This paper describes current and proposed protocols for mobility management for public land mobile network (PLMN)-based networks, mobile Internet protocol (IP), wireless asynchronous transfer mode (ATM), and satellite networks. The integration of these networks will be discussed in the context of the next evolutionary step of wireless communication networks. First, a review is provided of location management algorithms for personal communication systems (PCS) implemented over a PLMN network. The latest protocol changes for location registration and handoff are investigated for Mobile IP, followed by a discussion of proposed protocols for wireless ATM and satellite networks. Finally, an outline of open problems to be addressed by the next generation of wireless network service is discussed.

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In today's world, mobile phone penetration has reached a saturation point. As a result, subscriber churn has become an important issue for mobile operators as subscribers switch operators for a variety of reasons. Mobile operators typically employ churn prediction algorithms based on service usage metrics, network performance indicators, and traditional demographic information. A newly emerging technique is the use of social network analysis (SNA) to identify potential churners. Intuitively, a subscriber who is churning will have an impact on the churn propensity of his social circle. Call detail records are useful to understand the social connectivity of subscribers through call graphs but do not directly provide the strength of their relationship or have enough information to determine the diffusion of churn influence. In this paper, we present a way to address these challenges by developing a new churn prediction algorithm based on a social network analysis of the call graph. We provide a formulation that quantifies the strength of social ties between users based on multiple attributes and then apply an influence diffusion model over the call graph to determine the net accumulated influence from churners. We combine this influence and other social factors with more traditional metrics and apply machine‐learning methods to compute the propensity to churn for individual users. We evaluate the performance of our algorithm over a real data set and quantify the benefit of using SNA in churn prediction. © 2013 Alcatel‐Lucent.

Abstract:Low Earth Orbit (LEO) networks have dynamic, yet deterministic, topologies.varying connectivity pattern would result in the re connections passing through a link that is turned f the topology change. In this ations, routing, handover.

Critical Infrastructure Protection ModelingDisruptions to our critical national infrastructures (e.g., power, telecom, transportation, and emergency services) are an area of increasing national concern [21]. Each of the infrastructures is highly dependent on telecommunications [6] and each of the infrastructures is subject to