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Saturday, 3 February 2018

An Incentive Framework for Cellular Traffic Offloading(2014)


An Incentive Framework for Cellular

 Traffic Offloading(2014)

ABSTRACT:
Cellular networks (e.g., 3G) are currently facing severe traffic overload problems caused by excessive traffic demands. Offloading part of the cellular traffic through other forms of networks, such as Delay Tolerant Networks (DTNs) and WiFi hotspots, is a promising solution. However, since these networks can only provide intermittent connectivity to mobile users, utilizing them for cellular traffic offloading may result in a nonnegligible delay. As the delay increases, the users’ satisfaction decreases. In this paper, we investigate the tradeoff between the amount of traffic being offloaded and the users’ satisfaction. We provide a novel incentive framework to motivate users to leverage their delay tolerance for cellular traffic offloading. To minimize the incentive cost given an offloading target, users with high delay tolerance and large offloading potential should be prioritized for traffic offloading. To effectively capture the dynamic characteristics of users’ delay tolerance, our incentive framework is based on reverse auction to let users proactively express their delay tolerance by submitting bids. We further illustrate how to predict the offloading potential of the users by using stochastic analysis for both DTN and WiFi cases. Extensive trace-driven simulations verify the efficiency of our incentive framework for cellular traffic offloading.
EXISTING SYSTEM:
Existing offloading studies have not considered the satisfaction loss of the users when a longer delay is caused by traffic offloading.
DISADVANTAGES OF EXISTING SYSTEM:
Ø Not considered the satisfaction loss of the users when a longer delay is caused by traffic offloading.
Ø Only provide intermittent and opportunistic network connectivity to the users.
Ø Non-negligible data downloading delay.
PROPOSED SYSTEM:
In this paper, we focus on investigating the trade-off between the amount of traffic being offloaded and the users’ satisfaction, and propose a novel incentive framework to motivate users to leverage their delay tolerance for traffic offloading. Users are provided with incentives; i.e., receiving discount for their service charge if they are willing to wait longer for data downloading. During the delay, part of the cellular data traffic may be opportunistically off-loaded to other networks mentioned above, and the user is assured to receive the remaining part of the data via cellular network when the delay period ends.
ADVANTAGES OF PROPOSED SYSTEM:
Ø To motivate the mobile users with high delay tolerance and large offloading potential to offload their traffic to other intermittently connected networks such as DTN or WiFihotspots.
Ø To capture the dynamic characteristics of users’ delay tolerance.
Ø To predict users’ offloading potential based on their mobility patterns and the geographical distribution of WiFi hotspots in the WiFi case.
MODULES:
1.     Network Model.
2.     Reverse auction.
3.     Prediction of Offloading Potential: The DTN Case
4.     Prediction of Offloading Potential: The WiFi Case
MODULE DESCRIPTION:
Network Model
In this module, focusing on offloading cellular traffic to other forms of networks, such as DTNs and WiFi hotspots and they generally focus on maximizing the amount of cellular traffic that can be offloaded.
Reverse auction
In this module, we use a novel incentive framework, Win-Coupon, based on reverse auction, to motivate users to leverage their delay tolerance for cellular traffic offloading; Auction has been widely used in network design. Applying auction in the spectrum leasing is one of the most practical applications. Federal Communications Commission (FCC) has already auctioned the unused spectrum in the past decade, and there are a large amount of works on wireless spectrum auctions. Moreover, auction has also been applied for designing incentive mechanism to motivate selfish nodes to forward data for others. However, none of them has applied auction techniques to cellular traffic offloading.
Prediction of Offloading Potential: The DTN Case
Mobile users can share data via DTNs by contacting each other. In urban area with higher user density, mobile users have more chances to contact other users who have their requested data. Large data requests such as video clips tend to drain most of the cellular network resource, and such requests can also tolerate some delay. By offloading them via DTNs, the payload of cellular network can be significantly reduced.
 Prediction of Offloading Potential: The WiFi Case
In this module, we model node mobility by a Semi Markov Process, in which arbitrary distributed sojourn times are allowed. To avoid state space explosion, each Markov state represents a geographical area with a fixed size. The process of a user moving from a geographical area to another is modeled as a transition of Markov processes between two states.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
Ø System                          :         Pentium IV 2.4 GHz.
Ø Hard Disk                      :         40 GB.
Ø Floppy Drive                 :         1.44 Mb.
Ø Monitor                          :         15 VGA Colour.
Ø Mouse                            :         Logitech.
Ø Ram                               :         512 Mb.
SOFTWARE REQUIREMENTS:
Ø Operating system          :         Windows XP/7.
Ø Coding Language         :         JAVA/J2EE
Ø IDE                                   :         Netbeans 7.4
Ø Database                        :         MYSQL

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