Cross-layer Congestion Control and Routing Design for Wireless Sensor Networks: Distributed Newton Method
-
摘要: 无线传感网络应用广泛, 其性能与路由选择和拥塞控制密切相关. 致力于拥塞控制与多径路由的跨层优化, 以实现在链路容量受限和节点能量受限情况下的无线传感网络效用最大化. 针对对偶次梯度算法具有收敛速度慢与信息交互量大等缺陷, 设计了具有二阶收敛性能的分布式牛顿算法来实现网络效用最大化. 通过矩阵分裂技术, 实现了只需单跳信息交互的牛顿对偶方向的分布式求解方法. 仿真结果表明, 分布式牛顿算法的收敛性能显著优于对偶次梯度算法.Abstract: Wireless sensor networks have a wide range of extensive applications, and their performances are strongly dependent on routing and congestion control. Therefore, in this paper we address cross-layer optimization of congestion control and multi-hop routing to achieve network utility maximization for wireless sensor networks subject to link capacity and node power constraints. Considering that dual subgradient algorithm has a very slow convergence and large amount of information exchange, we design a distributed Newton method with quadratic convergence performance to maximize the network utility. We use matrix splitting technique to distributedly solve Newton dual direction through single-hop information exchange. Extensive simulation shows that distributed Newton algorithm converges faster than the dual subgradient algorithm.
-
[1] Yang Xiao-Jun, Xing Ke-Yi. Channel fault tolerant target tracking in multi-hop wireless sensor networks based on particle filtering. Acta Automatica Sinica, 2011, 37(4): 440-448(杨小军, 邢科义. 无线多跳传感器网络下基于粒子滤波的信道容错的目标跟踪方法. 自动化学报, 2011, 37(4): 440-448) [2] Peng Yu, Luo Qing-Hua, Wang Dan, Peng Xi-Yuan. WSN localization method using interval data clustering. Acta Automatica Sinica, 2012, 38(7): 1190-1199(彭宇, 罗清华, 王丹, 彭喜元. 基于区间数聚类的无线传感器网络定位方法. 自动化学报, 2012,38(7): 1190-1199) [3] Zhong Zhi, Luo Da-Yong, Liu Shao-Qiang, Fan Xiao-Ping, Qu Zhi-Hua. An adaptive localization approach for wireless sensor networks based on Gaussian-Markov mobility model. Acta Automatica Sinica, 2010, 36(11): 1557-1568(钟智, 罗大庸, 刘少强, 樊晓平, 瞿志华. 无线传感器网络中一种基于高斯马尔可夫移动模型的自适应定位方法. 自动化学报, 2010, 36(11): 1557-1568) [4] [4] Low S H, Lapsley D E. Optimization flow control, I: basic algorithm and convergence. IEEE/ACM Transactions on Networks, 1999, 7(6): 861-874 [5] [5] Kelly F P, Maulloo A K, Tan D K H. Rate control in communication networks: shadow prices, proportional fairness and stability. Journal of the Operational Research Society, 1998, 49(3): 237-252 [6] [6] Chiang M, Low S H, Calderbank A R, Doyle J C. Layering as optimization decomposition: a mathematical theory of network architectures. Proceedings of IEEE, 2007, 95(1): 255-312 [7] [7] Chen J M, Xu W Q, He S B. Utility-based asynchronous flow control algorithm for wireless sensor networks. IEEE Journal on Selected Areas in Communications, 2010, 28(7): 1116-1125 [8] [8] Nama H, Chiang M, Mandayam N. Utility-lifetime trade-off in self-regulating wireless sensor networks: a cross-layer design approach. In: Proceedings of IEEE International Conference on Communications. Istanbul, Turkey: IEEE, 2006. 3511-3516 [9] [9] Zhu J H, Hung K L, Bensaou B. Tradeoff between network lifetime and fair rate allocation in wireless sensor networks with multi-path routing. In: Proceedings of the 9th ACM International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems. New York, USA: ACM, 2006. 301-308 [10] Bertsekas D P, Gafni E M. Projected Newton methods and optimization of multi-commodity flows. IEEE Transactions on Automatic Control, 1983, 28(12): 1090-1096 [11] Bickson D, Tock Y, Zyrnnis A. Distributed large scale network utility maximization. In: Proceedings of the 2009 IEEE International Conference on Symposium on Information Theory. Seoul, Korea: IEEE. 829-833 [12] Wei E M, Ozdaglar A, Jadbabaie A. A distributed Newton method for network utility maximization. In: Proceedings of the 49th IEEE Conference on Decision and Control. Atlanta, GA: IEEE, 2010. 1816-1821 [13] Jadbabaie A, Ozdaglar A, Zargham M. A distributed newton method for network optimization. In: Proceedings of IEEE Conference on Decision and Control. Shanghai, China: IEEE, 2009. 15-18 [14] Liu J, Sherali H D. A distributed Newton's method for joint multi-hop routing and flow control: theory and algorithm. In: Proceedings of IEEE International Conference on Computer Communications. Orlando, FL, USA: IEEE, 2012. 2489-2497 [15] Boyd S, Vandenberghe L. Convex Optimization. Cambridge: Cambridge University Press, 2004. 484-496 [16] Woznicki Z I. Matrix splitting principles. International Journal of Mathematics and Mathematical Sciences, 2001, 28(5): 251-284 [17] Horn R, Johnson C R. Matrix Analysis. Cambridge: Cambridge University Press, 1985. 279-337
点击查看大图
计量
- 文章访问数: 1566
- HTML全文浏览量: 63
- PDF下载量: 980
- 被引次数: 0