Energy-efficient cloud integrated sensor based on clustering and multihop transmission

Main Article Content

Kalyan Das
Satyabrata Das


An energy-efficient sensor cloud model was proposed using the clustering algorithm and forwarding traffic to the node closet to the destination. Initially, the clustering algorithm created clusters of sensors using the closeness of measurements and distance between them. Following that, the cluster head algorithm selected the cluster head from the group whose measurement values were close to the mean value of all node measurements in that cluster. In the traditional approach, all nodes in the wireless sensor network were active and directly transmitted data to the sink. As a result, energy consumption was higher when compared to multihop transmission. The cluster heads generally communicated on behalf of all the cluster nodes in our proposed approach. That resulted in less number of active nodes that minimized energy consumption. After that, the cluster heads used multihop data transmission to forward the traffic to the destination using the shortest path algorithm. In our simulation, the accuracy of data transmitted by cluster heads was nearly equal to that of data transmitted by nodes. When compared to sending traffic directly to the destination, the multihop transmission of data could save energy. Our simulation showed that our proposed method can save energy, and also, the accuracy of transferred data is acceptable.


Download data is not yet available.

Article Details



Alamri, A., Ansari, W. S., Hassan, M. M., Hossain, M. S., Alelaiwi, A., and Hossain, M. A. (2013). A survey on sensor-cloud: architecture, applications, and approaches. International Journal of Distributed Sensor Networks, 9(2), 917923.

Bodik, P., Hong, W., Guestrin, C., Madden, S., Paskin, M., and Thibaux, R. (2004). Intel lab data. Intel Berkeley Research lab, [Online URL:] accessed on November 11, 2020.

Booranawong, A., Sopajarn, J., Sittiruk, T., and Jindapetch, N. (2018). Reduction of RSSI variations for indoor position estimation in wireless sensor networks. Engineering and Applied Science Research, 45(3), 212-220.

Das, K., Das, S., Darji, R. K., and Mishra, A. (2018). Survey of energy-efficient techniques for the cloud-integrated sensor network. Journal of Sensors, 2018, 1597089.

Das, K., Das, S., and Mohapatra, A. (2020). A novel energy-efficient sensor cloud model using data prediction and forecasting techniques. Karbala International Journal of Modern Science, 6(3), 267-274.

Guha, R. K., Gunter, C. A., and Sarkar, S. (2007). Fair coalitions for power-aware routing in wireless networks. IEEE Transactions on Mobile Computing, 6(2), 206220.

Ighravwe, D. E., Oke, S. A., and Adebiyi, K. A. (2018). Selection of an optimal neural network architecture for maintenance workforce size prediction using grey relational analysis. Engineering and Applied Science Research, 45(1), 1-7.

Kirtsaeng, S., Sukthawee, P., Khosuk, B., Masthawee, F., Pantong, N., and Taorat, K. (2016). Development of daily temperature prediction model for northeastern thailand using artificial neural networks. Engineering and Applied Science Research, 43(S3), 487-490.

Kumar, K., and Kumar, S. (2018). Energy efficient link stable routing in internet of things. International Journal of Information Technology, 10(4), 465-479.

Kurumbanshi, S., and Rathkanthiwar, S. (2018). Increasing the lifespan of wireless adhoc network using probabilistic approaches: a survey. International Journal of Information Technology, 10(4), 537-542.

Lemos, M., Rabelo, R., Mendes, D., Carvalho, C., and Holanda, R. (2019). An approach for provisioning virtual sensors in sensor clouds. International Journal of Network Management, 29(2), e2062.

Mishra, J., Sheetlani, J., and Reddy, K. H. K. (2022). Data center network energy consumption minimization: a hierarchical FAT-tree approach. International Journal of Information Technology, 14(1), 507-519.

Misra, S., Chatterjee, S., and Obaidat, S. M. (2017). On theoretical modeling of sensor cloud: a paradigm shift from wireless sensor network. IEEE Systems Journal, 11(2), 1084-1093.

Panedpojaman, P., and Intarit, P. (2016). Maximum temperature prediction for concrete sections during cooling phase. Engineering and Applied Science Research, 43(S2), 294-298.

Rao, M., and Kamila, N. K. (2018). Bayesian network based energy efficient ship motion monitoring. Karbala International Journal of Modern Science, 4(1), 69-85.

Samarah, S. (2015). A data predication model for integrating wireless sensor networks and cloud computing. Procedia Computer Science, 52, 1141-1146.

Sethi, D., and Anand, J. (2019). Big data and WBAN: prediction and analysis of the patient health condition in a remote area. Engineering and Applied Science Research, 46(3), 248-255.

Shi, H. L., Li, D., Qiu, J. F., Hou, C. D., and Cui, L. (2014). A task execution framework for cloud-assisted sensor networks. Journal of Computer Science and Technology, 29(2), 216-226.

Yuriyama, M., and Kushida, T. (2010). Sensor-Cloud infrastructure - Physical sensor management with virtualized sensors on cloud computing. In Proceeding of the 13th IEEE International Conference on Network-Based Information Systems (NBiS), pp. 1-8. Takayama, Japan.