Scalability of a Novel Shifting Balance Theory-Based Optimization Algorithm: A Comparative Study on a Cluster-Based Wireless Sensor Network

Scalability is one of the most important issues for optimization algorithms used in wireless sensor networks (WSNs) since there are often many parameters to be optimized at the same time. In this case it is very hard to ensure that an optimization algorithm can be smoothly scaled up from a low-dimensional optimization problem to the one with a high dimensionality. This paper addresses the scalability issue of a novel optimization algorithm inspired by the Shifting Balance Theory (SBT) of evolution in population genetics. Toward this end, a cluster-based WSN is employed in this paper as a benchmark to perform a comparative study. The total energy consumption is minimized under the required quality of service by jointly optimizing the transmission power and rate for each sensor node. The results obtained by the SBT-based algorithm are compared with the Metropolis algorithm (MA) and currently popular particle swarm optimizer (PSO) to assess the scaling performance of the three algorithms against the same WSN optimization problem.

[1]  S. Wright.  Evolution in mendelian populations , 1931 .

[2]  Andrea J. Goldsmith,et al.  Design challenges for energy-constrained ad hoc wireless networks , 2002, IEEE Wirel. Commun..

[3]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[4]  Majid Sarrafzadeh,et al.  Optimal Energy Aware Clustering in Sensor Networks , 2002 .

[5]  N. Barton.  ON THE SPREAD OF NEW GENE COMBINATIONS IN THE THIRD PHASE OF WRIGHT'S SHIFTING‐BALANCE , 1992, Evolution; international journal of organic evolution.

[6]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[7]  Andreas Willig,et al.  Protocols and Architectures for Wireless Sensor Networks , 2005 .

[8]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[9]  Mani B. Srivastava,et al.  Optimizing Sensor Networks in the Energy-Latency-Density Design Space , 2002, IEEE Trans. Mob. Comput..

[10]  Yong Ma,et al.  System lifetime optimization for heterogeneous sensor networks with a hub-spoke technology , 2004, IEEE Transactions on Mobile Computing.

[11]  Erfu Yang,et al.  Multiobjective Optimal Design of MEMS-Based Reconfigurable and Evolvable Sensor Networks for Space Applications , 2007, Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007).

[12]  Nicholas H. Barton,et al.  Adaptation and the shifting balance , 1993 .

[13]  Erfu Yang,et al.  A novel shifting balance theory-based approach to optimization of an energy-constrained modulation scheme for wireless sensor networks , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).