Research, Energy-saving Strategies For Wireless Sensor Networks Based On The Cc2420

Wireless sensor networks (WSN) are made up of many micro sensor nodes which have the ability of communication and computing. They are autonomy network system, are the intelligence system which can finish special tasks according to the different environment. Compared with the traditional networks, WSN have the characteristics, such as large-scale networks, random arrangement, multi-hop communication, self-organizing and working in coordination with each other, which make it have a bright future in military, in industry, in appliance, in environment and in many other fields. However, WSN bring immeasurable benefits for human society, while bring up some new challenges, such as resources restriction, super-scale, and highly dynamic.Energy problem is the biggest challenges which WSN come up against. The power is restrict to the mini- size of WSN, which limits the networks' lifetime. They can't be replaced the charge because of that the mount of nodes are huge and the places are not easy to reach. Compared with the existing wireless networks, WSN exist the serious problem called energy restriction. So, the chief design objects are to use the sensor node's energy effectively, and to prolong the networks' lifetime.Our paper address the research background of WSN, application value, the art of states at home and abroad firstly, introduce the WSN's architecture, characteristics and the IEEE 802.15.4 protocol it complied, analyze the RF module CC2420 chip how to apply to our WSN, study the empirical algorithms that are relate to sensor networks' energy saving, then we put forward to a new WSN' power saving idea.Through doing control with transmission power in WSN, the idea makes every effort to realize optimize the WSN's lifetime, and to enhance the communication quality between nodes. Our paper's innovations are coming up with a new power saving idea and having some innovation when implementing it. Finally, we prove our thoughts in experiments based on the platform which consists of CC2420 and EM78815.