Survey of Wireless Sensor Network based on Power Consumption and Data Aggregation

Abstract

Wireless sensor network (WSN), as one of the best emerging technologies of the 21st century, has started to develop at an accelerating pace in the past ten years. A lot of research has been done to improve it in various aspects, including its architecture, contract operating systems, routing protocols, data collection and merging, positioning mechanism, time synchronization, etc. Moreover, large numbers of promising applications have emerged and deployed in various geographies such as infrastructure protection, scientific exploration, military surveillance, traffic control and control, mining security and shipping, environmental protection, object tracking, military affairs, etc. With the conveniences provided by the WSN, it has affected our lives and changed greatly in many ways. However, there are still many problems plaguing the WSN. These include low reliability of wireless communication systems, limited available power, failure of nodes, etc. A WSN is an important way to study and interact with the physical world. A sensor network usually consists of a large number of small sensor nodes. Each sensing node has one or more sensing components for sensing ambient conditions (temperature, humidity, pressure) and a processing and communicating component to perform some simple operation on the data and communicate with its neighboring nodes. Sensor nodes are usually deployed extensively on a large scale and communicate with each other via wireless links. Control nodes process data collected from sensor nodes, collect control commands from sensor nodes, and connect the network to a traditional wired network. Sensor nodes are usually spread randomly, and then form a sensor network in a way that is customized to carry out specific tasks. There is usually no support for sensor network infrastructure. This paper first focuses on providing a survey of traditional WSN and then provides a detailed discussion based on Power Consumption and Data Aggregation.