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Plate tectonics and great earthquakes : 50 years of earth-shaking events
The theory of plate tectonics transformed earth science. The hypothesis that the earth's outermost layers consist of mostly rigid plates that move over an inner surface helped describe the growth of new seafloor, confirm continental drift, and explain why earthquakes and volcanoes occur in some places and not others. Lynn R. Sykes played a key role in the birth of plate tectonics, conducting revelatory research on earthquakes. In this book, he gives an invaluable insider's perspective on the theory's development and its implications. Sykes combines lucid explanation of how plate tectonics revolutionized geology with unparalleled personal reflections. He entered the field when it was on the cusp of radical discoveries. Studying the distribution and mechanisms of earthquakes, Sykes pioneered the identification of seismic gaps--regions that have not ruptured in great earthquakes for a long time--and methods to estimate the possibility of quake recurrence. He recounts the various phases of his career, including his antinuclear activism, and the stories of colleagues around the world who took part in changing the paradigm. Sykes delves into the controversies over earthquake prediction and their importance, especially in the wake of the giant 2011 Japanese earthquake and the accompanying Fukushima disaster. He highlights geology's lessons for nuclear safety, explaining why historic earthquake patterns are crucial to understanding the risks to power plants. Plate Tectonics and Great Earthquakes is the story of a scientist witnessing a revolution and playing an essential role in making it.
Book
Understanding predictability and exploration in human mobility
Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors - in terms of modeling approaches and spatio-temporal characteristics of the data sources - have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility.
Journal Article
LiMPO: lightweight mobility prediction and offloading framework using machine learning for mobile edge computing
Several applications have emerged with the proliferation of mobile devices to provide communication, learning, social networking, entertainment, and community computing services. Such applications include augmented reality, online gaming, and other real-time applications that need higher computational resources. These applications, executing on mobile devices, often need to access external computing resources and offload the application tasks to the cloud or mobile edge computing (MEC) servers. However, delivering task offloading results to the users in the MEC environment is a challenge, certainly when user mobility is high. Sub-optimal server selection at the offloading stage increases latency, energy consumption and deteriorates both quality of experience and quality of service. Existing techniques proposed in the literature handle computation offloading and mobility management separately. Without considering the real-time mobility factors, the solutions produced are sub-optimal. Some solutions exist to manage mobility, but they involve higher time complexity. We consider the user mobility in offloading decisions and present a lightweight mobility prediction and offloading (LiMPO) framework that offloads the compute-intensive tasks to the predicted user location using artificial neural networks with less complexity. In addition, we propose a multi-objective genetic algorithm based server selection technique that jointly optimizes latency and energy consumption while improving the resource utilization of MEC servers. The performance of the proposed framework is compared with two other techniques task-assignment with optimized mobility and dynamic mobility-aware offloading algorithm for edge computing. The simulation results show that LiMPO outperforms the others by latency reduction, energy efficiency, and enhanced resource utilization.
Journal Article
ZY3-02 Laser Altimeter Footprint Geolocation Prediction
Successfully launched on 30 May 2016, ZY3-02 is the first Chinese surveying and mapping satellite equipped with a lightweight laser altimeter. Calibration is necessary before the laser altimeter becomes operational. Laser footprint location prediction is the first step in calibration that is based on ground infrared detectors, and it is difficult because the sample frequency of the ZY3-02 laser altimeter is 2 Hz, and the distance between two adjacent laser footprints is about 3.5 km. In this paper, we build an on-orbit rigorous geometric prediction model referenced to the rigorous geometric model of optical remote sensing satellites. The model includes three kinds of data that must be predicted: pointing angle, orbit parameters, and attitude angles. The proposed method is verified by a ZY3-02 laser altimeter on-orbit geometric calibration test. Five laser footprint prediction experiments are conducted based on the model, and the laser footprint prediction accuracy is better than 150 m on the ground. The effectiveness and accuracy of the on-orbit rigorous geometric prediction model are confirmed by the test results. The geolocation is predicted precisely by the proposed method, and this will give a reference to the geolocation prediction of future land laser detectors in other laser altimeter calibration test.
Journal Article
Research on the Model of Rectifying the Longitude and Latitude of the Base Station in the Main Cell Based on the Adjacent Cell Data
In order to solve the problem that the location prediction rate of the base station is low due to the insufficient MR data within the coverage range of the base station, based on the inspiration of the grip rule, the longitude and latitude correction model of the base station in the main cell based on big data is established by using the coarse positioning data and fine positioning data of adjacent cells. Taking Guangdong Telecom as an example, the experimental results show that the prediction rate of base station location is significantly improved from 69% to 92%.
Journal Article
Location prediction on trajectory data: A review
Location prediction is the key technique in many location based services including route navigation, dining location recommendations, and traffic planning and control, to mention a few. This survey provides a comprehensive overview of location prediction, including basic definitions and concepts, algorithms, and applications. First, we introduce the types of trajectory data and related basic concepts. Then, we review existing location-prediction methods, ranging from temporal-pattern-based prediction to spatiotemporal-pattern-based prediction. We also discuss and analyze the advantages and disadvantages of these algorithms and briefly summarize current applications of location prediction in diverse fields. Finally, we identify the potential challenges and future research directions in location prediction.
Journal Article
A survey on next location prediction techniques, applications, and challenges
Next location prediction has recently gained great attention from researchers due to its importance in different application areas. Recent growth of location-based service applications has vast domain influence such as traffic-flow prediction, weather forecast, and network resource optimization. Nowadays, due to the explosive increasing of positioning and sensor devices, big trajectory data are produced related to human movement. Using this big location-based trajectory data, researchers tend to predict human next location. Research efforts are spent on the put forward overall picture of next location prediction, and number of works has been done so as to realize robust next location prediction systems. However, in-depth study of those state-of-the-art works is required to know well the applications and challenges. Therefore, the aim of this paper is an extensive review on existing different next location prediction approaches. This work offers an extensive overview of location prediction enveloping basic definitions and concepts, data sources, approaches, and applications. In next location prediction, trajectory is represented by a sequence of timestamped geographical locations. It is challenging to analyze and mine trajectory data due to the complex characteristics reflected in human mobility, which is affected by multiple contextual information. Heterogeneous data generated from different sources, users’ random movement behavior, and the time sensitivity of trajectory data are some of the challenges. In this manuscript, we have discussed various location prediction approaches, applications, and challenges, and it sheds light on important points regarding future research directions. Furthermore, application and challenges are addressed related to the user’s next location prediction. Finally, we draw the overall conclusion of the survey, which is important for the development of robust next location prediction systems.
Journal Article
COME-UP: Computation Offloading in Mobile Edge Computing with LSTM Based User Direction Prediction
In mobile edge computing (MEC), mobile devices limited to computation and memory resources offload compute-intensive tasks to nearby edge servers. User movement causes frequent handovers in 5G urban networks. The resultant delays in task execution due to unknown user position and base station lead to increased energy consumption and resource wastage. The current MEC offloading solutions separate computation offloading from user mobility. For task offloading, techniques that predict the user’s future location do not consider user direction. We propose a framework termed COME-UP Computation Offloading in mobile edge computing with Long-short term memory (LSTM) based user direction prediction. The nature of the mobility data is nonlinear and leads to a time series prediction problem. The LSTM considers the previous mobility features, such as location, velocity, and direction, as input to a feed-forward mechanism to train the learning model and predict the next location. The proposed architecture also uses a fitness function to calculate priority weights for selecting an optimum edge server for task offloading based on latency, energy, and server load. The simulation results show that the latency and energy consumption of COME-UP are lower than the baseline techniques, while the edge server utilization is enhanced.
Journal Article
Device-Driven Service Allocation in Mobile Edge Computing with Location Prediction
With the rapid deployment of edge base stations and the widespread application of 5G technology, Mobile Edge Computing (MEC)has gradually transitioned from a theoretical concept to practical implementation, playing a key role in emerging human-machine interactions and innovative mobile applications. In the MEC environment, efficiently allocating services, effectively utilizing edge device resources, and ensuring timely service responses have become critical research topics. Existing studies often treat MEC service allocation as an offline strategy, where the real-time location of users is used as input, and static optimization is applied. However, this approach overlooks dynamic factors such as user mobility. To address this limitation, this paper constructs a model based on constraints, optimization objectives, and server connection methods, determines experimental parameters and evaluation metrics, and sets up an experimental framework. We propose an Edge Location Prediction Model (ELPM) suitable for the MEC scenario, which integrates Spatial-Temporal Graph Neural Networks and attention mechanisms. By leveraging attention parameters, ELPM acquires spatio-temporal adaptive weights, enabling accurate location predictions. We also design an improved service allocation strategy, MESDA, based on the Gray Wolf Optimization (GWO) algorithm. MESDA dynamically adjusts its exploration and exploitation components, and introduces a random factor to enhance the algorithm’s ability to determine the direction during later stages. To validate the effectiveness of the proposed methods, we conduct multiple controlled experiments focusing on both location prediction models and service allocation algorithms. The results show that, compared to the baseline methods, our approach achieves improvements of 2.56%, 5.29%, and 2.16% in terms of the average user connection to edge servers, average service deployment cost, and average service allocation execution time, respectively, demonstrating the superiority and feasibility of the proposed methods.
Journal Article
Next Location Prediction Based on an Adaboost-Markov Model of Mobile Users
As an emerging class of spatial trajectory data, mobile user trajectory data can be used to analyze individual or group behavioral characteristics, hobbies and interests. Besides, the information extracted from original trajectory data is widely used in smart cities, transportation planning, and anti-terrorism maintenance. In order to identify the important locations of the target user from his trajectory data, a novel division method for preprocessing trajectory data is proposed, the feature points of original trajectory are extracted according to the change of trajectory structural, and then important locations are extracted by clustering the feature points, using an improved density peak clustering algorithm. Finally, in order to predict next location of mobile users, a multi-order fusion Markov model based on the Adaboost algorithm is proposed, the model order k is adaptively determined, and the weight coefficients of the 1~k-order models are given by the Adaboost algorithm according to the importance of various order models, a multi-order fusion Markov model is generated to predict next important location of the user. The experimental results on the real user trajectory dataset Geo-life show that the prediction performance of Adaboost-Markov model is better than the multi-order fusion Markov model with equal coefficient, and the universality and prediction performance of Adaboost-Markov model is better than the first to third order Markov models.
Journal Article