A case study of two-echelon multi-depot vehicle routing problem

The Vehicle Routing Problem (VRP) is a classic combinatorial optimization problem and a topic still studied for practical applications. Current research focuses on single echelon distribution systems such as distribution centers serving customers. However, in typical distribution, goods flows among regional distribution centers, local warehouses and customers, defined as a two-echelon network. The two-echelon multiple depot VRP problem is documented and applied to two stages illustrated by a small scale computational example. In the first stage, the simulated annealing algorithm is employed to determine the routes between local warehouses and final customers. For the second stage, trial-and-error is applied to obtain the number and location of regional distribution centers and the routes between regional distribution centers and local warehouses. Matlab is utilized to simulate annealing iterations and cost functions are analyzed. The convergence tendency of simulated annealing is depicted in figures by Matlab coding. Contributions include demonstration between the SA algorithm and a specific combinatorial optimization problem, and an application of the algorithm

Data Analytics and Performance Enhancement in Edge-Cloud Collaborative Internet of Things Systems

Based on the evolving communications, computing and embedded systems technologies, Internet of Things (IoT) systems can interconnect not only physical users and devices but also virtual services and objects, which have already been applied to many different application scenarios, such as smart home, smart healthcare, and intelligent transportation. With the rapid development, the number of involving devices increases tremendously. The huge number of devices and correspondingly generated data bring critical challenges to the IoT systems. To enhance the overall performance, this thesis aims to address the related technical issues on IoT data processing and physical topology discovery of the subnets self-organized by IoT devices. First of all, the issues on outlier detection and data aggregation are addressed through the development of recursive principal component analysis (R-PCA) based data analysis framework. The framework is developed in a cluster-based structure to fully exploit the spatial correlation of IoT data. Specifically, the sensing devices are gathered into clusters based on spatial data correlation. Edge devices are assigned to the clusters for the R-PCA based outlier detection and data aggregation. The outlier-free and aggregated data are forwarded to the remote cloud server for data reconstruction and storage. Moreover, a data reduction scheme is further proposed to relieve the burden on the trunk link for data uploading by utilizing the temporal data correlation. Kalman filters (KFs) with identical parameters are maintained at the edge and cloud for data prediction. The amount of data uploading is reduced by using the data predicted by the KF in the cloud instead of uploading all the practically measured data. Furthermore, an unmanned aerial vehicle (UAV) assisted IoT system is particularly designed for large-scale monitoring. Wireless sensor nodes are flexibly deployed for environmental sensing and self-organized into wireless sensor networks (WSNs). A physical topology discovery scheme is proposed to construct the physical topology of WSNs in the cloud server to facilitate performance optimization, where the physical topology indicates both the logical connectivity statuses of WSNs and the physical locations of WSN nodes. The physical topology discovery scheme is implemented through the newly developed parallel Metropolis-Hastings random walk based information sampling and network-wide 3D localization algorithms, where UAVs are served as the mobile edge devices and anchor nodes. Based on the physical topology constructed in the cloud, a UAV-enabled spatial data sampling scheme is further proposed to efficiently sample data from the monitoring area by using denoising autoencoder (DAE). By deploying the encoder of DAE at the UAV and decoder in the cloud, the data can be partially sampled from the sensing field and accurately reconstructed in the cloud. In the final part of the thesis, a novel autoencoder (AE) neural network based data outlier detection algorithm is proposed, where both encoder and decoder of AE are deployed at the edge devices. Data outliers can be accurately detected by the large fluctuations in the squared error generated by the data passing through the encoder and decoder of the AE

Exploitation of Data Correlation and Performance Enhancement in Wireless Sensor Networks

With the combination of wireless communications and embedded system, lots of progress has been made in the area of wireless sensor networks (WSNs). The networks have already been widely deployed, due to their self-organization capacity and low-cost advantage. However, there are still some technical challenges needed to be addressed. In the thesis, three algorithms are proposed in improving network energy efficiency, detecting data fault and reducing data redundancy. The basic principle behind the proposed algorithms is correlation in the data collected by WSNs. The first sensor scheduling algorithm is based on the spatial correlation between neighbor sensor readings. Given the spatial correlation, sensor nodes are clustered into groups. At each time instance, only one node within each group works as group representative, namely, sensing and transmitting sensor data. Sensor nodes take turns to be group representative. Therefore, the energy consumed by other sensor nodes within the same group can be saved. Due to the continuous nature of the data to be collected, temporal and spatial correlation of sensor data has been exploited to detect the faulty data. By exploitation of temporal correlation, the normal range of upcoming sensor data can be predicted by the historical observations. Based on spatial correlation, weighted neighbor voting can be used to diagnose whether the value of sensor data is reliable. The status of the sensor data, normal or faulty, is decided by the combination of these two proposed detection procedures. Similar to the sensor scheduling algorithm, the recursive principal component analysis (RPCA) based algorithm has been studied to detect faulty data and aggregate redundant data by exploitation of spatial correlation as well. The R-PCA model is used to process the sensor data, with the help of squared prediction error (SPE) score and cumulative percentage formula. When SPE score of a collected datum is distinctly larger than that of normal data, faults can be detected. The data dimension is reduced according to the calculation result of cumulative percentage formula. All the algorithms are simulated in OPNET or MATLAB based on practical and synthetic datasets. Performances of the proposed algorithms are evaluated in each chapter

Coastal Disasters and Remote Sensing Monitoring Methods

Coastal disaster is abnormal changes caused by climate change, human activities, geological movement or natural environment changes. According to formation cause, marine disasters as storm surges, waves, Tsunami coastal erosion, sea-level rise, red tide, seawater intrusion, marine oil spill and soil salinization. Remote sensing technology has real-time and large-area advantages in promoting the monitoring and forecast ability of coastal disaster. Relative to natural disasters, ones caused by human factors are more likely to be monitored and prevented. In this paper, we use several remote sensing methods to monitor or forecast three kinds of coastal disaster cause by human factors including red tide, sea-level rise and oil spilling, and make proposals for infrastructure based on the research results. The chosen method of monitoring red tide by inversing chlorophyll-a concentration is improved OC3M Model, which is more suitable for the coastal zone and higher spatial resolution than the MODIS chlorophyll-a production. We monitor the sea-level rise in coastal zone through coastline changes without artificial modifications. The improved Lagrangian model can simulate the trajectory of oil slick efficiently. Making the infrastructure planning according the coastal disasters and features of coastline contributes to prevent coastal disaster and coastal ecosystem protection. Multi-source remote sensing data can effectively monitor and prevent coastal disaster, and provide planning advices for coastal infrastructure construction

Feature-Based Matrix Factorization

Recommender system has been more and more popular and widely used in many applications recently. The increasing information available, not only in quantities but also in types, leads to a big challenge for recommender system that how to leverage these rich information to get a better performance. Most traditional approaches try to design a specific model for each scenario, which demands great efforts in developing and modifying models. In this technical report, we describe our implementation of feature-based matrix factorization. This model is an abstract of many variants of matrix factorization models, and new types of information can be utilized by simply defining new features, without modifying any lines of code. Using the toolkit, we built the best single model reported on track 1 of KDDCup'11.Comment: Minor update, add some related work

A Parallel and Efficient Algorithm for Learning to Match

Many tasks in data mining and related fields can be formalized as matching between objects in two heterogeneous domains, including collaborative filtering, link prediction, image tagging, and web search. Machine learning techniques, referred to as learning-to-match in this paper, have been successfully applied to the problems. Among them, a class of state-of-the-art methods, named feature-based matrix factorization, formalize the task as an extension to matrix factorization by incorporating auxiliary features into the model. Unfortunately, making those algorithms scale to real world problems is challenging, and simple parallelization strategies fail due to the complex cross talking patterns between sub-tasks. In this paper, we tackle this challenge with a novel parallel and efficient algorithm for feature-based matrix factorization. Our algorithm, based on coordinate descent, can easily handle hundreds of millions of instances and features on a single machine. The key recipe of this algorithm is an iterative relaxation of the objective to facilitate parallel updates of parameters, with guaranteed convergence on minimizing the original objective function. Experimental results demonstrate that the proposed method is effective on a wide range of matching problems, with efficiency significantly improved upon the baselines while accuracy retained unchanged.Comment: 10 pages, short version was published in ICDM 201

Spectral Efficiency Analysis of Uplink-Downlink Decoupled Access in C-V2X Networks

The uplink (UL)/downlink (DL) decoupled access has been emerging as a novel access architecture to improve the performance gains in cellular networks. In this paper, we investigate the UL/DL decoupled access performance in cellular vehicle-to-everything (C-V2X). We propose a unified analytical framework for the UL/DL decoupled access in C-V2X from the perspective of spectral efficiency (SE). By modeling the UL/DL decoupled access C-V2X as a Cox process and leveraging the stochastic geometry, we obtain the joint association probability, the UL/DL distance distributions to serving base stations and the SE for the UL/DL decoupled access in C-V2X networks with different association cases. We conduct extensive Monte Carlo simulations to verify the accuracy of the proposed unified analytical framework, and the results show a better system average SE of UL/DL decoupled access in C-V2X.Comment: 6pagaes,5 figures, globecom 202