## 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]}The method proposed is validated by using synthetic data as well as real mobility traffic data.

^{[9]}Simulation results that are provided, confirm that the proposed PBMT is more secure and efficient than traditional buffer management policies for opportunistic networks by using the Haggle INFOCOM 2006 real mobility data trace.

^{[10]}Based on this, we evaluated the privacy protection effects of optimal location obfuscation function against an adversary’s inference attack function using real mobility datasets.

^{[11]}Getting real mobility traces is not an easy task, but there has been some efforts to provide traces to the public through repositories.

^{[12]}We use real mobility data collected from a number of cities and countries to demonstrate the predictive ability of this simple model.

^{[13]}On the other hand, clustering techniques are used taking into account real mobility restrictions as a function of minimum distances, and the relationship of the PEV with different charge supply subregions.

^{[14]}Experiments show that the proposed approach outperforms the state-of-the-art approaches in finding the explanation for facts extracted from NYC taxi real mobility dataset.

^{[15]}However, generating the corresponding real mobility datasets has always been a big challenge although it is vital to the simulations of vehicular networks.

^{[16]}We demonstrated that the vitreous is indeed a major hurdle in the delivery of the cationic mRNA-complexes to retinal cells, both in terms of vitreal mobility and cellular uptake.

^{[17]}Typically, simulation tools use mobility trails to construct the network topology based on the mathematical formulation of a city mobility model, which attempts to emulate the real mobility of a given city.

^{[18]}Based on extensive evaluations using various task allocation strategies, representative workloads, and real mobility traces on a mobile MapReduce simulator validated against a platform running on an actual smartphone cluster, we show that MTA significantly outperformed the state-of-the-art task allocation algorithms by up to 3.

^{[19]}However, the existing methods do not fully consider the efficient mobile edge computing and the real mobility model of AUV in the underwater environment.

^{[20]}To increase the performance of hybrid networks in a real mobility model, this chapter analyses and devises a method to authenticate data streams for transmission.

^{[21]}Experimentation using real mobility traces from well-known OppNet scenarios show that our estimation functions greatly reduce the estimation error of the future values of both metrics when compared to representative state of the art proposals.

^{[22]}Typically, simulation tools use mobility trails to construct the network topology based on the mathematical formulation of a city mobility model, which attempts to emulate the real mobility of a given city.

^{[23]}Results over synthetic networks and real mobility traces indicate that these mechanisms improve efficiency and quality of content request discoveries, by reducing significantly collisions and increasing stability of discovered paths in dense pedestrian crowds.

^{[24]}The experiments with two real mobility traces show that our proposed approach can achieve better prediction accuracy than a state-of-the art location-unaware method (up to 44%) and a location-aware method (up to 17%).

^{[25]}First, we use the information entropy to analyze the past and future regularity of the nodes’ centrality in the real mobility traces, and verify that nodes’ centrality is predictable.

^{[26]}Analysis using real mobility traces from various types of urban locations shows that the proposal is valid and will ensure that all the locations within the city will gradually be claimed via the proposed type of transactions while providing independently verifiable proofs for each location.

^{[27]}Rather than creating the data to support proposed planning interventions, our method led to a much more sustainable, bottom-up planning strategy in line with the social and ecological benefits of an integrated transport planning approach and revealed the real mobility needs of people living in inner-city areas of Berlin.

^{[28]}First, we investigate V2V ranging accuracy on a highway under real mobility conditions.

^{[29]}We extensively evaluated MATRIX using user studies, popular location-driven apps and machine learning techniques, and demonstrate that it is portable to most Android devices globally, is reliable, has low-overhead, and generates synthetic trajectories that are difficult to differentiate from real mobility trajectories by an adversary.

^{[30]}In addition, we apply the proposed method in a real mobility dataset using different features and parameters.

^{[31]}The reliability of such applications are validated through real mobility information from a large vehicular testbed currently deployed.

^{[32]}The demand is modeled depicting the real mobility demand of a sample administrative district in Germany.

^{[33]}We run extensive testing and experimentation on a subset of a large real mobility trace database made publicly available through the CRAWDAD project.

^{[34]}As a first step towards this direction, behavior models are derived through extensive analysis of real mobility data.

^{[35]}Our analysis is based on a real mobility trace of buses from several days of Dublin, Ireland.

^{[36]}The effectiveness of the proposed algorithm is verified using a real mobility trace: Uber pick-up trace in the New York City.

^{[37]}Via extensive simulations under various environments including real mobility traces, we verify that the proposed DLS+OWD policy significantly reduces the average power consumption of mobile devices with a higher fairness compared to the existing algorithms.

^{[38]}In this paper, we propose a deep RNN architecture for building online travel mode detection models using Long-Short Term Memory (LSTM) cells and evaluate its performance using real mobility data collected with a wide variety of sensors.

^{[39]}Simulations using real mobility traces demonstrate that the proposed DIR and A-DIR protocols achieve their design goals.

^{[40]}Performance analysis is performed using real mobility traces.

^{[41]}

## 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]}Getting real mobility traces is not an easy task, but there has been some efforts to provide traces to the public through repositories.

^{[5]}Based on extensive evaluations using various task allocation strategies, representative workloads, and real mobility traces on a mobile MapReduce simulator validated against a platform running on an actual smartphone cluster, we show that MTA significantly outperformed the state-of-the-art task allocation algorithms by up to 3.

^{[6]}Experimentation using real mobility traces from well-known OppNet scenarios show that our estimation functions greatly reduce the estimation error of the future values of both metrics when compared to representative state of the art proposals.

^{[7]}Results over synthetic networks and real mobility traces indicate that these mechanisms improve efficiency and quality of content request discoveries, by reducing significantly collisions and increasing stability of discovered paths in dense pedestrian crowds.

^{[8]}The experiments with two real mobility traces show that our proposed approach can achieve better prediction accuracy than a state-of-the art location-unaware method (up to 44%) and a location-aware method (up to 17%).

^{[9]}First, we use the information entropy to analyze the past and future regularity of the nodes’ centrality in the real mobility traces, and verify that nodes’ centrality is predictable.

^{[10]}Analysis using real mobility traces from various types of urban locations shows that the proposal is valid and will ensure that all the locations within the city will gradually be claimed via the proposed type of transactions while providing independently verifiable proofs for each location.

^{[11]}We run extensive testing and experimentation on a subset of a large real mobility trace database made publicly available through the CRAWDAD project.

^{[12]}Our analysis is based on a real mobility trace of buses from several days of Dublin, Ireland.

^{[13]}The effectiveness of the proposed algorithm is verified using a real mobility trace: Uber pick-up trace in the New York City.

^{[14]}Via extensive simulations under various environments including real mobility traces, we verify that the proposed DLS+OWD policy significantly reduces the average power consumption of mobile devices with a higher fairness compared to the existing algorithms.

^{[15]}Simulations using real mobility traces demonstrate that the proposed DIR and A-DIR protocols achieve their design goals.

^{[16]}Performance analysis is performed using real mobility traces.

^{[17]}

## real mobility datum

We evaluate ApproxSSPS on Apache Spark Structured Streaming with real mobility data.^{[1]}Simulation results that are provided, confirm that the proposed PBMT is more secure and efficient than traditional buffer management policies for opportunistic networks by using the Haggle INFOCOM 2006 real mobility data trace.

^{[2]}We use real mobility data collected from a number of cities and countries to demonstrate the predictive ability of this simple model.

^{[3]}As a first step towards this direction, behavior models are derived through extensive analysis of real mobility data.

^{[4]}In this paper, we propose a deep RNN architecture for building online travel mode detection models using Long-Short Term Memory (LSTM) cells and evaluate its performance using real mobility data collected with a wide variety of sensors.

^{[5]}

## real mobility dataset

Extensive experimental evaluations are conducted on the real mobility dataset in Rome.^{[1]}Based on this, we evaluated the privacy protection effects of optimal location obfuscation function against an adversary’s inference attack function using real mobility datasets.

^{[2]}Experiments show that the proposed approach outperforms the state-of-the-art approaches in finding the explanation for facts extracted from NYC taxi real mobility dataset.

^{[3]}However, generating the corresponding real mobility datasets has always been a big challenge although it is vital to the simulations of vehicular networks.

^{[4]}In addition, we apply the proposed method in a real mobility dataset using different features and parameters.

^{[5]}

## real mobility model

However, the existing methods do not fully consider the efficient mobile edge computing and the real mobility model of AUV in the underwater environment.^{[1]}To increase the performance of hybrid networks in a real mobility model, this chapter analyses and devises a method to authenticate data streams for transmission.

^{[2]}