The week's pick
Random Articles
Reseach Article
Optimal Duty Cycling with Sleep-wake Schedule between Paired Nodes and Flexible Routing across Pairs
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 144 - Number 8 |
| Year of Publication: 2016 |
| Authors: Ginish, Munish Kumar, Amit Grover |
10.5120/ijca2016910419
|
Ginish, Munish Kumar, Amit Grover . Optimal Duty Cycling with Sleep-wake Schedule between Paired Nodes and Flexible Routing across Pairs. International Journal of Computer Applications. 144, 8 ( Jun 2016), 20-24. DOI=10.5120/ijca2016910419
Abstract
The sensor networks consist of the smaller sensor nodes which automatically constructs the routes between themselves to form the data delivery paths.There are several methods of energy conservation such as clustering, routing, duty cycling and many others. The duty cycling or sleep wake cycling is incorporated for the minimization for energy consumption. The effective duty cycling procedure requires the optimal selection of the sleep wake pairs adaptively constructed between the nodes, out of which one node goes sleep and another stays wake a particular point of time. In this paper, the adaptive optimal pairing with flexible routing has been utilized for the purpose of duty cycling in the sensor nodes. The proposed model has undergone several experiments and performed to be efficient enough. The proposed model has outperformed the previous models for duty cycling.
References
- Zhu, Chunsheng, Victor Leung, Laurence T. Yang, and Lei Shu. "Collaborative location-based sleep scheduling for wireless sensor networks integrated with mobile cloud computing." (2014).
- H. T. Dinh, C. Lee, D. Niyato, and P. Wang, “A survey of mobile cloud computing: Architecture, applications, and approaches,” Wireless Commun. Mobile Comput. vol. 13, no. 18, pp. 1587–1611, Dec. 2013.
- S. Wang and S. Dey, “Adaptive mobile cloud computing to enable rich mobile multimedia applications,” IEEE Trans. Multimedia, vol. 15, no. 4, pp. 870–883, Jun. 2013.
- R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, “Cloud computing and emerging it platforms: Vision, hype, and reality for delivering computing as the 5th utility,” Future Generation Comput. Syst., vol. 25, no. 6, pp. 599–616, Jun. 2009.
- C. Zhu, L. Shu, T. Hara, L. Wang, S. Nishio, and L. T. Yang, “A survey on communication and data management issues in mobile sensor networks,” Wirel. Commun. Mob. Comput., vol. 14, no. 1, pp. 19–36, Jan. 2014.
- M. Li and Y. Liu, “.Underground coal mine monitoring with wireless sensor networks,” ACM Trans. Sens. Netw, vol. 5, no. 2, Mar. 2009
- M. Yuriyama and T. Kushida, “Sensor-cloud infrastructure - physical sensor management with virtualized sensors on cloud computing,” in Proc. 13th Int. Conf. Netw.-Based Inf. Sys. (NBiS), 2010, pp. 1–8.
- G. Fortino, M. Pathan, and G. D. Fatta, “Bodycloud: Integration of cloud computing and body sensor networks,” in Proc. IEEE 4th Int. Conf. Cloud Comput. Technol. Sci. (CloudCom), 2012, pp. 851–856.
- R. Hummen, M. Henze, D. Catrein, and K. Wehrle, “A cloud design for user-controlled storage and processing of sensor data,” in Proc. IEEE 4th Int. Conf. Cloud Comput. Technol. Sci. (CloudCom), 2012, pp. 232–240.
- Y. Takabe, K. Matsumoto, M. Yamagiwa, and M. Uehara, “Proposed sensor network for living environments using cloud computing,” in Proc. 15th Int. Conf. Netw.-Based Inf. Sys. (NBiS), 2012, pp. 838–843.
Index Terms
Keywords