Multiple Mobile Data Offloading Through Disruption Tolerant Networks

Li, Yong; Qian, Mengjiong; Jin, Depeng; Hui, Pan; Hanzo, Lajos; Chen, Sheng

doi:10.1109/tmc.2013.61

Cited by 103 publications

(4 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Greedy, Heuristic and Random, which exploit information about the contact graph of mobile nodes in order to select the initial set of k mobile nodes in charge to trigger the opportunistic dissemination of the requested content. Li et al [41] propose to treat the selection of seed nodes as a maximisation problem in which the utility function to be optimised is subject to multiple linear constraints, expressing, for example, the mobility of users, traffic heterogeneity and the storage available on mobile nodes. A third solution based on mobility prediction, TOMP (Traffic Offloading with Movement Predictions), is proposed by Baier et al [42].…”

Section: Related Workmentioning

confidence: 99%

Cellular traffic offloading via opportunistic networking with reinforcement learning

Valerio

Bruno

Passarella

2015

Computer Communications

View full text Add to dashboard Cite

The widespread diffusion of mobile phones is triggering an exponential growth of mobile data traffic that is likely to cause, in the near future, considerable traffic overload issues even in last-generation cellular networks. Offloading part of the traffic to other networks is considered a very promising approach and, in particular, in this paper we consider offloading through opportunistic networks of users' devices. However, the performance of this solution strongly depends on the pattern of encounters between mobile nodes, which should therefore be taken into account when designing offloading control algorithms. In this paper we propose an adaptive offloading solution based on the Reinforcement Learning framework and we evaluate and compare the performance of two well known learning algorithms: Actor Critic and Q-Learning. More precisely, in our solution the controller of the dissemination process, once trained, is able to select a proper number of content replicas to be injected in the opportunistic network to guarantee the timely delivery of contents to all interested users. We show that our system based on Reinforcement Learning is able to automatically learn a very efficient strategy to reduce the traffic on the cellular network, without relying on any additional context information about the opportunistic network. Our solution achieves higher level of offloading with respect to other state-of-the-art approaches, in a range of different mobility settings. Moreover, we show that a more refined learning solution, based on the Actor-Critic algorithm, is significantly more efficient than a simpler solution based on Q-learning.

show abstract

Section: Related Workmentioning

confidence: 99%

Cellular traffic offloading via opportunistic networking with reinforcement learning

Valerio

Bruno

Passarella

2015

Computer Communications

View full text Add to dashboard Cite

show abstract

“…이 문제를 해결하기 위해 무선통신 사용량 증가에 따른 기반시설 확 충 비용을 줄이는 동시에 사용자의 데이터 통신 에 대한 필요를 충족시키기 위해 기회적 네트워 크를 (opportunistic networks) 활용한 무선통신 사용량 경감 기법이 제안 되었다 [3][4][5][6].…”

Section: 서론unclassified

Multimedia Contents Dissemination using Mobile Communication and Opportunistic Networks

Kim¹

2013

Journal of Digital Contents Society

View full text Add to dashboard Cite

The popularization of smart phones changes the usage patterns of mobile communication from voice-centric to data-centric communication. The demand for wireless data communications is rapidly increasing, and thus the need for expanding infrastructure for mobile communication is also rapidly increasing. In this paper, we propose a scheme for reducing the cost for the mobile communication infrastructure by exploiting opportunistic networks in dissemination of multimedia contents. By using this scheme, the large portion of the cost for mobile communication infrastructure could be saved, and the need of users for multimedia contents could be also fulfilled. Our scheme is evaluated using agent-based simulations. The simulation results show that about 70% of mobile communication can be replaced with the data communication through opportunistic networks.

show abstract

“…Consequently, the focus of operators is put on policy management signaling to sustain the profitability of their business and efficiently control the infrastructure. A wide range of research studies treated the topic of handling the growth of data services [2], [3]. However, litera-ture addressing the scalability issue of the systems handling the signaling associated with those services is difficult to find.…”

Section: Introductionmentioning

confidence: 99%

Network Function Virtualization: Mitigating the Impact of VoLTE on the Policy and the Charging System

Jouihri,

Guennoun,

Chagh

et al. 2016

JTIT

View full text Add to dashboard Cite

Needless to say that telecommunications’ operators are showing increasing interests toward solving the dilemma of optimizing network resources while offering state-of-the-art quality of service. Recently, operators have shown an increasing interest to investigate solutions for better control on network utilization, service usage and monetization. They also noticed a significant growth in Diameter signaling and more specifically in signaling related to policy management. A massive introduction of Voice over LTE (VoLTE) service will have a significant impact on the systems handling policy signaling, as VoLTE will reshape the landscape of Long Term Evolution (LTE) policies and completely change the way policy management works. However, this massive approach is meant to provide significant competitive advantages for operators offering LTE services and still require circuit-switched network to provide voice service. The biggest challenge for those operators is to find an appropriate solution, scalable enough to handle the unpredictable growth of Diameter signaling. In this paper, a model, based on Network Function Virtualization (NFV) technology is proposed, able to address the challenges of massively introducing VoLTE, without impacting existing services and without jeopardizing current revenues. In presented approach, the standard VoLTE call flows, referenced user’s behavior and latest experiments’ results on NFV technology are used.

show abstract

Multiple Mobile Data Offloading Through Disruption Tolerant Networks

Cited by 103 publications

References 38 publications

Cellular traffic offloading via opportunistic networking with reinforcement learning

Cellular traffic offloading via opportunistic networking with reinforcement learning

Multimedia Contents Dissemination using Mobile Communication and Opportunistic Networks

Network Function Virtualization: Mitigating the Impact of VoLTE on the Policy and the Charging System

Contact Info

Product

Resources

About