projects 2012

**Capacity of Data Collection in Arbitrary Wireless Sensor Networks -Â projects 2012
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Technology Used: Java

**ABSTRACT: Capacity of Data Collection in Arbitrary Wireless Sensor Networks -Â projects 2012**

Data collection is a fundamental function provided by wireless sensor networks. How to efficiently collect sensing data from all sensor nodes is critical to the performance of sensor networks. The proposed work aims to understand the theoretical limits of data collection in a TDMA-based sensor network in terms of possible and achievable maximum capacity. Previously, the study of data collection capacity has concentrated on large-scale random networks. However, in most of the practical sensor applications, the sensor network is not uniformly deployed and the number of sensors may not be as huge as in theory. Therefore, it is necessary to study the capacity of data collection in an arbitrary network. Proposed work concentrates on deriving the upper and lower bounds for data collection capacity in arbitrary networks under protocol interference and disk graph models. A simple BFS(Breadth First Search) treebased method can lead to order-optimal performance for any arbitrary sensor networks.

**AIM**

To understand the theoretical limits of data collection in a TDMA-based sensor network in terms of possible and achievable maximum capacity. To derive the upper and lower bounds for data collection capacity in arbitrary networks under protocol interference and disk graph models. To show that a simple BFS treebased method can lead to order-optimal performance for any arbitrary sensor networks.

**SCOPE**

Wireless sensor networks are used in wide range potential applications in various scenarios such as battlefield, emergency relief, and environment monitoring, wireless sensor networks have recently emerged as a premier research topic. The ultimate goal of a sensor network is often to deliver the sensing data from all sensors to a sink node and then conduct further analysis at the sink node. Thus, data collection is one of the most common services used in sensor network applications.

**OBJECTIVES**

Data collection is one of the most common services used in sensor network applications. How to efficiently collect sensing data from all sensor nodes is critical to the performance of sensor networks. In this paper, we study some fundamental capacity problems arising from the data collection in wireless sensor networks. However, in most of the practical sensor applications, the sensor network is not uniformly deployed and the number of sensors may not be as huge as in theory. Therefore, it is necessary to study the capacity of data collection in an arbitrary network.

**PROBLEM DEFINITION**

The performance of data collection in sensor networks can be characterized by the rate at which sensing data can be collected and transmitted to the sink node. In particular, the theoretical measure that captures the limits of collection processing in sensor networks is the capacity of many-to one data collection, i.e., the maximum data rate at the sink to continuously receive the snapshot of data from sensors. Data collection capacity reflects how fast the sink can collect sensing data from all sensors with interference constraint. It is critical to understand the limit of many-to-one information flows and devise efficient data collection algorithms to improve the performance of wireless sensor networks.

**PROBLEM SOLUTION**

Propose a simple data collection method which performs data collection on branches of the Breadth First Search (BFS) tree for arbitrary sensor networks under protocol interference and disk graph models (if two sensors are within the transmission ranges of each other, then they can communicate),

Since the disk graph model is idealistic, consider a more practical network model: general graph model. In the general graph model, two nearby nodes may be unable to communicate due to various reasons such as barriers and path fading. To prove that a greedy scheduling algorithm on the BFS tree can achieve capacity.

The data collection capacity under more general communication models: physical interference model and Gaussian channel model. For a physical interference model, the capacity of data collection is in the same order as the one under protocol interference model. For a Gaussian channel model, an upper bound of data collection capacity is derived.

**INTRODUCTION**

Due to their wide-range potential applications in various scenarios such as battlefield, emergency relief, and environment monitoring, wireless sensor networks have recently emerged as a premier research topic. The ultimate goal of a sensor network is often to deliver the sensing data from all sensors to a sink node and then conduct further analysis at the sink node. Thus, data collection is one of the most common services used in sensor network applications. In this paper, we study some fundamental capacity problems arising from the data collection in wireless sensor networks.

**ASSUMPTIONS MADE**

Assume that a successful transmission over a link has a fixed data rate W bits/second. It is also assumed that all packets have unit size b bits. The time is divided into time slots with t Â¼ b=W seconds. Thus, only one packet can be transmitted in a time slot between two neighboring nodes. TDMA scheduling is used at MAC layer. Assume that every node has a fixed transmission power P. Thus, a fixed transmission range r can be defined such that a node vj can successfully receive the signal sent by node uj.

We assume that there is no correlation among all sensing values and no network coding or aggregation technique is used during the data collection.

**EXISTING SYSTEM**

The many-to-one transport capacity in dense and random sensor networks under a protocol interference model has been studied.

The capacity of data collection with the complex physical layer techniques, such as antenna sharing, channel coding, and cooperative beam forming has been studied.

Recent study indicates the capacity of a general some-to some communication paradigm under a protocol interference model in random networks with multiple randomly selected sources and destinations.

The capacity of data collection under a protocol interference model with multiple sinks also studied.

**DISADVANTAGES**

All the research shares the standard assumption that a large number of sensor nodes are either located on a grid structure or randomly and uniformly distributed in a plane. Such an assumption is useful to simplify the analysis and derive nice theoretical limits, but may be invalid in many practical sensor applications.

**PROPOSED SYSTEM**

To deriving capacity bounds of data collection for arbitrary networks, where sensor nodes can be deployed in any distribution and can form any network topology.

Focus on the capacity of data collection in a many-to-one communication scenario.

Propose a simple data collection method based on the BFS tree to achieve capacity

Derive the upper and lower bounds for data collection capacity in arbitrary networks under protocol interference and disk graph models

**ADVANTAGES**

Simple BFS tree-based method can lead to order-optimal performance for any arbitrary sensor networks.

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