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real mobility trace

Through simulations using real mobility traces, the proposed RLFGRP protocol is shown to outperform the established Geocast Fuzzy-Based Check-and-Spray routing (FCSG) [4] and the Fuzzy logic-based Q-learning routing (FQLRP) [9] protocols in terms of overhead ratio, delivery ratio and average latency.

Reinforcement learning-based fuzzy geocast routing protocol for opportunistic networks

real mobility trace

Extensive simulations have been done based on the synthetic and real mobility traces, and the results show that our scheme can maximize the quality of reconstructed video and minimize the average replication times of each video packet.

VideoOR: A Quality Oriented Replication Scheme for Video Opportunistic Transmission

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10.3390/s21124160

We evaluate ApproxSSPS on Apache Spark Structured Streaming with real mobility data.

QoS-Aware Approximate Query Processing for Smart Cities Spatial Data Streams

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10.1016/J.IOT.2021.100384

Through simulations using real mobility traces, the proposed RLFGRP protocol is shown to outperform the established Geocast Fuzzy-Based Check-and-Spray routing (FCSG) [4] and the Fuzzy logic-based Q-learning routing (FQLRP) [9] protocols in terms of overhead ratio, delivery ratio and average latency.

Reinforcement learning-based fuzzy geocast routing protocol for opportunistic networks

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10.1007/S11277-021-08163-2

Extensive simulations have been done based on the synthetic and real mobility traces, and the results show that our scheme can maximize the quality of reconstructed video and minimize the average replication times of each video packet.

VideoOR: A Quality Oriented Replication Scheme for Video Opportunistic Transmission

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10.1109/tits.2020.3023446

Extensive experimental evaluations are conducted on the real mobility dataset in Rome.

A Deep Learning-Based Mobile Crowdsensing Scheme by Predicting Vehicle Mobility

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10.1109/ICCCN52240.2021.9522257

In this paper, we describe the main software components and provide an experimental evaluation that confirms the viability of the MOVO dApp in real mobility scenarios.

MOVO: a dApp for DLT-based Smart Mobility

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10.3390/ijgi10020071

To study the existing relationship between workspaces and living spaces, a new method to identify jobs-housing space is proposed, which not only considers the static spatial distribution of urban public facilities but also identifies the jobs-housing space by analyzing the real mobility characteristics of people from a humanistic perspective.

Research on the Method of Urban Jobs-Housing Space Recognition Combining Trajectory and POI Data

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10.1109/ICOA51614.2021.9442629

We recorded the real mobility traces of the users connected to a Wi-Fi access point of our research lab.

Predicting Device Availability in Mobile Crowd Computing using ConvLSTM

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10.1109/access.2021.3103903

To collect user mobility data, we considered a research lab scenario, where real mobility traces were recorded with respect to a Wi-Fi access point.

Deep Learning Based Resource Availability Prediction for Local Mobile Crowd Computing

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What is/are Real Mobility?

Real Mobility - We evaluate ApproxSSPS on Apache Spark Structured Streaming with real mobility data. [1] Through simulations using real mobility traces, the proposed RLFGRP protocol is shown to outperform the established Geocast Fuzzy-Based Check-and-Spray routing (FCSG) [4] and the Fuzzy logic-based Q-learning routing (FQLRP) [9] protocols in terms of overhead ratio, delivery ratio and average latency. [2] Extensive simulations have been done based on the synthetic and real mobility traces, and the results show that our scheme can maximize the quality of reconstructed video and minimize the average replication times of each video packet. [3] Extensive experimental evaluations are conducted on the real mobility dataset in Rome. [4] In this paper, we describe the main software components and provide an experimental evaluation that confirms the viability of the MOVO dApp in real mobility scenarios. [5] To study the existing relationship between workspaces and living spaces, a new method to identify jobs-housing space is proposed, which not only considers the static spatial distribution of urban public facilities but also identifies the jobs-housing space by analyzing the real mobility characteristics of people from a humanistic perspective. [6] We recorded the real mobility traces of the users connected to a Wi-Fi access point of our research lab. [7] To collect user mobility data, we considered a research lab scenario, where real mobility traces were recorded with respect to a Wi-Fi access point. [8]

real mobility trace

Through simulations using real mobility traces, the proposed RLFGRP protocol is shown to outperform the established Geocast Fuzzy-Based Check-and-Spray routing (FCSG) [4] and the Fuzzy logic-based Q-learning routing (FQLRP) [9] protocols in terms of overhead ratio, delivery ratio and average latency. [1] Extensive simulations have been done based on the synthetic and real mobility traces, and the results show that our scheme can maximize the quality of reconstructed video and minimize the average replication times of each video packet. [2] We recorded the real mobility traces of the users connected to a Wi-Fi access point of our research lab. [3] To collect user mobility data, we considered a research lab scenario, where real mobility traces were recorded with respect to a Wi-Fi access point. [4]