A load balancing virtual level routing (LBVLR) using mobile mule for large sensor networks
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In a large sensor network, the data are usually routed back to the static base station (BS) using multi-hop communication. The sensor nodes near the BS suffer from an energy hole problem owing to the heavier data traffic load on them. Hence, the nodes near to the BS die early and thereby minimize the network lifetime. The mobile carriers have been considered as a better approach to balance the energy consumption by minimizing the number of transmissions. In spite of several benefits, the mobile carriers have a lot of challenges in data collection from sensor nodes. This paper presents a load balancing virtual level-based routing (LBVLR) using a data mule as a mobile carrier to deal with energy holes and ensure reliable data transfer. Our scheme has attempted to palliate the energy hole problem by applying horizontal level routing in the left and right directions. Fixed virtual rectangular grids of equal size are formed with the help of a mobile data mule and the BS that minimizes the network overheads, which saves a significant amount of energy. This approach uses three different data collection schemes based on the mule speed during trajectory, and we name them uniform speed of mule, variable speed of mule and sojourn point of mule. To compare the performance with the other works, LBVLR is evaluated mathematically and extensive simulations are conducted. The simulation results show a significant improvement in terms of average energy usage, data delivery delay and data delivery ratio compared with the other two similar kinds of well-known routing protocols.
KeywordsLarge sensor network Grid-based cluster Mobile data mule Energy hole Grid head Virtual level routing Energy efficient
The authors would like to acknowledge the Ministry of Electronics and Information Technology (MeitY), Government of India, for supporting the financial assistant during research work through “Visvesvaraya PhD Scheme for Electronics and IT”.
- 5.Olariu S, Stojmenovic I (2006) Design guidelines for maximizing lifetime and avoiding energy holes in sensor networks with uniform distribution and uniform reporting, In: Proceedings IEEE INFOCOM 2006 25TH IEEE International Conference on Computer Communications, pp 1–12. https://doi.org/10.1109/INFOCOM.2006.296
- 6.Heinzelman W, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks, In: Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, 2000, p 10, vol 2. https://doi.org/10.1109/HICSS.2000.926982
- 10.Shah R, Roy S, Jain S, Brunette W (2003) Data MULEs: modeling a three-tier architecture for sparse sensor networks, In: 2003 IEEE International Workshop on Sensor Network Protocols and Applications, 2003. Proceedings of the First IEEE., pp 30–41. https://doi.org/10.1109/SNPA.2003.1203354
- 22.Luo J, Hubaux J (2005) Joint mobility and routing for lifetime elongation in wireless sensor networks, In: 2005 IEEE Symposium on INFOCOM 2005. FL, USA, Miami, pp 30–37Google Scholar
- 31.Khan AW, Abdullah AH, Razzaque MA, Bangash JI, Altameem A (2015) Vgdd: A virtual grid based data dissemination scheme for wireless sensor networks with mobile sink. Int J Distrib Sens Netw 2015(1):1Google Scholar
- 35.Prince B, Kumar P, Singh MP, Singh JP (2016) An energy efficient uneven grid clustering based routing protocol for wireless sensor networks. In: 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp 1580–1584Google Scholar
- 36.Singh SK, Kumar P, Singh JP (2016) An energy efficient Odd-Even round number based data collection using mules in WSNs, In: 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp 1255–1259. https://doi.org/10.1109/WiSPNET.2016.7566337
- 38.Yu Y, Krishnamachari B, Prasanna VK (2004) Energy-latency tradeoffs for data gathering in wireless sensor networks, In: IEEE INFOCOM 2004, vol 1, p 255. https://doi.org/10.1109/INFCOM.2004.1354498
- 39.Chakrabarti A, Sabharwal A, Aazhang B (2003) Using predictable observer mobility for power efficient design of sensor networks, In: Proceedings of the 2nd International Conference on Information Processing in Sensor Networks (Springer, Berlin), IPSN’03, pp 129–145. http://dl.acm.org/citation.cfm?id=1765991.1766001 CrossRefGoogle Scholar