2010 IEEE 30th International Conference on Distributed Computing Systems 2010
DOI: 10.1109/icdcs.2010.11
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An Energy-Efficient Distributed Algorithm for Minimum-Latency Aggregation Scheduling in Wireless Sensor Networks

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Cited by 78 publications
(41 citation statements)
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“…Afterwards, Huang et al [8] and Wan et al [13] propose improved scheduling algorithms with upper bounds of 23R+∆-18 and 15R+∆-4, respectively. Yu et al [3] propose the first distributed scheduling algorithm with an upper bound of 48R+6∆+16, which is further improved by Li et al [9] and Xu et al [14]. The latest work on MLAS is done by Bagaa et al [1], which differs from the previous work in adopting semi-structured and un-structured topology for data aggregation.…”
Section: Related Workmentioning
confidence: 99%
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“…Afterwards, Huang et al [8] and Wan et al [13] propose improved scheduling algorithms with upper bounds of 23R+∆-18 and 15R+∆-4, respectively. Yu et al [3] propose the first distributed scheduling algorithm with an upper bound of 48R+6∆+16, which is further improved by Li et al [9] and Xu et al [14]. The latest work on MLAS is done by Bagaa et al [1], which differs from the previous work in adopting semi-structured and un-structured topology for data aggregation.…”
Section: Related Workmentioning
confidence: 99%
“…In order to minimize the total delay for the sink to obtain the aggregated monitoring result, data aggregation scheduling is extensively researched to supply a collision-free scheduling for concurrent transmission links under certain interference constraints [8]. This is the so called Minimum Latency Aggregation Scheduling (MLAS) problem, which is proved to be NPhard in [4] and has been extensively investigated in [1,[3][4][8][9]. However, previous works on this topic usually assume that the sensor nodes are awake all the time and able to receive or transmit packets whenever they need to, which is, however, impractical when taking into account that sensor nodes are limited by power and idle listening consumes as much energy as wireless communication [16].…”
Section: Introductionmentioning
confidence: 99%
“…Distributed algorithms A number of distributed algorithms for solving DAS [28,27,20,15] have been proposed. How- in [28] presents an adaptive strategy for their DAS in the presence of crash failure.…”
Section: Related Workmentioning
confidence: 99%
“…One promising way of minimizing the delay is to maximize the throughput while data transmissions in WSNs face a fundamental challenge, i.e., the wireless interference. Previous work often focused on graphbased interference models in their protocol design for data aggregation [5], [12], [22], [27], [29]. Graph- Xu [16] of this paper appeared in IEEE MASS 2009. based models serve as a useful abstraction of WSNs; they facilitate the process of designing protocols and proving their efficiency, while they cannot reflect the superimposed effect of wireless interference.…”
Section: Introductionmentioning
confidence: 99%
“…11 for each node u in CDS do 12 i ← the level of u in tree T ; select only one as u's representative child and find its color index as σ g . This finishes the second phase.…”
mentioning
confidence: 99%