WSN  Vol.7 No.6 , June 2015
A Free Market Economy Model for Resource Management in Wireless Sensor Networks
This paper presents a free market economy model that can be used to facilitate fully distributed autonomous control of resources in massive heterogeneous wireless sensor networks (WSNs). In the future, it is expected that WSNs will exist as part of the global Internet of Things (IoT), and different WSNs can work together in a massive network of heterogeneous WSNs in order to solve common problems. Control of valuable processing, sensing and communication resources, determining which nodes will remain awake during specific time periods in order to provide sensing services, and determining which nodes will forward other nodes’ packets are difficult problems that must be dealt with. It is proposed that just as the free market economy model enables the global human society to function reasonably well when individuals simply attempt to trade money and services in order to maximize their individual profits, and a similar model and mechanism should enable a massive network of heterogeneous WSNs to function well in a fully distributed autonomous manner. The main contributions of this paper are the introduction of the free market economy model for use with WSNs, the formal definition of a maximum profit price problem for multihop packet relaying, and the proposal of a distributed genetic algorithm for the solution of the maximum profit price problem. Simulation results show that the proposed distributed solution produces results that are 70% - 80% similar to a pareto optimal solution for this problem.

Cite this paper
Bae, B. , Park, J. and Lee, S. (2015) A Free Market Economy Model for Resource Management in Wireless Sensor Networks. Wireless Sensor Network, 7, 76-82. doi: 10.4236/wsn.2015.76007.
[1]   Gubbi, J., Buyya, R., Marusic, S. and Palaniswami, M. (2013) Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions. Future Generation Computer Systems, 29, 1645-1660.

[2]   Yick, J., Mukherjee, B. and Ghosal, D. (2008) Wireless Sensor Network Survey. Computer Networks, 52, 2292-2330.

[3]   Jang, U., Lee, S. and Yoo, S. (2012) Optimal Wake-Up Scheduling of Data Gathering Trees for Wireless Sensor Networks. Journal of Parallel and Distributed Computing, 72, 536-546.

[4]   Marler, R.T. and Arora, J.S. (2004) Survey of Multi-Objective Optimization Methods for Engineering. Structural and Multidisciplinary Optimization, 26, 369-395.

[5]   Dijkstra, E.W. (1959) A Note on Two Problems in Connexion with Graphs. Numerische Mathematik, 1, 269-271.

[6]   Perkins, C.E. and Royer, E.M. (1999) Ad-Hoc On-Demand Distance Vector Routing. 2nd IEEE Workshop on Mobile Computing Systems and Applications, New Orleans, 25-26 February 1999, 90-100.

[7]   Zitzler, E. and Thiele, L. (1999) Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation, 3, 257-271.

[8]   Pareto, V. (1906) Manuale di Economia Politica. Vol. 13, Societa Editrice.

[9]   Garey, M.R. and Johnson, D.S. (1979) Computers and Intractability: A Guide to NP-Completeness. Freeman Press.

[10]   Deb, K. (2001) Multi-Objective Optimization Using Evolutionary Algorithms. Vol. 16, John Wiley & Sons, Hoboken.

[11]   Srinivas, N. and Deb, K. (1994) Muiltiobjective Optimization Using Non-Dominated Sorting in Genetic Algorithms. Evolutionary Computation, 2, 221-248.

[12]   Konstantinidis, A., Yang, K., Zhang, Q.F. and Zeinalipour-Yazti, D. (2010) A Multi-Objective Evolutionary Algorithm for the Deployment and Power Assignment Problem in Wireless Sensor Networks. Computer Networks, 54, 960-976.

[13]   Jia, J., Chen, J., Chang, G. and Tan, Z. (2009) Energy Efficient Coverage Control in Wireless Sensor Networks Based on Multi-Objective Genetic Algorithm. Computers and Mathematics with Applications, 57, 1756-1766.

[14]   Rajagopalan, R., Mohan, C.K., Varshney, P. and Mehrotra, K. (2005) Multi-Objective Mobile Agent Routing in Wireless Sensor Networks. The 2005 IEEE Congress on Evolutionary Computation, 2, 1730-1737.

[15]   Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. (2002) A Fast and Elitist Multiobjective Genetic Algorithm: NS-GA-II. IEEE Transactions on Evolutionary Computation, 6, 182-197.

[16]   Apaydin, T. and Ferhatosmanoglu, H. (2006) Access Structures for Angular Similarity Queries. IEEE Transactions on Knowledge and Data Engineering, 18, 1512-1525.