FAMACRO: Fuzzy and Ant Colony Optimization Based MAC/Routing Cross-layer Protocol for Wireless Sensor Networks

Abstract This paper presents Fuzzy and Ant Colony Optimization (ACO) based MAC/Routing cross-layer protocol (FAMACRO) for Wireless Sensor Networks that encompases cluster head selection, clustering and inter-cluster routing protocols. FAMACRO uses fuzzy logic with residual energy, number of neighboring nodes and quality of communication link as input variables for cluster head selection. To avoid “hot spots”, FAMACRO uses an unequal clustering mechanism with clusters closer to master station having smaller sizes than those far from it. Finally, ACO techinque is used for reliable and energy-efficient inter-cluster routing from cluster heads to master station. The inter-cluster routing protocol decides relay node considering its residual energy, distance from current cluster head, distance from master station and packet reception rate. A comparative analysis of FAMACRO with Distributed Energy Efficient Hierarchical Clustering, Unequal Hybrid Energy Efficient Distributed Clustering, Energy Efficient Unequal Clustering and Improved Fuzzy Unequal Clustering protocol shows that FAMACRO is 82% more energy-efficient, has 5% to 30% more network lifetime and sends 91% more packets compared to Improved Fuzzy Unequal Clustering protocol.

[1]  Ganesh K. Venayagamoorthy,et al.  Computational Intelligence in Wireless Sensor Networks: A Survey , 2011, IEEE Communications Surveys & Tutorials.

[2]  Sachin Gajjar,et al.  Performance analysis of cross layer protocols for wireless sensor networks , 2012, ICACCI '12.

[3]  Lan truyền,et al.  Wireless Communications Principles and Practice , 2015 .

[4]  Alfredo Navarra,et al.  UHEED - An Unequal Clustering Algorithm for Wireless Sensor Networks , 2012, SENSORNETS.

[5]  Layuan Li,et al.  Multihop Routing Protocol with Unequal Clustering for Wireless Sensor Networks , 2008, 2008 ISECS International Colloquium on Computing, Communication, Control, and Management.

[6]  Jie Wu,et al.  An energy-efficient unequal clustering mechanism for wireless sensor networks , 2005, IEEE International Conference on Mobile Adhoc and Sensor Systems Conference, 2005..

[7]  Ossama Younis,et al.  HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks , 2004, IEEE Transactions on Mobile Computing.

[8]  M. Motani,et al.  Cross-layer design: a survey and the road ahead , 2005, IEEE Communications Magazine.

[9]  Sachin Gajjar,et al.  Comparative analysis of wireless sensor network motes , 2014, 2014 International Conference on Signal Processing and Integrated Networks (SPIN).

[10]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..

[11]  Kaveh Pahlavan,et al.  Wireless Information Networks , 1995 .

[12]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[13]  K. S. Dasgupta,et al.  Wireless Sensor Network: Application led research perspective , 2011, 2011 IEEE Recent Advances in Intelligent Computational Systems.

[14]  Nj Piscataway,et al.  Wireless LAN medium access control (MAC) and physical layer (PHY) specifications , 1996 .

[15]  Adnan Yazici,et al.  An energy aware fuzzy unequal clustering algorithm for wireless sensor networks , 2010, International Conference on Fuzzy Systems.

[16]  S.H. Gajjar,et al.  Cross layer architectural approaches for Wireless Sensor Networks , 2011, 2011 IEEE Recent Advances in Intelligent Computational Systems.

[17]  Wendi B. Heinzelman,et al.  Prolonging the lifetime of wireless sensor networks via unequal clustering , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[18]  Shugong Xu,et al.  Does the ieee 802 , 2001 .

[19]  Guanghui Wang,et al.  An energy-driven unequal clustering protocol for heterogeneous wireless sensor networks , 2011 .

[20]  Petri Mähönen,et al.  Designing a reliable and stable link quality metric for wireless sensor networks , 2008, REALWSN '08.

[21]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[22]  Shugong Xu,et al.  Does the IEEE 802.11 MAC protocol work well in multihop wireless ad hoc networks? , 2001, IEEE Commun. Mag..

[23]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[24]  Neng Wang,et al.  An unequal layered clustering approach for large scale wireless sensor networks , 2010, 2010 2nd International Conference on Future Computer and Communication.

[25]  Song Mao,et al.  An Improved Fuzzy Unequal Clustering Algorithm for Wireless Sensor Network , 2013, Mob. Networks Appl..

[26]  JoAnne Holliday,et al.  Distributed Energy-Efficient Hierarchical Clustering for Wireless Sensor Networks , 2005, DCOSS.

[27]  Sachin Gajjar,et al.  PERFORMANCE ANALYSIS OF CLUSTERING PROTOCOLS FOR WIRELESS SENSOR NETWORKS , 2013 .

[28]  Jie Wu,et al.  EECS: an energy efficient clustering scheme in wireless sensor networks , 2005, PCCC 2005. 24th IEEE International Performance, Computing, and Communications Conference, 2005..