Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks

The geographic routing protocol only requires the location information of local nodes for routing decisions, and is considered very efficient in multi-hop wireless sensor networks. However, in dynamic wireless sensor networks, it increases the routing overhead while obtaining the location information of destination nodes by using a location server algorithm. In addition, the routing void problem and location inaccuracy problem also occur in geographic routing. To solve these problems, a novel fuzzy logic-based geographic routing protocol (FLGR) is proposed. The selection criteria and parameters for the assessment of the next forwarding node are also proposed. In FLGR protocol, the next forward node can be selected based on the fuzzy location region of the destination node. Finally, the feasibility of the FLGR forwarding mode is verified and the performance of FLGR protocol is analyzed via simulation. Simulation results show that the proposed FLGR forwarding mode can effectively avoid the routing void problem. Compared with existing protocols, the FLGR protocol has lower routing overhead, and a higher packet delivery rate in a sparse network.

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

[2]  Huanhuan Hou,et al.  WSN中基于位置预测的地理路由算法 (Geographic Routing Algorithm Based on Location Prediction in WSN) , 2018, 计算机科学.

[3]  Sankaran Mahadevan,et al.  A Game Theoretic Approach to Network Reliability Assessment , 2017, IEEE Transactions on Reliability.

[4]  Zhen He,et al.  Extensions of Atanassov's Intuitionistic Fuzzy Interaction Bonferroni Means and Their Application to Multiple-Attribute Decision Making , 2016, IEEE Transactions on Fuzzy Systems.

[5]  Wang Huai-cai,et al.  New routing algorithm based on geographical location:GPSR-AD , 2009 .

[6]  Jaesung Park,et al.  A forwarder selection method for greedy mode operation of a geographic routing protocol in a WSN , 2013, 2013 Fifth International Conference on Ubiquitous and Future Networks (ICUFN).

[7]  Pei Chen,et al.  Fast Phase-Only Positioning with Triple-Frequency GPS , 2018, Sensors.

[8]  Sanghyun Ahn,et al.  A geographic routing scheme with dead-end avoidance for large-scale MANETs , 2015, 2015 International Conference on Information and Communication Technology Convergence (ICTC).

[9]  Zhao Xiang-ning.  Survey on Geographic Routing in Wireless Sensor Networks , 2008 .

[10]  Ghulam Bhatti,et al.  Machine Learning Based Localization in Large-Scale Wireless Sensor Networks , 2018, Sensors.

[11]  Witold Pedrycz,et al.  Fuzzy Systems Engineering - Toward Human-Centric Computing , 2007 .

[12]  Madan M. Gupta,et al.  Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems , 2003 .

[13]  Geng-Sheng Kuo,et al.  A Novel Location-fault-tolerant Geographic Routing Scheme for Wireless Ad Hoc Networks , 2006, 2006 IEEE 63rd Vehicular Technology Conference.

[14]  David R. Karger,et al.  A scalable location service for geographic ad hoc routing , 2000, MobiCom '00.

[15]  Hai-yan Liu,et al.  New routing algorithm based on geographical location: GPSR-AD: New routing algorithm based on geographical location: GPSR-AD , 2010 .

[16]  Jean-Marie Bonnin,et al.  Wireless sensor networks: a survey on recent developments and potential synergies , 2013, The Journal of Supercomputing.

[17]  Brad Karp,et al.  GPSR : Greedy Perimeter Stateless Routing for Wireless , 2000, MobiCom 2000.

[18]  Muhammad Shoaib Khan,et al.  Exponential utility function based criteria for network selection in heterogeneous wireless networks , 2018 .

[19]  Zheng Bo.  Improvement of greedy forwarding schemes based on fuzzy logic control , 2011 .

[20]  S H Chen,et al.  REPRESENTATION, RANKING AND DISTANCE OF FUZZY NUMBER WITH EXPONENTIAL MEMBERSHIP FUNCTION USING GRADED MEAN INTEGRATION METHOD , 2000 .