Two Phased Routing Protocol Incorporating Distributed Genetic Algorithm and Gradient Based Heuristic in Clustered WSN

In wireless cluster networks with a single non mobile sink, finding the optimal cluster assignment is a non-trivial problem. The inherently non centralized nature of wireless sensor networks poses a problem as majority of the learning algorithms are centralized. It is also desirable that single routing algorithm be applicable regardless of whether the sensor network is a dense single-hop network or a sparse multi-hop network. In this paper we present the two phased routing incorporating distributed genetic algorithm and gradient based heuristic (TRIGGER) as an attempt to solve these problems. In the first phase of TRIGGER a distributed (island model) genetic algorithm based clustering is employed to find a spatially optimal cluster assignment. In the second phase a gradient based routing forwards the already aggregated data to the sink. We discuss the rationale behind the two phased nature of TRIGGER. We demonstrate the effectiveness of TRIGGER with extensive simulations and discuss the results.

[1]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[2]  Kalyanmoy Deb,et al.  Sensor network design of linear processes using genetic algorithms , 1998 .

[3]  Martin,et al.  Finite size and dimensional dependence in the Euclidean traveling salesman problem. , 1996, Physical review letters.

[4]  Konstantinos P. Ferentinos,et al.  Adaptive design optimization of wireless sensor networks using genetic algorithms , 2007, Comput. Networks.

[5]  L. Darrell Whitley,et al.  Island Model genetic Algorithms and Linearly Separable Problems , 1997, Evolutionary Computing, AISB Workshop.

[6]  Nei Kato,et al.  HYMN: A Novel Hybrid Multi-Hop Routing Algorithm to Improve the Longevity of WSNs , 2012, IEEE Transactions on Wireless Communications.

[7]  J. Wheeler,et al.  Physicist’s version of traveling salesman problem: statistical analysis , 1983 .

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

[9]  Yookun Cho,et al.  PEACH: Power-efficient and adaptive clustering hierarchy protocol for wireless sensor networks , 2007, Comput. Commun..

[10]  Abdul Wasey Matin,et al.  Genetic Algorithm for Hierarchical Wireless Sensor Networks , 2007, J. Networks.

[11]  S. Hussain,et al.  Genetic Algorithm for Energy Efficient Clusters in Wireless Sensor Networks , 2007, Fourth International Conference on Information Technology (ITNG'07).

[12]  S. Chandrasekhar.  Stochastic problems in Physics and Astronomy , 1943 .

[13]  Prasanta K. Jana,et al.  Energy Efficient Clustering and Routing Algorithms for Wireless Sensor Networks: GA Based Approach , 2015, Wireless Personal Communications.

[14]  Leslie Lamport,et al.  Time, clocks, and the ordering of events in a distributed system , 1978, CACM.

[15]  Ying Song,et al.  A Genetic Algorithm for Energy-Efficient Based Multipath Routing in Wireless Sensor Networks , 2015, Wirel. Pers. Commun..

[16]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  William Feller,et al.  An Introduction to Probability Theory and Its Applications , 1951 .

[18]  O. Bohigas,et al.  The random link approximation for the Euclidean traveling salesman problem , 1996, cond-mat/9607080.

[19]  Cauligi S. Raghavendra,et al.  PEGASIS: Power-efficient gathering in sensor information systems , 2002, Proceedings, IEEE Aerospace Conference.

[20]  Sajid Hussain,et al.  Genetic Algorithm for Energy-Efficient Trees in Wireless Sensor Networks , 2009 .

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

[22]  Bikas K. Chakrabarti,et al.  The mean distance to the nth neighbour in a uniform distribution of random points: an application of probability theory , 2002 .

[23]  Andrew McCallum,et al.  Efficient clustering of high-dimensional data sets with application to reference matching , 2000, KDD '00.

[24]  Nauman Aslam,et al.  A multi-criterion optimization technique for energy efficient cluster formation in wireless sensor networks , 2011, Inf. Fusion.

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

[26]  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.

[27]  Ujjwal Maulik,et al.  Genetic algorithm-based clustering technique , 2000, Pattern Recognit..

[28]  O. C. Martin,et al.  Scaling Universalities ofkth-Nearest Neighbor Distances on Closed Manifolds , 1998 .

[29]  J. Beardwood,et al.  The shortest path through many points , 1959, Mathematical Proceedings of the Cambridge Philosophical Society.

[30]  Annie S. Wu,et al.  Sensor Network Optimization Using a Genetic Algorithm , 2003 .

[31]  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..

[32]  Stephan Olariu,et al.  On training a sensor network , 2003, Proceedings International Parallel and Distributed Processing Symposium.