Mobility prediction and location management based on data mining

Daoui, Mehammed; Belkadi, Malika; Chamek, Lynda; Lalam, Mustapha; Hamrioui, Sofiane; Berqia, Amine

doi:10.1109/ngns.2012.6656095

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…The famous discovered relationship is that if a customer bought bread, butter, and coffee, it is likely that he/she would also buy milk. In our case, this technology can be used to discover the relationship between cells and obtain some types of information, such as users' movement history, roads, and locations of BSs [70]. In mobility prediction, most methods only consider the spatial factors, as it is directly related to users' future locations.…”

Section: Data Miningmentioning

confidence: 99%

“…Lastly, find the next cell-ID in the predicted region using the matched rule in preceding phases. Besides, the authors of [70] investigate using data mining to find the relationship between user location and other information, such as users' movement history, roads, and locations of BSs. The Apriori algorithm is used to get the association rules and predict the target cell.…”

Section: Data Miningmentioning

confidence: 99%

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A Survey on Next-Cell Prediction in Cellular Networks: Schemes and Applications

2020

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Mobility prediction is a powerful tool for network operators to optimize network performance. From cell level, if network operators know the cells to which the users will be connected in advance, wireless resources can be pre-allocated to improve network performance and better user experience can be provided in location-based services. Many next-cell prediction models and methods have been suggested and implemented. This paper is devoted to next-cell prediction (cell level mobility prediction) in cellular networks, and provides a thorough survey of the prediction schemes and applications. Particularly, a two-level classification methodology was proposed and applied. We first divided the prediction schemes into three categories based on the mobility data used for prediction, i.e. Current Movement State based Approaches (CMSA), Historical Movement Pattern based Approaches (HMPA), and HybriD Approaches (HDA). Prediction schemes in each category were further classified based on the used prediction methods. The typical application scenarios were introduced as well, including handover management, resource allocation, etc. Finally, current challenges and potential trends in the near future were further discussed.

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Section: Data Miningmentioning

confidence: 99%

Section: Data Miningmentioning

confidence: 99%

A Survey on Next-Cell Prediction in Cellular Networks: Schemes and Applications

2020

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“…DM refers to discovering knowledge in very large or huge amounts of data. Association Rule Mining (ARM) is one of the most important techniques in data mining [10]. It finds strong and interesting relationships among large sets of data items.…”

Section: Data Mining (Dm)mentioning

confidence: 99%

“…Where µ represents the total weight of the cell k. W r (C k ) represents the weight of roads in cell k as expressed in (10). W f (C k ) represents the weight of famous places in cell k as expressed in (11):…”

Section: Weighted Ant Colony Predictor (Wacp)mentioning

confidence: 99%

A new strategy for mobility prediction in the PCS network

Hekal¹,

Saleh

Zakria

2018

Mansoura Journal for Computer and Information Sciences

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Mobility prediction is one of the main challenges that faced Personal Communication Service (PCS) network. It is probable for many users to move among cells (coverage areas) during their calls. Therefore, the network needs to predict their next location in order to reserve another resource in that next cell to keep their calls going on. In this paper, a new strategy is proposed for mobility prediction named Mixed Mobility Prediction (MMP). It is composed of two predictors. The first one is named Association Rules Predictor (ARP), and the second one is called Weighted Ant Colony Predictor (WACP). In ARP the prediction is based on Association rules in data mining and detecting the time of calls. In WACP the prediction is based on Ant Colony (AC) in swarm intelligence. In addition to that, roads lead to their predicted next locations, and priority of famous places found in those locations. Finally, MMP merges the decisions of both predictors to get the final accurate decision in the absence of sufficient history for a MT. The proposed approach outperformed the compared the state-of-arts methods in terms of; Prediction Accuracy (PA), and Quality of Measure (QM).

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“…The other category is based on popular movement histories [21], [22]. In this situation, mobility prediction focuses on group of similar mobility behavior instead of eliminating random movements from the entire body of mobile users' profiles.…”

Section: Introductionmentioning

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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Mobility prediction and location management based on data mining

Cited by 9 publications

References 11 publications

A Survey on Next-Cell Prediction in Cellular Networks: Schemes and Applications

A Survey on Next-Cell Prediction in Cellular Networks: Schemes and Applications

A new strategy for mobility prediction in the PCS network

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

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