Crossing over the bounded domain: from exponential to power-law inter-meeting time in manet.
01/2007; pp.159-170 In proceeding of: Proceedings of the 13th Annual International Conference on Mobile Computing and Networking, MOBICOM 2007, Montréal, Québec, Canada, September 9-14, 2007
Conference Proceeding: User location forecasting at points of interestProceedings of the 2012 RecSys workshop on Personalizing the local mobile experience, Dublin, Ireland; 01/2012
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ABSTRACT: We introduce a wait-and-chase scheme that models the contact times between moving agents within a connectionist construct. The idea that elementary processors move within a network to get a proper position is borne out both by biological neurons in the brain morphogenesis and by agents within social networks. From the former, we take inspiration to devise a medium-term project for new artificial neural network training procedures where mobile neurons exchange data only when they are close to one another in a proper space (are in contact). From the latter, we accumulate mobility tracks experience. We focus on the preliminary step of characterizing the elapsed time between neuron contacts, which results from a spatial process fitting in the family of random processes with memory, where chasing neurons are stochastically driven by the goal of hitting target neurons. Thus, we add an unprecedented mobility model to the literature in the field, introducing a distribution law of the intercontact times that merges features of both negative exponential and Pareto distribution laws. We give a constructive description and implementation of our model, as well as a short analytical form whose parameters are suitably estimated in terms of confidence intervals from experimental data. Numerical experiments show the model and related inference tools to be sufficiently robust to cope with two main requisites for its exploitation in a neural network: the nonindependence of the observed intercontact times and the feasibility of the model inversion problem to infer suitable mobility parameters.IEEE Transactions on Neural Networks 10/2011; 22(12):2032-49. · 2.95 Impact Factor
Article: Personal Marks and Community Certificates: Detecting Clones in Mobile Wireless Networks of Smart-Phones[show abstract] [hide abstract]
ABSTRACT: We consider the problem of detecting clones in wireless mobile adhoc networks. We assume that one of the devices of the network has been cloned. Everything, including certificates and secret keys. This can happen quite easily, because of a virus that immediately after sending all the content of the infected device to the adversary destroys itself, or just because the owner has left his device unattended for a few minutes in a hostile environment. The problem is to detect this attack. We propose a solution in networks of mobile devices carried by individuals. These networks are composed by nodes that have the capability of using short-range communication technology like blue-tooth or Wi-Fi, where nodes are carried by mobile users, and where links appear and disappear according to the social relationships between the users. Our idea is to use social physical contacts, securely collected by wireless personal smart-phones, as a biometric way to authenticate the legitimate owner of the device and detect the clone attack. We introduce two mechanisms: Personal Marks and Community Certificates. Personal Marks is a simple cryptographic protocol that works very well when the adversary is an insider, a malicious node in the network that is part, or not very far, from the social community of the original device that has been cloned. Community Certificates work very well when the adversary is an outsider, a node that has the goal of using the stolen credentials when interacting with other nodes that are far in the social network from the original device. When combined, these mechanisms provide an excellent protection against this very strong attack. We prove our ideas and solutions with extensive simulations in a real world scenario-with mobility traces collected in a real life experiment05/2011;
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