ε-Approximation and Weighted Fairness Guaranteed Congestion Control Algorithm for Wireless Sensor Networks

Abstract

In a wireless sensor network,congestion problem not only causes packet loss,but also leads to delay increase and energy consumption.Most of the existing congestion control algorithms for wireless sensor networks seldom concern the combined problem of data compression and weighted fairness.In order to address this problem,a greedy piecewise constant e-approximation algorithm,GPCA,and a novel decentralized congestion control algorithm WFCC are proposed in this paper.GPCA approximates a subsequence using a constant and guarantees that the error between the real sequence and the approximation sequence is less than or equal to e.In addition,we prove the optimality of constant e-approximation in theory.WFCC not only mitigates congestion,but also guarantees the weighted fairness among all sensor nodes.Importantly,we give a lower bound of weighted fairness metric 1-(10c/9)2 where c is a constant and 0c0.2.We evaluate GPCA using Intel Lab Data,and the results show that GPCA has great compression performance.In addition,we evaluate the algorithm WFCC in a 22-node wireless sensor network by TOSSIM.The results of simulation demonstrate that WFCC achieves high throughput and weighted fairness(above 95% on average).