Future location prediction of mobile subscriber over mobile network using Intra Cell Movement pattern algorithm

Abo‐Zahhad, Mohammed; Ahmed, Sabah M.; Mabrook, M. Mourad

doi:10.1109/iccspa.2013.6487272

Cited by 13 publications

(10 citation statements)

References 7 publications

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“…Furthermore, Abo-Zahhad et al [23] presented a multi-scale version of the algorithm. The algorithm is able to compute movement predictions on different scales depending on the user's needs.…”

Section: Related Workmentioning

confidence: 99%

Predicting User Mobility in Mobile Radio Networks to Proactively Anticipate Traffic Hotspots

Göndör

Uzun

Rohrmann³

et al. 2013

2013 International Conference on MOBILe Wireless MiddleWARE, Operating Systems, and Applications

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With approx. 6 million macro cells worldwide and a gross energy consumption of approx. 100 TWh per year as of 2013, mobile networks are one of the major energy consumers in the ICT sector. As trends, such as cloud-based services and other traffic-intensive mobile applications, fuel the growth of mobile traffic demands, operators of mobile telephony networks are forced to continuously extend the capacity of the existing infrastructure by both implementing new technologies as well as by installing new cell towers to provide more bandwidth for mobile users and improve the network's coverage. In order to implement energy-efficient reconfiguration mechanisms in mobile telephony networks as proposed by the project Communicate Green, it is essential to anticipate traffic hotspots, so that a network's configuration can be adjusted in time accordingly. Hence, predicting the movement of mobile users on a cellular level of the mobile network is a crucial task. In this paper, we propose a Movement Prediction System based on the algorithm of Yavas et al. that allows to determine the future movement of a user on a cellular level using precomputed movement patterns. We extended the algorithm to be capable of preselecting patterns based on time and contextual data in order to improve prediction accuracy. The performance of the algorithm is evaluated based on real and live user movement data from the OpenMobileNetwork, which is a platform providing estimated mobile network topology data. We found that the algorithm's prediction quality is sufficient, but requires an extensive amount of recorded user movements to perform well.

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Section: Related Workmentioning

confidence: 99%

Predicting User Mobility in Mobile Radio Networks to Proactively Anticipate Traffic Hotspots

Göndör

Uzun

Rohrmann³

et al. 2013

2013 International Conference on MOBILe Wireless MiddleWARE, Operating Systems, and Applications

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“…Abo-Zahhad et al [93] proposed a future location prediction scheme based on three levels of location detection, including intra-sector prediction, sector to sector prediction and cell to cell prediction. All of these three levels of predictions consisted of four main processes.…”

Section: ) User Behavioral Informationmentioning

confidence: 99%

Mobility Prediction: A Survey on State-of-the-Art Schemes and Future Applications

2019

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Recently, mobility has gathered tremendous interest as the users' desire for consecutive connections and better quality of service has increased. An accurate prediction of user mobility in mobile networks provides efficient resource and handover management, which can avoid unacceptable degradation of the perceived quality. Therefore, mobility prediction in wireless networks is of great importance and many works have been dedicated to this issue. In this paper, the necessity of mobility prediction, together with its intrinsic characteristics in terms of movement predictability, prediction outputs, and performance metrics is discussed. Moreover, the learning perspective of solutions to mobility prediction has been studied. Specifically, an overview of the state-of-the-art approaches is provided, including Markov chain, hidden Markov model, artificial neural network, Bayesian network, and data mining based on different kinds of knowledge. At last, this paper also explores the open research challenges due to the advent of the fifthgeneration mobile system and puts forward some potential trends in the near future. INDEX TERMS Mobility prediction, quality of service (QoS), resource reservation, handover management, the fifth-generation mobile system (5G).

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“…A simple Multi Layer Perception (MLP) model which is able to resolve non-linearly separable problems was described by authors in [8]. To build a multilayer perception, a number of neurons are connected in layers where each of them is able to identify small linearly separable sections of the inputs and is used to get the final output in the network.…”

Section: Related Workmentioning

confidence: 99%

Handoff Delay Optimization Using Hybrid Prediction Model

Regitha¹,

Paul²,

Sivan³

et al. 2018

IJNDC

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Future location prediction of mobile subscriber over mobile network using Intra Cell Movement pattern algorithm

Cited by 13 publications

References 7 publications

Predicting User Mobility in Mobile Radio Networks to Proactively Anticipate Traffic Hotspots

Predicting User Mobility in Mobile Radio Networks to Proactively Anticipate Traffic Hotspots

Mobility Prediction: A Survey on State-of-the-Art Schemes and Future Applications

Handoff Delay Optimization Using Hybrid Prediction Model

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