UK
- EPSRC Hub for Quantitative Modelling in Healthcare, University of Exeter, Exeter, UK
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
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7
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Feng H, Jin D, Li J, Li Y, Zou Q, Liu T. Matrix reconstruction with reliable neighbors for predicting potential MiRNA-disease associations. Brief Bioinform 2023; 24:6960615. [PMID: 36567252 DOI: 10.1093/bib/bbac571] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 10/16/2022] [Accepted: 11/23/2022] [Indexed: 12/27/2022] Open
Abstract
Numerous experimental studies have indicated that alteration and dysregulation in mircroRNAs (miRNAs) are associated with serious diseases. Identifying disease-related miRNAs is therefore an essential and challenging task in bioinformatics research. Computational methods are an efficient and economical alternative to conventional biomedical studies and can reveal underlying miRNA-disease associations for subsequent experimental confirmation with reasonable confidence. Despite the success of existing computational approaches, most of them only rely on the known miRNA-disease associations to predict associations without adding other data to increase the prediction accuracy, and they are affected by issues of data sparsity. In this paper, we present MRRN, a model that combines matrix reconstruction with node reliability to predict probable miRNA-disease associations. In MRRN, the most reliable neighbors of miRNA and disease are used to update the original miRNA-disease association matrix, which significantly reduces data sparsity. Unknown miRNA-disease associations are reconstructed by aggregating the most reliable first-order neighbors to increase prediction accuracy by representing the local and global structure of the heterogeneous network. Five-fold cross-validation of MRRN produced an area under the curve (AUC) of 0.9355 and area under the precision-recall curve (AUPR) of 0.2646, values that were greater than those produced by comparable models. Two different types of case studies using three diseases were conducted to demonstrate the accuracy of MRRN, and all top 30 predicted miRNAs were verified.
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Affiliation(s)
- Hailin Feng
- School of mathematics and computer science, Zhejiang A&F University, No.666 Wusu Street,Lin'an District, 311300, Hangzhou, China
| | - Dongdong Jin
- School of mathematics and computer science, Zhejiang A&F University, No.666 Wusu Street,Lin'an District, 311300, Hangzhou, China
| | - Jian Li
- School of mathematics and computer science, Zhejiang A&F University, No.666 Wusu Street,Lin'an District, 311300, Hangzhou, China
| | - Yane Li
- School of mathematics and computer science, Zhejiang A&F University, No.666 Wusu Street,Lin'an District, 311300, Hangzhou, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, West District, high tech Zone, 611731, Chengdu, China
| | - Tongcun Liu
- School of mathematics and computer science, Zhejiang A&F University, No.666 Wusu Street,Lin'an District, 311300, Hangzhou, China
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8
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Doncheva NT, Morris JH, Holze H, Kirsch R, Nastou KC, Cuesta-Astroz Y, Rattei T, Szklarczyk D, von Mering C, Jensen LJ. Cytoscape stringApp 2.0: Analysis and Visualization of Heterogeneous Biological Networks. J Proteome Res 2022; 22:637-646. [PMID: 36512705 PMCID: PMC9904289 DOI: 10.1021/acs.jproteome.2c00651] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Biological networks are often used to represent complex biological systems, which can contain several types of entities. Analysis and visualization of such networks is supported by the Cytoscape software tool and its many apps. While earlier versions of stringApp focused on providing intraspecies protein-protein interactions from the STRING database, the new stringApp 2.0 greatly improves the support for heterogeneous networks. Here, we highlight new functionality that makes it possible to create networks that contain proteins and interactions from STRING as well as other biological entities and associations from other sources. We exemplify this by complementing a published SARS-CoV-2 interactome with interactions from STRING. We have also extended stringApp with new data and query functionality for protein-protein interactions between eukaryotic parasites and their hosts. We show how this can be used to retrieve and visualize a cross-species network for a malaria parasite, its host, and its vector. Finally, the latest stringApp version has an improved user interface, allows retrieval of both functional associations and physical interactions, and supports group-wise enrichment analysis of different parts of a network to aid biological interpretation. stringApp is freely available at https://apps.cytoscape.org/apps/stringapp.
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Affiliation(s)
- Nadezhda T. Doncheva
- Novo
Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark,
| | - John H. Morris
- Resource
on Biocomputing, Visualization, and Informatics, University of California, San
Francisco, California 94143, United States
| | - Henrietta Holze
- Novo
Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Rebecca Kirsch
- Novo
Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Katerina C. Nastou
- Novo
Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Yesid Cuesta-Astroz
- Instituto
Colombiano de Medicina Tropical, Universidad
CES, 055413 Sabaneta, Colombia
| | - Thomas Rattei
- Centre
for Microbiology and Environmental Systems Science, University of Vienna, 1030 Vienna, Austria
| | - Damian Szklarczyk
- Department
of Molecular Life Sciences, University of
Zurich, 8057 Zurich, Switzerland,SIB
Swiss
Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Christian von Mering
- Department
of Molecular Life Sciences, University of
Zurich, 8057 Zurich, Switzerland,SIB
Swiss
Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Lars J. Jensen
- Novo
Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark,
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9
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Hernández-Carlón JJ, Pérez-Romero J, Sallent O, Vilà I, Casadevall F. A Deep Q-Network-Based Algorithm for Multi-Connectivity Optimization in Heterogeneous Cellular-Networks. Sensors (Basel) 2022; 22:6179. [PMID: 36015940 PMCID: PMC9414990 DOI: 10.3390/s22166179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 08/07/2022] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
The use of multi-connectivity has become a useful tool to manage the traffic in heterogeneous cellular network deployments, since it allows a device to be simultaneously connected to multiple cells. The proper exploitation of this technique requires to adequately configure the traffic sent through each cell depending on the experienced conditions. This motivates this work, which tackles the problem of how to optimally split the traffic among the cells when the multi-connectivity feature is used. To this end, the paper proposes the use of a deep reinforcement learning solution based on a Deep Q-Network (DQN) in order to determine the amount of traffic of a device that needs to be delivered through each cell, making the decision as a function of the current traffic and radio conditions. The obtained results show a near-optimal performance of the DQN-based solution with an average difference of only 3.9% in terms of reward with respect to the optimum strategy. Moreover, the solution clearly outperforms a reference scheme based on Signal to Interference Noise Ratio (SINR) with differences of up to 50% in terms of reward and up to 166% in terms of throughput for certain situations. Overall, the presented results show the promising performance of the DQN-based approach that establishes a basis for further research in the topic of multi-connectivity and for the application of this type of techniques in other problems of the radio access network.
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10
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Ivanov A, Tonchev K, Poulkov V, Manolova A, Neshov NN. Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications. Sensors (Basel) 2022; 22:s22155778. [PMID: 35957333 PMCID: PMC9371046 DOI: 10.3390/s22155778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 07/01/2022] [Accepted: 07/26/2022] [Indexed: 05/14/2023]
Abstract
Resource allocation (RA) has always had a prominent place in wireless communications research due to its significance for network throughput maximization, and its inherent complexity. Concurrently, graph-based solutions for RA have also grown in importance, providing opportunities for higher throughput and efficiency due to their representational capabilities, as well as challenges for realizing scalable algorithms. This article presents a comprehensive review and analysis of graph-based RA methods in three major wireless network types: cellular homogeneous and heterogeneous, device-to-device, and cognitive radio networks. The main design characteristics, as well as directions for future research, are provided for each of these categories. On the basis of this review, the concept of Graph-based Resource allocation for Integrated Space and Terrestrial communications (GRIST) is proposed. It describes the inter-connectivity and coexistence of various terrestrial and non-terrestrial networks via a hypergraph and its attributes. In addition, the implementation challenges of GRIST are explained in detail. Finally, to complement GRIST, a scheme for determining the appropriate balance between different design considerations is introduced. It is described via a simplified complete graph-based design process for resource management algorithms.
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11
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Ortega A, Tralli V. QoS-Aware Resource Allocation with Pilot-Aided Channel Estimation for Heterogeneous Wireless Networks. Sensors (Basel) 2022; 22:4545. [PMID: 35746328 PMCID: PMC9227623 DOI: 10.3390/s22124545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 06/09/2022] [Accepted: 06/13/2022] [Indexed: 06/15/2023]
Abstract
The deployment of heterogeneous networks (HetNets) is a way to increase the network capacity and release part of the traffic generated by users inside a cell to small-scale wireless networks for service. In this context, the main problem is managing the interference due to the coexistence of small cells and macro cells. In this paper, a QoS-aware Resource Allocation (RA) algorithm jointly working with admission control (AC) over a two-tier HetNet scenario is investigated in the presence of both the pilot-symbols for channel estimation and the channel estimation error. The RA algorithm allows two users, the macro cell user (CU) and small cell user (SU), to simultaneously share the same resource block. Moreover, system performance and fairness are improved by including adaptive power allocation to users over resource blocks. In the framework of RA with proportional rate constraints, a novel algorithm is designed by including the effects of pilot-aided channel estimation. The algorithm is able to distribute the same proportional rate to all CUs and SUs, even in the presence of channel estimation error. Relevant numerical results for the downlink of a two-tier HetNet with pilot-aided channel estimation show that the rate dispersion is driven to zero while the sum-rate is maximized, and the average user rate penalty with respect to a perfect-CSI scenario may rise to 20%.
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Affiliation(s)
- Andres Ortega
- Telecommunications Engineering, Center for Studies and Sustainable Development (CEDS), Universidad Tecnológica Ecotec, Guayaquil 092301, Ecuador
| | - Velio Tralli
- Engineering Department, University of Ferrara—CNIT, I-44122 Ferrara, Italy;
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12
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Liu E, He R, Chen X, Yu C. Deep Reinforcement Learning Based Optical and Acoustic Dual Channel Multiple Access in Heterogeneous Underwater Sensor Networks. Sensors (Basel) 2022; 22:s22041628. [PMID: 35214530 PMCID: PMC8880241 DOI: 10.3390/s22041628] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 02/06/2022] [Accepted: 02/14/2022] [Indexed: 05/27/2023]
Abstract
In this paper, we investigate how to efficiently utilize channel bandwidth in heterogeneous hybrid optical and acoustic underwater sensor networks, where sensor nodes adopt different Media Access Control (MAC) protocols to transmit data packets to a common relay node on optical or acoustic channels. We propose a new MAC protocol based on deep reinforcement learning (DRL), referred to as optical and acoustic dual-channel deep-reinforcement learning multiple access (OA-DLMA), in which the sensor nodes utilizing the OA-DLMA protocol are called agents, and the remainder are non-agents. The agents can learn the transmission patterns of coexisting non-agents and find an optimal channel access strategy without any prior information. Moreover, in order to further enhance network performance, we develop a differentiated reward policy that rewards specific actions over optical and acoustic channels differently, with priority compensation being given to the optical channel to achieve greater data transmission. Furthermore, we have derived the optimal short-term sum throughput and channel utilization analytically and conducted extensive simulations to evaluate the OA-DLMA protocol. Simulation results show that our protocol performs with near-optimal performance and significantly outperforms other existing protocols in terms of short-term sum throughput and channel utilization.
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Affiliation(s)
- Enhong Liu
- College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China; (E.L.); (X.C.); (C.Y.)
| | - Rongxi He
- College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China; (E.L.); (X.C.); (C.Y.)
| | - Xiaojing Chen
- College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China; (E.L.); (X.C.); (C.Y.)
- School of Electrical Engineering, Dalian University of Science and Technology, Dalian 116052, China
| | - Cunqian Yu
- College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China; (E.L.); (X.C.); (C.Y.)
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Li J, Kong M, Wang D, Yang Z, Hao X. Prediction of lncRNA-Disease Associations via Closest Node Weight Graphs of the Spatial Neighborhood Based on the Edge Attention Graph Convolutional Network. Front Genet 2022; 12:808962. [PMID: 35058974 PMCID: PMC8763691 DOI: 10.3389/fgene.2021.808962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 11/29/2021] [Indexed: 11/24/2022] Open
Abstract
Accumulated evidence of biological clinical trials has shown that long non-coding RNAs (lncRNAs) are closely related to the occurrence and development of various complex human diseases. Research works on lncRNA–disease relations will benefit to further understand the pathogenesis of human complex diseases at the molecular level, but only a small proportion of lncRNA–disease associations has been confirmed. Considering the high cost of biological experiments, exploring potential lncRNA–disease associations with computational approaches has become very urgent. In this study, a model based on closest node weight graph of the spatial neighborhood (CNWGSN) and edge attention graph convolutional network (EAGCN), LDA-EAGCN, was developed to uncover potential lncRNA–disease associations by integrating disease semantic similarity, lncRNA functional similarity, and known lncRNA–disease associations. Inspired by the great success of the EAGCN method on the chemical molecule property recognition problem, the prediction of lncRNA–disease associations could be regarded as a component recognition problem of lncRNA–disease characteristic graphs. The CNWGSN features of lncRNA–disease associations combined with known lncRNA–disease associations were introduced to train EAGCN, and correlation scores of input data were predicted with EAGCN for judging whether the input lncRNAs would be associated with the input diseases. LDA-EAGCN achieved a reliable AUC value of 0.9853 in the ten-fold cross-over experiments, which was the highest among five state-of-the-art models. Furthermore, case studies of renal cancer, laryngeal carcinoma, and liver cancer were implemented, and most of the top-ranking lncRNA–disease associations have been proven by recently published experimental literature works. It can be seen that LDA-EAGCN is an effective model for predicting potential lncRNA–disease associations. Its source code and experimental data are available at https://github.com/HGDKMF/LDA-EAGCN.
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Affiliation(s)
- Jianwei Li
- Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China.,Hebei Province Key Laboratory of Big Data Calculation, Hebei University of Technology, Tianjin, China
| | - Mengfan Kong
- Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| | - Duanyang Wang
- Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| | - Zhenwu Yang
- Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| | - Xiaoke Hao
- Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
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14
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Jin S, Niu Z, Jiang C, Huang W, Xia F, Jin X, Liu X, Zeng X. HeTDR: Drug repositioning based on heterogeneous networks and text mining. Patterns (N Y) 2021; 2:100307. [PMID: 34430926 PMCID: PMC8369234 DOI: 10.1016/j.patter.2021.100307] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 05/11/2021] [Accepted: 06/14/2021] [Indexed: 12/14/2022]
Abstract
Using existing knowledge to carry out drug-disease associations prediction is a vital method for drug repositioning. However, effectively fusing the biomedical text and biological network information is one of the great challenges for most current drug repositioning methods. In this study, we propose a drug repositioning method based on heterogeneous networks and text mining (HeTDR). This model can combine drug features from multiple drug-related networks, disease features from biomedical corpora with the known drug-disease associations network to predict the correlation scores between drug and disease. Experiments demonstrate that HeTDR has excellent performance that is superior to that of state-of-the-art models. We present the top 10 novel HeTDR-predicted approved drugs for five diseases and prove our model is capable of discovering potential candidate drugs for disease indications. We developed a novel DL-based method for drug repositioning (HeTDR) HeTDR succeeds in fusing networks topology information and text mining information HeTDR obtains high accuracy, excessing most state-of-the-art models HeTDR could represent an algorithm integrating multiple sources of information
Traditional drug discovery and development are often time consuming and high risk. Drug repositioning aims to expand existing indications or discover new targets by studying the approved drug compounds, thereby reducing the time, costs, and risk of drug development. We propose a novel method in drug repositioning based on heterogeneous networks and text mining (HeTDR), which combines drugs features from multiple networks and diseases features from biomedical corpora to predict the correlation scores between drugs and diseases. This prediction model has provided a potential solution for multiple information fusion and to exhibit accurate performance leading to the discovery of new drugs for indications. This algorithm could contribute a new idea to the acceleration and development of future drug repositioning by using computational methods and provide computer-aided guidance for biologists in clinical settings.
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Affiliation(s)
- Shuting Jin
- Department of Computer Science, Xiamen University, Xiamen 361005, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China.,Shenzhen Research Institute of Xiamen University, Shenzhen 518000, China
| | | | - Changzhi Jiang
- Department of Computer Science, Xiamen University, Xiamen 361005, China
| | - Wei Huang
- Department of Computer Science, Xiamen University, Xiamen 361005, China
| | - Feng Xia
- Department of Computer Science, Xiamen University, Xiamen 361005, China
| | - Xurui Jin
- MindRank AI Ltd., Hangzhou, Zhejiang 311113, China
| | - Xiangrong Liu
- Department of Computer Science, Xiamen University, Xiamen 361005, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
| | - Xiangxiang Zeng
- School of Information Science and Engineering, Hunan University, Changsha 410082, China
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15
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Asada M, Gunasekaran N, Miwa M, Sasaki Y. Representing a Heterogeneous Pharmaceutical Knowledge-Graph with Textual Information. Front Res Metr Anal 2021; 6:670206. [PMID: 34278204 PMCID: PMC8281808 DOI: 10.3389/frma.2021.670206] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Accepted: 05/28/2021] [Indexed: 11/25/2022] Open
Abstract
We deal with a heterogeneous pharmaceutical knowledge-graph containing textual information built from several databases. The knowledge graph is a heterogeneous graph that includes a wide variety of concepts and attributes, some of which are provided in the form of textual pieces of information which have not been targeted in the conventional graph completion tasks. To investigate the utility of textual information for knowledge graph completion, we generate embeddings from textual descriptions given to heterogeneous items, such as drugs and proteins, while learning knowledge graph embeddings. We evaluate the obtained graph embeddings on the link prediction task for knowledge graph completion, which can be used for drug discovery and repurposing. We also compare the results with existing methods and discuss the utility of the textual information.
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Affiliation(s)
- Masaki Asada
- Computational Intelligence Laboratory, Toyota Technological Institute, Nagoya, Japan
| | - Nallappan Gunasekaran
- Computational Intelligence Laboratory, Toyota Technological Institute, Nagoya, Japan
| | - Makoto Miwa
- Computational Intelligence Laboratory, Toyota Technological Institute, Nagoya, Japan
| | - Yutaka Sasaki
- Computational Intelligence Laboratory, Toyota Technological Institute, Nagoya, Japan
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16
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Lee KH, Kim D. Cross-Layer Optimization for Heterogeneous MU-MIMO/OFDMA Networks. Sensors (Basel) 2021; 21:s21082744. [PMID: 33924620 PMCID: PMC8068908 DOI: 10.3390/s21082744] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/26/2021] [Accepted: 04/10/2021] [Indexed: 12/02/2022]
Abstract
To enable the full benefits from MU-MIMO (Multiuser-Multiple Input Multiple Output) and OFDMA (Orthogonal Frequency Division Multiple Access) to be achieved, the optimal use of these two technologies for a given set of network resources has been investigated in a rich body of literature. However, most of these studies have focused either on maximizing the performance of only one of these schemes, or have considered both but only for single-hop networks, in which the effect of the interference between nodes is relatively limited, thus causing the network performance to be overestimated. In addition, the heterogeneity of the nodes has not been sufficiently considered, and in particular, the joint use of OFDMA and MU-MIMO has been assumed to be always available at all nodes. In this paper, we propose a cross-layer optimization framework that considers both OFDMA and MU-MIMO for heterogeneous wireless networks. Not only does our model assume that the nodes have different capabilities, in terms of bandwidth and the number of antennas, but it also supports practical use cases in which nodes can support either OFDMA or MU-MIMO, or both at the same time. Our optimization model carefully takes into account the interactions between the key elements of the physical layer to the network layer. In addition, we consider multi-hop networks, and capture the complicated interference relationships between nodes as well as multi-path routing via multi-user transmissions. We formulate the proposed model as a Mixed Integer Linear Programming (MILP) problem, and initially model the case in which each node can selectively use either OFDMA or MU-MIMO; we then extend this to scenarios in which they are jointly used. As a case study, we apply the proposed model to sum-rate maximization and max–min fair allocation, and verify through MATLAB numerical evaluations that it can take appropriate advantage of each technology for a given set of network resources. Based on the optimization results, we also observe that when the two technologies are jointly used, more multi-user transmissions are enabled thanks to flexible resource allocation, meaning that greater use of the link capacity is achieved.
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Affiliation(s)
- Kyu-haeng Lee
- Department of Mobile Systems Engineering, Dankook University, Yongin-si 16890, Korea;
| | - Daehee Kim
- Department of Internet of Things, Soonchunhyang University, Asan-si 31538, Korea
- Correspondence:
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17
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Lee K. Distributed Beamforming and Power Allocation for Heterogeneous Networks with MISO Interference Channel. Sensors (Basel) 2021; 21:2606. [PMID: 33917693 DOI: 10.3390/s21082606] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 03/23/2021] [Accepted: 04/06/2021] [Indexed: 11/16/2022]
Abstract
To address the limitations of centralized resource allocation, i.e., high computational complexity and signaling overhead, a distributed beamforming and power allocation strategy is proposed for heterogeneous networks with multiple-input-single-output (MISO) interference channels. In the proposed scheme, each secondary user transceiver pair (SU TP) determines the beamforming vector and transmits power to maximize its own spectral efficiency (SE) while keeping the interference to the primary user below a predetermined threshold, and such resource management for each SU TP is updated iteratively without any information sharing until the strategies for all SU TPs converge. The simulation confirms that the proposed scheme can achieve a performance comparable to that of a centralized approach with a much lower computation time, e.g., less than 5% degradation in SE while improving computation time by more than 10 times.
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18
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Phan LA, Kim T. Breaking Down the Compatibility Problem in Smart Homes: A Dynamically Updatable Gateway Platform. Sensors (Basel) 2020; 20:E2783. [PMID: 32422946 DOI: 10.3390/s20102783] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 05/08/2020] [Accepted: 05/12/2020] [Indexed: 11/17/2022]
Abstract
Smart home is one of the most promising applications of the Internet of Things. Although there have been studies about this technology in recent years, the adoption rate of smart homes is still low. One of the largest barriers is technological fragmentation within the smart home ecosystem. Currently, there are many protocols used in a connected home, increasing the confusion of consumers when choosing a product for their house. One possible solution for this fragmentation is to make a gateway to handle the diverse protocols as a central hub in the home. However, this solution brings about another issue for manufacturers: compatibility. Because of the various smart devices on the market, supporting all possible devices in one gateway is also an enormous challenge. In this paper, we propose a software architecture for a gateway in a smart home system to solve the compatibility problem. By creating a mechanism to dynamically download and update a device profile from a server, the gateway can easily handle new devices. Moreover, the proposed gateway also supports unified control over heterogeneous networks. We implemented a prototype to prove the feasibility of the proposed gateway architecture and evaluated its performance from the viewpoint of message execution time over heterogeneous networks, as well as the latency for device profile downloads and updates, and the overhead needed for handling unknown commands.
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19
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Codeluppi G, Cilfone A, Davoli L, Ferrari G. LoRaFarM: A LoRaWAN-Based Smart Farming Modular IoT Architecture. Sensors (Basel) 2020; 20:s20072028. [PMID: 32260338 PMCID: PMC7180486 DOI: 10.3390/s20072028] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Revised: 03/30/2020] [Accepted: 03/30/2020] [Indexed: 11/16/2022]
Abstract
Presently, the adoption of Internet of Things (IoT)-related technologies in the Smart Farming domain is rapidly emerging. The ultimate goal is to collect, monitor, and effectively employ relevant data for agricultural processes, with the purpose of achieving an optimized and more environmentally sustainable agriculture. In this paper, a low-cost, modular, and Long-Range Wide-Area Network (LoRaWAN)-based IoT platform, denoted as “LoRaWAN-based Smart Farming Modular IoT Architecture” (LoRaFarM), and aimed at improving the management of generic farms in a highly customizable way, is presented. The platform, built around a core middleware, is easily extensible with ad-hoc low-level modules (feeding the middleware with data coming from the sensors deployed in the farm) or high-level modules (providing advanced functionalities to the farmer). The proposed platform has been evaluated in a real farm in Italy, collecting environmental data (air/soil temperature and humidity) related to the growth of farm products (namely grapes and greenhouse vegetables) over a period of three months. A web-based visualization tool for the collected data is also presented, to validate the LoRaFarM architecture.
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20
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Deng D, Li X, Zhao M, Rabie KM, Kharel R. Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks. Sensors (Basel) 2020; 20:E1730. [PMID: 32244857 DOI: 10.3390/s20061730] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 03/18/2020] [Accepted: 03/18/2020] [Indexed: 11/23/2022]
Abstract
Perfect channel state information (CSI) is required in most of the classical physical-layer security techniques, while it is difficult to obtain the ideal CSI due to the time-varying wireless fading channel. Although imperfect CSI has a great impact on the security of MIMO communications, deep learning is becoming a promising solution to handle the negative effect of imperfect CSI. In this work, we propose two types of deep learning-based secure MIMO detectors for heterogeneous networks, where the macro base station (BS) chooses the null-space eigenvectors to prevent information leakage to the femto BS. Thus, the bit error rate of the associated user is adopted as the metric to evaluate the system performance. With the help of deep convolutional neural networks (CNNs), the macro BS obtains the refined version from the imperfect CSI. Simulation results are provided to validate the proposed algorithms. The impacts of system parameters, such as the correlation factor of imperfect CSI, the normalized doppler frequency, the number of antennas is investigated in different setup scenarios. The results show that considerable performance gains can be obtained from the deep learning-based detectors compared with the classical maximum likelihood algorithm.
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21
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Alsafasfeh Q, Saraereh OA, Ali A, Al-Tarawneh L, Khan I, Silva A. Efficient Power Control Framework for Small-Cell Heterogeneous Networks. Sensors (Basel) 2020; 20:s20051467. [PMID: 32155972 PMCID: PMC7085631 DOI: 10.3390/s20051467] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 02/17/2020] [Accepted: 03/05/2020] [Indexed: 11/21/2022]
Abstract
Heterogeneous networks are rapidly emerging as one of the key enablers of beyond fifth-generation (5G) wireless networks. It is gradually becoming clear to the network operators that existing cellular networks may not be able to support the traffic demands of the future. Thus, there is an upsurge in the interest of efficiently deploying small-cell networks for accommodating a growing number of user equipment (UEs). This work further extends the state-of-the-art by proposing an optimization framework for reducing the power consumption of small-cell base stations (BSs). Specifically, a novel algorithm has been proposed which dynamically switches off the redundant small-cell BSs based on the traffic demands of the network. Due to the dynamicity of the formulated problem, a new UE admission control policy has been presented when the problem becomes infeasible to solve. To validate the effectiveness of the proposed solution, the simulation results are compared with conventional techniques. It is shown that the proposed power control solution outperforms the conventional approaches both in terms of accommodating more UEs and reducing power consumption.
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Affiliation(s)
- Qais Alsafasfeh
- Department of Electrical Power and Mechatronics, Tafila Technical University, Tafila 11183, Jordan;
| | - Omar A. Saraereh
- Department of Electrical Engineering, Hashemite University, Zarqa 13133, Jordan; (O.A.S.); (A.A.)
| | - Ashraf Ali
- Department of Electrical Engineering, Hashemite University, Zarqa 13133, Jordan; (O.A.S.); (A.A.)
| | - Luae Al-Tarawneh
- Communications Engineering Department, Princess Sumayya University for Technology, P.O.Box 1438, Amman 11941, Jordan;
| | - Imran Khan
- Department of Electrical Engineering, University of Engineering and Technology Peshawar, KPK P.O.Box 814, Pakistan;
| | - Adão Silva
- Instituto de Telecomunicações (IT) and Departamento de Eletrónica, Telecomunicações e Informática (DETI), University of Aveiro, 3810-193 Aveiro, Portugal
- Correspondence:
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22
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Suratanee A, Plaimas K. Heterogeneous Network Model to Identify Potential Associations Between Plasmodium vivax and Human Proteins. Int J Mol Sci 2020; 21:E1310. [PMID: 32075230 DOI: 10.3390/ijms21041310] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Revised: 01/29/2020] [Accepted: 02/12/2020] [Indexed: 02/06/2023] Open
Abstract
Integration of multiple sources and data levels provides a great insight into the complex associations between human and malaria systems. In this study, a meta-analysis framework was developed based on a heterogeneous network model for integrating human-malaria protein similarities, a human protein interaction network, and a Plasmodium vivax protein interaction network. An iterative network propagation was performed on the heterogeneous network until we obtained stabilized weights. The association scores were calculated for qualifying a novel potential human-malaria protein association. This method provided a better performance compared to random experiments. After that, the stabilized network was clustered into association modules. The potential association candidates were then thoroughly analyzed by statistical enrichment analysis with protein complexes and known drug targets. The most promising target proteins were the succinate dehydrogenase protein complex in the human citrate (TCA) cycle pathway and the nicotinic acetylcholine receptor in the human central nervous system. Promising associations and potential drug targets were also provided for further studies and designs in therapeutic approaches for malaria at a systematic level. In conclusion, this method is efficient to identify new human-malaria protein associations and can be generalized to infer other types of association studies to further advance biomedical science.
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23
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Zhang S, Kang G. User Association and Power Control for Energy Efficiency Maximization in M2M-Enabled Uplink Heterogeneous Networks with NOMA. Sensors (Basel) 2019; 19:E5307. [PMID: 31810276 DOI: 10.3390/s19235307] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 11/14/2019] [Accepted: 11/25/2019] [Indexed: 11/30/2022]
Abstract
To support a vast number of devices with less energy consumption, we propose a new user association and power control scheme for machine to machine enabled heterogeneous networks with non-orthogonal multiple access (NOMA), where a mobile user (MU) acting as a machine-type communication gateway can decode and forward both the information of machine-type communication devices and its own data to the base station (BS) directly. MU association and power control are jointly considered in the formulated as optimization problem for energy efficiency (EE) maximization under the constraints of minimum data rate requirements of MUs. A many-to-one MU association matching algorithm is firstly proposed based on the theory of matching game. By taking swap matching operations among MUs, BSs, and sub-channels, the original problem can be solved by dealing with the EE maximization for each sub-channel. Then, two power control algorithms are proposed, where the tools of sequential optimization, fractional programming, and exhaustive search have been employed. Simulation results are provided to demonstrate the optimality properties of our algorithms under different parameter settings.
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24
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Jeong S, Baek Y, Son SH. Hierarchical Network Architecture for Non-Safety Applications in Urban Vehicular Ad-Hoc Networks. Sensors (Basel) 2019; 19:E4306. [PMID: 31590260 DOI: 10.3390/s19194306] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Revised: 09/27/2019] [Accepted: 10/01/2019] [Indexed: 11/24/2022]
Abstract
In the vehicular ad-hoc networks (VANETs), wireless access in vehicular environments (WAVE) as the core networking technology is suitable for supporting safety-critical applications, but it is difficult to guarantee its performance when transmitting non-safety data, especially high volumes of data, in a multi-hop manner. Therefore, to provide non-safety applications effectively and reliably for users, we propose a hybrid V2V communication system (HVCS) using hierarchical networking architecture: a centralized control model for the establishment of a fast connection and a local data propagation model for efficient and reliable transmissions. The centralized control model had the functionality of node discovery, local ad-hoc group (LAG) formation, a LAG owner (LAGO) determination, and LAG management. The local data propagation indicates that data are transmitted only within the LAG under the management of the LAGO. To support the end-to-end multi-hop transmission over V2V communication, vehicles outside the LAG employ the store and forward model. We designed three phases consisting of concise device discovery (CDD), concise provisioning (CP), and data transmission, so that the HVCS is highly efficient and robust on the hierarchical networking architecture. Under the centralized control, the phase of the CDD operates to improve connection establishment time, and the CP is to simplify operations required for security establishment. Our HVCS is implemented as a two-tier system using a traffic controller for centralized control using cellular networks and a smartphone for local data propagation over Wi-Fi Direct. The HVCS’ performance was evaluated using Veins, and compared with WAVE in terms of throughput, connectivity, and quality of service (QoS). The effectiveness of the centralized control was demonstrated in comparative experiments with Wi-Fi Direct. The connection establishment time measured was only 0.95 s for the HVCS. In the case of video streaming services through the HVCS, about 98% of the events could be played over 16 frames per second. The throughput for the streaming data was between 74% to 81% when the vehicle density was over 50%. We demonstrated that the proposed system has high throughput and satisfies the QoS of streaming services even though the end-to-end delay is a bit longer when compared to that of WAVE.
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25
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Lysogor I, Voskov L, Rolich A, Efremov S. Study of Data Transfer in a Heterogeneous LoRa-Satellite Network for the Internet of Remote Things. Sensors (Basel) 2019; 19:s19153384. [PMID: 31374980 PMCID: PMC6695685 DOI: 10.3390/s19153384] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Revised: 07/30/2019] [Accepted: 07/30/2019] [Indexed: 11/16/2022]
Abstract
In the absence of traditional communication infrastructures, the choice of available technologies for building data collection and control systems in remote areas is very limited. This paper reviews and analyzes protocols and technologies for transferring Internet of Things (IoT) data and presents an architecture for a hybrid IoT-satellite network, which includes a long range (LoRa) low power wide area network (LPWAN) terrestrial network for data collection and an Iridium satellite system for backhaul connectivity. Simulation modelling, together with a specialized experimental stand, allowed us to study the applicability of different methods of information presentation for the case of transmitting IoT data over low-speed satellite communication channels. We proposed a data encoding and packaging scheme called GDEP (Gateway Data Encoding and Packaging). It is based on the combination of data format conversion at the connection points of a heterogeneous network and message packaging. GDEP enabled the reduction of the number of utilized Short Burst Data (SBD) containers and the overall transmitted data size by almost five times.
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Affiliation(s)
- Ivan Lysogor
- Laboratory of the Internet of Things and Cyber-physical Systems, National Research University Higher School of Economics, Moscow 101000, Russia
| | - Leonid Voskov
- Department of Computer Engineering, National Research University Higher School of Economics, Moscow 101000, Russia
| | - Alexey Rolich
- Department of Computer Engineering, National Research University Higher School of Economics, Moscow 101000, Russia.
| | - Sergey Efremov
- School of Business Informatics, National Research University Higher School of Economics, Moscow 101000, Russia
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26
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Tripathi B, Parthasarathy S, Sinha H, Raman K, Ravindran B. Adapting Community Detection Algorithms for Disease Module Identification in Heterogeneous Biological Networks. Front Genet 2019; 10:164. [PMID: 30918511 PMCID: PMC6424898 DOI: 10.3389/fgene.2019.00164] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 02/14/2019] [Indexed: 11/13/2022] Open
Abstract
Biological networks catalog the complex web of interactions happening between different molecules, typically proteins, within a cell. These networks are known to be highly modular, with groups of proteins associated with specific biological functions. Human diseases often arise from the dysfunction of one or more such proteins of the biological functional group. The ability, to identify and automatically extract these modules has implications for understanding the etiology of different diseases as well as the functional roles of different protein modules in disease. The recent DREAM challenge posed the problem of identifying disease modules from six heterogeneous networks of proteins/genes. There exist many community detection algorithms, but all of them are not adaptable to the biological context, as these networks are densely connected and the size of biologically relevant modules is quite small. The contribution of this study is 3-fold: first, we present a comprehensive assessment of many classic community detection algorithms for biological networks to identify non-overlapping communities, and propose heuristics to identify small and structurally well-defined communities-core modules. We evaluated our performance over 180 GWAS datasets. In comparison to traditional approaches, with our proposed approach we could identify 50% more number of disease-relevant modules. Thus, we show that it is important to identify more compact modules for better performance. Next, we sought to understand the peculiar characteristics of disease-enriched modules and what causes standard community detection algorithms to detect so few of them. We performed a comprehensive analysis of the interaction patterns of known disease genes to understand the structure of disease modules and show that merely considering the known disease genes set as a module does not give good quality clusters, as measured by typical metrics such as modularity and conductance. We go on to present a methodology leveraging these known disease genes, to also include the neighboring nodes of these genes into a module, to form good quality clusters and subsequently extract a "gold-standard set" of disease modules. Lastly, we demonstrate, with justification, that "overlapping" community detection algorithms should be the preferred choice for disease module identification since several genes participate in multiple biological functions.
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Affiliation(s)
- Beethika Tripathi
- Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, India.,Initiative for Biological Systems Engineering, Indian Institute of Technology Madras, Chennai, India.,Robert Bosch Centre for Data Science and AI, Indian Institute of Technology Madras, Chennai, India
| | - Srinivasan Parthasarathy
- Department of Computer Science and Engineering, Ohio State University, Columbus, OH, United States.,Department of Biomedical Informatics, Ohio State University, Columbus, OH, United States
| | - Himanshu Sinha
- Initiative for Biological Systems Engineering, Indian Institute of Technology Madras, Chennai, India.,Robert Bosch Centre for Data Science and AI, Indian Institute of Technology Madras, Chennai, India.,Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Karthik Raman
- Initiative for Biological Systems Engineering, Indian Institute of Technology Madras, Chennai, India.,Robert Bosch Centre for Data Science and AI, Indian Institute of Technology Madras, Chennai, India.,Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Balaraman Ravindran
- Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, India.,Initiative for Biological Systems Engineering, Indian Institute of Technology Madras, Chennai, India.,Robert Bosch Centre for Data Science and AI, Indian Institute of Technology Madras, Chennai, India
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27
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Navarro C, Martínez V, Blanco A, Cano C. ProphTools: general prioritization tools for heterogeneous biological networks. Gigascience 2018; 6:1-8. [PMID: 29186475 PMCID: PMC5751048 DOI: 10.1093/gigascience/gix111] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 11/09/2017] [Indexed: 12/17/2022] Open
Abstract
Background Networks have been proven effective representations for the analysis of biological data. As such, there exist multiple methods to extract knowledge from biological networks. However, these approaches usually limit their scope to a single biological entity type of interest or they lack the flexibility to analyze user-defined data. Results We developed ProphTools, a flexible open-source command-line tool that performs prioritization on a heterogeneous network. ProphTools prioritization combines a Flow Propagation algorithm similar to a Random Walk with Restarts and a weighted propagation method. A flexible model for the representation of a heterogeneous network allows the user to define a prioritization problem involving an arbitrary number of entity types and their interconnections. Furthermore, ProphTools provides functionality to perform cross-validation tests, allowing users to select the best network configuration for a given problem. ProphTools core prioritization methodology has already been proven effective in gene-disease prioritization and drug repositioning. Here we make ProphTools available to the scientific community as flexible, open-source software and perform a new proof-of-concept case study on long noncoding RNAs (lncRNAs) to disease prioritization. Conclusions ProphTools is robust prioritization software that provides the flexibility not present in other state-of-the-art network analysis approaches, enabling researchers to perform prioritization tasks on any user-defined heterogeneous network. Furthermore, the application to lncRNA-disease prioritization shows that ProphTools can reach the performance levels of ad hoc prioritization tools without losing its generality.
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Affiliation(s)
- Carmen Navarro
- Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
| | - Victor Martínez
- Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
| | - Armando Blanco
- Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
| | - Carlos Cano
- Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
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Stamatakis G, Tragos EZ, Traganitis A. Energy Efficient Policies for Data Transmission in Disruption Tolerant Heterogeneous IoT Networks. Sensors (Basel) 2018; 18:E2891. [PMID: 30200375 DOI: 10.3390/s18092891] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 08/27/2018] [Accepted: 08/30/2018] [Indexed: 11/16/2022]
Abstract
The Internet-of-things facilitates the development of many groundbreaking applications. A large number of these applications involve mobile end nodes and a sparsely deployed network of base stations that operate as gateways to the Internet. Most of the mobile nodes, at least within city areas, are connected through low power wide area networking technologies (LPWAN) using public frequencies. Mobility and sparse network coverage result in long delays and intermittent connectivity for the end nodes. Disruption Tolerant Networks and utilization of heterogeneous wireless interfaces have emerged as key technologies to tackle the problem at hand. The first technology renders communication resilient to intermittent connectivity by storing and carrying data while the later increases the communication opportunities of the end nodes and at the same time reduces energy consumption whenever short-range communication is possible. However, one has to consider that end nodes are typically both memory and energy constrained devices which makes finding an energy efficient data transmission policy for heterogeneous disruption tolerant networks imperative. In this work we utilize information related to the spatial availability of network resources and localization information to formulate the problem at hand as a dynamic programming problem. Next, we utilize the framework of Markov Decision Processes to derive approximately optimal and suboptimal data transmission policies. We also prove that we can achieve improved packet transmission policies and reduce energy consumption, extending battery lifetime. This is achieved by knowing the spatial availability of heterogeneous network resources combined with the mobile node’s location information. Numerical resultsshow significant gains achieved by utilizing the derived approximately optimal and suboptimal policies.
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29
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Tian R, Ma L, Wang Z, Tan X. Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks. Sensors (Basel) 2018; 18:s18082548. [PMID: 30081529 PMCID: PMC6111842 DOI: 10.3390/s18082548] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Revised: 07/18/2018] [Accepted: 07/29/2018] [Indexed: 11/16/2022]
Abstract
This paper considers interference management and capacity improvement for Internet of Things (IoT) oriented two-tier networks by exploiting cognition between network tiers with interference alignment (IA). More specifically, we target our efforts on the next generation two-tier networks, where a tier of femtocell serving multiple IoT devices shares the licensed spectrum with a tier of pre-existing macrocell via a cognitive radio. Aiming to manage the cross-tier interference caused by cognitive spectrum sharing as well as ensure an optimal capacity of the femtocell, two novel self-organizing cognitive IA schemes are proposed. First, we propose an interference nulling based cognitive IA scheme. In such a scheme, both co-tier and cross-tier interferences are aligned into the orthogonal subspace at each IoT receiver, which means all the interference can be perfectly eliminated without causing any performance degradation on the macrocell. However, it is known that the interference nulling based IA algorithm achieves its optimum only in high signal to noise ratio (SNR) scenarios, where the noise power is negligible. Consequently, when the imposed interference-free constraint on the femtocell can be relaxed, we also present a partial cognitive IA scheme that further enhances the network performance under a low and intermediate SNR. Additionally, the feasibility conditions and capacity analyses of the proposed schemes are provided. Both theoretical and numerical results demonstrate that the proposed cognitive IA schemes outperform the traditional orthogonal precoding methods in terms of network capacity, while preserving for macrocell users the desired quality of service.
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Affiliation(s)
- Run Tian
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China.
- School of Engineering, Michigan State University, East Lansing, MI 48824, USA.
| | - Lin Ma
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China.
| | - Zhe Wang
- School of Engineering, Michigan State University, East Lansing, MI 48824, USA.
| | - Xuezhi Tan
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China.
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30
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Yao K, Luo Y, Yang Y, Liu X, Zhang Y, Yao C. Location-Aware Incentive Mechanism for Traffic Offloading in Heterogeneous Networks: A Stackelberg Game Approach. Entropy (Basel) 2018; 20:e20040302. [PMID: 33265393 PMCID: PMC7512820 DOI: 10.3390/e20040302] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 04/01/2018] [Accepted: 04/04/2018] [Indexed: 11/16/2022]
Abstract
This article investigates the traffic offloading problem in the heterogeneous network. The location of small cells is considered as an important factor in two aspects: the amount of resources they share for offloaded macrocell users and the performance enhancement they bring after offloading. A location-aware incentive mechanism is therefore designed to incentivize small cells to serve macrocell users. Instead of taking the amount of resources shared as the basis of the reward division, the performance promotion brought to the macro network is taken. Meanwhile, in order to ensure the superiority of small cell users, the significance of them weighs heavier than macrocell users instead of being treated equally. The offloading problem is formulated as a Stackelberg game where the macro cell base station is the leader and small cells are followers. The Stackelberg equilibrium of the game is proved to be existing and unique. It is also proved to be the optimum of the proposed problem. Simulation and numerical results verify the effectiveness of the proposed method.
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Affiliation(s)
- Kailing Yao
- College of Communications Engineering, Army Engineering University, Nanjing 210007, China
| | - Yunpeng Luo
- College of Communications Engineering, Army Engineering University, Nanjing 210007, China
| | - Yang Yang
- College of Communications Engineering, Army Engineering University, Nanjing 210007, China
- Correspondence: ; Tel.: +86-139-1395-3836
| | - Xin Liu
- College of Information Science and Engineering, Guilin University of Technology, Guangxi 541000, China
| | - Yuli Zhang
- College of Communications Engineering, Army Engineering University, Nanjing 210007, China
| | - Changhua Yao
- College of Communications Engineering, Army Engineering University, Nanjing 210007, China
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31
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Masini BM, Bazzi A, Zanella A. Vehicular Visible Light Networks for Urban Mobile Crowd Sensing. Sensors (Basel) 2018; 18:E1177. [PMID: 29649149 DOI: 10.3390/s18041177] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 04/06/2018] [Accepted: 04/10/2018] [Indexed: 11/24/2022]
Abstract
Crowd sensing is a powerful tool to map and predict interests and events. In the future, it could be boosted by an increasing number of connected vehicles sharing information and intentions. This will be made available by on board wireless connected devices able to continuously communicate with other vehicles and with the environment. Among the enabling technologies, visible light communication (VLC) represents a low cost solution in the short term. In spite of the fact that vehicular communications cannot rely on the sole VLC due to the limitation provided by the light which allows communications in visibility only, VLC can however be considered to complement other wireless communication technologies which could be overloaded in dense scenarios. In this paper we evaluate the performance of VLC connected vehicles when urban crowd sensing is addressed and we compare the performance of sole vehicular visible light networks with that of VLC as a complementary technology of IEEE 802.11p. Results, obtained through a realistic simulation tool taking into account both the roadmap constraints and the technologies protocols, help to understand when VLC provides the major improvement in terms of delivered data varying the number and position of RSUs and the FOV of the receiver.
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Himmelstein DS, Lizee A, Hessler C, Brueggeman L, Chen SL, Hadley D, Green A, Khankhanian P, Baranzini SE. Systematic integration of biomedical knowledge prioritizes drugs for repurposing. eLife 2017; 6:26726. [PMID: 28936969 PMCID: PMC5640425 DOI: 10.7554/elife.26726] [Citation(s) in RCA: 201] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2017] [Accepted: 09/11/2017] [Indexed: 12/16/2022] Open
Abstract
The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound-disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.
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Affiliation(s)
- Daniel Scott Himmelstein
- Biological and Medical Informatics Program, University of California, San Francisco, San Francisco, United States.,Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, United States
| | - Antoine Lizee
- Department of Neurology, University of California, San Francisco, San Francisco, United States.,ITUN-CRTI-UMR 1064 Inserm, University of Nantes, Nantes, France
| | - Christine Hessler
- Department of Neurology, University of California, San Francisco, San Francisco, United States
| | - Leo Brueggeman
- Department of Neurology, University of California, San Francisco, San Francisco, United States.,University of Iowa, Iowa City, United States
| | - Sabrina L Chen
- Department of Neurology, University of California, San Francisco, San Francisco, United States.,Johns Hopkins University, Baltimore, United States
| | - Dexter Hadley
- Department of Pediatrics, University of California, San Fransisco, San Fransisco, United States.,Institute for Computational Health Sciences, University of California, San Francisco, San Francisco, United States
| | - Ari Green
- Department of Neurology, University of California, San Francisco, San Francisco, United States
| | - Pouya Khankhanian
- Department of Neurology, University of California, San Francisco, San Francisco, United States.,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, United States
| | - Sergio E Baranzini
- Biological and Medical Informatics Program, University of California, San Francisco, San Francisco, United States.,Department of Neurology, University of California, San Francisco, San Francisco, United States
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Zhang Z, Chen Y. Position Fingerprint-Based Beam Selection in Millimeter Wave Heterogeneous Networks. Sensors (Basel) 2017; 17:s17092009. [PMID: 28862684 PMCID: PMC5621152 DOI: 10.3390/s17092009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 08/28/2017] [Accepted: 08/30/2017] [Indexed: 11/16/2022]
Abstract
The traditional beam selection algorithms determine the optimal beam direction by feeding back the perfect channel state information (CSI) in a millimeter wave (mmWave) massive Multiple-Input Multiple-Output (MIMO) system. Popular beam selection algorithms mostly focus on the methods of feedback and exhaustive search. In order to reduce the extra computational complexity coming from the redundant feedback and exhaustive search, a position fingerprint (PFP)-based mmWave multi-cell beam selection scheme is proposed in this paper. In the proposed scheme, the best beam identity (ID) and the strongest interference beam IDs from adjacent cells of each fingerprint spot are stored in a fingerprint database (FPDB), then the optimal beam and the strongest interference beams can be determined by matching the current PFP of the user equipment (UE) with the PFP in the FPDB instead of exhaustive search, and the orthogonal codes are also allocated to the optimal beam and the strongest interference beams. Simulation results show that the proposed PFP-based beam selection scheme can reduce the computational complexity and inter-cell interference and produce less feedback, and the system sum-rate for the mmWave heterogeneous networks is also improved.
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Affiliation(s)
- Zufan Zhang
- School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
| | - Yanbo Chen
- School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
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Cho H, Berger B, Peng J. Compact Integration of Multi-Network Topology for Functional Analysis of Genes. Cell Syst 2016; 3:540-548.e5. [PMID: 27889536 DOI: 10.1016/j.cels.2016.10.017] [Citation(s) in RCA: 132] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Revised: 08/14/2016] [Accepted: 10/19/2016] [Indexed: 01/18/2023]
Abstract
The topological landscape of molecular or functional interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, a pressing yet-unsolved challenge is how to combine multiple heterogeneous networks, each having different connectivity patterns, to achieve more accurate inference. Here, we describe the Mashup framework for scalable and robust network integration. In Mashup, the diffusion in each network is first analyzed to characterize the topological context of each node. Next, the high-dimensional topological patterns in individual networks are canonically represented using low-dimensional vectors, one per gene or protein. These vectors can then be plugged into off-the-shelf machine learning methods to derive functional insights about genes or proteins. We present tools based on Mashup that achieve state-of-the-art performance in three diverse functional inference tasks: protein function prediction, gene ontology reconstruction, and genetic interaction prediction. Mashup enables deeper insights into the structure of rapidly accumulating and diverse biological network data and can be broadly applied to other network science domains.
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Affiliation(s)
- Hyunghoon Cho
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA; Department of Mathematics, MIT, Cambridge, MA 02139, USA.
| | - Jian Peng
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA; Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA.
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35
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Xiao Z, Liu H, Havyarimana V, Li T, Wang D. Analytical Study on Multi-Tier 5G Heterogeneous Small Cell Networks: Coverage Performance and Energy Efficiency. Sensors (Basel) 2016; 16:s16111854. [PMID: 27827917 PMCID: PMC5134513 DOI: 10.3390/s16111854] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 10/29/2016] [Accepted: 11/01/2016] [Indexed: 12/03/2022]
Abstract
In this paper, we investigate the coverage performance and energy efficiency of multi-tier heterogeneous cellular networks (HetNets) which are composed of macrocells and different types of small cells, i.e., picocells and femtocells. By virtue of stochastic geometry tools, we model the multi-tier HetNets based on a Poisson point process (PPP) and analyze the Signal to Interference Ratio (SIR) via studying the cumulative interference from pico-tier and femto-tier. We then derive the analytical expressions of coverage probabilities in order to evaluate coverage performance in different tiers and investigate how it varies with the small cells’ deployment density. By taking the fairness and user experience into consideration, we propose a disjoint channel allocation scheme and derive the system channel throughput for various tiers. Further, we formulate the energy efficiency optimization problem for multi-tier HetNets in terms of throughput performance and resource allocation fairness. To solve this problem, we devise a linear programming based approach to obtain the available area of the feasible solutions. System-level simulations demonstrate that the small cells’ deployment density has a significant effect on the coverage performance and energy efficiency. Simulation results also reveal that there exits an optimal small cell base station (SBS) density ratio between pico-tier and femto-tier which can be applied to maximize the energy efficiency and at the same time enhance the system performance. Our findings provide guidance for the design of multi-tier HetNets for improving the coverage performance as well as the energy efficiency.
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Affiliation(s)
- Zhu Xiao
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
- State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an 710071, China.
| | - Hongjing Liu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
| | - Vincent Havyarimana
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
| | - Tong Li
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
| | - Dong Wang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
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36
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Fraga-Lamas P, Fernández-Caramés TM, Suárez-Albela M, Castedo L, González-López M. A Review on Internet of Things for Defense and Public Safety. Sensors (Basel) 2016; 16:E1644. [PMID: 27782052 DOI: 10.3390/s16101644] [Citation(s) in RCA: 143] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Accepted: 09/29/2016] [Indexed: 11/17/2022]
Abstract
The Internet of Things (IoT) is undeniably transforming the way that organizations communicate and organize everyday businesses and industrial procedures. Its adoption has proven well suited for sectors that manage a large number of assets and coordinate complex and distributed processes. This survey analyzes the great potential for applying IoT technologies (i.e., data-driven applications or embedded automation and intelligent adaptive systems) to revolutionize modern warfare and provide benefits similar to those in industry. It identifies scenarios where Defense and Public Safety (PS) could leverage better commercial IoT capabilities to deliver greater survivability to the warfighter or first responders, while reducing costs and increasing operation efficiency and effectiveness. This article reviews the main tactical requirements and the architecture, examining gaps and shortcomings in existing IoT systems across the military field and mission-critical scenarios. The review characterizes the open challenges for a broad deployment and presents a research roadmap for enabling an affordable IoT for defense and PS.
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Okasaka S, Weiler RJ, Keusgen W, Pudeyev A, Maltsev A, Karls I, Sakaguchi K. Proof-of-Concept of a Millimeter-Wave Integrated Heterogeneous Network for 5G Cellular. Sensors (Basel) 2016; 16:E1362. [PMID: 27571074 DOI: 10.3390/s16091362] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Revised: 08/14/2016] [Accepted: 08/15/2016] [Indexed: 11/23/2022]
Abstract
The fifth-generation mobile networks (5G) will not only enhance mobile broadband services, but also enable connectivity for a massive number of Internet-of-Things devices, such as wireless sensors, meters or actuators. Thus, 5G is expected to achieve a 1000-fold or more increase in capacity over 4G. The use of the millimeter-wave (mmWave) spectrum is a key enabler to allowing 5G to achieve such enhancement in capacity. To fully utilize the mmWave spectrum, 5G is expected to adopt a heterogeneous network (HetNet) architecture, wherein mmWave small cells are overlaid onto a conventional macro-cellular network. In the mmWave-integrated HetNet, splitting of the control plane (CP) and user plane (UP) will allow continuous connectivity and increase the capacity of the mmWave small cells. mmWave communication can be used not only for access linking, but also for wireless backhaul linking, which will facilitate the installation of mmWave small cells. In this study, a proof-of-concept (PoC) was conducted to demonstrate the practicality of a prototype mmWave-integrated HetNet, using mmWave technologies for both backhaul and access.
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