Spatio-temporal analysis and prediction of cellular traffic in metropolis

ISSN:1536-1233ISSN:1558-066

Multi-Agent Learning of Efficient Fulfilment and Routing Strategies in E-Commerce

This paper presents an integrated algorithmic framework for minimising product delivery costs in e-commerce (known as the cost-to-serve or C2S). One of the major challenges in e-commerce is the large volume of spatio-temporally diverse orders from multiple customers, each of which has to be fulfilled from one of several warehouses using a fleet of vehicles. This results in two levels of decision-making: (i) selection of a fulfillment node for each order (including the option of deferral to a future time), and then (ii) routing of vehicles (each of which can carry multiple orders originating from the same warehouse). We propose an approach that combines graph neural networks and reinforcement learning to train the node selection and vehicle routing agents. We include real-world constraints such as warehouse inventory capacity, vehicle characteristics such as travel times, service times, carrying capacity, and customer constraints including time windows for delivery. The complexity of this problem arises from the fact that outcomes (rewards) are driven both by the fulfillment node mapping as well as the routing algorithms, and are spatio-temporally distributed. Our experiments show that this algorithmic pipeline outperforms pure heuristic policies

Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey

Modern communication systems and networks, e.g., Internet of Things (IoT) and cellular networks, generate a massive and heterogeneous amount of traffic data. In such networks, the traditional network management techniques for monitoring and data analytics face some challenges and issues, e.g., accuracy, and effective processing of big data in a real-time fashion. Moreover, the pattern of network traffic, especially in cellular networks, shows very complex behavior because of various factors, such as device mobility and network heterogeneity. Deep learning has been efficiently employed to facilitate analytics and knowledge discovery in big data systems to recognize hidden and complex patterns. Motivated by these successes, researchers in the field of networking apply deep learning models for Network Traffic Monitoring and Analysis (NTMA) applications, e.g., traffic classification and prediction. This paper provides a comprehensive review on applications of deep learning in NTMA. We first provide fundamental background relevant to our review. Then, we give an insight into the confluence of deep learning and NTMA, and review deep learning techniques proposed for NTMA applications. Finally, we discuss key challenges, open issues, and future research directions for using deep learning in NTMA applications.publishedVersio

Advanced time-varying approaches for modeling the multipath channel in wireless network

This dissertation proposes the use of advanced time-varying approaches for modeling the dynamics of the multipath channel in wireless communication networks. These advanced time-varying approaches include linear Kalman innovation models in observable block companion form, and neural network-based models. The e˙ectiveness of these type of models is evaluated through three case studies. The first case study involves the identification of a linear time-varying Kalman innovation model, for describing measured received signal strength (RSSI) as a function of the speed of the link in an indoor multipath wireless channel. Results for this first case study show that the model exhibits both accuracy and robustness. The second case study evaluates the suitability of using a linear time-varying Kalman innovation model of the RSSI, for secret key generation in the physical layer of multipath wireless channels. It was found that the residuals of the Kalman model, due to their significant randomness, exhibit a notable potential for secret key generation; indeed, improved values of maximum channel capacity for secret key generation were achieved. At last, the third case study includes the identification of a neural network-based autoregressive moving average with exogenous inputs (NN-ARMAX) model and of a neural network-based autoregressive with exogenous inputs (NN-ARX) model, for describing traÿc in a 4G-LTE network. Both models showed similar performance, but the NN-ARMAX has the advantage that it can be converted to a linear time-varying Kalman innovation model, and thus can be used for the implementation of advanced strategies for controlling the operation of the network