Energy Map Construction using Adaptive Alpha Grey Prediction Model in WSNs
Wireless Sensor Networks can be used to monitor the
physical phenomenon in such areas where human approach is nearly
impossible. Hence the limited power supply is the major constraint of
the WSNs due to the use of non-rechargeable batteries in sensor
nodes. A lot of researches are going on to reduce the energy
consumption of sensor nodes. Energy map can be used with
clustering, data dissemination and routing techniques to reduce the
power consumption of WSNs. Energy map can also be used to know
which part of the network is going to fail in near future. In this paper,
Energy map is constructed using the prediction based approach.
Adaptive alpha GM(1,1) model is used as the prediction model.
GM(1,1) is being used worldwide in many applications for predicting
future values of time series using some past values due to its high
computational efficiency and accuracy.
[1] Raquel A.F.Mini, Max do Val Machado, Antonio A. F. Loureiro and
Badri Nath, "Prediction-based energy map for wireless sensor
networks," Ad Hoc Networks, vol. 3, 2005, pp. 235-253.
[2] Edward Chan and Song Han, "Energy Efficient Residual Energy
Monitoring in Wireless Sensor Networks," International Journal of
Distributed Sensor Networks, vol.5, 2009, pp.1-23.
[3] Raquel A.F. Minia, Antonio A.F. Loureiro, Badri Nath, "The distinctive
design characteristic of a wireless sensor network:the energy map,"
Computer Communication, vol. 27, 2004, pp.935-945.
[4] Erdal Kayacan, Baris Ulutas, Okyay Kaynak, "Grey system theorybased
models in time series prediction," Expert Systems with
Applications, vol. 37, 2010, pp.1784-1789.
[5] Ujjwal Kumar, V.K. Jain, "Time series models (Grey-Markov, Grey
Model with rolling mechanism and singular spectrum analysis) to
forecast energy consumption in India," Energy, vol. 35, 2010, pp.1709-
1716.
[6] Sifeng Liu, Yi Lin, "Grey Systems Theory And Applications", Springer,
2010.C. J. Kaufman, Rocky Mountain Research Lab., Boulder, CO,
private communication, May 1995.
[7] Yao, A.W.L., Chi, S.C., and Chen, J.H., "An Improved Grey-Based
Approach for Electricity Demand Forecasting," Electric Power Systems
Research, vol. 67, 2003, pp. 217 -224.
[8] L.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, "Wireless
Sensor Networks: A Survey," Computer Network, vol. 38, 2002, pp.
393-422.
[1] Raquel A.F.Mini, Max do Val Machado, Antonio A. F. Loureiro and
Badri Nath, "Prediction-based energy map for wireless sensor
networks," Ad Hoc Networks, vol. 3, 2005, pp. 235-253.
[2] Edward Chan and Song Han, "Energy Efficient Residual Energy
Monitoring in Wireless Sensor Networks," International Journal of
Distributed Sensor Networks, vol.5, 2009, pp.1-23.
[3] Raquel A.F. Minia, Antonio A.F. Loureiro, Badri Nath, "The distinctive
design characteristic of a wireless sensor network:the energy map,"
Computer Communication, vol. 27, 2004, pp.935-945.
[4] Erdal Kayacan, Baris Ulutas, Okyay Kaynak, "Grey system theorybased
models in time series prediction," Expert Systems with
Applications, vol. 37, 2010, pp.1784-1789.
[5] Ujjwal Kumar, V.K. Jain, "Time series models (Grey-Markov, Grey
Model with rolling mechanism and singular spectrum analysis) to
forecast energy consumption in India," Energy, vol. 35, 2010, pp.1709-
1716.
[6] Sifeng Liu, Yi Lin, "Grey Systems Theory And Applications", Springer,
2010.C. J. Kaufman, Rocky Mountain Research Lab., Boulder, CO,
private communication, May 1995.
[7] Yao, A.W.L., Chi, S.C., and Chen, J.H., "An Improved Grey-Based
Approach for Electricity Demand Forecasting," Electric Power Systems
Research, vol. 67, 2003, pp. 217 -224.
[8] L.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, "Wireless
Sensor Networks: A Survey," Computer Network, vol. 38, 2002, pp.
393-422.
@article{"International Journal of Electrical, Electronic and Communication Sciences:60804", author = "Surender Kumar Soni and Dhirendra Pratap Singh", title = "Energy Map Construction using Adaptive Alpha Grey Prediction Model in WSNs", abstract = "Wireless Sensor Networks can be used to monitor the
physical phenomenon in such areas where human approach is nearly
impossible. Hence the limited power supply is the major constraint of
the WSNs due to the use of non-rechargeable batteries in sensor
nodes. A lot of researches are going on to reduce the energy
consumption of sensor nodes. Energy map can be used with
clustering, data dissemination and routing techniques to reduce the
power consumption of WSNs. Energy map can also be used to know
which part of the network is going to fail in near future. In this paper,
Energy map is constructed using the prediction based approach.
Adaptive alpha GM(1,1) model is used as the prediction model.
GM(1,1) is being used worldwide in many applications for predicting
future values of time series using some past values due to its high
computational efficiency and accuracy.", keywords = "Adaptive Alpha GM(1,1) Model, Energy Map,
Prediction Based Data Reduction, Wireless Sensor Networks", volume = "6", number = "3", pages = "351-4", }