Sleep Scheduling Schemes Based on Location of Mobile User in Sensor-Cloud

Authors: N. Mahendran, R. Priya

Abstract:

The mobile cloud computing (MCC) with wireless sensor networks (WSNs) technology gets more attraction by research scholars because its combines the sensors data gathering ability with the cloud data processing capacity. This approach overcomes the limitation of data storage capacity and computational ability of sensor nodes. Finally, the stored data are sent to the mobile users when the user sends the request. The most of the integrated sensor-cloud schemes fail to observe the following criteria: 1) The mobile users request the specific data to the cloud based on their present location. 2) Power consumption since most of them are equipped with non-rechargeable batteries. Mostly, the sensors are deployed in hazardous and remote areas. This paper focuses on above observations and introduces an approach known as collaborative location-based sleep scheduling (CLSS) scheme. Both awake and asleep status of each sensor node is dynamically devised by schedulers and the scheduling is done purely based on the of mobile users’ current location; in this manner, large amount of energy consumption is minimized at WSN. CLSS work depends on two different methods; CLSS1 scheme provides lower energy consumption and CLSS2 provides the scalability and robustness of the integrated WSN.

Keywords: Sleep scheduling, mobile cloud computing, wireless sensor network, integration, location, network lifetime.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1127380

References:

[1] H. T. Dinh, C. Lee, D. Niyato, and Pang, “A Survey of Mobile Comput., volume13, no.18, pp. 1587–1611 Dec. 2013.
[2] 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.
[3] 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,” Wireless Commun. Mobile Comput., vol. 14, no. 1, pp. 19–36, Jan. 2014.
[4] Alamri, W.S.Ansari, M. M. Hassan, M. S. Hossain A. Alelaiwi, and M. A. Hossain, “A survey on sensor-cloud: Architecture, applications, and approaches,” Int. J. Distrib. Sensor Netw., vol. 2013, pp. 1–18, 2013.
[5] Zhu, H. Wang, X. Liu, L. Shu, L. T. Yang, and Leung, “A novel sensory data processing framework to integrate sensor networks with the mobile cloud,” IEEE Syst. vol. PP, no.9. pp. 1–12, Jan. 2014.
[6] P. Zhang, Z. Yan, and H. Sun, “A novel architecture based on cloud computing for wireless sensor network,” in Proc. 2nd Int. Conf. Computer. Sci. Electron. Eng., 2013, pp. 472–475.
[7] Yong Ding, Chen Wang, and Li Xiao, ‘An Adaptive Partitioning Scheme for Sleep Scheduling and Topology Control in Wireless Sensor Networks’’ IEEE Trans on Parallel and distributive system, vol. 20, no. 9, September 2009.
[8] Yanwei Wu, Member, Xiang-Yang Li, Yun Hao Liu, Senior Member, and Wei Lou on ‘‘Energy-Efficient Wake-Up Scheduling for Data Collection and Aggregation’’ IEEE Trans on parallel and distributed system, vol.21, no. 2, February 2010.
[9] Tarik Yardibi, Ezhan, Karasan, “A distributed activity scheduling algorithm for wireless sensor network with partial coverage” Published online:1 Springer Science Business Media, LLC 2008, August 2008.
[10] Li-Hsing Yen, Yang-Min, Cheng ‘‘Range-Based Sleep Scheduling (RBSS) for Wireless Sensor Networks” Published online: 11 Springer Science+Business Media, LLC. June2008.
[11] Soumyadip Sengupta, Swagatam Das, Md.Nasir, Athanasios, V. Vasilakos, and Witold Pedrycz “An Evolutionary Multiobjective Sleep-Scheduling Scheme for Differentiated Coverage in Wireless Sensor Networks’’ IEEE Trans, on Sys, Man, and Cybernetics, vol.42, No.6, November 2012.
[12] G. Ananthanarayanan, M. Haridasan, I. Mohomed, D. Terry, and C. A. Thekkath, “Startrack A framework for enabling track-based applications,” in Proc.7th Int. Conf. Mobile Syst., Appl., Serv.,2009 pp.207–220.
[13] Chunsheng Zhu and Victor C. M. Leung “Collaborative Location-Based Sleep Scheduling for Wireless Sensor Networks Integrated with Mobile Cloud Computing’’ IEEE transactions on computers, vol. 64, no. 7, July 2015.