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...

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Bibliographic Details
Published in2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC) pp. 1679 - 1684
Main Authors Chenggang Shan, Jie Mou, Wei Zhang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2015
Subjects
AODV
Data communication
Energy consumption
Mesh networks
OMNeT
Routing
self-learning
SMSA
Storms
Wireless sensor networks
Zigbee
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Summary: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.
DOI:10.1109/IMCCC.2015.356