A dynamic programming algorithm for resolving transmit-ambiguities in the localization of WSN

In application cases where millimeter-sized (≤ 5mm diameter) sensor motes are inevitable to gather environmental data, the motes' inherent energy constraints require a radical change in the communication procedure. For application cases like the surveying of hardly permeable underground structures, as they exist e.g. in mining areas of oil sands, ten-thousands of miniature sensors are needed to examine e.g. the oil-resource wealth of the area. This combination of extremely resource limited sensor motes and the need to communicate with several ten-thousands of motes requires changes with respect to the normal communication procedure. As reducing the DS-CDMA code length, which is used for the identification of the motes, saves signification amount of energy, we consider the scenario where a localization of sensor motes is required by means of non-relatable distance measurements. The resulting ambiguities in the distance measurements are due to the reassigning of DS-CDMA codes. In this work, we consider the problem of re-enabling the localization, i.e. of resolving the ambiguities due to non-unique identification of the sensor motes, to allow classical localization algorithms to obtain a positioning of the motes. For this problem, which has already been shown to be NP-hard in general, we present an algorithm that is capable of solving this problem optimally in the maximum a posteriori sense. Further, we show that our algorithm offers significant complexity reductions for large problem instances.

[1]  Hans L. Bodlaender,et al.  A Tourist Guide through Treewidth , 1993, Acta Cybern..

[2]  Gerd Ascheid,et al.  Localization of wireless sensor networks with concurrently used identification sequences , 2015, 2015 14th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET).

[3]  Panos M. Pardalos,et al.  Encyclopedia of Optimization, Second Edition , 2009 .

[4]  Abbas Jamalipour,et al.  Wireless communications , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[5]  Jörg Flum,et al.  Parameterized Complexity Theory (Texts in Theoretical Computer Science. An EATCS Series) , 2006 .

[6]  W. Marsden I and J , 2012 .

[7]  Mingzhu Wei,et al.  Position estimation from relative distance measurements in multi-agents formations , 2010, 18th Mediterranean Conference on Control and Automation, MED'10.

[8]  D. Du,et al.  Theory of Computational Complexity , 2000 .

[9]  Gerd Ascheid,et al.  A Novel Low-Complexity Numerical Localization Method for Dynamic Wireless Sensor Networks , 2015, IEEE Transactions on Signal Processing.

[10]  Ding‐Zhu Du,et al.  Wiley Series in Discrete Mathematics and Optimization , 2014 .

[11]  Sigve Hortemo Sæther,et al.  Faster algorithms for vertex partitioning problems parameterized by clique-width , 2013, Theor. Comput. Sci..

[12]  Libertario Demi,et al.  Environment mapping and localization with an uncontrolled swarm of ultrasound sensor motes , 2013 .

[13]  Udi Rotics,et al.  Clique-Width is NP-Complete , 2009, SIAM J. Discret. Math..

[14]  Heinrich J. Wörtche,et al.  Micro Motes: A Highly Penetrating Probe for Inaccessible Environments , 2015 .

[15]  Frank Gurski,et al.  A comparison of two approaches for polynomial time algorithms computing basic graph parameters , 2008, ArXiv.

[16]  Jörg Flum,et al.  Parameterized Complexity Theory , 2006, Texts in Theoretical Computer Science. An EATCS Series.

[17]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[18]  Panos M. Pardalos,et al.  Encyclopedia of Optimization , 2006 .

[19]  Bruno Courcelle,et al.  Upper bounds to the clique width of graphs , 2000, Discret. Appl. Math..

[20]  Jörg Rothe,et al.  Exakte Algorithmen für schwere Graphenprobleme , 2010, eXamen.press.