Title
CACA: Link-Based Channel Allocation Exploiting Capture Effect for Channel Reuse in Wireless Sensor Networks
Abstract
In this paper, we exploit the capture-effect for channel allocation. We experimentally show the characteristics of capture-effect across different channels, over time, and in different network densities. Then, we introduce CACA, an effective channel assignment protocol for wireless sensor networks. Traditional channel assignment protocols utilize all available channels to minimize interferences between any adjacent links. However, their performances are often not much better than the case of using single channel only. This is mainly due to an assumption that all channels are independent and quality of all channels are similar. However, this is a false assumption. In fact, there are only a few channels that show very good quality at any given time. The CACA avoids this problem by utilizing a few good channels and reuse these channels. When the channels are reused it relies on the capture-effect to ensure at least one of the contending nodes to transmit successfully. Maximizing this capture probability is a main objective of the CACA whenever the channels need to be reused. We evaluate the CACA on a 140-node wireless sensor network testbed and compare its performance with another benchmark protocol. Our results indicate that the CACA can improve the packet reception ratio of every link. As a result of this improvement, end-to-end throughput increases upto 100% in the case of a wireless sensor network with bursty traffic.
Year
DOI
Venue
2016
10.1109/ICDCS.2016.65
2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS)
Keywords
Field
DocType
channel diversity,capture-effect,wireless sensor network
Key distribution in wireless sensor networks,Capture effect,Computer science,Computer network,Communication channel,Packet loss,Throughput,Wi-Fi array,Wireless sensor network,Channel allocation schemes,Distributed computing
Conference
ISSN
ISBN
Citations 
1063-6927
978-1-5090-1484-2
1
PageRank 
References 
Authors
0.35
23
5
Name
Order
Citations
PageRank
Jung Hyun Jun1405.52
Solchan Yeon210.35
Titir Kundu310.35
Dharma P. Agrawal44804443.40
Jaehoon Jeong538734.96