Deep Learning Techniques for Mobility Prediction and Management in Mobile Networks

Trajectory prediction is an important research topic in modern mobile networks (e.g., 5G and beyond 5G) to enhance the network quality of service by accurately predicting the future locations of mobile users, such as pedestrians and vehicles, based on their past mobility patterns. A trajectory is defined as the sequence of locations the user visits over time. The primary objective of this thesis is to improve the modeling of mobility data and establish personalized, scalable, collective-intelligent, distributed, and strategic trajectory prediction techniques that can effectively adapt to the dynamics of urban environments in order to facilitate the optimal delivery of mobility-aware network services. Our proposed approaches aim to increase the accuracy of trajectory prediction while minimizing communication and computational costs leading to more efficient mobile networks. The thesis begins by introducing a personalized trajectory prediction technique using deep learning and reinforcement learning. It adapts the neural network architecture to capture the distinct characteristics of mobile users’ data. Furthermore, it introduces advanced anticipatory handover management and dynamic service migration techniques that optimize network management using our high-performance trajectory predictor. This approach ensures seamless connectivity and proactively migrates network services, enhancing the quality of service in dense wireless networks. The second contribution of the thesis introduces cluster-level prediction to extend the reinforcement learning-based trajectory prediction, addressing scalability challenges in large-scale networks. Cluster-level trajectory prediction leverages users’ similarities within clusters to train only a few representatives. This enables efficient transfer learning of pre-trained mobility models and reduces computational overhead enhancing the network scalability. The third contribution proposes a collaborative social-aware multi-agent trajectory prediction technique that accounts for the interactions between multiple intra-cluster agents in a dynamic urban environment, increasing the prediction accuracy but decreasing the algorithm complexity and computational resource usage. The fourth contribution proposes a federated learning-driven multi-agent trajectory prediction technique that leverages the collaborative power of multiple local data sources in a decentralized manner to enhance user privacy and improve the accuracy of trajectory prediction while jointly minimizing computational and communication costs. The fifth contribution proposes a game theoretic non-cooperative multi-agent prediction technique that considers the strategic behaviors among competitive inter-cluster mobile users. The proposed approaches are evaluated on small-scale and large-scale location-based mobility datasets, where locations could be GPS coordinates or cellular base station IDs. Our experiments demonstrate that our proposed approaches outperform state-of-the-art trajectory prediction methods making significant contributions to the field of mobile networks

Attention-based Neural Networks for Multi-modal Trajectory Prediction

Trajectory prediction is of great importance in wireless and intelligent networks. Accurate forecast of users’ trajectories can provide efficient handover management, continuous network connection, and generally better network quality of service. A trajectory is defined as the sequence of location logs, e.g., GPS coordinates or cellular antenna IDs, over time. We present a trajectory predictor based on Transformers Neural Networks acquiring the self-attention mechanism [1]. Mobile objects’ mobility patterns are influenced by their nearby neighbors. Thus, learning spatio-temporal dependencies among neighbor-trajectory users can help to better predict their trajectories [2]. In this direction, unlike our previously proposed mobility predictor (based on LSTM and CNN) designed for single agents [3], [4], [5], where agents were acting in isolation, we now propose the INteractive TRAnsformers ReinFORCEd (INTRAFORCE) social-aware neural network. We further employ a reinforcement learning agent to design the highest-performance transformers neural architecture based on the multi-modal trajectory scenario. Evaluations show that using the Orange dataset [4], our transformer-based predictor can remarkably increase the accuracy and decrease the training time and computations concerning our models based on LSTM and CNN [4]. Furthermore, on ETH+UCY datasets [6], INTRAFORCE achieves the least Mean Square Error compared to numerous state-of-the-art mechanisms on this popular dataset

The unfinished agenda of communicable diseases among children and adolescents before the COVID-19 pandemic, 1990-2019: a systematic analysis of the Global Burden of Disease Study 2019

BACKGROUND: Communicable disease control has long been a focus of global health policy. There have been substantial reductions in the burden and mortality of communicable diseases among children younger than 5 years, but we know less about this burden in older children and adolescents, and it is unclear whether current programmes and policies remain aligned with targets for intervention. This knowledge is especially important for policy and programmes in the context of the COVID-19 pandemic. We aimed to use the Global Burden of Disease (GBD) Study 2019 to systematically characterise the burden of communicable diseases across childhood and adolescence. METHODS: In this systematic analysis of the GBD study from 1990 to 2019, all communicable diseases and their manifestations as modelled within GBD 2019 were included, categorised as 16 subgroups of common diseases or presentations. Data were reported for absolute count, prevalence, and incidence across measures of cause-specific mortality (deaths and years of life lost), disability (years lived with disability [YLDs]), and disease burden (disability-adjusted life-years [DALYs]) for children and adolescents aged 0-24 years. Data were reported across the Socio-demographic Index (SDI) and across time (1990-2019), and for 204 countries and territories. For HIV, we reported the mortality-to-incidence ratio (MIR) as a measure of health system performance. FINDINGS: In 2019, there were 3·0 million deaths and 30·0 million years of healthy life lost to disability (as measured by YLDs), corresponding to 288·4 million DALYs from communicable diseases among children and adolescents globally (57·3% of total communicable disease burden across all ages). Over time, there has been a shift in communicable disease burden from young children to older children and adolescents (largely driven by the considerable reductions in children younger than 5 years and slower progress elsewhere), although children younger than 5 years still accounted for most of the communicable disease burden in 2019. Disease burden and mortality were predominantly in low-SDI settings, with high and high-middle SDI settings also having an appreciable burden of communicable disease morbidity (4·0 million YLDs in 2019 alone). Three cause groups (enteric infections, lower-respiratory-tract infections, and malaria) accounted for 59·8% of the global communicable disease burden in children and adolescents, with tuberculosis and HIV both emerging as important causes during adolescence. HIV was the only cause for which disease burden increased over time, particularly in children and adolescents older than 5 years, and especially in females. Excess MIRs for HIV were observed for males aged 15-19 years in low-SDI settings. INTERPRETATION: Our analysis supports continued policy focus on enteric infections and lower-respiratory-tract infections, with orientation to children younger than 5 years in settings of low socioeconomic development. However, efforts should also be targeted to other conditions, particularly HIV, given its increased burden in older children and adolescents. Older children and adolescents also experience a large burden of communicable disease, further highlighting the need for efforts to extend beyond the first 5 years of life. Our analysis also identified substantial morbidity caused by communicable diseases affecting child and adolescent health across the world. FUNDING: The Australian National Health and Medical Research Council Centre for Research Excellence for Driving Investment in Global Adolescent Health and the Bill & Melinda Gates Foundation

Global, regional, and national progress towards Sustainable Development Goal 3.2 for neonatal and child health: all-cause and cause-specific mortality findings from the Global Burden of Disease Study 2019

Background Sustainable Development Goal 3.2 has targeted elimination of preventable child mortality, reduction of neonatal death to less than 12 per 1000 livebirths, and reduction of death of children younger than 5 years to less than 25 per 1000 livebirths, for each country by 2030. To understand current rates, recent trends, and potential trajectories of child mortality for the next decade, we present the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 findings for all-cause mortality and cause-specific mortality in children younger than 5 years of age, with multiple scenarios for child mortality in 2030 that include the consideration of potential effects of COVID-19, and a novel framework for quantifying optimal child survival. Methods We completed all-cause mortality and cause-specific mortality analyses from 204 countries and territories for detailed age groups separately, with aggregated mortality probabilities per 1000 livebirths computed for neonatal mortality rate (NMR) and under-5 mortality rate (USMR). Scenarios for 2030 represent different potential trajectories, notably including potential effects of the COVID-19 pandemic and the potential impact of improvements preferentially targeting neonatal survival. Optimal child survival metrics were developed by age, sex, and cause of death across all GBD location-years. The first metric is a global optimum and is based on the lowest observed mortality, and the second is a survival potential frontier that is based on stochastic frontier analysis of observed mortality and Healthcare Access and Quality Index. Findings Global U5MR decreased from 71.2 deaths per 1000 livebirths (95% uncertainty interval WI] 68.3-74-0) in 2000 to 37.1 (33.2-41.7) in 2019 while global NMR correspondingly declined more slowly from 28.0 deaths per 1000 live births (26.8-29-5) in 2000 to 17.9 (16.3-19-8) in 2019. In 2019,136 (67%) of 204 countries had a USMR at or below the SDG 3.2 threshold and 133 (65%) had an NMR at or below the SDG 3.2 threshold, and the reference scenario suggests that by 2030,154 (75%) of all countries could meet the U5MR targets, and 139 (68%) could meet the NMR targets. Deaths of children younger than 5 years totalled 9.65 million (95% UI 9.05-10.30) in 2000 and 5.05 million (4.27-6.02) in 2019, with the neonatal fraction of these deaths increasing from 39% (3.76 million 95% UI 3.53-4.021) in 2000 to 48% (2.42 million; 2.06-2.86) in 2019. NMR and U5MR were generally higher in males than in females, although there was no statistically significant difference at the global level. Neonatal disorders remained the leading cause of death in children younger than 5 years in 2019, followed by lower respiratory infections, diarrhoeal diseases, congenital birth defects, and malaria. The global optimum analysis suggests NMR could be reduced to as low as 0.80 (95% UI 0.71-0.86) deaths per 1000 livebirths and U5MR to 1.44 (95% UI 1-27-1.58) deaths per 1000 livebirths, and in 2019, there were as many as 1.87 million (95% UI 1-35-2.58; 37% 95% UI 32-43]) of 5.05 million more deaths of children younger than 5 years than the survival potential frontier. Interpretation Global child mortality declined by almost half between 2000 and 2019, but progress remains slower in neonates and 65 (32%) of 204 countries, mostly in sub-Saharan Africa and south Asia, are not on track to meet either SDG 3.2 target by 2030. Focused improvements in perinatal and newborn care, continued and expanded delivery of essential interventions such as vaccination and infection prevention, an enhanced focus on equity, continued focus on poverty reduction and education, and investment in strengthening health systems across the development spectrum have the potential to substantially improve USMR. Given the widespread effects of COVID-19, considerable effort will be required to maintain and accelerate progress. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd

Reinforcement-supported Artificial Neural Network-based Trajectory Prediction

With the advent of 5G, Beyond 5G, and 6G cellular networks, mobility prediction has become a crucial task to enable a wide range of services such as handover and resource management. Mobility predictors analyze users’ historical mobility data, extract meaningful information, and learn moving patterns to forecast future locations and trajectories of users’ end systems. A trajectory is defined as the sequence of GPS coordinates or cellular antennas’ IDs over time. We present a trajectory predictor based on Long Short Term Memory (LSTM), a variant of Recurrent Neural Networks (RNNs), to improve the performance of network applications, namely handover management and service migration for the Context Awareness Engine project with Orange SA [1]. When a mobile user moves within a mobile cellular network, transferring the cellular connection from one base station antenna to another is called handover, which requires many signaling operations. The knowledge of the user’s future location can offer a proactive handover mechanism so that the signaling procedure can be done before the mobile user’s arrival at a target antenna. In this way, the total system delay can remarkably decrease. Modern network services aim for reliable communication, low latency, and high throughput, especially in the case of user mobility, where services might easier experience interruption and quality degradation. Distributed technologies such as Multi-access Edge Computing (MEC) and Service Migration are introduced to offload requested data from the cloud to edge servers closer to users. In this context, trajectory prediction enables designing efficient service migration that guarantees service continuity and Quality of Service (QoS) in a proactive manner. Hence, attaining a reliable, accurate, and optimal trajectory predictor is a pivotal task. Within various machine learning (ML) and artificial neural network (ANN) approaches, LSTM models have achieved a remarkable performance in the trajectory prediction task. However, most state-of-the-art works choose the neural networks heuristically and apply the same model to every user type, which does not guarantee optimal performance. We use Reinforcement Learning (RL) as a self-learning approach that is able to automate the LSTM architecture search process and hyperparameter optimization to explore the best neural architecture for each mobile user individually. To accelerate RL’s optimization process, we apply a Transfer Learning (TL) method. TL is a technique for reusing one task’s developed model as the starting point for another task in order not to initialize the second task from scratch. This way, we transfer the knowledge from a pre-trained RL-suggested neural network to a newly RL-suggested neural network. Although LSTMs have achieved excellent prediction accuracy, high training time and computational power still stay as their main bottlenecks. LSTMs are extremely slow by nature due to the fact that they learn spatio-temporal dependencies in sequential order. As an extension for our work [1], we propose the combination of RL and Convolutional Neural Networks (CNNs) as the optimal solution to save more computational resources. CNNs support parallelism and are computationally faster. One-dimensional CNNs concentrate on sequential data from a global perspective and extract features by applying efficient convolutional operations. To evaluate the proposed predictors’ performance, we have done our computations on the High-Performance Computing Cluster of the University of Bern known as HPC Cluster - UBELIX, which supports parallel execution of multiple user predictions. Our RL-LSTM achieves on average 69.7% accuracy, which is almost 10% better than other ML approaches. The suggested RL-LSTM predictor consumes 176 minutes to explore-exploit the best neural architecture and to train data, which is only 28% of the time that a Grid Search-based LSTM requires (625 minutes) for performing the same task. On the other hand, our RL-CNN predictor achieves 67.7% accuracy, which is slightly less than RL-LSTM’s accuracy, while its optimization and training time is only 55 minutes, 31% of the time RL-LSTM already had spent. Moreover, in a real distributed scenario, applying thousands of individual RL-CNNs is impossible due to limited computational resources. Therefore, we detect similar trajectory users, build an exclusive RL-CNN per cluster based on a few users’ data, and transfer the pre-trained neural network knowledge between group members. With this approach, we can save up to 90% of computational resources while losing a few couples of percentages of the average accuracy

INTRAFORCE: Intra-Cluster Reinforced Social Transformer for Trajectory Prediction

Predicting mobile users’ trajectories accurately is essential for improving the performance of wireless networks and autonomous systems. In this paper, we tackle the problem of trajectory prediction in a multi-agent scenario where the social interaction among users is taken into consideration.We propose Intra- Cluster Reinforced Social Transformer (INTRAFORCE), a novel system to design and train Social-Transformer neural networks that learn the spatio-temporal interactions among neighboring mobile users and predict their joint future trajectories. Unlike state-of-the-art social-aware trajectory predictors that either miss the large-distance interactions or are computationally expensive due to the pooling of all users’ interactions, INTRAFORCE clusters users with similar trajectories and learns their interactions. INTRAFORCE performs Neural Architecture Search to optimize each transformer’s architecture to fit each cluster’s user mobility features using Reinforcement Learning. Through experimental validation, we show that INTRAFORCE outperforms several state-of-the-art trajectory predictors on five widely used smallscale pedestrian mobility datasets and one large-scale privacyoriented cellular mobility dataset by achieving lower prediction error, training time, and computational complexity. Keywords: Social-aware Trajectory Prediction, Transformers, Reinforcement Learning, Neural Architecture Search, Clustering

GTP-Force: Game-Theoretic Trajectory Prediction through Distributed Reinforcement Learning

This paper introduces Game-theoretic Trajectory Prediction through distributed reinForcement learning (GTPForce), a system that tackles the challenge of predicting joint pedestrian trajectories in multi-agent scenarios. GTP-Force utilizes decentralized reinforcement learning agents to personalize neural networks for each competing player based on their noncooperative preferences and social interactions with others. By identifying the Nash Equilibria, GTP-Force accurately predicts joint trajectories while minimizing overall system loss in noncooperative environments. The system outperforms existing stateof- the-art trajectory predictors, achieving an average displacement error of 0.19m on the ETH+UCY dataset and 80% accuracy on the Orange dataset, which is -0.03m and 5% better than the best-performing baseline, respectively. Additionally, GTP-Force considerably reduces the model size of social mobility predictors compared to approaches with classical game theory

FedForce: Network-adaptive Federated Learning for Reinforced Mobility Prediction

Federated Learning (FL) has become popular in the field of mobility and trajectory prediction due to its privacy-preserving and scalability capabilities. Deploying FL over resource-constrained devices and varying network conditions is challenging for achieving a good tradeoff among prediction performance, computational load, and communication volume. On the other hand, the design of FL’s distributed neural architectures is complex, time-consuming, and dependent on experts’ prior knowledge. To tackle the above limitations, we propose the network-adaptive FEDerated learning for reinFORCEd mobility prediction (FedForce) system. FedForce employs reinforcement learning to design a transformer neural network whose architecture jointly optimizes the prediction accuracy, training time, and transmission time based on the mobility dataset’s unique features, the client’s computing capacity, and the available network throughput. FedForce outperforms several state-of-theart trajectory predictors and achieves an average displacement error of 0.20m on the ETH+UCY dataset and an accuracy of 76% on the Orange dataset (-0.02m and 10% higher than the bestperforming baseline, respectively), while cutting the FL training and transmission time by half. FedForce can save up to 80% of computational resources and 96% of communication overheads with a negligible accuracy decrease

RC-TL: Reinforcement Convolutional Transfer Learning for Large-scale Trajectory Prediction

Anticipating future locations of mobile users plays a pivotal role in intelligent services supporting mobile networks. Predicting user trajectories is a crucial task not only from the perspective of facilitating smart cities but also of significant importance in network management, such as handover optimization, service migration, and the caching of services in a mobile and edge-computing network. Convolutional Neural Networks (CNNs) have proven to be successful to tackle the forecasting of mobile users’ future locations. However, designing effective CNN architectures is challenging due to their large hyper-parameter space. Reinforcement Learning (RL)-based Neural Architecture Search (NAS) mechanisms have been proposed to optimize the neural network design process, but they are computationally expensive and they have not been used to predict user mobility. In large urban scenarios, the rate at which mobility information is generated makes it a challenge to optimize, train, and maintain prediction models for individual users. However, considering that user trajectories are not independent, a common trajectory-prediction model can be built and shared among a set of users characterized by similar mobility features. In the present work, we introduce Reinforcement Convolutional Transfer Learning (RC-TL), a CNN-based trajectory-prediction system that clusters users with similar trajectories, dedicates a single RL agent per cluster to optimize a CNN neural architecture, trains one model per cluster using the data of a small user subset, and transfers it to the other users in the cluster. Experimental results on a large-scale dataset show that our proposed RL-based CNN achieves up to 12% higher trajectory-prediction accuracy, with no training speed reduction, over other state-of-the-art approaches on a large-scale, real-world mobility dataset. Moreover, RC-TL’s clustering strategy saves up to 90% of the computational resources needed for training compared to single-user models, in exchange for a 3% accuracy reduction

Protective effects of quercetin on thioacetamide-induced acute liver damage and its related biochemical and pathological alterations

Background Acute liver damage may be followed by biochemical, behavioral, and pathological alterations, which can end up in serious complications and even death. Aim The aim of this study was to determine whether quercetin, a flavonoid compound, which is also known to have cell-protective, antioxidant, and anti-inflammatory effects, has any protective impacts against thioacetamide (TAA)-induced liver damage in rats. Methods Thirty-six Sprague–Dawley rats were divided into three groups: group C1, normal rats; group C2, rats that received a single dose of TAA (350 mg/kg) intraperitoneally; and group E, rats that received a single dose of TAA (350 mg/kg)+300 mg/kg quercetin intraperitoneally. At the end, liver enzymes and plasma ammonia (NH4) were measured, and pathological analysis of the liver carried out. Results The measured serological markers except for total bilirubin (alanine aminotransferase, aspartate aminotransferase, and NH4) showed a significant decrease in group E compared with group C2. The quercetin-treated group showed a significantly lower clinical grade of encephalopathy. Pathological findings showed a significantly lower piecemeal necrosis in group E compared with group C2. Moreover, there was a nonsignificant decrease in focal necrosis, apoptosis, and focal inflammation in group E compared with group C2. Portal inflammation scores were lower in group E than in group C2. Therefore, quercetin significantly affected the grade of liver damage, as group E had lower grades compared with group C2 (P<0.05). Conclusion Overall, quercetin showed positive effects on both the liver injury and its related behavioral and biochemical changes