An Improvement of AODV Algorithm in a Large-scale Outdoor Area for ZigBee Networks
AODV routing algorithm is widely used in a large-scale outdoor area for ZigBee wireless sensors networks. However, a large number of routing requests in AODV algorithm can easily lead to broadcast storms, which consume a lot of bandwidth and node energy, cause a large amount of signal collision and reduce the quality of communication. To solve these problems, this paper proposes self-learning multi-route selection-optimised algorithm (SMSA) based on improvements of ZigBee AODV. By making full use of the learning function of the routing table and taking collision frequency of sending data as a reference index of the cost of the path, this algorithm can reduce effectively the number of forwarding RREQ, inhibit the broadcast storm and reduce the collision of data, thus improving the success rate of data transmission. Meanwhile, it can also reduce the transmission delay and eventually save the overall energy of the network. In the simulation platform of OMNeT++, the algorithm proves to have a good effect on the large scale ZigBee network.
ZigBee self-learning SMSA AODV OMNeT++
Chenggang Shan Jie Mou Wei Zhang
College of Information Science and Engineering Zaozhuang University Zaozhuang,China Shenzhen Huiding Technology Co.Ltd. Shenzhen,China
国际会议
秦皇岛
英文
1679-1684
2015-09-18（万方平台首次上网日期，不代表论文的发表时间）