Mobility Prediction Research Articles

P2-TaskMP: Privacy-Preserving Task Allocation Optimization Based on Mobility Prediction

P2-TaskMP: Privacy-Preserving Task Allocation Optimization Based on Mobility Prediction

Tractive performance of rigid wheel in granular media using coarse-scale DEM models

Understanding interactions between wheel and granular media in variable loading conditions is critical for prediction of mobility of wheeled vehicles in off-road environments. The discrete element method (DEM) is routinely used for modeling vehicle off-road performance, but the method’s accuracy is often not fully established.In this work, the DEM modeling accuracy is assessed by the comparison of ten DEM soil models with laboratory soil-bin measurements of net traction, gross traction, and sinkage of a wheel operating in sand. Laboratory soil-bin measurements, serving as reference for DEM simulations, were taken from physical experiments by Shinone et al. (2010), examining a 165/60R13 wheel with circumferential velocity of 97.6 mm/s and vertical contact load of 980 N in varying slip conditions.The set of ten DEM models was selected from the Generic EDEM Material Model database from Altair®’s EDEM™ software package, choosing the materials matching the bulk density and angle of repose for dry sand.Given the large particle size and no additional calibration of the DEM models, finding overall reasonable match with the gross traction from lab measurements and identifying a material predicting the net traction with a satisfiable accuracy encourages further use, refinement, and calibration of the DEM-based soil models.

A novel operational water quality mobile prediction system with LSTM-Seq2Seq model

A novel operational water quality mobile prediction system with LSTM-Seq2Seq model

Individual mobility prediction by considering current traveling features and historical activity chain

ABSTRACT Individual mobility prediction forecasts traveling activities of an individual traveler, and has wide applications in location-based services, public health, and transportation planning. Whereas, it remains challenging due to the complexity and uncertainty of human mobility. Existing methods mainly consider spatiotemporal contexts in current traveling, but overlook those in historical trips, as well as relationships between traversed road intersections. These issues hinder the model from effectively capturing complex mobility patterns. To fill this gap, we propose a novel method that incorporates current traveling features and historical activity chain to predict the coordinates of traveling destination. Specifically, (1) we construct current traveling features by extracting real-time moving states, and represent spatiotemporal correlations between traversed road intersections using word embedding; (2) we learn travel intentions as a probability vector for each historical trip, and combine it with spatiotemporal features to construct historical activity chain; (3) we construct an individual mobility prediction model using Long Short-Term Memory (LSTM) network and spatiotemporal scoring mechanism, to capture short-term and long-term dependencies in current trip and historical activity chain, respectively. Experiments on 21,890 trajectories over the whole Year 2019 of 20 representatives selected from 1916 private car travelers in Shenzhen City, reveal the effectiveness of our model. It outperforms four baselines, Random Forest (RF), Distant Neighboring Dependencies (DND), Location Semantics and Location Importance (LSI)-LSTM, as well as Intersection Transfer Preference and Current Movement Mode (ITP-CMM), by approximately 10%-15% improvement in accuracy. In addition, we further explore the impact of historical activity chain length, and destination visiting frequency on prediction, as well as the relationship between predictability and eight mobility pattern features. This study benefits potential applications such as personalized location-based service recommendations and targeted advertising, and also provides implications for understanding human mobility.

Prediction of Urban Air Mobility and Drone Accident Rates and the Role of Urban Management Systems

Prediction of Urban Air Mobility and Drone Accident Rates and the Role of Urban Management Systems

W-GPSR Routing Based on Mobility Prediction for Vehicular Ad-Hoc Network (VANET)

In recent years, Vehicular Ad-Hoc Networks (VANETs) innovation has been regarded as a significant research area. This is owing to the increasing popularity of vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications in the area of Intelligent Transportation System (ITS) to improve traffic management, safety, CO2 emission mitigation, and other applications. A variety of routing protocols for VANETs are being recently developed. More specifically, geographic-based routing algorithms such as Greedy Perimeter Stateless Routing (GPSR) have provoked the most interest in VANETs due to their compatibility with a regularly changing network structure and the highly unsteady nature of automobile nodes. This paper proposes an efficient weight based mobility method in VANET to improve the mechanism of the GPSR protocol through optimizing the greedy forwarding strategy; which is so called O-Greedy Mode. Therefore, the key goal is to achieve the optimal data forwarding paths. The next hop is determined by estimating the neighbors' mobility based on each neighbor's Greedy Link Weight Factor (GLWF). The Weighted GPSR (W-GPSR) based on Mobility Prediction is then evaluated using OMNeT++ simulator with Inet, Veins and SUMO traffic simulator. The results demonstrate the efficiency of W-GPSR in contrast with the traditional existing protocols for essential metrics of Packet Delivery Ratio (PDR), throughput, End-to-End Delay (E2ED), Normalized Routing Load (NRL) and Packet Loss Ratio (PLR).

Incorporating Mobility Prediction in Handover Procedure for Frequent-Handover Mitigation in Small-Cell Networks

Incorporating Mobility Prediction in Handover Procedure for Frequent-Handover Mitigation in Small-Cell Networks

Predictive Smart Mobility Systems for Sustainable and Efficient Urban Transportation

Abstract: The rapid urbanization and growth of cities have placed increasing pressure on transportation systems, leading to traffic congestion, environmental degradation, and inefficiencies in mobility. The Smart Mobility Prediction System (SMPS) is designed to address these challenges by leveraging advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and big data analytics to optimize transportation networks. This system gathers realtime data from sensors, traffic cameras, GPS devices, and social media feeds to predict traffic patterns, identify congestion hotspots, and suggest alternative routes to users. By utilizing predictive analytics, the SMPS can forecast traffic conditions, demand for public transportation, and the availability of shared mobility options such as ride-hailing and bike-sharing services. Furthermore, the system can support urban planners and policymakers by providing insights into the performance of transportation systems, enabling them to make informed decisions regarding infrastructure development and traffic management. The implementation of the Smart Mobility Prediction System aims to enhance the efficiency, sustainability, and safety of urban transportation networks, contributing to improved quality of life for city dwellers while reducing environmental impacts.

Optimal Routing Protocol to Reduce Frequent Disconnection Problem with Improvement of Link Quality in Vehicle Network

Vehicular Ad-hoc Networks (VANETs) are currently receiving significant interest from academics because to the escalating traffic issues, particularly in densely populated nations. The rising incidence of accidents necessitates the implementation of an Intelligent Transportation System (ITS) that can effectively minimize and alleviate this trend. The existing routing protocols for VANETs address various scenarios and techniques for establishing dependable communication between vehicles and infrastructures. However, certain conditions, such as the stability of the link between vehicles during packet exchange, have not been well handled. This work presents the Link Reliability Data Forwarding Routing Protocol (LRDF-RP) as a solution to address the issue of frequent disconnections and improve route selection based on reliability in Mobility prediction in VANET. This study encompasses two essential contributions: (1) Enhancing the prediction routing protocol to transmit packets over a dependable route, and (2) Incorporating more logic in the selection of intermediate nodes towards the destination to get an increased Packet Delivery Ratio (PDR). The LRDF-RP employs the Prims Greedy algorithm for forwarding and the Predictive Perimeter method for recovery, with changes made to both algorithms to fulfil the specific demands of VANETs. The approach is evaluated based on several criteria including packet delivery ratio, throughput, end-to-end delay, consumption of energy, and average hop count.

A Unified Method for the Mobility Prediction of an Inelastic Non-Newtonian Fluid Through Complex Porous Media

A Unified Method for the Mobility Prediction of an Inelastic Non-Newtonian Fluid Through Complex Porous Media

Dear: vehicle mobility prediction using diffusion-expanded attention network based on IoV trajectory data

Dear: vehicle mobility prediction using diffusion-expanded attention network based on IoV trajectory data

Mobility Prediction Algorithms for Handover Management in Heterogeneous LiFi and RF Networks: An Ensemble Approach

Light Fidelity (LiFi) is a communication technology that operates in the Visible Light (VL) region, using light as a medium to enable ultra-high-speed communication. The spectrum occupied by LiFi does not overlap with the Radio Frequency (RF) spectrum. Thus, they can be used in a hybrid manner to enhance the Quality of Service (QoS) for users. However, in a heterogeneous LiFi and RF network, users experience constant handovers due to the small coverage area of the LiFi and their frequent movement. This study proposes an intelligent handover scheme, where the network parameters of the users are used to train four machine learning models, namely an Artificial Neural Network (ANN), an Adaptive Neurofuzzy Inference System (ANFIS), a Support Vector Machine (SVM), and a Regression Tree (RT), to predict the mobility of the users, so that the central network can have a priori mobility information to ensure seamless connectivity. Furthermore, the performance of the standalone models was enhanced by integrating ensemble learning techniques such as the Simple Averaging Ensemble (SAE), Weighted Averaging Ensemble (WAE), and a Meta-Learning Ensemble (MLE). The results show that the ensemble algorithms improved prediction performance, with an average error decrease of 44.40%, 53.53%, and 61.03% for SAE, WAE, and MLE, respectively, which further demonstrated the effectiveness and robustness of using ensemble algorithms to predict user mobility.

Passive plasticity and a sex difference in the predictability of mobility.

Behavioural predictability describes the behavioural variability of an individual. Unpredictability can arise from many sources including non-adaptive passive plasticity in which an environmental factor acts directly on the individual to create non-adaptive phenotypic variation. In this study, I use radiotelemetry to field test the hypothesis that Cook Strait giant weta Deinacrida rugosa (Orthoptera: Anostostomatidae) exhibit a sex difference in the predictability of their nightly travel distance due to passive behavioural plasticity. As predicted, I found that male mobility (i.e. nightly travel distance) was less predictable than female mobility. Females travel short and predictable distances each night for food and refuges that are close by and readily available. In contrast, male travel is less predictable because they search for female mates that are stochastically dispersed across the landscape. Therefore, their travel distance can vary considerably across nights.

Cardiovascular wellness in low-resource settings: A mobile app-based risk prediction study among fuel filling station employees in Puducherry district.

India is witnessing a significant increase in the prevalence of non-communicable diseases (NCDs), and addressing this requires a comprehensive and multi-faceted approach. The burden of NCDs puts a strain on the healthcare system, requiring an increased focus on preventive measures, early detection, and management of chronic conditions. Adopting a risk-based approach to cardiovascular diseases (CVDs) in resource-poor settings offers several economic and social advantages. The aim of the study was to assess the prevalence of CVD risk factors among fuel filling station employees in the Puducherry district and the 10-year CVD risk prediction score among the study participants with the World Health Organisation package of essential non-communicable (WHO PEN) app and package for resource-poor settings. A community-based cross-sectional study was conducted among the fuel filling station employees in Puducherry. A universal sampling method was employed. The data were collected using a pilot-tested, predesigned, structured questionnaire and the WHO PEN app was used to estimate the CVD risk score. The data were collected from February 2021 to January 2022 and analysed using Statistical Package for Social Sciences (SPSS) version 20. Frequency distribution along with the Chi-square test was employed to test statistical significance. Out of 212 subjects, 170 (80.2%) were males, out of which 116 (54.7%) were between 40 and 50 years old. Nearly half the participants (48%) had CVD risk scores ranging from 5 to 20%, with an increased prevalence of CVD risk factors, namely, obesity/overweight (65.5%), physical inactivity (58.5%), hypertension (52%), alcohol consumption (51%) and tobacco consumption in any form (25.5%). This study sheds light on the sedentary nature of the occupation and the increased prevalence of CVD risk factors among the study participants. It is also evident that the participants had higher CVD risk scores for developing CVDs in the future. Recommendations: The use of mobile-based apps can be used as a feasible strategy to save scarce resources in delivering primary health care. We also propose that the nature of occupation be taken into account as one of the parameters for risk prediction. Risk prediction assessment should be made mandatory during the annual examination of employees.

Appformer: A novel framework for mobile app usage prediction leveraging progressive multi-modal data fusion and feature extraction

This article presents Appformer, a novel mobile application prediction framework inspired by the efficiency of Transformer-like architectures in processing sequential data through self-attention mechanisms. Combining a Multi-Modal Data Progressive Fusion Module with a sophisticated Feature Extraction Module, Appformer leverages the synergies of multi-modal data fusion and data mining techniques while maintaining user privacy. The framework employs Points of Interest (POIs) associated with base stations, optimizing them through comprehensive comparative experiments to identify the most effective clustering method. These refined inputs are seamlessly integrated into the initial phases of cross-modal data fusion, where temporal units are encoded via word embeddings and subsequently merged in later stages. The Feature Extraction Module, employing Transformer-like architectures specialized for time series analysis, adeptly distils comprehensive features. It meticulously fine-tunes the outputs from the fusion module, facilitating the extraction of high-caliber, multi-modal features, thus guaranteeing a robust and efficient extraction process. Extensive experimental validation confirms Appformer’s effectiveness, attaining state-of-the-art (SOTA) metrics in mobile app usage prediction, thereby signifying a notable progression in this field. The implementation is available at https://github.com/SunChuike/Appformer-ESWA.

Federated Learning‐Based Mobile Traffic Prediction in Satellite‐Terrestrial Integrated Networks

ABSTRACTIntroductionWith the development and integration of satellite and terrestrial networks, mobile traffic prediction has become more important than before, which is the basis for service provision and resource scheduling when supporting various vertical applications. However, existing traffic prediction methods, especially deep learning‐based methods, require massive data for model training. Due to data privacy concerns, mobile traffic data are not easily shared among different parties, making it difficult to obtain a precise prediction model.MethodsTo mitigate the data leakage risk, a federated learning framework is proposed in this study for mobile traffic prediction in satellite‐terrestrial integrated networks to achieve a tradeoff between data privacy and prediction accuracy. In the proposed framework, local models are trained in base stations on the ground, and a global model is aggregated in the satellite edge server in space.ResultsA deep learning‐based prediction model with an adaptive graph convolutional network (AGCN) and long short‐term memory (LSTM) modules is proposed and validated in numerical experiments, which achieves the lowest prediction error with a real‐world traffic dataset when compared with other graph neural network (GNN) variants in the federated learning setting.ConclusionNumerical experiments with a real‐world mobile traffic dataset demonstrate the effectiveness of the proposed approach, which outperforms other GNN variants with lower prediction errors.

Distributed service caching with deep reinforcement learning for sustainable edge computing in large-scale AI

Increasing reliance on large-scale AI models has led to rising demand for intelligent services. The centralized cloud computing approach has limitations in terms of data transfer efficiency and response time, and as a result many service providers have begun to deploy edge servers to cache intelligent services in order to reduce transmission delay and communication energy consumption. However, finding the optimal service caching strategy remains a significant challenge due to the stochastic nature of service requests and the bulky nature of intelligent services. To deal with this we propose a distributed service caching scheme integrating deep reinforcement learning (DRL) with mobility prediction, which we refer to as DSDM. Specifically, we employ the D3QN (Deep Double Dueling Q-Network) framework to integrate Long Short-Term Memory (LSTM) predicted mobile device locations into the service caching replacement algorithm and adopt the distributed multi-agent approach for learning and training. Experimental results demonstrate that DSDM achieves significant performance improvements in reducing communication energy consumption compared to traditional methods across various scenarios.

Joint Cooperative Caching and UAV Trajectory Optimization Based on Mobility Prediction in the Internet of Connected Vehicles

Joint Cooperative Caching and UAV Trajectory Optimization Based on Mobility Prediction in the Internet of Connected Vehicles

Mobile single-lead ECG atrial fibrillation prediction enhancement integrated by standard ECG algorithm with Deep learning model

Abstract Background Artificial intelligence (AI) using electrocardiogram (ECG) enabled to predict atrial fibrillation (AF) in patients without documented AF. Mobile single-lead ECG is more convenient to surveil cardiac rhythm with simple measurement. However, AI-enabled arrhythmia predictability by mobile ECG is limited due to single channel utilization and longer duration for arrhythmia diagnosis. We aimed to enhance mobile single-lead ECG AF prediction AI algorithm integrated with 12-lead ECG using deep learning model. Method Based on 552,372 12-lead ECG data of 318,321 patients, a statistical AF prediction model employing a deep-learning approach was constituted. Out of single-lead 13,509 ECGs from a total of 6,719 patients, we utilized a total of 10,287 normal sinus rhythm ECGs from 5,170 patients. Resnet structure was utilized to distinguish subtle changes of the vicinity of P-wave. Single-lead mobile ECGs were adjusted with noise filtering and segmented every 10 seconds. A random under-sampling was applied to reduce bias from data imbalance. Both 12-lead ECG and single-lead ECG were allocated to training, validation, testing datasets in a 6:2:2 ratio. Then, we conducted transfer learning using the standard 12-lead ECG’s deep learning model to improve performance of single-lead mobile ECG deep learning model. Results AF was annotated in 26,541 (4.8%) with 12-lead ECG whereas 1,443 (21.2%) with single-lead mobile ECG. The area under the curve (AUC) value for predicting AF was 0.910 with 12-lead ECG, and 0.742 with mobile ECG. The predictive performance of mobile ECG was 67.5% in accuracy, 64.2% in sensitivity and 66.8% in F1-score. The AUC value of mobile ECG after applying transfer learning based on 12-lead ECG for AF prediction was increased to 0.790 with accuracy of 73.8%, sensitivity of 65.5% and F1-score of 71.0%. Conclusion Integration with deep learning algorithm of standard 12-lead ECG significantly improved the model performance of single-lead ECG AF prediction model compared to single-lead mobile ECG only based model. Easy application of mobile ECG with enhanced AF predictability might serve a more convenient method as a pre-emptive assistive tool to provide probabilistic prediction for PAF screening rather than 12-lead ECG.

Proactive Scheduling for mmWave Wireless LANs

To cope with growing wireless bandwidth demand, millimeter wave (mmWave) communication has been identified as a promising technology to deliver Gbps throughput. However, due to the susceptibility of mmWave signals to blockage, applications can experience significant performance variability as users move around due to rapid and significant variation in channel conditions. In this context, proactive schedulers that make use of future data rate prediction have potential to bring a significant performance improvement as compared to traditional schedulers. In this work, we explore the possibility of proactive scheduling that uses mobility prediction and some knowledge of the environment to predict future channel conditions. We present both an optimal proactive scheduler, which is based on an integer linear programming formulation and provides an upper bound on proactive scheduling performance, and a greedy heuristic proactive scheduler that is suitable for practical implementation. Extensive simulation results show that proactive scheduling has the potential to increase average user data rate by up to 35% over the classic proportional fair scheduler without any loss of fairness and incurring only a small increase in jitter. The results also show that the efficient proactive heuristic scheduler achieves from 60% to 75% of the performance gains of the optimal proactive scheduler. Finally, the results show that proactive scheduling performance is sensitive to the quality of mobility prediction and, thus, use of state-of-the-art mobility prediction techniques will be necessary to realize its full potential.