Abstract
The sensor-cloud architecture rises the opportunity to overcome intrinsic shortages of wireless sensors, such as computing capacity, storage space and communication range. However, before realizing these complementary effects of cloud computing, there is a challenge of how to plan efficient routes for mobile sinks to gather distributedly sensed data to centralized computing resources on cloud, especially where practical environment limits the travelling range of mobile sinks. This paper models the route planning problem into multi-echelon vehicle routing problem, and formulates it into integer linear programming. To solve this problem, a GPU-based parallel algorithm is proposed. The experimental results verify the accuracy and efficiency of the algorithm.
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References
Liu, X., Obaidat, M.S., Lin, C., Wang, T., Liu, A.: Movement-based solutions to energy limitation in wireless sensor networks: State of the art and future trends. IEEE Network (2020). https://doi.org/10.1109/MNET.011.2000445
Li, M., Jiang, Y., Sun, Y., Tian, Z.: Answering the min-cost quality-aware query on multi-sources in sensor-cloud systems. In: Wang, G., Chen, J., Yang, L. (eds.) SpaCCS 2018. LNCS, vol. 11342, pp. 156–165. Springer, Cham (2018)
Liu, X., Lin, P., Liu, T., Wang, T., Liu, A., XU, W.: Objective-variable tour planning for mobile data collection in partitioned sensor networks. IEEE Trans. Mob. Comput. (2020). https://doi.org/10.1109/TMC.2020.3003004
Wang, T., Peng, Z., Liang, J., Wen, S., Bhuiyan, M.Z.A., Cai, Y., Cao, J.: Following targets for mobile tracking in wireless sensor networks. ACM Trans. Sensor Networks 12(4), Article 31 (2016)
Fei, C., Zhao, B., Yu, W., Wu, C.: An approximate data collection algorithm in space-based internet of things. In: Wang, G., Feng, J., Bhuiyan, M., Lu, R. (eds.) SpaCCS 2019. LNCS, vol. 11637, pp. 170–184. Springer, Cham (2019)
Wang, T., Ke, H., Wang, K., Sangaiah, A.K., Liu, A.: Big data cleaning based on mobile edge computing in industrial sensor-cloud. IEEE Trans. Industr. Inf. 16(2), 1321–1329 (2020)
Li, Y., Wang, T., Wang, G., Liang, J., Chen, H.: Efficient data collection in sensor-cloud system with multiple mobile sinks. In: Wang, G., Han, Y., MartÃnez Pérez, G. (eds.) APSCC 2016. LNCS, vol. 10065, pp. 130–143. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49178-3_10
Huang, M., Liu, A., Wang, T., Huang, C.: Green data gathering under delay differentiated services constraint for internet of things. Wireless Communications and Mobile Computing 2018, Article ID 9715428 (2018)
Dantzig, G.B., Ramser, J.H.: The truck dispatching problem. Manage. Sci. 6(1), 80–91 (1959)
Zeng, J., Wang, T., Lai, Y.: Data delivery from wsns to cloud based on a fog structure. In: Proceedings of 2016 International Conference on Advanced Cloud and Big Data (CBD), pp. 104–109. IEEE (2016)
Deng, X., Li, J., Liu, E., Zhang, H.: Task allocation algorithm and optimization model on edge collaboration. J. Syst. Archit. 110, Article 101778 (2020). https://doi.org/10.1016/j.sysarc.2020.101778
Huang, R., Sun, Y., Huang, C., Zhao, G., Ma, Y.: A survey on fog computing. In: Wang, G., Feng, J., Bhuiyan, M., Lu, R. (eds.) SpaCCS 2019. LNCS, vol. 11637, pp. 160–169. Springer, Cham (2019)
Toth, P., Vigo, D. (eds.): The vehicle routing problem, chap. 1. An overview of vehicle routing problmes. SIAM, Philadelpia (2000)
Du, D.Z., Ko, K.I., Hu, X.: Design and Analysis of Approximation Algorithms, chap. 1. Introduction. Springer, New York (2011)
Contardo, C., Hemmelmayr, V., Crainic, T.: Lower and upper bounds for the two-echelon capacitated location-routing problem. Comput. Oper. Res. 39, 3185–3199 (2012)
Jepson, M., Spoorendonk, S., Ropke, S.: A branch-and-cut algorithm for the symmetric two-echelon capacitated vehicle routing problem. Transp. Sci. 47, 23–37 (2013)
Baldacci, R., Mingozzi, A., Roberti, R., Calvo, R.: An exact algorithm for the two-echelon capacitated vehicle routing problem. Oper. Res. 61, 298–314 (2013)
Song, L., Gu, H., Huang, H.: A lower bound for the adaptive two-echelon capacitated vehicle routing problem. J. Comb. Optim. 33(4), 1145–1167 (2016). https://doi.org/10.1007/s10878-016-0028-6
Bao, C., Zhang, S.: Algorithm-based fault tolerance for discrete wavelet transform implemented on gpus. J. Syst. Architect. 108, Article 101823 (2020). https://doi.org/10.1016/j.sysarc.2020.101823
Chang, Y.M., Liao, W.C., Wang, S.C., Yang, C.C., Hwang, Y.S.: A framework for scheduling dependent programs on GPU architectures. J. Syst. Architect. 106, Article 101712 (2020). https://doi.org/10.1016/j.sysarc.2020.101712
Acknowledgments
This work was supported in part by China Scholarship Council, the Fundamental Research Funds for the Central Universities of China under Grant No. 20720190028, National Key R&D Program of China under Grant No. 2017YFB0803002, National Natural Science Foundation of China under Grants No. 61672195, No. 61732022, No.61772154, and the Shenzhen Basic Research Program (Project No. JCYJ20190806143011274).
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Song, L., Chen, H., Huang, H., Du, H. (2021). Multi-echelon Vehicle Routing Problem in Sensor-Cloud Architecture with Mobile Sinks. In: Wang, G., Chen, B., Li, W., Di Pietro, R., Yan, X., Han, H. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2020. Lecture Notes in Computer Science(), vol 12383. Springer, Cham. https://doi.org/10.1007/978-3-030-68884-4_20
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