Category: CSE Project Reports

This paper discussed about ubiquitous computing, which is using a new computing technology that easily adapts to user environment with the help of computing infrastructure. This paper discussed about the differences between ubiquitous computing and traditional distributed computing and also mentioned about context aware ubiquitous computing applications which can manage user data by using context. 

Overview: 

In the entire Ubiquitous computing research, the main issue is how to create context- aware applications. For mobile users there is a middleware architecture available called Context Based Storage system which helps to build context- aware applications. Information about the user in both physical and virtual worlds is stored in context. CBS system finds the accessed files that are stored elsewhere using context.  In Ubiquitous computing, context-aware computing technology supports new interaction styles and also makes the interactions between users and computers much easier. CBS system uses context to avoid additional programming effort or traditionally performed programming effort  By using this system data can retrieve easily based on context of user priority, data can organize easily based on user preference and the context can also used to constrain the data quantity presented to the user. Every document will links to the context attributes and can retrieve those documents from storage using the corresponding context attributes. The main terminologies need to be highlighted in CBS system are logical context data model, entities, attributes, relationships and groups. File Browser and calendar are the two applications which implemented as part of capnet system on top of CBS system. These two applications describe how context is used to find relevant user information. In CBS system, we have both normal and context-aware modes. 

Conclusion: 

In CBS system there is architecture to organize the data in ubiquitous environment.  There is a different type of context information’s that supports useful functions in context-aware application and also exists context information that can be integrated with data management system.

Download Context Based Storage System for Managing Data Ubiquitous Computing Environment .

Introduction to Extending lifetime of sensor surveillance systems Data fusion model:

This paper discussed about the sensor surveillance system and how to increase the network lifetime of this system in data fusion model. The sensors need to be scheduled to monitor the respective targets, and then only the surveillance system network lifetime can be improved. The time duration when each target is monitored by sensor can be termed as network lifetime. A set of wireless sensor nodes and target will be there in a sensor surveillance system. Always, each target must be monitored by sensors. The sensors has short battery life so there is a severe need in increase the lifetime of a sensor surveillance system as it is difficult to recharge the sensors battery whenever required. 

Data fusion model- overview of sensor surveillance systems: 

To discuss about the network lifetime extension of a sensor surveillance system, there is a need to discuss about sensing model and data fusion model.

A. Sensing model: We usually assume targets will get detected by sensors with the help of signals energy which emitted by targets.

B. Data fusion model:  By using multiple sensors, data fusion can improve the performance of detecting targets.  Sensors need to be organized into clusters in order to use in data fusion. In every cluster, cluster head is responsible to take decisions about whether the target is present or not based on the information gathered from the member sensors in the cluster. The main goal is to extend network lifetime of the sensor network. Target is monitored only if the sensors in its fusion range informs that the target is exists based on the data fusion model. Sensors are organized into number of no disjoint cover sets and each cover set totally monitors all the targets. 

Conclusion: 

Energy consumption must monitor to extend the network lifetime of a sensor surveillance systems.  If we implement data fusion model in wireless sensor networks then sensing performance will get improved. As per this paper Data fusion model is helpful to extend the lifetime of sensor surveillance systems.

Introduction to Reducing File Download Time Stochastic Peer-to-Peer Networks:

This paper is about how to reduce download time in Peer-to-Peer networks and also discussed about the challenges encountered in using Peer-to-Peer networks in real world. In server-client architecture as many users working on limited resources, resultant poor performance in downloading files. Even P2P networks tries to overcomes this issue but it has its own drawbacks related to file downloads in reality. 

Characterizing the download time in a P2P network: 

In P2P network once a peer completes its downloading files it has to become a server to provide service to other peers. As the servicing peers increasing gradually, average download time for every user is shorter when compared to that of centralized network architecture. The challenging tasks of P2P network are lies in peer selection, data search, routing a file download session and downloading a file from source peers.

In order to reduce the downloading time there is a need to reduce file transfer time i.e., reduce the download time for all users to finish their downloads. As per total download time of an individual user, the per-user performance in P2P network is much worse when compared to that of centralized network architecture. In reality file download time for per-user should increase. The problem of reducing the download time can be solved by knowing average service capacity of each peer in the network or get to know the global constraints of each source peer. In reality gathering global information of a peer in the network is not possible. So in order to reduce the download time there is a need to know the capacity of each peer in the network. In real data networks, average capacities for different source peers are different.  When we calculate the average file download time there is a need to calculate the effect of correlation. 

Conclusion:

In real P2P network world, there is a need of increase the network efficiency by reducing per user download time. Spatial heterogeneity and the temporal correlations can increase the average download time of an individual user in the network.  There is a need to focus on time rather than on bytes and also average service capacity is not along useful to define average user performance in P2P network.

 Download Reducing File Download Time Stochastic Peer-to-Peer Networks .