Research On Energy Efficient Data Gathering Technology Of Wireless Sensor Networks

Wireless sensor network is one of the most promising operations in the 21st century, and it has been widely used in military surveillance, health monitoring, environmental monitoring, emergency rescue and other fields. Data gathering is one of the most basic operations in wireless sensor networks. Depending on the application requirements, data gathering can be divided into two different modes—data gathering with or without aggregation. For example, in some applications (such as image information gathering, etc), each sensor node needs to transmit the information to the server, that is data gathering without aggregation. For some other applications (such as temperature query, humidity query, etc), data gathering without aggregation often leads to heavy traffic and although it can meet the needs of these applications. But data gathering with aggregation can use the fusion technology to fuse data, and it not only can meet the needs of these applications but also can reduce the network traffic so that the energy consumption of the node will be reduced and the lifetime of the network will get extended. This study will focus on these two different modes of data gathering.Data gathering is widely used in many applications, and because of this, for different applications, the performance requirements are also different too. In the continuous target tracking, environmental monitoring and other applications, how to extend the network lifetime efficiently is the primary task for data gathering because the energy of sensor nodes is limit and in some other applications, for example, emergency rescue, battlefield surveillance, they require real-time monitoring so that they can make timely feedback according to the information from monitoring area, so low transmission delay is one target for data gathering. Although there are quite a number of research results, but most of them focus on a single performance and ignore the trade-off between multiple performances.Based on the two different modes of data gathering, this paper studies the balance between energy and delay. Our study includes the following two aspects:1) The previous studies about network lifetime often incur high delay and other issues. So in this paper, for data gathering without aggregation, we study how to extend the lifetime efficiently under delay constraint. We consider the problem from the perspective of weighted load balancing and propose a delay-constraint and maximum lifetime data gathering algorithm-DCDR. DCDR first constructs a data gathering tree in which the network lifetime is near optimal, and then deals with the"bottleneck"nodes iteratively to meet the delay requirements and reduces the algorithm's time complexity through pruning techniques. The simulation results show that compared with MITT algorithm with no delay constraint, the lifetime of DCDR algorithm can achieve over 90% of MITT's in most cases on the premise of guaranteeing the network transmission delay.2) In the previous studies about data gathering with aggregation, most of them assume that each node has a fixed transmission radius, but in the actual system, each node has multiple available transmission level so its transmission radius is variable. In this paper, we first analysis the impact of transmission level to network lifetime and delay, and then propose an efficient data gathering algorithm (EDG). EDG first estimates the changes of node's load and then meets the delay constraint through assigning each node a weight and achieves energy efficient through path optimization and level adjustment. The simulation results show that compared with MLA algorithm with no delay constraint, the lifetime of EDG can achieve over 53% of MLA under different network conditions, and compared with DCML algorithm with delay constraint, the lifetime can increase 10%.The main contribution of this paper and the innovations are as follows:1,For data gathering without aggregation, we propose a delay-constraint maximum lifetime data gathering algorithm. And this algorithm first constructs a data gathering tree in which the network lifetime is near optimal, and then deals with the"bottleneck"nodes iteratively to meet the delay requirements.2,For data gathering with aggregation, based on the node's model of actual system we study the balance between network lifetime and transmission delay, and then propose an efficient algorithm. The algorithm can achieve energy efficient through path optimization and level adjustment.