Large-Scale Synthetic Social Mobile Networks with SWIM

LARGE-SCALE SYNTHETIC SOCIAL MOBILE NETWORKS WITH SWIM

ABSTRACT:

This paper presents small world in motion (SWIM), a new mobility model for ad hoc networking. SWIM is relatively simple, is easily tuned by setting just a few parameters, and generates traces that look real—synthetic traces have the same statistical properties of real traces in terms of intercontact times, contact duration, and frequency among node couples. Furthermore, it generates social behavior among nodes and models networks with complex social communities as the ones observed in the real traces. SWIM shows experimentally and theoretically the presence of the power-law and exponential decay dichotomy of intercontact times, and, most importantly, our experiments show that predicts very accurately the performance of forwarding protocols for PSNs like Epidemic, Delegation, Spray&Wait, and more complex, social-based ones like BUBBLE. Moreover, we propose a methodology to assess protocols on model with a large number of nodes. To the best of our knowledge, this is the first such study. Scaling of mobility models is a fundamental issue, yet never considered in the literature. Thanks to SWIM, here we present the first analysis of the scaling capabilities of Epidemic Forwarding, Delegation Forwarding, Spray&Wait, and BUBBLE.

EXISTING SYSTEM:

The complexity of these networks derives mostly from the difficulty of predicting human mobility. Much research has been dedicated to the study of real-life experimental data traces so as to compute statistical properties of human mobility and, therefore, of PSNs. Another large flow of works have been dedicated to uncovering structural properties of PSNs such as the presence of social-based community substructures  and to using these properties to design efficient message forwarding. Additionally, in the authors discuss on the limits of experiments based on logging contacts and show how to infer plausible mobility patterns from them. Also have a large number of works been presented on designing models for human mobility. Most of these works validate their models with real-life data traces available online and unfortunately not very large.

DISADVANTAGES OF EXISTING SYSTEM:

v Difficulty of predicting human mobility.

v Another large flow of works have been dedicated to uncovering structural properties of PSNs such as the presence of social-based community substructures and to using these properties to design efficient message.

PROPOSED SYSTEM:

The model is very simple to implement and very efficient in simulations. By implementing this simple rule, SWIM is able to raise social behavior among nodes, a fundamental ingredient of human mobility in real life. We validate the model using four different real traces and compare the distributions of inter contact times, contact durations, and number of contacts between nodes, showing that synthetic data that SWIM generate match very well each of the four real scenarios simulated.

ADVANTAGES OF PROPOSED SYSTEM:

v It generates traces with similar statistical properties.

v It validates correctly sophisticated protocols based on the social structure.

v Generating large scale synthetic social mobile networks.

SYSTEM CONFIGURATION:-

HARDWARE REQUIREMENTS:-

Processor             -       Pentium –IV

Speed                  -       1.1 Ghz

RAM                   -       512 MB(min)

Hard Disk            -       40 GB

Key Board           -       Standard Windows Keyboard

Mouse                 -       Two or Three Button Mouse

Monitor                       -       LCD/LED

SOFTWARE REQUIREMENTS:

Operating system        :       Windows XP.

Coding Language       :       JAVA

Data Base                    :       SQL

Tool                            :       Netbeans

REFERENCE:

Sokol Kosta, Alessandro Mei, Member, IEEE, and Julinda Stefa, Member, IEEE, “Large-Scale Synthetic Social Mobile Networks with SWIM” IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 13, NO. 1, JANUARY 2014