scholarly journals Opportunistic Network Algorithms for Internet Traffic Offloading in Music Festival Scenarios

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3315
Author(s):  
Aida-Ștefania Manole ◽  
Radu-Ioan Ciobanu ◽  
Ciprian Dobre ◽  
Raluca Purnichescu-Purtan
Keyword(s):  
High Performance ◽  
Geographical Area ◽  
Mobility Model ◽  
Routing Algorithms ◽  
Message Delivery ◽  
Opportunistic Network ◽  
Key Factors ◽  
Network Algorithms ◽  
Music Festival ◽  
Social Metrics

Constant Internet connectivity has become a necessity in our lives. Hence, music festival organizers allocate part of their budget for temporary Wi-Fi equipment in order to sustain the high network traffic generated in such a small geographical area, but this naturally leads to high costs that need to be decreased. Thus, in this paper, we propose a solution that can help offload some of that traffic to an opportunistic network created with the attendees’ smartphones, therefore minimizing the costs of the temporary network infrastructure. Using a music festival-based mobility model that we propose and analyze, we introduce two routing algorithms which can enable end-to-end message delivery between participants. The key factors for high performance are social metrics and limiting the number of message copies at any given time. We show that the proposed solutions are able to offer high delivery rates and low delivery delays for various scenarios at a music festival.

scholarly journals A Routing Algorithm for the Sparse Opportunistic Networks Based on Node Intimacy

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Gang Xu ◽  
Xinyue Wang ◽  
Na Zhang ◽  
Zhifei Wang ◽  
Lin Yu ◽  
...  
Keyword(s):  
Routing Algorithm ◽  
Opportunistic Networks ◽  
Network Routing ◽  
Routing Algorithms ◽  
Time Slice ◽  
Message Delivery ◽  
Opportunistic Network ◽  
Network Resources ◽  
Subsequent Time

Opportunistic networks are becoming more and more important in the Internet of Things. The opportunistic network routing algorithm is a very important algorithm, especially based on the historical encounters of the nodes. Such an algorithm can improve message delivery quality in scenarios where nodes meet regularly. At present, many kinds of opportunistic network routing algorithms based on historical message have been provided. According to the encounter information of the nodes in the last time slice, the routing algorithms predict probability that nodes will meet in the subsequent time slice. However, if opportunistic network is constructed in remote rural and pastoral areas with few nodes, there are few encounters in the network. Then, due to the inability to obtain sufficient encounter information, the existing routing algorithms cannot accurately predict whether there are encounters between nodes in subsequent time slices. For the purpose of improving the accuracy in the environment of sparse opportunistic networks, a prediction model based on nodes intimacy is proposed. And opportunistic network routing algorithm is designed. The experimental results show that the ONBTM model effectively improves the delivery quality of messages in sparse opportunistic networks and reduces network resources consumed during message delivery.

Executing tree routing algorithms on a high-performance pattern associative router

1998 ◽  
Vol 44 (11) ◽  
pp. 849-866
Author(s):  
Douglas H. Summerville ◽  
José G. Delgado-Frias ◽  
Stamatis Vassiliadis
Keyword(s):  
High Performance ◽  
Routing Algorithms ◽  
Tree Routing ◽  
Performance Pattern

High Performance Teams

2016 ◽  
Vol 7 (2) ◽  
pp. 72-82 ◽  
Author(s):  
Caroline Dominguez ◽  
Isabel C. Moura ◽  
João Varajão
Keyword(s):  
High Performance ◽  
Business Environment ◽  
Reward Systems ◽  
Team Management ◽  
Key Factors ◽  
Team Members ◽  
High Performing ◽  
Exploratory Case Study ◽  
Performance Teams

Effective team management is one of the key factors that allow companies to tackle the challenges of today's demanding business environment. Although high-performing teams have been studied for some time, very little has been written on them from the construction industry's perspective. Based on the conclusions of previous work and on a project involving 44 professionals of seven teams, this exploratory case study intends to evaluate if there is a gap between what team members and leaders perceive as being (a) the most important features for managing teams into high performance and (b) the features that are present in their teams. The present study shows that, although teams under investigation had some high-performing features at the leadership dimension, there is room for improvement, in particular when it comes to empowering team members, involving them in planning the work, and creating proper reward systems.

scholarly journals LATEST INFORMATION ON THE DISTRIBUTION OF FUSARIUM LANGSETHIAE, THE PRODUCER OF T-2 AND HT-2 TOXINS, IN RUSSIA

2020 ◽  
Vol 103 (3) ◽  
pp. 201-206
Author(s):  
O. P. Gavrilova ◽  
T. Yu. Gagkaeva
Keyword(s):  
High Performance ◽  
Geographical Area ◽  
Intraspecific Diversity ◽  
Specific Primers ◽  
Toxic Metabolites ◽  
Central Regions ◽  
Species Specific ◽  
High Level ◽  
Grain Contamination ◽  
Fusarium Fungi

The annual monitoring of grain contamination with Fusarium fungi and the identification of their species composition showed the widespread distribution of F. langsethiae producing dangerous T-2 and HT-2 toxins in the Northwestern and Central regions of Russia. Mycological analysis of grain samples harvested in 2018–2019 allowed revealing the new places of F. langsethiae distribution, including Urals. The top infection rate of the oats grain by F. langsethiae in 2019 reached 14 %. The identification of F. langsethiae strains was supported by PCR with species-specific primers. The analysis of toxic metabolites in F. langsethiae by the combination of high-performance liquid chromatography and tandem mass spectrometry revealed the high level of T-2 and HT-2 toxins. The considerable total amounts of T-2 and HT-2 toxins (165–1230 μg/kg) were found in the grain samples infected with this species. Further clarification of the geographical area of F. langsethiae and the study of its intraspecific diversity are needed to understand the distribution of this toxin-producing fungus.

scholarly journals A Survey to Analyse Routing Algorithms for Opportunistic Network

2020 ◽  
Vol 171 ◽  
pp. 2501-2511
Author(s):  
Soamdeep Singha ◽  
Biswapati Jana ◽  
Sharmistha Halder Jana ◽  
Niranjan Kumar Mandal
Keyword(s):  
Routing Algorithms ◽  
Opportunistic Network

scholarly journals Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Weidong Song ◽  
Guohui Jia ◽  
Hong Zhu ◽  
Di Jia ◽  
Lin Gao
Keyword(s):  
High Performance ◽  
Crack Detection ◽  
Automated Detection ◽  
Key Factors ◽  
The Road ◽  
Intelligent Maintenance ◽  
Road Pavement ◽  
Maintenance Systems ◽  
High Level ◽  
Pavement Crack Detection

Road pavement cracks automated detection is one of the key factors to evaluate the road distress quality, and it is a difficult issue for the construction of intelligent maintenance systems. However, pavement cracks automated detection has been a challenging task, including strong nonuniformity, complex topology, and strong noise-like problems in the crack images, and so on. To address these challenges, we propose the CrackSeg—an end-to-end trainable deep convolutional neural network for pavement crack detection, which is effective in achieving pixel-level, and automated detection via high-level features. In this work, we introduce a novel multiscale dilated convolutional module that can learn rich deep convolutional features, making the crack features acquired under a complex background more discriminant. Moreover, in the upsampling module process, the high spatial resolution features of the shallow network are fused to obtain more refined pixel-level pavement crack detection results. We train and evaluate the CrackSeg net on our CrackDataset, the experimental results prove that the CrackSeg achieves high performance with a precision of 98.00%, recall of 97.85%, F-score of 97.92%, and a mIoU of 73.53%. Compared with other state-of-the-art methods, the CrackSeg performs more efficiently, and robustly for automated pavement crack detection.

A review: crystal growth for high-performance all-inorganic perovskite solar cells

2020 ◽  
Vol 13 (7) ◽  
pp. 1971-1996 ◽  
Author(s):  
Weijie Chen ◽  
Xinqi Li ◽  
Yaowen Li ◽  
Yongfang Li
Keyword(s):  
Solar Cells ◽  
Crystal Growth ◽  
High Performance ◽  
Perovskite Solar Cells ◽  
Key Factors ◽  
High Quality ◽  
Inorganic Perovskite ◽  
Perovskite Crystal

The key factors for high-quality all-inorganic perovskite crystal growth.

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