Multi-objective node deployment in WSNs: In search of an optimal trade-off among coverage, lifetime, energy consumption, and connectivity

The increased demand of Wireless Sensor Networks (WSNs) in different areas of application have intensified studies dedicated to the deployment of sensor nodes in recent past. For deployment of sensor nodes some of the key objectives that need to be satisfied are coverage of the area to be monitored, net energy consumed by the WSN, lifetime of the network, and connectivity and number of deployed sensors. In this article the sensor node deployment task has been formulated as a constrained multi-objective optimization (MO) problem where the aim is to find a deployed sensor node arrangement to maximize the area of coverage, minimize the net energy consumption, maximize the network lifetime, and minimize the number of deployed sensor nodes while maintaining connectivity between each sensor node and the sink node for proper data transmission. We assume a tree structure between the deployed nodes and the sink node for data transmission. Our method employs a recently developed and very competitive multi-objective evolutionary algorithm (MOEA) known as MOEA/D-DE that uses a decomposition approach for converting the problem of approximation of the Pareto fronts (PF) into a number of single-objective optimization problems. This algorithm employs differential evolution (DE), one of the most powerful real parameter optimizers in current use, as its search method. The original MOEA/D has been modified by introducing a new fuzzy dominance based decomposition technique. The algorithm introduces a fuzzy Pareto dominance concept to compare two solutions and uses the scalar decomposition method only when one of the solutions fails to dominate the other in terms of a fuzzy dominance level. We have compared the performance of the resulting algorithm, called MOEA/DFD, with the original MOEA/D-DE and another very popular MOEA called Non-dominated Sorting Genetic Algorithm (NSGA-II). The best trade-off solutions from MOEA/DFD based node deployment scheme have also been compared with a few single-objective node deployment schemes based on the original DE, an adaptive DE-variant (JADE), original particle swarm optimization (PSO), and a state-of-the art variant of PSO (Comprehensive Learning PSO). In all the test instances, MOEA/DFD performs better than all other algorithms. Also the proposed multi-objective formulation of the problem adds more flexibility to the decision maker for choosing the necessary threshold of the objectives to be satisfied.

[1]  S. Sitharama Iyengar,et al.  Grid Coverage for Surveillance and Target Location in Distributed Sensor Networks , 2002, IEEE Trans. Computers.

[2]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[3]  Marco Farina,et al.  A fuzzy definition of "optimality" for many-criteria optimization problems , 2004, IEEE Trans. Syst. Man Cybern. Part A.

[4]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[5]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[6]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[7]  Jason R. Schott.  Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. , 1995 .

[8]  Qingfu Zhang,et al.  Multiobjective optimization Test Instances for the CEC 2009 Special Session and Competition , 2009 .

[9]  Zoran Bojkovic,et al.  A survey on wireless sensor networks deployment , 2008 .

[10]  Flávio V. C. Martins,et al.  Model and Algorithms for the Density , Coverage and Connectivity Control Problem in Flat WSNs , 2007 .

[11]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[12]  Robert E. Tarjan,et al.  Finding Minimum Spanning Trees , 1976, SIAM J. Comput..

[13]  E. G. Carrano,et al.  A Hybrid Multiobjective Evolutionary Approach for Improving the Performance of Wireless Sensor Networks , 2011, IEEE Sensors Journal.

[14]  Leonidas J. Guibas,et al.  Wireless sensor networks - an information processing approach , 2004, The Morgan Kaufmann series in networking.

[15]  Mohammad Ilyas,et al.  Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems , 2004 .

[16]  Guy Pujolle,et al.  Potential Field Approach to Ensure Connectivity and Differentiated Detection in WSN Deployment , 2009, 2009 IEEE International Conference on Communications.

[17]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[18]  Ajith Abraham,et al.  An improved Multiobjective Evolutionary Algorithm based on decomposition with fuzzy dominance , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[19]  Sanjay Jha,et al.  Wireless Sensor Networks , 2005 .

[20]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[21]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[22]  Qingfu Zhang,et al.  The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances , 2009, 2009 IEEE Congress on Evolutionary Computation.

[23]  Ivan Stojmenović,et al.  Handbook of Sensor Networks: Algorithms and Architectures , 2005, Handbook of Sensor Networks.

[24]  Sanjoy Das,et al.  Fuzzy Dominance Based Multi-objective GA-Simplex Hybrid Algorithms Applied to Gene Network Models , 2004, GECCO.

[25]  Mohamed F. Younis,et al.  Strategies and techniques for node placement in wireless sensor networks: A survey , 2008, Ad Hoc Networks.

[26]  S. Sitharama Iyengar,et al.  On efficient deployment of sensors on planar grid , 2007, Comput. Commun..

[27]  Aura Ganz,et al.  On the lifetime of large scale sensor networks , 2006, Comput. Commun..

[28]  J. J.A,et al.  Nonlinear multi-objective optimization of metal forming process , 2003 .

[29]  Miodrag Potkonjak,et al.  Coverage problems in wireless ad-hoc sensor networks , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[30]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[31]  JAMAL N. AL-KARAKI,et al.  Routing techniques in wireless sensor networks: a survey , 2004, IEEE Wireless Communications.

[32]  Qingfu Zhang,et al.  Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II , 2009, IEEE Transactions on Evolutionary Computation.

[33]  Mani B. Srivastava,et al.  Simulating networks of wireless sensors , 2001, Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304).

[34]  Guy Pujolle,et al.  A Tabu Search WSN Deployment Method for Monitoring Geographically Irregular Distributed Events , 2009, Sensors.

[35]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[36]  Biswanath Mukherjee,et al.  Wireless sensor network survey , 2008, Comput. Networks.

[37]  Edgar H. Callaway,et al.  Wireless Sensor Networks: Architectures and Protocols , 2003 .

[38]  P. Fleming,et al.  Convergence Acceleration Operator for Multiobjective Optimization , 2007, IEEE Transactions on Evolutionary Computation.

[39]  Prasant Mohapatra,et al.  On the deployment of wireless data back-haul networks , 2007, IEEE Transactions on Wireless Communications.

[40]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[41]  J. Mendel.  Fuzzy logic systems for engineering: a tutorial , 1995, Proc. IEEE.