Cooperation Between Stations in Wireless Networks

In a wireless network, mobile nodes (MNs) repeatedly perform tasks such as layer 2 (L2) handoff, layer 3 (L3) handoff and authentication. These tasks are critical, particularly for real-time applications such as VoIP. We propose a novel approach, namely Cooperative Roaming (CR), in which MNs can collaborate with each other and share useful information about the network in which they move. We show how we can achieve seamless L2 and L3 handoffs regardless of the authentication mechanism used and without any changes to either the infrastructure or the protocol. In particular, we provide a working implementation of CR and show how, with CR, MNs can achieve a total L2+L3 handoff time of less than 16 ms in an open network and of about 21 ms in an IEEE 802.11i network. We consider behaviors typical of IEEE 802.11 networks, although many of the concepts and problems addressed here apply to any kind of mobile network

Seamless Layer-2 Handoff using Two Radios in IEEE 802.11 Wireless Networks

We reduces the false handoff probability significantly by introducing selective passive scanning

Passive Duplicate Address Detection for Dynamic Host Configuration Protocol (DHCP)

During a layer-3 handoff, address acquisition via DHCP is often the dominant source of handoff delay, duplicate address detection (DAD) being responsible for most of the delay. We propose a new DAD algorithm, passive DAD (pDAD), which we show to be effective, yet introduce only a few milliseconds of delay. Unlike traditional DAD, pDAD also detects the unauthorized use of an IP address before it is assigned to a DHCP client

Generic Models for Mobility Management in Next Generation Networks

In the network community different mobility management techniques have been proposed over the years. However, many of these techniques share a surprisingly high number of similarities. In this technical report we analyze and evaluate the most relevant mobility management techniques, pointing out differences and similarities. For macro-mobility we consider Mobile IP (MIP), the Session Initiation Protocol (SIP) and mobility management techniques typical of a GSM network; for micro-mobility we describe and analyze several protocols such as: Hierarchical MIP, TeleMIP, IDMP, Cellular IP and HAWAII

Neural Network Parametrization of Deep-Inelastic Structure Functions

We construct a parametrization of deep-inelastic structure functions which retains information on experimental errors and correlations, and which does not introduce any theoretical bias while interpolating between existing data points. We generate a Monte Carlo sample of pseudo-data configurations and we train an ensemble of neural networks on them. This effectively provides us with a probability measure in the space of structure functions, within the whole kinematic region where data are available. This measure can then be used to determine the value of the structure function, its error, point-to-point correlations and generally the value and uncertainty of any function of the structure function itself. We apply this technique to the determination of the structure function F_2 of the proton and deuteron, and a precision determination of the isotriplet combination F_2[p-d]. We discuss in detail these results, check their stability and accuracy, and make them available in various formats for applications.Comment: Latex, 43 pages, 22 figures. (v2) Final version, published in JHEP; Sect.5.2 and Fig.9 improved, a few typos corrected and other minor improvements. (v3) Some inconsequential typos in Tab.1 and Tab 5 corrected. Neural parametrization available at http://sophia.ecm.ub.es/f2neura

Unbiased determination of the proton structure function F_2^p with faithful uncertainty estimation

We construct a parametrization of the deep-inelastic structure function of the proton F_2 based on all available experimental information from charged lepton deep-inelastic scattering experiments. The parametrization effectively provides a bias-free determination of the probability measure in the space of structure functions, which retains information on experimental errors and correlations. The result is obtained in the form of a Monte Carlo sample of neural networks trained on an ensemble of replicas of the experimental data. We discuss in detail the techniques required for the construction of bias-free parameterizations of large amounts of structure function data, in view of future applications to the determination of parton distributions based on the same method.We construct a parametrization of the deep-inelastic structure function of the proton F_2 based on all available experimental information from charged lepton deep-inelastic scattering experiments. The parametrization effectively provides a bias-free determination of the probability measure in the space of structure functions, which retains information on experimental errors and correlations. The result is obtained in the form of a Monte Carlo sample of neural networks trained on an ensemble of replicas of the experimental data. We discuss in detail the techniques required for the construction of bias-free parameterizations of large amounts of structure function data, in view of future applications to the determination of parton distributions based on the same method

Notulae to the Italian native vascular flora: 8

In this contribution, new data concerning the distribution of native vascular flora in Italy are presented. It includes new records, confirmations, exclusions, and status changes to the Italian administrative regions for taxa in the genera Ajuga, Chamaemelum, Clematis, Convolvulus, Cytisus, Deschampsia, Eleocharis, Epipactis, Euphorbia, Groenlandia, Hedera, Hieracium, Hydrocharis, Jacobaea, Juncus, Klasea, Lagurus, Leersia, Linum, Nerium, Onopordum, Persicaria, Phlomis, Polypogon, Potamogeton, Securigera, Sedum, Soleirolia, Stachys, Umbilicus, Valerianella, and Vinca. Nomenclatural and distribution updates, published elsewhere, and corrigenda are provided as Suppl. material 1