1,721,485 research outputs found
Optical Network Design with Mixed Line Rates and Multiple ModulationFormats
No full textWe propose a design method for mixed-line-rate (MLR) optical networks with transceivers employing different modulation formats. Our results demonstrate the tradeoff between a transceiver’s cost and its optical reach in overall network design
Analytical modelling of users' behaviour and performance metrics in key distribution schemes
No full textOptical network design with mixed line rates
No full textFuture telecommunication networks employing optical wavelength-division multiplexing (WDM) are expected to be increasingly heterogeneous and support a wide variety of traffic demands. Based on the nature of the demands, it may be convenient to set up lightpaths on these networks with different bit rates. Then, the network design cost could be reduced because low-bit-rate services will need less grooming (i.e., less multiplexing with other low-bit-rate services onto high-capacity wavelengths) while high-bit-rate services can be accommodated on a wavelength itself. Future optical networks may support mixed line rates (say over 10/40/100 Gbps). Since a lightpath may travel a long distance, for high bit rates, the effect of the physical impairments along a lightpath may become very significant (leading to high bit-error rate (BER)); and the signal’s maximum transmission range, which depends on the bit rate, will become limited.
In this study, we propose a novel, cost-effective approach to design a mixed-line-rate (MLR) network with transmission-range (TR) constraint. By intelligent assignment of channel rates to lightpaths, based on their TR constraint, the need for signal regeneration can be minimized, and a “transparent” optical network can be designed to support all-optical end-to-end lightpaths. The design problem is formulated as an integer linear program (ILP). A heuristic algorithm is also proposed. Our results show that, with mixed line rates and maximum transmission range constraints, one can design a cost-effective network
Measurement and control of geo-location privacy on Twitter
No full textThe widespread diffusion of Online Social Networks and Media (OSNEM) has generated a huge amount of users’ personal data. As this data is often publicly available, users’ privacy is at risk. To address this issue, users may control the release of their sensitive data on OSNEM. An example of data that users rarely publish is their location. Besides being a privacy-sensitive information, location is a business-relevant data that third parties, e.g., Location-Based Service (LBS) providers, may be interested to obtain. It is, therefore, of paramount importance to understand to what extent the secrecy of location information can be violated. In this work, we investigate how users can measure the privacy of their geo-location on OSNEM and to control the factors affecting it. We define the privacy of a target user as the geographical distance between her actual unexposed location and the location estimated by an attacker. To measure privacy, we propose a novel deep learning architecture that uncovers a target user’s position based only on the publicly-available locations shared by users on Twitter. Results show that locations can be accurately unveiled for the majority of the users, thus suggesting the need for countermeasures to improve their privacy. To control privacy, we propose data perturbation techniques that users can apply to tune the public exposure of their location, and we show the resulting privacy improvements. To shed light on the factors influencing privacy, we then propose a machine learning model that measures privacy based on several users’ features (e.g., social and behavioral characteristics). Unlike the aforementioned deep learning approach, this model also allows to quantify the impact that each feature has on privacy. We observe that features related to the history of users’ visited locations proved to be the most relevant factors affecting privacy. Finally, we explore potential side effects resulting from the application of data perturbation strategies. In particular, we examine, as a study case, the trade-off between users’ privacy and the effectiveness of a proximity marketing LBS. Results suggest that privacy can be guaranteed while not significantly lowering the effectiveness of the LBS
Survivable multipath routing of anycast and unicast traffic in elastic optical networks
No full textIn this paper, we focus on the survivability of elastic optical networks (EONs) that jointly support two types of traffic demands: unicast and anycast. To provide network survivability, we apply multipath routing; i.e., we allow the splitting of a demand into a number of routing paths if the paths' combination guarantees the realization of a specific demand volume in the case of a single link failure. We formulate the corresponding optimization problem as an integer linear program (ILP) and propose a survivable multipath allocation (SMA) algorithm to solve the problem in a reasonable amount of time. Next, we perform numerical experiments to compare the efficiency (ability to provide a good-quality solution in a reasonable amount of time) of the ILP model and SMA as well as to evaluate the impact of survivable multipath routing on the objective defined as a maximum spectrum usage in EONs. Our results show that the SMA method finds good-quality solutions in a reasonable amount of time and that survivable multipath routing in EONs requires additional spectrum resources, up to 45%. However, the amount of additional resources depends on the required protection level, amount of anycast traffic, the maximum number of paths used for demand realization, and the considered network topology
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