时限约束下最大质量聚合树的构建
On the Construction of Maximum-Quality Aggregation Trees in Deadline-Constrained WSNs
摘要：
在有期限约束的无线传感器网络数据聚合中，由于Sink节点的期限约束和干扰约束，使得所有传感器节点都不能参与数据聚合。因此，利用群体范例的智慧，数据聚合中的参与节点的总数确定聚合的质量（$QoA$）。虽然以前的研究已经提出了最优算法，最大化$QoA$在一个强加的截止日期和一个给定的聚合树，有没有工作在这种情况下构建最优树。在本文中，我们投下一个优化问题，以解决最佳的树构建的最后期限约束的数据聚合在无线传感器网络。我们证明，最佳和最差的聚合树的最大可实现的$QoA$之间的比率是一样大的$O(2^D)$，其中$D$是汇的最后期限，从而使设计有效的解决方案的问题的最重要的价值的问题。然而，问题是具有挑战性的解决，因为我们证明了它是NP-难的。我们应用最近提出的马尔可夫近似框架，设计两个分布式算法具有不同的计算开销，收敛到一个有界邻域的最优解。大量的模拟在一组代表性的随机生成的情况下表明，所提出的算法显着提高$QoA$的%101和%93，平均相比，最好的，据我们所知，现有的替代方法。
英文摘要：
In deadline-constrained data aggregation in wireless sensor networks (WSNs), the imposed sink deadline along with the interference constraint hinders participation of all sensor nodes in data aggregation. Thus, exploiting the wisdom of the crowd paradigm, the total number of participant nodes in data aggregation determines the quality of aggregation ($QoA$). Although the previous studies have proposed optimal algorithms to maximize $QoA$ under an imposed deadline and a given aggregation tree, there is no work on constructing optimal tree in this context. In this paper, we cast an optimization problem to address optimal tree construction for deadline-constrained data aggregation in WSNs. We demonstrate that the ratio between the maximum achievable $QoA$s of the optimal and the worst aggregation trees is as large as $O(2^D)$, where $D$ is the sink deadline and thus makes devising efficient solution of the problem an issue of paramount value. However, the problem is challenging to solve since we prove that it is NP-hard. We apply the recently-proposed Markov approximation framework to devise two distributed algorithms with different computation overheads that converge to a bounded neighborhood of the optimal solution. Extensive simulations in a set of representative randomly-generated scenarios show that the proposed algorithms significantly improve $QoA$ by %101 and %93 in average compared to the best, to our knowledge, existing alternative methods.
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