• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.872-885
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• 2015

The rapid increase of smart mobile devices and mobile applications has led to explosive growth of data traffic in cellular network. Offloading data traffic becomes one of the most urgent technical problems. Recent work has proposed to exploit opportunistic communications to offload cellular traffic for mobile data dissemination services, especially for accepting large delayed data. The basic idea is to deliver the data to only part of subscribers (called target-nodes) via the cellular network, and allow target-nodes to disseminate the data through opportunistic communications. Human mobility shows temporal and spatial characteristics and predictability, which can be used as effective guidance efficient opportunistic communication. Therefore, based on the regularity of human mobility we propose NodeRank algorithm which uses the encounter characteristics between nodes to choose target nodes. Different from the existing work which only using encounter frequency, NodeRank algorithm combined the contact time and inter-contact time meanwhile to ensure integrity and availability of message delivery. The simulation results based on real-world mobility traces show the performance advantages of NodeRank in offloading efficiency and network redundant copies.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.39-46
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• 2019

Human mobility estimation plays a key factor in a lot of promising applications including location-based recommendation systems, urban planning, and disease outbreak control. We study the human mobility estimation problem in the case where recent locations of a person-of-interest are unknown. Since matrix decomposition is used to perform latent semantic analysis of multi-dimensional data, we propose a human location estimation algorithm based on matrix factorization to reconstruct the human movement patterns through the use of information of persons with correlated movements. Specifically, the optimization problem which minimizes the difference between the reconstructed and actual movement data is first formulated. Then, the gradient descent algorithm is applied to adjust parameters which contribute to reconstructed mobility data. The experiment results show that the proposed framework can be used for the prediction of human location and achieves higher predictive accuracy than a baseline model.

• Journal of Korea Spatial Information System Society
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• v.10 no.3
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• pp.45-54
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• 2008

This paper designs and Implements a connectivity analyzer for the hybrid vehicular network based on the real-life movement history data achieved from the Taxi telematics system currently in operation, aiming at providing a useful guideline and information to build a telematics network. The simulator traces the location of each vehicle, sets the vehicle type, either gateway or normal, decides whether it can be connected to a mobile gateway, keeps track of status of the vehicle, and calculates the duration of disconnected state. With this analysis considering the transmission range and gateway ratio, we can decide the cost-effective number of mobile gateways having both cellular and ad-hoc network interfaces, and buffer space requirement based on the measured disconnection time and message generation ratio.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5712-5728
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• 2017

Recently, there are many studies, that considering green wireless cellular networks, have taken the energy consumption of the base station (BS) into consideration. In this work, we first introduce an energy consumption model of multi-mode sharing BS powered by multiple energy sources including renewable energy, local storage and power grid. Then communication load requests of the BS are transformed to energy demand queues, and battery energy level and worst-case delay constraints are considered into the virtual queue to ensure the network QoS when our objective is to minimize the long term electricity cost of BSs. Lyapunov optimization method is applied to work out the optimization objective without knowing the future information of the communication load, real-time electricity market price and renewable energy availability. Finally, linear programming is used, and the corresponding energy efficient scheduling policy is obtained. The performance analysis of our proposed online algorithm based on real-world traces demonstrates that it can greatly reduce one day's electricity cost of individual BS.