UAV-assisted data gathering in wireless sensor networks

Springer Science and Business Media LLC - Tập 70 - Trang 1142-1155 - 2014
Mianxiong Dong1,2, Kaoru Ota2,3, Man Lin4, Zunyi Tang5, Suguo Du6, Haojin Zhu6
1National Institute of Information and Communications Technology, Kyoto, Japan
2The State Key Lab of Integrated Services Networks, Xidian University, Xi’an, China
3Muroran Institute of Technology, Muroran , Japan
4St Francis Xavier University, Antigonish, Canada
5Osaka Electro-Communication University, Neyagawa, Japan
6Shanghai Jiao Tong University, Shanghai, China

Tóm tắt

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.

Tài liệu tham khảo

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