On the Information Flow Required for Tracking
Control in Networks of Mobile Sensing Agents
Abstract:
We design controllers that permit mobile agents with distributed or networked sensing capabilities to track (follow) desired trajectories, identify what trajectory information must be distributed to each agent for tracking, and develop methods to minimize the communication needed for the trajectory information distribution.
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 Our tracking controller, 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.
MODULES
1. System Module
2. Social Motion Influence
3. Twin Social Behavior
4. Spatial and Time Characteristics
5. Interaction Based Mobility
MODULE DESCRIPTION
SYSTEM MODULE
Client-server computing or networking is a distributed application architecture that partitions tasks or workloads between service providers (servers) and service requesters, called clients. Often clients and servers operate over a computer network on separate hardware. A server machine is a high-performance host that is running one or more server programs which share its resources with clients. A client also shares any of its resources; Clients therefore initiate communication sessions with servers which await (listen to) incoming requests.
SOCIAL MOTION INFLUENCE
Our proposed tracking controller is composed of two parts: social motion influence and motion execution unit. The social motion influence updates an individual’s current behavior to either socialize or isolate. The motion execution unit is responsible for translating the behavior adopted by an individual into motion. we will translate this sociostation into the domain of pedestrian mobility. Although many other influences are at play in any individual’s mobility, such as collision avoidance, activity planning and constraints, we wish here to gauge the effect of this process alone, aside from any other influence. Simulating complete pedestrian mobility is therefore out of the scope of this paper.
TWIN SOCIAL BEHAVIOUR
Our proposed tracking controller relies on social graphs from which motion influence behaviors are derived. Social graphs do not represent physical proximity, but only relationships among individuals. Nevertheless, the former influences the latter, since close acquaintances tend to get physically closer. The originality in Our proposed tracking controller resides in its twin behavior (socialize and isolate) and their interplay. In its expression, Our proposed tracking controller combines gravitational attraction and preferential attachment.
SPATIAL AND TIME CHARACTERISTICS
This parameter describes the space in which individuals move. The boundary conditions can be of three types: infinite, finite, and periodic. If finite, the topology can be a square, a disc, or any kind of bounded geometric space. If periodic, the topology can be a square (with toroidal boundary mapping). In the remainder of this paper, we investigate the properties exhibited by Our proposed tracking controller alone. Time characteristics concern the total duration for which motion is considered, and the time quantization step used for motion rendering. These two values, although more related to implementation than to model definition, are of prime concern since their choice can directly influence the outcome of the synthesized motion. It is then of major importance to distinguish inherent characteristics of our model from eventual bias due to time sampling. In the analysis below, we explore the effects of time quantization and total considered duration on the results of Our proposed tracking controller.
INTERACTION BASED MOBILITY
Our proposed tracking controller also breaks the barrier between individual and group mobility: collective motion emerges in this model, without the help of explicit grouping. The influence of time on Our proposed tracking controller outcomes appears in two ways: time quantization step and total simulation time. The first aspect is related to the very common problem of sampling on measurements. Individuals present different mobility characteristics depending on the space they evolve in. We have two parameters defining motion space. The first one, namely space type, defines if individual evolve in finite, infinite, or periodic space.
REFERENCE:
Liang Chen, Sandip Roy and Ali Saberi, “On the information flow required for tracking control in networks of mobile sensing agents”, IEEE Transactions on Mobile Computing, Vol. 10, No.5, April 2011.
