Abstract
Sensing network of the Internet of Things (IoT) has become the infrastructure for facilitating the monitoring of potential events, where the accuracy and energy-efficiency are essential factors to be considered when determining the boundary of continuous objects. This article proposes an energy-efficient boundary detection mechanism in IoT sensing network. Specifically, a sleeping mechanism is adopted to detect the relatively coarse boundary through applying the convex hull algorithm. Leveraging the analysis of the relation for corresponding boundary nodes, the area around a boundary node is categorized as three types of sub-areas with descending possibility of event occurrence. An optimized greedy algorithm is adopted to selectively activate certain numbers of 1-hop neighboring IoT nodes in respective sub-areas, to avoid the activation of all 1-hop neighboring nodes in a flooding manner. Consequently, the boundary is refined and optimized according to sensory data of these activated IoT nodes. Experimental results demonstrate that our method can achieve better detection accuracy, while reducing energy consumption to a large extent, compared to the state of arts.
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References
Xiong, S., Ni, Q., Wang, X., Su, Y.: A connectivity enhancement scheme based on link transformation in iot sensing networks. IEEE Internet Things J. 4(6), 2297–2308 (2017)
Kavitha, B.C., Vallikannu, R.: IoT based intelligent industry monitoring system. In: 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 63–65 (2019)
Dong, L., et al.: The gas leak detection based on a wireless monitoring system. IEEE Trans. Ind. Inf. (2019)
Chao, C., Jiao, S., Zhang, S., Liu, W., Feng, L., Wang, Y.: TripImputor: real-time imputing taxi trip purpose leveraging multi-sourced urban data. IEEE Trans. Intell. Transp. Syst. 99, 1–13 (2018)
Olatinwo, S.O., Joubert, T.H.: Energy efficient solutions in wireless sensor system for monitoring the quality of water: a review. IEEE Sens. J. (2018)
Shu, L., Chen, Y., Sun, Z., Tong, F., Mukherjee, M.: Detecting the dangerous area of toxic gases with wireless sensor networks. IEEE Trans. Emerg. Top. Comput. (2017)
Lei, F., Yao, L., Zhao, D., Duan, Y.: Energy-efficient abnormal nodes detection and handlings in wireless sensor networks. IEEE Access 5, 3393–3409 (2017)
Zhang, Y., Wang, Z., Meng, L., Zhou, Z.: Boundary region detection for continuous objects in wireless sensor networks. Wirel. Commun. Mob. Comput. (2018)
Hong, Z., Wang, R., Li, X.: A clustering-tree topology control based on the energy forecast for heterogeneous wireless sensor networks. IEEE/CAA J. Autom. Sinica 3(1), 68–77 (2016)
Heinzelman, W.B., Chandrakasan, A.P., Balakrishnan, H.: An application specific protocol architecture for wireless microsensor networks. IEEE Trans. Wirel. Commun. 1(4), 660–670 (2002)
Bandyopadhyay, S., Coyle, E.J.: Minimizing communication costs in hierarchically-clustered networks of wireless sensors. Comput. Netw. 44(1), 1–16 (2004)
Bartier, P.M., Keller, C.P.: Multivariate interpolation to incorporate thematic surface data using inverse distance weighting (IDW). Comput. Geosci. 22(7), 795–799 (1996)
Li, X.Y., Calinescu, G., Wan, P.J., Wang, Y.: Localized delaunay triangulation with application in ad hoc wireless networks. IEEE Trans. Parallel Distrib. Syst. 14(10), 1035–1047 (2003)
Ping, H., Zhou, Z., Shi, Z., Rahman, T.: Accurate and energy-efficient boundary detection of continuous objects in duty-cycled wireless sensor networks. Pers. Ubiquit. Comput. 22(3), 597–613 (2018)
Liu, L., Han, G., Shen, J., Zhang, W., Liu, Y.: Diffusion distance-based predictive tracking for continuous objects in industrial wireless sensor networks. Mob. Netw. Appl. 1–12 (2018)
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This work was supported by the National Natural Science Foundation of China (Grant no. 61772479 and 61662021).
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Diao, J., Zhao, D., Tang, J., Cheng, Z., Zhou, Z. (2019). Continuous Objects Detection Based on Optimized Greedy Algorithm in IoT Sensing Networks. In: Wang, G., Feng, J., Bhuiyan, M., Lu, R. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2019. Lecture Notes in Computer Science(), vol 11637. Springer, Cham. https://doi.org/10.1007/978-3-030-24900-7_22
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DOI: https://doi.org/10.1007/978-3-030-24900-7_22
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