Avoidance of the energy hole in wireless sensor networks using a layered-based routing tree

Prolonging the network lifetime is one of the main challenges in wireless sensor networks WSNs. This paper proposes a layered-based routing tree called LBRT that solves the energy holes in WSNs. It uses several layers in a ring network in a way that each layer is composed of four sectors. The number of nodes in first layer is more than that of in other layers. The nodes construct a cluster at each sector, except sectors of first layer, to transmit the data packets via cluster head CH nodes. CH nodes are selected based on their remaining energy and average distance to neighbouring nodes. They receive data packets from non-cluster head NCH nodes and forward them toward the sink through CH nodes located at the lower layer. Simulation results demonstrate that the proposed method surpasses than another routing method in terms of network lifetime, network traffic, system throughput, and delivery time.

[1]  Wendi Heinzelman,et al.  Energy-efficient communication protocol for wireless microsensor networks , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[2]  Sohrab Khanmohammadi,et al.  Static Three-Dimensional Fuzzy Routing Based on the Receiving Probability in Wireless Sensor Networks , 2013, Comput..

[3]  S. Peng,et al.  Energy Neutral Clustering for energy harvesting wireless sensors networks , 2013, 2013 19th IEEE International Conference on Networks (ICON).

[4]  Song Chao,et al.  ACO-Based Algorithm for Solving Energy Hole Problems in Wireless Sensor Networks , 2009 .

[5]  Ivan Stojmenovic,et al.  Design Guidelines for Maximizing Lifetime and Avoiding Energy Holes in Sensor Networks with Uniform Distribution and Uniform Reporting , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[6]  Ahmed Wasif Reza,et al.  Energizing wireless sensor networks by energy harvesting systems: Scopes, challenges and approaches , 2014 .

[7]  Jian Li,et al.  Analytical modeling and mitigation techniques for the energy hole problem in sensor networks , 2007, Pervasive Mob. Comput..

[8]  Mohd Fauzi Othman,et al.  Wireless Sensor Network Applications: A Study in Environment Monitoring System , 2012 .

[9]  Hannes Hartenstein,et al.  Stochastic Properties of the Random Waypoint Mobility Model , 2004, Wirel. Networks.

[10]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[11]  Yu Xue,et al.  An efficient energy hole alleviating algorithm for wireless sensor networks , 2014, IEEE Transactions on Consumer Electronics.

[12]  Mohammad Samadi Gharajeh Determining the State of the Sensor Nodes Based on Fuzzy Theory in WSNs , 2014, Int. J. Comput. Commun. Control.

[13]  Yacine Challal,et al.  Energy efficiency in wireless sensor networks: A top-down survey , 2014, Comput. Networks.

[14]  Quazi Mamun,et al.  A Qualitative Comparison of Different Logical Topologies for Wireless Sensor Networks , 2012, Sensors.

[15]  Majid Haghparast,et al.  A Novel Fault-Tolerant LEACH Clustering Protocol for Wireless Sensor Networks , 2014, J. Circuits Syst. Comput..

[16]  Yuhua Liu,et al.  A Hybrid Routing Tree to Avoid the Energy Hole Problem in Wireless Sensor Network , 2012 .