Mobile data offloading via urban public transportation networks

Mobile data traffic is increasing at an exponential rate with the proliferation of mobile devices and easy access to large contents such as video. Traffic demand is expected to soar in the next 5 years and a new generation of mobile networks (5G) is currently being developed to address the looming bandwidth crunch. However, significant 5G deployments are not expected until 2020 or even beyond. As such, any solution that offloads cellular traffic to other available networks is of high interest, the main example being the successful offloading of cellular traffic onto WiFi. In this context, we propose to leverage public transportation networks (PTNs) created by regular bus lines in urban centers to create another offloading option for delay tolerant data such as video on demand. This PhD proposes a novel content delivery infrastructure where wireless access points (APs) are installed on both bus stops and buses. Buses act as data mules, creating a delay tolerant network capable of carrying content users can access while commuting using public transportation. Building such a network raises several core challenges such as: (i) selecting the bus stops on which it is best to install APs, (ii) efficiently routing the data, (iii) relieving congestion points in major hubs and (iv) minimizing the cost of the full architecture. These challenges are addressed in the three parts of this thesis. The first part of the thesis presents our content delivery infrastructure whose primary aim is to carry large volumes of data. We show that it is beneficial to install APs at the end stations of bus lines by analyzing the publicly available time tables of PTN providers of different cities. Knowing the underlying topology and schedule of PTNs, we propose to pre-calculate static routes between stations. This leads to a dramatic decrease in message replications and transfers compared to the state-of-the-art Epidemic delay tolerant protocol. Simulation results for three cities demonstrate that our routing policy increases by 4 to 8 times the number of delivered messages while reducing the overhead ratio. The second part of the thesis addresses the problem of relieving congestion at stations where several bus lines converge and have to exchange data through the AP. The solution proposed leverages XOR network coding where encoding and decoding are performed hop-by-hop for flows crossing at an AP. We conduct a theoretical analysis of the delivery probability and overhead ratio for a general setting. This analysis indicates that the maximum delivery probability is increased by 50% while the overhead ratio is reduced by 50%, if such network coding is applied. Simulations of this general setting corroborate these points, showing, in addition, that the average delay is reduced as well. Introducing our XOR network coding to our content delivery infrastructure using real bus timetables, we demonstrate a 35% - 48% improvement in the number of messages delivered. The third part of the thesis proposes a cost-effective architecture. It classifies PTN bus stops into three categories, each equipped with different types of wireless APs, allowing for a fine-grained cost control. Simulation results demonstrate the viability of our design choices. In particular, the 3-Tier architecture is shown to guarantee end-to-end connectivity and reduce the deployment cost by a factor of 3 while delivering 30% more packets than a baseline architecture. It can offload a large amount of mobile data, as for instance 4.7 terabytes within 12 hours in the Paris topology

Siamese-DETR for Generic Multi-Object Tracking

The ability to detect and track the dynamic objects in different scenes is fundamental to real-world applications, e.g., autonomous driving and robot navigation. However, traditional Multi-Object Tracking (MOT) is limited to tracking objects belonging to the pre-defined closed-set categories. Recently, Open-Vocabulary MOT (OVMOT) and Generic MOT (GMOT) are proposed to track interested objects beyond pre-defined categories with the given text prompt and template image. However, the expensive well pre-trained (vision-)language model and fine-grained category annotations are required to train OVMOT models. In this paper, we focus on GMOT and propose a simple but effective method, Siamese-DETR, for GMOT. Only the commonly used detection datasets (e.g., COCO) are required for training. Different from existing GMOT methods, which train a Single Object Tracking (SOT) based detector to detect interested objects and then apply a data association based MOT tracker to get the trajectories, we leverage the inherent object queries in DETR variants. Specifically: 1) The multi-scale object queries are designed based on the given template image, which are effective for detecting different scales of objects with the same category as the template image; 2) A dynamic matching training strategy is introduced to train Siamese-DETR on commonly used detection datasets, which takes full advantage of provided annotations; 3) The online tracking pipeline is simplified through a tracking-by-query manner by incorporating the tracked boxes in previous frame as additional query boxes. The complex data association is replaced with the much simpler Non-Maximum Suppression (NMS). Extensive experimental results show that Siamese-DETR surpasses existing MOT methods on GMOT-40 dataset by a large margin

Synchronization of Chaotic Neural Networks with Leakage Delay and Mixed Time-Varying Delays via Sampled-Data Control

This paper investigates the synchronization problem for neural networks with leakage delay and both discrete and distributed time-varying delays under sampled-data control. By employing the Lyapunov functional method and using the matrix inequality techniques, a delay-dependent LMIs criterion is given to ensure that the master systems and the slave systems are synchronous. An example with simulations is given to show the effectiveness of the proposed criterion

XOR Network Coding for Data Mule Delay Tolerant Networks

International audienceWe propose a simple yet efficient scalable scheme for improving the performance of Delay Tolerant Networks (DTNs) with data mules by using XOR network coding. We carry out a theoretical analysis based on a model abstracted from the Village Communication Networks (VCNs), beginning with two villages and then extending to N villages. We also examine how the delivery probability is affected by the different overlapping intervals of two data mules. The theoretical analysis indicates that the maximum delivery probability increases by 50% and our simulation results illustrate this point, showing that the overhead ratio and average delay are reduced as well. Finally, our scheme is applied to a real network, the Toulouse public transportation network. We analyze the dataset, calculate the overlapping intervals of inter-vehicles and the amount of data that transit vehicles can exchange in one day, showing a 54:4% improvement in throughput