A Location Prediction Algorithm for Mobile Communications Using Directional Antennas

Lu, Xiaofeng; Lió, Píetro; Hui, Pan; Jin, Haifei

doi:10.1155/2013/418606

Cited by 6 publications

(5 citation statements)

References 16 publications

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“…Similarly, our previous work on PPMLE [25] is also extended to distributed sensor networks. Additionally, if our distributed parameter estimation procedure is equipped with localization technologies [35][36][37][38], it can be utilized in more mobile situations.…”

Section: Discussionmentioning

confidence: 99%

Distributed Risk Aversion Parameter Estimation for First-Price Auction in Sensor Networks

Chen

et al. 2013

International Journal of Distributed Sensor Networks

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Following the Internet, the Internet of Things (IoT) becomes a prime vehicle for supporting auction. The use of market mechanisms to solve computer science problems is gaining significant traction. More and more clues show that the bidders tend to be risk-averse ones. However, traditional nonparametric approach is only applicable for the case of risk neutrality in a centralized server. This study proposes a generalized nonparametric structural estimation procedure for the first-price auctions in the distributed sensor networks. To evaluate the performance of the aggregated parameter estimators, extensive Monte Carlo simulation experiments are conducted for ten different values of risk aversion parameters including the risk neutrality case in multiple classic scenes. Moreover, in order to improve the usability of the aggregated parameter estimators, some guidance is also given for real-world applications.

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Section: Discussionmentioning

confidence: 99%

Distributed Risk Aversion Parameter Estimation for First-Price Auction in Sensor Networks

Chen

et al. 2013

International Journal of Distributed Sensor Networks

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“…Other approaches, such as the work [26], attempt to reduce the handoff issue through an efficient beam system control; a similar work is presented in [27]; in this paper, authors try to mitigate the handoff problem by proposing a predictive location model in order to advance the future position movements of the nodes. Nevertheless, these works are suitable for Vehicular ad Hoc Network (VANET) environments and do not properly address the deafness problem which is very relevant in MANET.…”

Section: Mobility Issues Directional Mac Workmentioning

confidence: 99%

An Adaptive Beamforming Time with Round Robin MAC Algorithm for Reducing Energy Consumption in MANET

Inzillo¹,

Rango²,

Quintana³

et al. 2018

Preprint

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The use of Smart Antenna Systems (SAS) in pervasive environments such the Mobile Ad hoc Networks (MANET) has been promoted as the best choice to improve Spatial Division Multiple Access (SDMA) and throughput. Although directional communications are expected to provide great advantages in terms of network performance, directional MAC (Medium Access Control) protocols introduce several issues. One of the most known problems in this context is represented by the fact that, attempting to solve or at least mitigate the problems introduced by these kinds of antennas especially at MAC layer, a large amount of energy consumption is achieved ; for example, due to excessive retransmissions introduced by very frequently issue such as deafness and handoff. The expedients proposed in order to reduce these drawbacks attempting to limit beamforming time of nodes in cooperation with a Round-Robin scheduling, can grant high performance in terms of fairness and throughput. However the overall energy consumption in the network is not efficent due to the static approach. In view of this, we propose an Adaptive Beamforming Time with Round-Robin MAC providing for a dynamic assignment of the beamforming time with the purpose to limit the waste of energy of nodes.

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“…Because the OpenCellid database does not supply cell information such as radius, azimuth and directivity, which can be used to further improve location prediction as in [11], we assumed the cells are omnidirectional and we estimated a mean coverage radius for a cell in an urban environment to be around 400 meters, by measuring the medium distance between two cells.…”

Section: Resources Involvedmentioning

confidence: 99%

“…Xiaofeng et al [11] propose a location prediction algorithm that will make use of directional antennas. Their work is mainly oriented at predicting and tracking location of moving vehicles.…”

Section: Introductionmentioning

confidence: 99%

Significant Location Detection & Prediction in Cellular Networks using Artificial Neural Networks

Leca¹,

Nicolaescu²,

Rîncu³

2015

csit

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Location services and applications, based on network data or global positioning systems, are greatly influencing and changing the way people use mobile phone networks by improving not only user-applications but also the network management part. These applications and services can be further developed by introducing location prediction. We design a system that logs cell id and timestamp data from the users' mobile device, detects the significance of the location to the user, such as home and workplace, and predicts future locations over a chosen time period using artificial neural networks. A novel method is designed for location detection that automatically determines the significance of the location to the user, by spatial and temporal analysis. In our approach, the neural network is automatically adapted, with the help of the location detection algorithm, to the period of the week for which a prediction is desired, achieving accurate weekday and weekend location prediction.

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A Location Prediction Algorithm for Mobile Communications Using Directional Antennas

Cited by 6 publications

References 16 publications

Distributed Risk Aversion Parameter Estimation for First-Price Auction in Sensor Networks

Distributed Risk Aversion Parameter Estimation for First-Price Auction in Sensor Networks

An Adaptive Beamforming Time with Round Robin MAC Algorithm for Reducing Energy Consumption in MANET

Significant Location Detection & Prediction in Cellular Networks using Artificial Neural Networks

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