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.

[1]  Hakan Ferhatosmanoglu,et al.  Access Structures for Angular Similarity Queries , 2006, IEEE Transactions on Knowledge and Data Engineering.

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

[3]  Jasbir S. Arora,et al.  Survey of multi-objective optimization methods for engineering , 2004 .

[4]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[5]  David S. Johnson,et al.  Computers and In stractability: A Guide to the Theory of NP-Completeness. W. H Freeman, San Fran , 1979 .

[6]  Charles E. Perkins,et al.  Ad-hoc on-demand distance vector routing , 1999, Proceedings WMCSA'99. Second IEEE Workshop on Mobile Computing Systems and Applications.

[7]  Sunggu Lee,et al.  Optimal wake-up scheduling of data gathering trees for wireless sensor networks , 2012, J. Parallel Distributed Comput..

[8]  Vilfredo Pareto,et al.  Manuale di economia politica , 1965 .

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

[10]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

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

[12]  Guiran Chang,et al.  Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm , 2009, Comput. Math. Appl..

[13]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[14]  Pramod K. Varshney,et al.  Multi-objective mobile agent routing in wireless sensor networks , 2005, 2005 IEEE Congress on Evolutionary Computation.

[15]  Qingfu Zhang,et al.  A multi-objective evolutionary algorithm for the deployment and power assignment problem in wireless sensor networks , 2010, Comput. Networks.

[16]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

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