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Towards Maximizing Timely Content Delivery

in Delay Tolerant Networks

ABSTRACT

The state-of-the-arts capture mobility patterns or social properties of mobile devices. Such solutions do not capture patterns of delivered content in order to optimize content delivery. Without such optimization, the content demanded by a large number of subscribers could follow the same forwarding path as the content by only one subscriber, leading to traffic congestion and packet drop. To address the challenge, in this paper, we develop a solution framework, namely Ameba, for timely delivery. In detail, we first leverage content properties to derive an optimal routing hop count of each content to maximize the number of needed nodes. Next, we develop node utilities to capture interests, capacity and locations of mobile devices. Finally, the distributed forwarding scheme leverages the optimal routing hop count and node utilities to deliver content towards the needed nodes in a timely manner. Illustrative results verify that Ameba achieves comparable delivery ratio as Epidemic but with much lower overhead.

EXISTING SYSTEM	PROPOSED SYSTEM
EXISTING CONCEPT:- Exchanges of advertisements occur if and only if the associated mobile nodes carrying such advertisements encounter each other.  Thus, the forwarding of advertisements from publishers towards needed subscribers depends upon the opportunistic encounters of mobile nodes.	PROPOSED CONCEPT: - The proposed Ameba essentially is a hybrid of multicast and unicast, and we highlight its basic idea as follows. After an advertisement is published by its publisher, the advertisement is forwarded from the publisher to needed subscribers.
EXISTING TECHNIQUE:- Optimal Advertisement Relay Strategy	PROPOSED TECHNIQUE:- Ameba solution framework
TECHNIQUE DEFINITION:- During the optimization, the key is to derive the function between the opportunistic encounters of mobile nodes used to exchange advertisements and the number of nodes to receive advertisements of interest.	TECHNIQUE DEFINITION:- To solve the above maximization problem, in this project, we propose a solution framework, namely Ameba, by considering two sub problems and designing the associated techniques. First, Ameba considers a simple case by assuming that each node in a DTN has an equal probability to forward a given advertisement towards needed subscribers
DRAWBACKS:- Delayed communication Low performance.	ADVANTAGES:- Fast communication  High performance