Unified analytical models for Location Management costs and optimum design of location areas

Martin, Eladio; Liu, Ling; Weber, Matt; Pesti, Peter; Woodward, M.E.

doi:10.4108/icst.collaboratecom2009.8320

Cited by 11 publications

(8 citation statements)

References 31 publications

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“…Recent developments of TA optimization are presented in [10,24]. The authors of [10] combine rule base paging with movement-based location update, and derive analytical results of the performance improvement that is achievable by the integrated scheme.…”

Section: Related Workmentioning

confidence: 99%

“…The authors of [10] combine rule base paging with movement-based location update, and derive analytical results of the performance improvement that is achievable by the integrated scheme. In [24], the authors apply stochastic models to characterize the trade-off between paging and update, and provide a unified analytical model for the minimum overall overhead. The results enable sensitivity analysis of the impact of paging and update parameters on 6 TA size.…”

Section: Related Workmentioning

confidence: 99%

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Performance and cost trade-off in Tracking Area reconfiguration: A Pareto-optimization approach

et al. 2012

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Tracking Area (TA) design is one of the key tasks in location management of Long Term Evolution (LTE) networks. TA enables to trace and page User Equipments (UEs). As UEs distribution and mobility patterns change over time, TA design may have to undergo revisions. For revising the TA design, the cells to be reconfigured typically have to be temporary torn down.Consequently, this will result in service interruption and "cost". There is always a trade-off between the performance in terms of the overall signaling overhead of the network and the reconfiguration cost. In this paper, we model this trade-off as a bi-objective optimization problem to which the solutions are characterized by Pareto-optimality. Solving the problem delivers a host of potential trade-offs among which the selection can be based on the preferences of a decision maker. An integer programming model has been developed and applied to the problem. Solving the integer programming model for various cost budget levels leads to an exact scheme for Pareto-optimization.In order to deliver Pareto-optimal solutions for large networks in one single run, a Genetic Algorithm (GA) embedded with Local Search (LS) is applied. * Corresponding author. E-mail: sarmo@itn.liu.se Preprint submitted to Computer Networks April 2, 2012Unlike many commonly adopted approaches in multi-objective optimization, our algorithm does not consider any weighted combination of the objectives.Comprehensive numerical results are presented in this study, using largescale realistic or real-life network scenarios. The experiments demonstrate the effectiveness of the proposed approach.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Performance and cost trade-off in Tracking Area reconfiguration: A Pareto-optimization approach

et al. 2012

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“…Mobile clients do not contribute directly to the spatial alarm processing task. The server obtains the current location of its mobile clients through localization services [14,9] which are orthogonal to the spatial alarm processing service.…”

Section: Distributed Client-server Architecturementioning

confidence: 99%

“…At one extreme, the alarm processing server will handle the check of all alarms for all mobile clients at high frequency, which can be very expensive and not scalable. Furthermore, even in this server centric case, clients still need to be constantly connected in order for the location server to continuously track their current location through signal probing [9], which is known to be energy inefficient compared to running a simple application on the mobile client [18,10]. Contributions and scope of the paper.…”

Section: Introductionmentioning

confidence: 99%

Efficient indexing structure for scalable processing of spatial alarms

Doo

Liu

Narasimhan

et al. 2010

Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems

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Spatial Alarms are reminders for mobile users upon their arrival of certain spatial location of interest. Spatial alarm processing requires meeting two demanding objectives: high accuracy, which ensures zero or very low alarm misses, and high scalability, which requires highly efficient and optimal processing of spatial alarms. Existing techniques for processing spatial alarms cannot solve these two problems at the same time. In this paper we present the design and implementation of a new indexing technique, Mondrian tree. The Mondrian tree indexing method partitions the entire universe of discourse into spatial alarm monitoring regions and alarm-free regions. This enables us to reduce the number of on-demand alarm-free region computations, significant saving of both server load and client to server communication cost. We evaluate the efficiency of the Mondrian tree indexing approach using a road network simulator and show that the Mondrian tree offers significant performance enhancements on spatial alarm processing at both the server side and the client side.

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“…In order to minimize this signaling burden, the location area concept (a set of cells) is used, whereby the mobile terminal will inform the network about a change in its position only when the location area's border has been crossed. The employment of the call and mobility patterns of the user can help optimize the location area's dimensions and minimize signaling costs [11]. In fact, user profile-based algorithms for Location Management have proved to significantly reduce signaling costs [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Resource Optimization in Mobile Communication Networks With User Profile-Based Algorithms

Martin¹

2012

IJCNC

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Unified analytical models for Location Management costs and optimum design of location areas

Cited by 11 publications

References 31 publications

Performance and cost trade-off in Tracking Area reconfiguration: A Pareto-optimization approach

Performance and cost trade-off in Tracking Area reconfiguration: A Pareto-optimization approach

Efficient indexing structure for scalable processing of spatial alarms

Resource Optimization in Mobile Communication Networks With User Profile-Based Algorithms

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