Student Seminar Report & Project Report With Presentation (PPT,PDF,DOC,ZIP)

SIMPS: Using Sociology for Personal Mobility

Abstract”

Assessing mobility in a thorough fashion is a crucial step toward more efficient mobile network design. Recent research on mobility has focused on two main points: analyzing models and studying their impact on data transport. These works investigate the consequences of mobility. This model defines a process called sociostation, rendered by two complimentary behaviors, namely socialize and isolate, that regulate an individual with regard to her/his own sociability level. SIMPS leads to results that agree with scaling laws observed both in small-scale and large-scale human motion. Although our model defines only two simple individual behaviors, we observe many emerging collective behaviors (group formation/splitting, path formation, and evolution).

Algorithm / Technique used:


SIMPS Technique


Existing System:

Almost all work on mobile ad hoc networks relies on simulations, which, in turn, rely on realistic movement models for their credibility. Since there is a total absence of realistic data in the public domain, synthetic models for movement pattern generation must be used and the most widely used models are currently very simplistic, the focus being ease of implementation rather than soundness of foundation. Whilst it would be preferable to have models that better reflect the movement of real users, it is currently impossible to validate any movement model against real data. However, it is lazy to conclude from this that all models are equally likely to be invalid so any will do. We note that movement is strongly affected by the needs of humans to socialize in one form or another. Fortunately, humans are known to associate in particular ways that can be mathematically modeled, and that are likely to bias their movement patterns. Thus, we propose a new mobility model that is founded on social network theory, because this has empirically been shown to be useful as a means of describing human relationships. In particular, the model allows collections of hosts to be grouped together in a way that is based on social relationships among the individuals. This grouping is only then mapped to a topographical space, with topography biased by the strength of social tie. We discuss the implementation of this mobility model and we evaluate emergent properties of the generated networks.





Proposed System:

We focus on the causes of mobility. Starting from established research in sociology, we propose SIMPS, a mobility model of human crowds with pedestrian motion.

We propose Sociological Interaction Mobility for Population, a mobility model aimed at pedestrian crowd motion that explores recent sociological findings driving human interactions:

(i) Each human has specific socialization needs, quantified by a target social interaction level, which corresponds to her personal status (e.g., age and social class.
(ii) Humans make acquaintances in order to meet their social interaction needs. We show that these two components can be translated into a coherent set of behaviors, called sociostation.

Hardware Requirements
¢ SYSTEM : Pentium IV 2.4 GHz
¢ HARD DISK : 40 GB
¢ FLOPPY DRIVE : 1.44 MB
¢ MONITOR : 15 VGA colour
¢ MOUSE : Logitech.
¢ RAM : 256 MB
¢ KEYBOARD : 110 keys enhanced.
Software Requirements

¢ Operating system :- Windows XP Professional
¢ Front End : - Java Technology.

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SIMPS: USING SOCIOLOGY FOR PERSONAL MOBILITY

INTRODUCTION

SIMPS: USING SOCIOLOGY FOR PERSONAL MOBILITY
Mobility modeling aims at describing in the most accurate and simplest way the motion of mobile entities. They are fundamental tools in a large variety of domains, such as physics, biology, sociology, networking, electronic gaming, and many others. As of now, there is a growing number of mobility models used in the design and analysis of communication systems, but how many of them fully represent the aspects characterizing the mobility of human beings. This is a fundamental issue, since in many situations the mobility of communicating and sensing equipments follows human mobility. The characteristic of that approach is very important, since we consider mobility by its causes. While we will study the characteristics outputted by our mobility proposal throughout its parameter set, and compare them with characteristics recorded from mobility traces, our goal here is clearly not to gauge the consequences of our work in terms of routing performance (consequences).

Sir,
Can you tell what are the modules used in this project(SIMPS:Using Sociology For Personal Mobility).


can you give the diagram for each modules?

[attachment=10264]
SIMPS: Using Sociology for Personal Mobility
Abstract:
Assessing mobility in a thorough fashion is a crucial step toward more efficient mobile network design. Recent research on mobility has focused on two main points: analyzing models and studying their impact on data transport. These works investigate the consequences of mobility.
This model defines a process called sociostation, rendered by two complimentary behaviors, namely socialize and isolate, that regulate an individual with regard to her/his own sociability level. SIMPS leads to results that agree with scaling laws observed both in small-scale and large-scale human motion. Although our model defines only two simple individual behaviors, we observe many emerging collective behaviors (group formation/splitting, path formation, and evolution).
Algorithm / Technique used:
SIMPS Technique
Existing System:
Almost all work on mobile ad hoc networks relies on simulations, which, in turn, rely on realistic movement models for their credibility. Since there is a total absence of realistic data in the public domain, synthetic models for movement pattern generation must be used and the most widely used models are currently very simplistic, the focus being ease of implementation rather than soundness of foundation. Whilst it would be preferable to have models that better reflect the movement of real users, it is currently impossible to validate any movement model against real data. However, it is lazy to conclude from this that all models are equally likely to be invalid so any will do. We note that movement is strongly affected by the needs of humans to socialize in one form or another. Fortunately, humans are known to associate in particular ways that can be mathematically modeled, and that are likely to bias their movement patterns. Thus, we propose a new mobility model that is founded on social network theory, because this has empirically been shown to be useful as a means of describing human relationships. In particular, the model allows collections of hosts to be grouped together in a way that is based on social relationships among the individuals. This grouping is only then mapped to a topographical space, with topography biased by the strength of social tie. We discuss the implementation of this mobility model and we evaluate emergent properties of the generated networks.
Proposed System:
We focus on the causes of mobility. Starting from established research in sociology, we propose SIMPS, a mobility model of human crowds with pedestrian motion.
We propose Sociological Interaction Mobility for Population, a mobility model aimed at pedestrian crowd motion that explores recent sociological findings driving human interactions:
(i) Each human has specific socialization needs, quantified by a target social interaction level, which corresponds to her personal status (e.g., age and social class.
(ii) Humans make acquaintances in order to meet their social interaction needs. We show that these two components can be translated into a coherent set of behaviors, called sociostation.
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• Ram : 256 Mb.
Software Requirements:
• Operating system : - Windows XP Professional.
• Coding Language : - Java.
• Tool Used : - Eclipse.

[attachment=11641]
INTRODUCTION
SIMPS: USING SOCIOLOGY FOR PERSONAL MOBILITY
Mobility modeling aims at describing in the most accurate and simplest way the motion of mobile entities. They are fundamental tools in a large variety of domains, such as physics, biology, sociology, networking, electronic gaming, and many others. As of now, there is a growing number of mobility models used in the design and analysis of communication systems, but how many of them fully represent the aspects characterizing the mobility of human beings. This is a fundamental issue, since in many situations the mobility of communicating and sensing equipments follows human mobility. The characteristic of that approach is very important, since we consider mobility by its causes. While we will study the characteristics outputted by our mobility proposal throughout its parameter set, and compare them with characteristics recorded from mobility traces, our goal here is clearly not to gauge the consequences of our work in terms of routing performance (consequences).
1.1 OBJECTIVES
• To provide, High-resolution scalable models of complex socio-technical systems
• Service-oriented architecture and delivery mechanism for facilitating the use of these models by domain experts
• To generate mobility patterns that spans a wide range of empirical observation in an extremely robust fashion.
• It adopts outer knowledge from sociology that has not been used before.
• Distributed coordinating architecture for information fusion, model execution and data processing
• Scalable data management architecture and system to support model execution and analytics
• Scalable methods for visual and data analytics to support analysts
1.2 SCOPE OF PROJECT
• After considering all disadvantages in RWP, achieved a mobility model aimed at pedestrian crowd motion that explores recent sociological findings driving human interactions
• Also translated the two component (i) each human has specific socialization needs, quantified by a target social interaction level, which corresponds to her personal status (ii) humans make acquaintances in order to meet their social interaction needs into a coherent set of behaviors, called sociostation, driving the dynamics of simulated entities.
• Our measurements are based on many other influences are at play in any individual’s mobility, such as collision avoidance, activity planning and constraints.
• To address the roots of mobility.
1.2 FEATURES OF PROJECT
• SIMPS is a Mobility modeling that aims at describing in the most accurate and simplest way the motion of mobile entities.
• It’s main feature is being simple and analytically tractable.
• Another important feature is, it explores recent sociological findings driving human interactions.
• It updates an individual’s current behavior to either socialize or isolate.
• It is responsible for translating the behavior adopted by an individual into motion.
LITERATURE SURVEY
CHAPTER 2
LITERATURE SURVEY
There is a growing number of mobility models used in the design and analysis of communication systems. But, the question is ‘How many of them fully represent the aspects characterizing the mobility of the human beings?’ Considering the problem, many algorithms have been developed.
• Mobility modeling refers in general Random Way Point (RWP), which is the de-facto standard for both theoretical analysis and simulation results.
• RWP belongs to the same class as Brownian motion, also called Random Walk. This technique has many advantages; however, the simplicity provided by the RWP fails in capturing realistic behaviors observed in the human mobility, as shown in the recent literatures.
• To avoid this problem, network and communication technicians deploy various other techniques.
• Vehicular based mobility models have been developed first as a first set of models based on expectations how the mobility is performed in the particular situations.
• Dynamic source routing for adhoc networks is taken as one of the key technique to develop the SIMPS methodology.
• Trace based mobility models have been proposed both indoors and outdoors and the various results are obtained.
• Various other analyses show that both contact and inner-contact distributions, as well as location popularity distribution are developed.