Energy Efficient Routing in Multiple Sink Sensor Networks

Wireless sensor networks have been widely used in many fields with the developments of the related techniques. But there are many problems in traditional single sink sensor networks. The energy of the sensors near the sink or on the critical paths consumes too fast causing unbalanced energy consumption. The routing algorithms mainly focus on the nearest path or minimum hops. The invalidation of the single sink node causes the breakdown of the whole sensor network. In this paper, through the analysis of the disadvantages of single-sink sensor networks, we propose the system architecture of multi-sink sensor networks and new routing algorithms ELBR (Energy Level Based Routing) and PBR (Primary Based Routing) in multi-sink sensor networks. Experiment results show ELBR and PBR have better performances than traditional methods and balance the energy consumption in sensor networks.

[1]  J V Tu,et al.  Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. , 1996, Journal of clinical epidemiology.

[2]  Yanghee Choi,et al.  Optimal Multi-sink Positioning and Energy-Efficient Routing in Wireless Sensor Networks , 2005, ICOIN.

[3]  Ebrahim Nasrabadi,et al.  Fuzzy linear regression models with least square errors , 2005, Appl. Math. Comput..

[4]  Yu Xue,et al.  Fuzzy regression method for prediction and control the bead width in the robotic arc-welding process , 2005 .

[5]  Wei Hong,et al.  Proceedings of the 5th Symposium on Operating Systems Design and Implementation Tag: a Tiny Aggregation Service for Ad-hoc Sensor Networks , 2022 .

[6]  Fang-Mei Tseng,et al.  A quadratic interval logit model for forecasting bankruptcy , 2005 .

[7]  Hsiao-Fan Wang,et al.  Insight of a fuzzy regression model , 2000, Fuzzy Sets Syst..

[8]  John Anderson,et al.  Wireless sensor networks for habitat monitoring , 2002, WSNA '02.

[9]  Lucila Ohno-Machado,et al.  Logistic regression and artificial neural network classification models: a methodology review , 2002, J. Biomed. Informatics.

[10]  Calton Pu,et al.  Research challenges in environmental observation and forecasting systems , 2000, MobiCom '00.

[11]  Sandeep K. S. Gupta,et al.  Research challenges in wireless networks of biomedical sensors , 2001, MobiCom '01.

[12]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[13]  Klara Nahrstedt,et al.  Maximizing Lifetime for Data Aggregation in Wireless Sensor Networks , 2005, Mob. Networks Appl..

[14]  David J. Brady,et al.  Tracking and imaging humans on heterogeneous infrared sensor arrays for law enforcement applications , 2002, SPIE Defense + Commercial Sensing.

[15]  Mohsen Shanbeh,et al.  Comparison of Statistical Regression, Fuzzy Regression and Artificial Neural Network Modeling Methodologies in Polyester Dyeing , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[16]  Yong Wang,et al.  Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with ZebraNet , 2002, ASPLOS X.

[17]  Abhimanyu Das,et al.  Data acquisition in multiple-sink sensor networks , 2005, MOCO.