Planning the Deployment of Indoor Wireless Sensor Networks Through Multiobjective Evolutionary Techniques

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• 作者：Jose M. Lanza-Gutierrez (15) (16)
  Juan A. Gomez-Pulido (15)
  S. Priem-Mendes (16) (17)
  M. Ferreira (16) (17)
  J. S. Pereira (16) (17) (18)
  
  15. Department of Computers and Communications Technologies ; Polytechnic School ; University of Extremadura ; Caceres ; Spain
  16. Center for Research in Informatics and Communications ; Polytechnic Institute of Leiria ; Leiria ; Portugal
  17. School of Technology and Management ; Polytechnic Institute of Leiria ; Leiria ; Portugal
  18. Instituto de Telecomunica莽玫es ; Leiria Branch ; Leiria ; Portugal
  
• 关键词：Coverage ; Deployment ; Energy ; Indoor ; Multiobjective ; NSGA ; II ; SPEA2 ; Reliability ; Wireless sensor network
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9028
• 期：1
• 页码：128-139
• 全文大小：816 KB
• 参考文献：1. Mukherjee, JYB, Ghosal, D (2008) Wireless sensor network survey. Comput. Netw. 52: pp. 2292-2330 CrossRef
  2. Akyildiz, I, Su, W, Sankarasubramaniam, Y, Cayirci, E (2002) A survey on sensor networks. IEEE Commun. Mag. 40: pp. 102-114 CrossRef
  3. Cheng, X, Narahari, B, Simha, R, Cheng, M, Liu, D (2003) Strong minimum energy topology in wireless sensor networks: Np-completeness and heuristics. IEEE Trans. Mob. Comput. 2: pp. 248-256 CrossRef
  4. Chang, JH, Tassiulas, L (2004) Maximum lifetime routing in wireless sensor networks. IEEE/ACM Trans. Netw. 12: pp. 609-619 CrossRef
  5. Zitzler, E.: Evolutionary algorithms for multiobjective optimization: methods and applications. (Doctoral dissertation). Swiss Federal Institute of Technology (ETH) (1999)
  6. Yun, J., Kim, J.: Deployment support for sensor networks in indoor climate monitoring. Int. J. Distrib. Sens. Netw., 1鈥?0 (2013)
  7. Zhang, Z, Zhu, J, Ruan, J, Song, G (2014) Distance measurement for the indoor WSN nodes using WTR method. Int. J. Distrib. Sens. Netw. 2014: pp. 1-13
  8. Song, G., Zhuang, W., Song, A.: Self-deployment of mobile sensor networks in complex indoor environments. In: IEEE Conference WCICA, pp. 4543鈥?546 (2006)
  9. Lin, CH, King, CT (2010) Sensor-deployment strategies for indoor robot navigation. IEEE Trans. Syst. Man Cybern. B Cybern. - Part A: Syst. Hum. 40: pp. 388-398 CrossRef
  10. Seok, J.-H., Lee, J.-Y., Oh, C., Lee, J.-J., Lee, H.J.: Rfid sensor deployment using differential evolution for indoor mobile robot localization. In: IEEE Conference IROS, pp. 3719鈥?724 (2010)
  11. Tarng, JH, Chuang, BW, Liu, PC (2009) A relay node deployment method for disconnected wireless sensor networks: Applied in indoor environments. J. Netw. Comput. Appl. 32: pp. 652-659 CrossRef
  12. Yu, M., Song, J.K., Mah, P.: RNIndoor: A relay node deployment method for disconnected wireless sensor networks in indoor environments. In: ICUFN Conference, pp. 19鈥?4 (2011)
  13. Deb, K, Pratap, A, Agarwal, S, Meyarivan, T (2000) A fast elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6: pp. 182-197 CrossRef
  14. Zitzler, E., Laumanns, M., Thiele, L.: Spea 2: Improving the strength pareto evolutionary algorithm. Technical report, Computer Engineering and Networks Laboratory (TIK), ETH Zurich (2001)
  15. Lanza-Guti茅rrez, JM, G贸mez-Pulido, JA, Vega-Rodr铆guez, MA A trajectory-based heuristic to solve a three-objective optimization problemfor wireless sensor network deployment. In: Esparcia-Alc谩zar, AI, Mora, AM eds. (2014) EvoApplications 2014. Springer, Heidelberg, pp. 27-38
  16. Lanza-Gutierrez, JM, Gomez-Pulido, JA, Vega-Rodriguez, MA (2014) Intelligent relay node placement in heterogeneous wireless sensor networks for energy efficiency. Int. J. Robot. Autom. 29: pp. 1-13
  Cormen, TH, Leiserson, CE, Rivest, RL, Stein, C eds. (2009) Introduction to Algorithms. The MIT Press, Cambridge
  17. Chipcom, A.S.: Smartrf cc2420 preliminary datasheet (2004). http://inst.eecs.berkeley.edu/cs150/Documents/CC2420.pdf
  18. Wilson, R.: Propagation losses through common building materials: 2.4 ghz vs 5 ghz. reflection and transmission losses through common building materials. Technical Report E10589, Magis Networks, Inc. (2002)
  19. Konstantinidis, A, Yang, K (2011) Multi-objective k-connected deployment and power assignment in wsns using a problem-specific constrained evolutionary algorithm based on decomposition. Comput. Commun. 34: pp. 83-98 CrossRef
  20. Deb, B., Bhatnagar, S., Nath, B.: Reliable information forwarding using multiple paths in sensor networks. In: Proceedings of IEEE LCN, pp. 406鈥?15 (2003)
  21. Suurballe, JW (1974) Disjoint paths in a network. Networks 4: pp. 125-145 CrossRef
  22. Lanza-Gutierrez, J.M., Gomez-Pulido, J.A.: Instance sets for indoor optimization in wireless sensor networks (2014). http://arco.unex.es/wsnopt
  23. Mahboubi, H, Moezzi, K, Aghdam, A, Sayrafian-Pour, K, Marbukh, V (2014) Distributed deployment algorithms for improved coverage in a network of wireless mobile sensors. IEEE Trans. Industr. Inf. 10: pp. 163-174 CrossRef
  24. Martins, F, Carrano, E, Wanner, E, Takahashi, R, Mateus, G (2011) A hybrid multiobjective evolutionary approach for improving the performance of wireless sensor networks. IEEE Sens. J. 11: pp. 545-554 CrossRef
  25. Zitzler, E, Thiele, L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3: pp. 257-271 CrossRef
  
• 作者单位：Applications of Evolutionary Computation
• 丛书名：978-3-319-16548-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

This work deals with how to efficiently deploy an indoor wireless sensor network, assuming a novel approach in which we try to leverage existing infrastructure. Thus, given a set of low-cost sensors, which can be plugged into the grid or powered by batteries, a collector node, and a building plan, including walls and plugs, the purpose is to deploy the sensors optimising three conflicting objectives: average coverage, average energy cost, and average reliability. Two MultiObjective (MO) genetic algorithms are assumed to solve this issue, NSGA-II and SPEA2. These metaheuristics are applied to solve the problem using a freely available data set. The results obtained are analysed considering two MO quality metrics: hypervolume and set coverage. After applying a statistical methodology widely accepted, we conclude that SPEA2 provides the best performance on average considering such data set.