99
Views
2
CrossRef citations to date
0
Altmetric
Articles

GLS and VNS based heuristics for conflict-free minimum-latency aggregation scheduling in WSN

ORCID Icon, ORCID Icon & ORCID Icon
Pages 697-719 | Received 31 Jan 2019, Accepted 12 Dec 2019, Published online: 08 Jan 2020

References

  • R.A. Aziz, M. Ayob, Z. Othman, and H. Mohd Sarim, Adaptive guided variable neighborhood search, J. Appl. Sci. 13(6) (2013), pp. 883–888. doi:10.3923/jas.2013.883.888.
  • M. Bagaa, Y. Challal, A. Ksentini, A. Derhab, and N. Badache, Data aggregation scheduling algorithms in wireless sensor networks: Solutions and challenges, IEEE Commun. Surv. Tutor. 16 (2014), pp. 1339–1367. doi: 10.1109/SURV.2014.031914.00029
  • R. Beier and J.F. Sibeyn, A Powerful Heuristic for Telephone Gossiping, Proceedings of 17th International Colloquium on Structural Information & Communication Complexity (SIROCCO 2000), 2000, pp. 17–36.
  • X. Chen, X. Hu, and J. Zhu, Minimum data aggregation time problem in wireless sensor networks, Lecture Notes Comput. Sci. 3794 (2005), pp. 133–142. doi: 10.1007/11599463_14
  • B.N. Clark, C.J. Colbourn, and D.S. Johnson, Unit disk graphs, Discrete Math. 86(1–3) (1990), pp. 165–177. doi: 10.1016/0012-365X(90)90358-O
  • E. de Souza and I. Nikolaidis, An exploration of aggregation convergecast scheduling, Ad Hoc Netw. 11 (2013), pp. 2391–2407. doi: 10.1016/j.adhoc.2013.06.004
  • W. Du, W. Ying, P. Yang, X. Cao, G. Yan, K. Tang, and D. Wu, Network-based heterogeneous particle swarm optimization and its application in uav communication coverage, in IEEE Transactions on Emerging Topics in Computational Intelligence, 2019, pp. 1–12. doi:10.1109/TETCI.2019.2899604.
  • J. Edmonds, Optimum branchings, J. Res. Natl. Bur. Stand. B 71 (1967), pp. 233–240. doi: 10.6028/jres.071B.032
  • A. Erzin and A. Pyatkin, Convergecast Scheduling Problem in Case of Given Aggregation Tree: The Complexity Status and Some Special Cases, Proceedings of 10th International Symposium on Communication Systems Networks and Digital Signal Processing (CSNDSP), IEEE, 2016, pp. 1–6. doi:10.1109/CSNDSP.2016.7574007.
  • F. García-López, B. Melián-Batista, J.A. Moreno-Pérez, and J.M. Moreno-Vega, The parallel variable neighborhood search for the p-Median problem, J. Heuristics 8(3) (2002), pp. 375–388. doi:10.1023/A:1015013919497.
  • M. Gruber, J. van Hemert, and G.R. Raidl, Neighbourhood Searches for the Bounded Diameter Minimum Spanning Tree Problem Embedded in a VNS, EA, and ACO, Proceedings of the 8th annual conference on Genetic and evolutionary computation, July 08–12, Seattle, Washington, 2006. doi:10.1145/1143997.1144185.
  • E. Guney, I.K. Altinel, N. Aras, and C. Ersoy, A Variable Neighbourhood Search Heuristic for Point Coverage, Sink Location and Data Routing in Wireless Sensor Networks, Proceedings of 2nd International Conference on Communication Theory, Reliability, and Quality of Service (CTRQ '09), 2009, pp. 81–86.
  • P. Gupta and P.R. Kumar, The capacity of wireless networks, IEEE Trans. Inf. Theory 46 (2000), pp. 388–404. doi: 10.1109/18.825799
  • P. Hansen and N. Mladenovic, Variable neighborhood search: Principles and applications, Eur. J. Oper. Res. 130 (2001), pp. 449–467. doi: 10.1016/S0377-2217(00)00100-4
  • P. Hansen, N. Mladenovic, and D. Perez-Britos, Variable neighborhood decomposition search, J. Heuristics 7(4) (2001), pp. 335–350. doi:10.1023/A:1011336210885.
  • J. Hromkovic, R. Klasing, B. Monien, and R. Peine, Dissemination of information in interconnection networks (broadcasting & gossiping), in Combinatorial Network Theory. Applied Optimization, Vol. 1, Springer, Boston, MA, 1996, pp. 125–212.
  • S.C.-H. Huang, P.J. Wan, C.T. Vu, Y. Li, and F. Yao, Nearly Constant Approximation for Data Aggregation Scheduling in Wireless Sensor Networks, IEEE Conference on Computer Communications (INFOCOM 2007), 2007, pp. 366–472.
  • S. Hussain, A.W. Matin, and O. Islam, Genetic algorithm for hierarchical wireless sensor networks, J. Netw. 2 (2007), pp. 87–97.
  • O.D. Incel, A. Ghosh, B. Krishnamachari, and K. Chintalapudi, Fast data collection in tree-based wireless sensor networks, IEEE Trans. Mobile Comput. 11 (2011), pp. 86–99. doi: 10.1109/TMC.2011.22
  • Ailian Jiang and Lihong Zheng, An effective hybrid routing algorithm in WSN: Ant colony optimization in combination with hop count minimization, Sensors 18(4) (2018), pp. 1020. doi:10.3390/s18041020.
  • Supreet Kaur and Rajiv Mahajan, Hybrid meta-heuristic optimization based energy efficient protocol for wireless sensor networks, Egypt. Inform. J. 19(3) (2018), pp. 145–150. doi: 10.1016/j.eij.2018.01.002
  • Husheng Li, Network topology design, Commun. Contr. Cyber Phys. Syst. 2016), pp. 149–180. doi:10.1016/B978-0-12-801950-4.00006-8.
  • J. Liu and C.V. Ravishankar, LEACH-GA: genetic algorithm-based energy-efficient adaptive clustering protocol for wireless sensor networks, Int. J. Mach. Learn. Comput. 1 (2011), pp. 79–85. doi: 10.7763/IJMLC.2011.V1.12
  • B. Malhotra, I. Nikolaidis, and M.A. Nascimento, Aggregation convergecast scheduling in wireless sensor networks, Wireless Netw. 17 (2011), pp. 319–335. doi: 10.1007/s11276-010-0282-y
  • T.D. Nguyen, V. Zalyubovskiy, and H. Choo, Efficient time latency of data aggregation based on neighboring dominators in WSNs, in IEEE Globecom, 2011. doi:10.1109/GLOCOM.2011.6133827.
  • Ch. Pan and H. Zhang, A time efficient aggregation convergecast scheduling algorithm for wireless sensor networks, Wireless Netw. 22 (2016), pp. 2469–2483. doi:10.1007/s11276-016-1337-5.
  • Jun Pei, Zorica Darzic, Milan Drazic, Nenad Mladenovic, and Panos Pardalos, Continuous variable neighborhood search (C-VNS) for solving systems of nonlinear equations, INFORMS. J. Comput. 2018. doi:10.1287/ijoc.2018.0876.
  • Jun Pei, Xinbao Liu, Wenjuan Fan, Panos M. Pardalos, and Shaojun Lu, A hybrid BA-VNS algorithm for coordinated serial-batching scheduling with deteriorating jobs, financial budget, and resource constraint in multiple manufacturers, Omega 82 (2019), pp. 55–69. doi: 10.1016/j.omega.2017.12.003
  • R. Plotnikov, A. Erzin, and N. Mladenovic, Variable neighborhood search-based heuristics for min-power symmetric connectivity problem in wireless networks, Lect. Notes Comput. Sci. 9869 (2016), pp. 220–232. doi: 10.1007/978-3-319-44914-2_18
  • R. Plotnikov, A. Erzin, and V. Zalyubovskiy, Genetic Local Search for Conflict-Free Minimum-Latency Aggregation Scheduling in Wireless Sensor Networks, Proceedings of Optimization and Applications. OPTIMA 2018. Communications in Computer and Information Science, Vol. 974, Springer, 2019, pp. 216–231.
  • R. Plotnikov, A. Erzin, and V. Zalyubovsky, Convergecast with Unbounded Number of Channels, in MATEC Web of Conferences 125, 2017. doi:10.1051/matecconf/201712503001.
  • S. Sivanandam and S. Deepa, Introduction to Genetic Algorithms, Springer, doi:Heidelberg, 2008. 10.1007/978-3-540-73190-0.
  • S. Su and H. Yu, Minimizing tardiness in data aggregation scheduling with due date consideration for single-hop wireless sensor networks, Wireless Netw. 21 (2015), pp. 1259–1273. doi: 10.1007/s11276-014-0853-4
  • N. Sudha, M.L. Valarmathi, and T.C. Neyandar, Optimizing Energy in WSN Using Evolutionary Algorithm, Proceeding of IJCA on International Conference on VLSI, Communications and Instrumentation (ICVCI 2011), Kerala, India, 7–9 April 2011, 2011, pp. 26–29.
  • C. Tian, H. Jiang, C. Wang, C. Z. Wu, J. Chen, and W. Liu, Neither shortest path nor dominating set: Aggregation scheduling by greedy growing tree in multihop wireless sensor networks, IEEE Trans. Veh. Technol. 60 (2011), pp. 3462–3472. doi: 10.1109/TVT.2011.2162086
  • Rostislav Vodák, Michal Bíl, and Zuzana Křivánková, A modified ant colony optimization algorithm to increase the speed of the road network recovery process after disasters, Int. J. Disast. Risk Re. 31 (2018), pp. 1092–1106.
  • X. Xu, X.Y. Li, X. Mao, S. Tang, and S. Wang, A delay-efficient algorithm for data aggregation in multihop wireless sensor networks, IEEE Trans. Parallel Distrib. Syst. 22 (2011), pp. 163–175. doi: 10.1109/TPDS.2010.95
  • Y.S. Yen, H.C. Chao, R.S. Chang, and A. Vasilakos, Flooding-limited and multi-constrained QoS multicast routing based on the genetic algorithm for MANETs, Math. Comput. Model. 53 (2011), pp. 2238–2250. doi: 10.1016/j.mcm.2010.10.008
  • J. Zhu and X. Hu, Improved algorithm for minimum data aggregation time problem in wireless sensor networks, J. Syst. Sci. Complex. 21 (2018), pp. 626–636. doi: 10.1007/s11424-008-9139-1
  • N. Zhu and I. O'Connor, iMASKO: A genetic algorithm based optimization framework for wireless sensor networks, J. Sens. Actuator Netw. 2 (2013), pp. 675–699. doi:10.3390/jsan2040675.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.