scholarly journals On Lookahead Strategy for Movement-Based Location Update: A General Formulation

2011 ◽  
pp. 153-166 ◽  
Author(s):  
Vicente Casares-Giner
Keyword(s):  
Location Update

Tunnel MTU discovery for nested mobile networks based on location update

2013 ◽  
Vol 32 (8) ◽  
pp. 2090-2094
Author(s):  
Long CHEN ◽  
Hong-bo TANG ◽  
Ling-wei WANG
Keyword(s):  
Mobile Networks ◽  
Location Update

scholarly journals Distributed Group Location Update Algorithm for Massive Machine Type Communication

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7336
Author(s):  
Mincheol Paik ◽  
Haneul Ko
Keyword(s):  
Outage Probability ◽  
Stochastic Game ◽  
Location Update ◽  
Response Dynamics ◽  
Small Energy ◽  
Machine Type ◽  
Best Response ◽  
Machine Type Communication ◽  
Iot Devices ◽  
The Individual

Frequent location updates of individual Internet of Things (IoT) devices can cause several problems (e.g., signaling overhead in networks and energy depletion of IoT devices) in massive machine type communication (mMTC) systems. To alleviate these problems, we design a distributed group location update algorithm (DGLU) in which geographically proximate IoT devices determine whether to conduct the location update in a distributed manner. To maximize the accuracy of the locations of IoT devices while maintaining a sufficiently small energy outage probability, we formulate a constrained stochastic game model. We then introduce a best response dynamics-based algorithm to obtain a multi-policy constrained Nash equilibrium. From the evaluation results, it is demonstrated that DGLU can achieve an accuracy of location information that is comparable with that of the individual location update scheme, with a sufficiently small energy outage probability.

Nature-Inspired-Algorithms-Based Cellular Location Management

2018 ◽  
pp. 346-362
Author(s):  
Swati Swayamsiddha ◽  
Chetna Singhal ◽  
Rajarshi Roy
Keyword(s):  
Potential Application ◽  
Optimization Problems ◽  
Location Management ◽  
Cellular Communication ◽  
Location Update ◽  
Mobile Location ◽  
Vital Importance ◽  
Cellular Location ◽  
Complex Optimization ◽  
Nature Inspired Algorithms

Nature-Inspired algorithms have gained relevance particularly for solving complex optimization problems in engineering domain. An overview of implementation modeling of the established algorithms to newly developed algorithms is outlined. Mobile location management has vital importance in wireless cellular communication and can be viewed as an optimization problem. It has two aspects: location update and paging where the objective is to reduce the overall cost incurred corresponding to these two operations. The potential application of the Nature-Inspired algorithms to mobile location management is studied. Many such algorithms are recently being explored along with incremental modifications to the existing techniques. Finally, analysis and insights highlight the further scopes of the Nature-Inspired algorithms to mobile location management application.

Particle Swarm Optimization for Cost Reduction in Mobile Location Management Using Reporting Cell Planning Approach

2017 ◽  
pp. 171-189 ◽  
Author(s):  
Smita Parija ◽  
Sudhansu Sekhar Singh ◽  
Swati Swayamsiddha
Keyword(s):  
Particle Swarm Optimization ◽  
Cellular Network ◽  
Cost Minimization ◽  
Location Management ◽  
Particle Swarm ◽  
Location Update ◽  
Swarm Optimization ◽  
Cell Planning ◽  
Wireless Mobile Communication ◽  
Cost Minimization Problem

Location management is a very critical and intricate problem in wireless mobile communication which involves tracking the movement of the mobile users in the cellular network. Particle Swarm Optimization (PSO) is proposed for the optimal design of the cellular network using reporting cell planning (RCP) strategy. In this state-of-the-art approach, the proposed algorithm reduces the involved total cost such as location update and paging cost for the location management issue. The same technique is proved to be a competitive approach to different existing test network problems showing the efficacy of the proposed method through simulation results. The result obtained is also validated for real network data obtained from BSNL, Odisha. Particle Swarm Optimization is used to find the optimal set of reporting cells in a given cellular network by minimizing the location management cost. This RCP technique applied to this cost minimization problem has given improved result as compared to the results obtained in the previous literature.

Autonomic Group Location Update for Mobile Networks

2006 ◽  
pp. 133-142
Author(s):  
Lai Tu ◽  
Furong Wang ◽  
Fan Zhang ◽  
Jian Zhang
Keyword(s):  
Mobile Networks ◽  
Location Update

Mobility Management in Mobile Computing and Networking Environments

2009 ◽  
pp. 650-681
Author(s):  
Samuel Pierre
Keyword(s):  
Mobile Networks ◽  
Mobility Management ◽  
Location Management ◽  
Cost Savings ◽  
Memory Model ◽  
Location Update ◽  
Management Scheme ◽  
Location Register ◽  
Wireless Communications Systems ◽  
The Cost

This chapter analyzes and proposes some mobility management models and schemes by taking into account their capability to reduce search and location update costs in wireless mobile networks. The first model proposed is called the built-in memory model; it is based on the architecture of the IS-41 network and aims at reducing the home-location-register (HLR) access overhead. The performance of this model was investigated by comparing it with the IS-41 scheme for different call-to-mobility ratios (CMRs). Experimental results indicate that the proposed model is potentially beneficial for large classes of users and can yield substantial reductions in total user-location management costs, particularly for users who have a low CMR. These results also show that the cost reduction obtained on the location update is very significant while the extra costs paid to locate a mobile unit simply amount to the costs of crossing a single pointer between two location areas. The built-in memory model is also compared with the forwarding pointers’ scheme. The results show that this model consistently outperforms the forwarding pointers’ strategy. A second location management model to manage mobility in wireless communications systems is also proposed. The results show that significant cost savings can be obtained compared with the IS-41 standard location-management scheme depending on the value of the mobile units’ CMR.

Movement Prediction Oriented Adaptive Location Management

2011 ◽  
pp. 464-483
Author(s):  
Tania Das
Keyword(s):  
Location Management ◽  
Personal Communication ◽  
Location Tracking ◽  
Location Update ◽  
Signaling Cost ◽  
Home Location ◽  
Movement Prediction ◽  
Location Register ◽  
Visitor Location Register ◽  
Two Level Systems

Movement prediction oriented adaptive location management provides a major role in personal communication service (PCS) system. Generally the GSM system supports two level architecture. Because it supports two kinds of databases-Home Location register and Visitor Location Register. Every time when the user crosses the location area it has to register with the HLR. This creates high cost for registration and location tracking as it involves the use of costly bandwidth between the Mobile Switching Center (MSC) and the HLR. In this paper the technique for reducing the costs during the location tracking and location update is proposed. Taking the movement prediction of the users it creates the block and the user registers with the HLR only after crossing the block instead of crossing the single cell. This movement prediction is generated using one neural network model for all the users. The block register (BR) is introduced between the block and the HLR in two level systems, thus introduces three level architecture. In this architecture some signaling cost values between the MSC-BR, BR-HLR and BR-BR are maintained to get the better performance. In this proposed system the aim is to set the value between the MSC and BR and the two BR as small as possible and the value between the BR and the HLR must be higher to get the better performance.

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