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Approximate solutions and performance bounds for the sensor placement problem

Muhammad Uddin, Anthony Kuh, Aleksandar Kavcic, Toshihisa Tanaka
2012 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)  
Using properties of matrices, we come up with lower and upper bounds for the optimal solution performance.  ...  The solution for large m and n is infeasible, requiring us to look at approximate algorithms.  ...  Acknowledgment This work was supported in part by NSF grants ECCS-098344, 1029081, DOE grant DE-OE0000394, and the University of Hawaii REIS project.  ... 
doi:10.1109/smartgridcomm.2012.6485955 dblp:conf/smartgridcomm/UddinKKT12 fatcat:oix5xrdkcrceninwh3hgdrpcai

Nested performance bounds and approximate solutions for the sensor placement problem

Muhammad Sharif Uddin, Anthony Kuh, Aleksandar Kavcic, Toshihisa Tanaka
2014 APSIPA Transactions on Signal and Information Processing  
Computing the solution for large m and n is infeasible, requiring us to look at approximate algorithms and bounding optimal performance.  ...  This paperdevelops a number of tools and algorithms to analyze the sensor placement problem.  ...  Approximation algorithms perform well and we could apply these algorithms for placement of meters at the distribution level for microgrid state estimation problems.  ... 
doi:10.1017/atsip.2014.3 fatcat:qhomcc23a5aj7h7e63focby7b4

Simultaneous Optimization of Sensor Placements and Balanced Schedules

Andreas Krause, Ram Rajagopal, Anupam Gupta, Carlos Guestrin
2011 IEEE Transactions on Automatic Control  
We prove that ESPASS provides a constant-factor approximation to the optimal solution of this NP-hard optimization problem.  ...  Traditionally, these two problems of sensor placement and scheduling have been considered separately; one first decides where to place the sensors, and then when to activate them.  ...  Varaiya and Dr. S. Coleri Ergen for valuable discussions, the staff of Sensys Networks for providing technical advice on their sensor, and K. El-Arini and J.  ... 
doi:10.1109/tac.2011.2164010 fatcat:tgouanpgu5bqneofwxgp44x4ui

Efficient Sensor Placement Optimization for Securing Large Water Distribution Networks

Andreas Krause, Jure Leskovec, Carlos Guestrin, Jeanne VanBriesen, Christos Faloutsos
2008 Journal of water resources planning and management  
It is shown how the method presented here can be extended to multicriteria optimization, selecting placements robust to sensor failures and optimizing minimax criteria.  ...  The submodularity of these objectives is exploited in order to design efficient placement algorithms with provable performance guarantees.  ...  For example, suppose we have an approximate solution AЈ, and that the optimal penalty reduction OPT is upper-bounded by R͑AЈ͒ ഛ OPTഛ M for some value M.  ... 
doi:10.1061/(asce)0733-9496(2008)134:6(516) fatcat:n6fidcyzavalvbj65neweqqqhy

Sensor placement minimizing the state estimation mean square error: Performance guarantees of greedy solutions [article]

Akira Kohara, Kunihisa Okano, Kentaro Hirata, Yukinori Nakamura
2020 arXiv   pre-print
By using the properties of the MSE function, we approximately compute these quantities and derive a performance guarantee for the greedy solutions.  ...  Since the MSE function is not submodular nor supermodular, the well-known performance guarantees for the greedy solutions do not hold in the present case.  ...  Acknowledgment: The authors would like to thank the anonymous reviewers for their helpful comments, especially on the proof of Theorem 1.  ... 
arXiv:2004.04355v3 fatcat:wx52jiwr3rfpjkvfet72gw4fki

Near-Optimal Sensor Placement for Linear Inverse Problems

Juri Ranieri, Amina Chebira, Martin Vetterli
2014 IEEE Transactions on Signal Processing  
Unfortunately, the selection of the optimal sensor locations is intrinsically combinatorial and the available approximation algorithms are not guaranteed to generate good solutions in all cases of interest  ...  The number of sensors is often limited by physical or economical constraints and their placement is of fundamental importance to obtain accurate estimates.  ...  ACKNOWLEDGMENT The authors would like to thank Ivan Dokmanić and Prof. Ola Svensson whose suggestions were fundamental to strengthen the results described in the paper.  ... 
doi:10.1109/tsp.2014.2299518 fatcat:rpz3mreuzrc3dnkaxt2e75szg4

Approximating relay placement in sensor networks

Jukka Suomela
2006 Proceedings of the 3rd ACM international workshop on Performance evaluation of wireless ad hoc, sensor and ubiquitous networks - PE-WASUN '06  
• Heuristic algorithm for finding both lower and upper bounds for the Euclidean problem • Tighten the upper bound by partitioning the plane into cells • Make division denser in the areas where relays are  ...  centre points of the cells) , upper bound finite subproblem, lower bound full problem, lower bound full problem, solution Timings for the examples in Figures (i), (ii), (iii); in seconds Proof outline  ... 
doi:10.1145/1163610.1163635 dblp:conf/pe-wasun/Suomela06 fatcat:bjbb5oztanbmbfns5q42ybthti

Approximation Algorithm for Base Station Placement in Wireless Sensor Networks

Yi Shi, Y. Thomas Hou
2007 2007 4th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks  
This paper presents an approximation algorithm that can guarantee (1 -) optimal network lifetime performance for base station placement problem with any desired error bound £ > 0.  ...  For a multihop sensor network, this problem is particular challenging as we need to jointly consider base station placement and data routing strategy to maximize network lifetime performance.  ...  The main result is an approximation algorithm that can guarantee (1-) optimal network lifetime performance for base station placement problem with any desired error bound E > 0.  ... 
doi:10.1109/sahcn.2007.4292863 dblp:conf/secon/ShiH07 fatcat:pr4wyk7qwvha3kp5sydhwsrnq4

Limited-Memory Techniques for Sensor Placement in Water Distribution Networks [chapter]

William E. Hart, Jonathan W. Berry, Erik Boman, Cynthia A. Phillips, Lee Ann Riesen, Jean-Paul Watson
2008 Lecture Notes in Computer Science  
The target computing platforms for this application have motivated limited-memory techniques that can optimize large-scale sensor placement problems.  ...  These include the scale of problem instances, which in practice drives the development of approximate solution techniques, and constraints imposed by the target computing platforms.  ...  Thus, the time needed for simulation does not impact the time spent performing sensor placement.  ... 
doi:10.1007/978-3-540-92695-5_10 fatcat:v7kz5zp7bvebbkxicn6tf5g3je

New formulation and optimization methods for water sensor placement

Yue Zhao, Rafi Schwartz, Elad Salomons, Avi Ostfeld, H. Vincent Poor
2016 Environmental Modelling & Software  
A branch and bound sensor placement algorithm is proposed based on greedy heuristics and convex relaxation.  ...  For any sensor placement, the average consumption of contaminated water prior to event detection amongst all simulated events is employed as the sensing performance metric.  ...  The performance metric for any sensor placement solution consists of two numbers: • The average consumption of the contaminated water for all detected events: g avg : g avg = E k ∈E I (min m∈M g mk ≥ 0  ... 
doi:10.1016/j.envsoft.2015.10.030 fatcat:3utdi3zmcjgjvobylchnadvtdq

Optimal base station placement in wireless sensor networks

Yi Shi, Y. Thomas Hou
2009 ACM transactions on sensor networks  
This article presents an approximation algorithm that can guarantee (1 − ε)-optimal network lifetime performance for base station placement problem with any desired error bound ε > 0.  ...  This approximation algorithm is simpler and faster than a state-of-the-art algorithm and represents the best known result to the base station placement problem.  ...  ACKNOWLEDGMENTS The authors wish to thank the anonymous reviewers for their constructive comments and suggestions, which helped to improve the presentation of this paper.  ... 
doi:10.1145/1614379.1614384 fatcat:6u73v36zcbgg3pj2s5djicxxve

Sparsity-Exploiting Anchor Placement for Localization in Sensor Networks [article]

Sundeep Prabhakar Chepuri and Geert Leus and Alle-Jan van der Veen
2013 arXiv   pre-print
We make abstraction of the localization algorithm and instead use the Cram\'er-Rao lower bound (CRB) as the performance constraint.  ...  We consider the anchor placement problem in localization based on one-way ranging, in which either the sensor or the anchors send the ranging signals.  ...  The SDP in (9) provides a good approximation for the Boolean problem in (8) , and the solutions for (8) and (9) are upper bounds for the dual feasible w, i.e., w * bp ≤ w * sdp ≤ 1 T w.  ... 
arXiv:1303.4085v1 fatcat:ojgpyrrehzhynaclt4hezxwuvq

Algorithm design for base station placement problems in sensor networks

Yi Shi, Y. Thomas Hou, Alon Efrat
2006 Proceedings of the 3rd international conference on Quality of service in heterogeneous wired/wireless networks - QShine '06  
This paper proposes a set of procedure to design´½ µ approximation algorithms for base station placement problems under any desired small error bound ¼.  ...  Base station placement has significant impact on sensor network performance.  ...  Acknowledgements The work of Y.T. Hou and Y. Shi has been supported in part by NSF under Grants ANI-0312655 and CNS-0347390 and ONR under Grant No. N00014-03-1-0521. The work of A.  ... 
doi:10.1145/1185373.1185391 dblp:conf/qshine/ShiHE06 fatcat:f6ssrdoodbhfjauuk44zq7xo4i

The delay-constrained information coverage problem in mobile sensor networks: single hop case

Gabriel Y. Keung, Qian Zhang, Bo Li
2010 Wireless networks  
We prove that this problem is NP-hard even under finite search space approximation and we develop theoretical analysis to derive its upper and lower performance bounds.  ...  Keywords Mobile sensor network Á Mobility Á Information coverage Á Sink node placement List of symbols The number of mobile sensors @ The set of sink nodes N The number of sink nodes, and N C 1, and @  ...  Acknowledgment The research was support in part by grants from RGC under the contracts 615608, and 616207, by a grant from NSFC/ RGC under the contract N_HKUST603/07.  ... 
doi:10.1007/s11276-010-0238-2 fatcat:cva7ozeqnvbuvjdrtfs4ujxglq

Sparsity-Exploiting Anchor Placement For Localization In Sensor Networks

Sundeep Prabhakar Chepuri, Geert Leus, Alle Jan van der Veen
2013 Zenodo  
Publication in the conference proceedings of EUSIPCO, Marrakech, Morocco, 2013  ...  The SDP in (9) provides a good approximation for the Boolean problem in (8) , and the solutions for (8) and (9) are upper bounds for the dual feasible w, i.e., w * bp ≤ w * sdp ≤ 1 T w.  ...  For the OW-A case, the sparse solution yields the ranging energies that the anchors should adopt leading to a solution for the joint ranging energy optimization and anchor placement problem, and for the  ... 
doi:10.5281/zenodo.43478 fatcat:zoonlmkq4fdvtmkllzw3tzkz2q
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