On the feasibility of monitoring DTN: Impacts of fine tuning on routing protocols and the user experience

The “machine to machine” communication paradigm will become a central element for mobile networks. This paradigm can be easily constructed by a contact-based network, notably a disruption/delay tolerant networks (DTN). To characterize a DTN, we can use the Inter-contact time among the nodes. The better understanding of inter-contact time (ICT) has practical applications on the tuning of forwarding strategies, and hence in the quality of the User Experience. Nevertheless, the fine tuning of those parameters is tight to a set of assumptions about the regularity of movement or periodicity of patterns in an usually non complete and cumbersome statistical analysis. That is why in a dynamic environment where we cannot assume any previous information the tuning of parameters is usually overestimated. In this work we study how monitoring can help to adapt those parameters to give a better understanding of both natural evolution of the network and non periodical events

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Integration of heterogeneous devices and communication models via the cloud in the constrained internet of things

As the Internet of Things continues to expand in the coming years, the need for services that span multiple IoT application domains will continue to increase in order to realize the efficiency gains promised by the IoT. Today, however, service developers looking to add value on top of existing IoT systems are faced with very heterogeneous devices and systems. These systems implement a wide variety of network connectivity options, protocols (proprietary or standards-based), and communication methods all of which are unknown to a service developer that is new to the IoT. Even within one IoT standard, a device typically has multiple options for communicating with others. In order to alleviate service developers from these concerns, this paper presents a cloud-based platform for integrating heterogeneous constrained IoT devices and communication models into services. Our evaluation shows that the impact of our approach on the operation of constrained devices is minimal while providing a tangible benefit in service integration of low-resource IoT devices. A proof of concept demonstrates the latter by means of a control and management dashboard for constrained devices that was implemented on top of the presented platform. The results of our work enable service developers to more easily implement and deploy services that span a wide variety of IoT application domains

Fast estimation of point-to-point travel times for real-time vehicle routing

Abstract To provide the optimal allocation of requests to the available fleet vehicles, routing algorithms typically assume the availability of complete and correct information about point-to-point travel times. Actually, in real applications non-recurrent events and traffic conditions make the estimation and the prediction of such travel times a difficult task, further complicated in real-time applications by the dynamicity of the information and the number of needed estimates. In this paper we present a complete methodology to achieve a computation of point-to-point travel times on a large network which proves to be both extremely fast and consistent with dynamically updated traffic information

Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things

In a typical Internet of Things (IoT) deployment such as smart cities and Industry 4.0, the amount of sensory data collected from physical world is significant and wide-ranging. Processing large amount of real-time data from the diverse IoT devices is challenging. For example, in IoT environment, wireless sensor networks (WSN) are typically used for the monitoring and collecting of data in some geographic area. Spatial range queries with location constraints to facilitate data indexing are traditionally employed in such applications, which allows the querying and managing the data based on SQL structure. One particular challenge is to minimize communication cost and storage requirements in multi-dimensional data indexing approaches. In this paper, we present an energy- and time-efficient multidimensional data indexing scheme, which is designed to answer range query. Specifically, we propose data indexing methods which utilize hierarchical indexing structures, using binary space partitioning (BSP), such as kd-tree, quad-tree, k-means clustering, and Voronoi-based methods to provide more efficient routing with less latency. Simulation results demonstrate that the Voronoi Diagram-based algorithm minimizes the average energy consumption and query response time