Abstract
We consider the problem of aggregating data in a dynamic graph, that is, aggregating the data that originates from all nodes in the graph to a specific node, the sink. We are interested in giving lower bounds for this problem, under different kinds of adversaries.
In our model, nodes are endowed with unlimited memory and unlimited computational power. Yet, we assume that communications between nodes are carried out with pairwise interactions, where nodes can exchange control information before deciding whether they transmit their data or not, given that each node is allowed to transmit its data at most once. When a node receives a data from a neighbor, the node may aggregate it with its own data.
We consider three possible adversaries: the online adaptive adversary, the oblivious adversary, and the randomized adversary that chooses the pairwise interactions uniformly at random. For the online adaptive and the oblivious adversaries, we give impossibility results when nodes have no knowledge about the graph and are not aware of the future. Also, we give several tight bounds depending on the knowledge (be it topology related or time related) of the nodes. For the randomized adversary, we show that the Gathering algorithm, which always commands a node to transmit, is optimal if nodes have no knowledge at all. Also, we propose an algorithm called Waiting Greedy, where a node either waits or transmits depending on some parameter, that is optimal when each node knows its future pairwise interactions with the sink.
This work was performed within the Labex SMART supported by French state funds managed by the ANR within the Investissements d’Avenir program under reference ANR-11-IDEX-0004-02. A preliminary 2 pages poster of this work appeared in IEEE ICDCS 2016 [4].
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
An event A occurs with high probability if \(P(A) > 1-O\left( 1/\log (n)\right) \).
References
Abshoff, S., Meyer auf der Heide, F.: Continuous aggregation in dynamic ad-hoc networks. In: Halldórsson, M.M. (ed.) SIROCCO 2014. LNCS, vol. 8576, pp. 194–209. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09620-9_16
Angluin, D., Aspnes, J., Eisenstat, D., Ruppert, E.: The computational power of population protocols. Distrib. Comput. 20(4), 279–304 (2007)
Annamalai, V., Gupta, S.K.S., Schwiebert, L.: On tree-based convergecasting in wireless sensor networks. In: 2003 IEEE Wireless Communications and Networking, WCNC 2003, vol. 3, pp. 1942–1947. IEEE (2003)
Bramas, Q., Masuzawa, T., Tixeuil, S.: Distributed online data aggregation in dynamic graphs. In: 36th IEEE International Conference on Distributed Computing Systems, ICDCS 2016, Nara, Japan, 27–30 June 2016, pp. 747–748. IEEE Computer Society (2016)
Bramas, Q., Masuzawa, T., Tixeuil, S.: Distributed online data aggregation in dynamic graphs. arXiv preprint arXiv:1602.01065 (2016)
Bramas, Q., Tixeuil, S.: The complexity of data aggregation in static and dynamic wireless sensor networks. In: Pelc, A., Schwarzmann, A.A. (eds.) SSS 2015. LNCS, vol. 9212, pp. 36–50. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21741-3_3
Casteigts, A., Chaumette, S., Ferreira, A.: Characterizing topological assumptions of distributed algorithms in dynamic networks. In: Kutten, S., Žerovnik, J. (eds.) SIROCCO 2009. LNCS, vol. 5869, pp. 126–140. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-11476-2_11
Casteigts, A., Flocchini, P., Quattrociocchi, W., Santoro, N.: Time-varying graphs and dynamic networks. In: Frey, H., Li, X., Ruehrup, S. (eds.) ADHOC-NOW 2011. LNCS, vol. 6811, pp. 346–359. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22450-8_27
Chen, X., Hu, X., Zhu, J.: Minimum data aggregation time problem in wireless sensor networks. In: Jia, X., Wu, J., He, Y. (eds.) MSN 2005. LNCS, vol. 3794, pp. 133–142. Springer, Heidelberg (2005). https://doi.org/10.1007/11599463_14
Cornejo, A., Gilbert, S., Newport, C.: Aggregation in dynamic networks. In: Proceedings of the 2012 ACM Symposium on Principles of Distributed Computing, pp. 195–204. ACM (2012)
Fasolo, E., Rossi, M., Widmer, J., Zorzi, M.: In-network aggregation techniques for wireless sensor networks: a survey. IEEE Wirel. Commun. 14(2), 70–87 (2007)
Nguyen, T.D., Zalyubovskiy, V., Choo, H.: Efficient time latency of data aggregation based on neighboring dominators in WSNs. In: 2011 IEEE Global Telecommunications Conference (GLOBECOM 2011), pp. 1–6. IEEE (2011)
Ren, M., Guo, L., Li, J.: A new scheduling algorithm for reducing data aggregation latency in wireless sensor networks. Int. J. Commun. Netw. Syst. Sci. 3(8), 679 (2010)
XiaoHua, X., Li, M., Mao, X.F., Tang, S., Wang, S.G.: A delay-efficient algorithm for data aggregation in multihop wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 22(1), 163–175 (2011)
Yamauchi, Y., Tixeuil, S., Kijima, S., Yamashita, M.: Brief announcement: probabilistic stabilization under probabilistic schedulers. In: Aguilera, M.K. (ed.) DISC 2012. LNCS, vol. 7611, pp. 413–414. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33651-5_34
Yu, B., Li, J., Li, Y.: Distributed data aggregation scheduling in wireless sensor networks. In: IEEE INFOCOM 2009, pp. 2159–2167. IEEE (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Bramas, Q., Masuzawa, T., Tixeuil, S. (2019). Distributed Online Data Aggregation in Dynamic Graphs. In: Atig, M., Schwarzmann, A. (eds) Networked Systems. NETYS 2019. Lecture Notes in Computer Science(), vol 11704. Springer, Cham. https://doi.org/10.1007/978-3-030-31277-0_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-31277-0_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31276-3
Online ISBN: 978-3-030-31277-0
eBook Packages: Computer ScienceComputer Science (R0)