Abstract
An unmanned aerial vehicle (UAV) is a promising carriage for data gathering in wireless sensor networks since it has sufficient as well as efficient resources both in terms of time and energy due to its direct communication between the UAV and sensor nodes. On the other hand, to realize the data gathering system with UAV in wireless sensor networks, there are still some challenging issues remain such that the highly affected problem by the speed of UAVs and network density, also the heavy conflicts if a lot of sensor nodes concurrently send its own data to the UAV. To solve those problems, we propose a new data gathering algorithm, leveraging both the UAV and mobile agents (MAs) to autonomously collect and process data in wireless sensor networks. Specifically, the UAV dispatches MAs to the network and every MA is responsible for collecting and processing the data from sensor nodes in an area of the network by traveling around that area. The UAV gets desired information via MAs with aggregated sensory data. In this paper, we design a itinerary of MA migration with considering the network density. Simulation results demonstrate that our proposed method is time- and energy-efficient for any density of the network.





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References
Benson K, Venkatasubramanian N (2013) Improving sensor data delivery during disaster scenarios with resilient overlay networks. In: Proceedings of 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp 547–552. doi:10.1109/PerComW.2013.6529556
Chellappan S, Paruchuri V, McDonald D, Durresi A (2008) Localizing sensor networks in un-friendly environments. In: Proceedings of IEEE military communications conference, 2008 (MILCOM 2008), pp 1–7. doi:10.1109/MILCOM.2008.4753635
Cheng CT, Leung H, Maupin P (2013) A delay-aware network structure for wireless sensor networks with in network data fusion. IEEE Sens J 13(5):1622–1631. doi:10.1109/JSEN.2013.2240617
Dasgupta K, Kalpakis K, Namjoshi P (2003) An efficient clustering-based heuristic for data gathering and aggregation in sensor networks. In: Proceedings of 2003 IEEE wireless communications and networking (WCNC 2003), vol 3, pp 1948–1953. doi:10.1109/WCNC.2003.1200685
Duan H, Luo Q, Shi Y, Ma G (2013) Hybrid particle swarm optimization and genetic algorithm for multi-uav formation reconfiguration. IEEE Comput Intell Mag 8(3):16–27
Fu Y, Ding M, Zhou C (2012) Phase angle-encoded and quantum-behaved particle swarm optimization applied to three-dimensional route planning for UAV. In: IEEE transactions on systems, man and cybernetics, part A: systems and humans 42(2):511–526
Giorgetti A, Lucchi M, Chiani M, Win M (2011) Throughput per pass for data aggregation from a wireless sensor network via a UAV. IEEE Trans Aerosp Electr Syst 47(4):2610–2626
Gupta P, Kumar P (2000) The capacity of wireless networks. IEEE Trans Inf Theory 46(2):388–404. doi:10.1109/18.825799
Li J, Blake C, De Couto DS, Lee HI, Morris R (2001) Capacity of ad hoc wireless networks. In: Proceedings of the 7th annual international conference on Mobile computing and networking, ACM, New York, MobiCom ’01, pp 61–69. doi:10.1145/381677.381684. http://doi.acm.org/10.1145/381677.381684
Li M, Liu Y, Chen L (2008) Nonthreshold-based event detection for 3d environment monitoring in sensor networks. IEEE Trans Knowl Data Eng 20(12):1699–1711
Liu M, Gong H, Wen Y, Chen G, Cao J (2011) The last minute: Efficient data evacuation strategy for sensor networks in post-disaster applications. In: Proceedings of 2011 IEEE international conference on computer communications (IEEE INFOCOM 2011), pp 291–295. doi:10.1109/INFCOM.2011.5935131
Luo H, Luo J, Liu Y, Das S (2006) Adaptive data fusion for energy efficient routing in wireless sensor networks. IEEE Trans Comput 55(10):1286–1299. doi:10.1109/TC.2006.157
Ota K, Dong M, Wang J, Guo S, Cheng Z, Guo M (2010) Dynamic itinerary planning for mobile agents with a content-specific approach in wireless sensor networks. In: Proceedings of 2010 IEEE 72nd vehicular technology conference fall (VTC 2010-Fall), pp 1–5. doi:10.1109/VETECF.2010.5594122
Ota K, Dong M, Cheng Z, Wang J, Li X, Shen XS (2012) Oracle: mobility control in wireless sensor and actor networks. Comput Commun 35(9):1029–1037
Pai HT, Han YS (2008) Power-efficient direct-voting assurance for data fusion in wireless sensor networks. IEEE Trans Comput 57(2):261–273. doi:10.1109/TC.2007.70805
Riva G, Finochietto J (2012) Pheromone-based in-network processing for wireless sensor network monitoring systems. In: Proceedings of 2012 IEEE international conference on communications (ICC), pp 6560–6564. doi:10.1109/ICC.2012.6364847
Tisue S, Wilensky U (2004) NetLogo: a simple environment for modeling complexity. In: Minai A, Bar-Yam Y (eds) Proceedings of the fifth international conference on complex systems ICCS 2004, pp 16–21
Xu Y, Qi H (2007) Dynamic mobile agent migration in wireless sensor networks. Int J Ad Hoc Ubiquitous Comput 2(1/2):73–82. doi:10.1504/IJAHUC.2007.011605. http://dx.doi.org/10.1504/IJAHUC.2007.011605
Acknowledgments
This work is partially supported by JSPS KAKENHI Grant Number 25880002, JSPS A3 Foresight Program, NEC C&C Foundation, National Science Foundation of China (Grant No. 70971086, 61003218, 61272444, 61161140320, 61033014, 71061005), Doctoral Fund of Ministry of Education of China (Grant No. 20100073120065) and Sino-Japan project (ZR2012-03) sponsored by The State Key Lab of Integrated Services Networks, Xidian University, China. The main part of this work was done when Mianxiong Dong was with The University of Aizu, Japan.
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Dong, M., Ota, K., Lin, M. et al. UAV-assisted data gathering in wireless sensor networks. J Supercomput 70, 1142–1155 (2014). https://doi.org/10.1007/s11227-014-1161-6
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DOI: https://doi.org/10.1007/s11227-014-1161-6