A Service Negotiation Model for Selfish Nodes in the Mobile Ad Hoc Networks

In the open MANETs, nodes with different goals expect to benefit from others, but are unwilling to share their own resources. These selfish behaviors have posed increasing research challenges for cooperation. Negotiation as a key form of interaction for two or more parties enables nodes to announce their contradictory demands and seek to an agreement by concession. In the paper, the Service Negotiation model for Selfish nodes in the MANETs (SNSM) combines the policies of imitating rivals' behaviors and fast-approaching reserve prices presented to generate mutual offer and counter-offer for service bargaining. Specially, the model provides three types of changing rates of bids to speculate the rivals' behaviors. In addition, we improve the Weber-Fechner's law to self-adjust the deadline in the negotiation. Simulation results demonstrate our model has superior performances in increasing the negotiation efficiency, achieving mutual benefits between the service buyer and seller.

[1]  Yang Yang,et al.  A self-adaptive method of task allocation in clustering-based MANETs , 2010, 2010 IEEE Network Operations and Management Symposium - NOMS 2010.

[2]  Frank Dignum,et al.  A formal analysis of interest-based negotiation , 2009, Annals of Mathematics and Artificial Intelligence.

[3]  Nicholas R. Jennings,et al.  Optimal negotiation of multiple issues in incomplete information settings , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[4]  Raymond Y. K. Lau,et al.  An evolutionary learning approach for adaptive negotiation agents , 2006, Int. J. Intell. Syst..

[5]  J.-Y. Le Boudec,et al.  Toward self-organized mobile ad hoc networks: the terminodes project , 2001, IEEE Commun. Mag..

[6]  Gwendal Simon,et al.  Efficient route discovery in hybrid networks , 2009, Ad Hoc Networks.

[7]  Kwang Mong Sim,et al.  BLGAN: Bayesian Learning and Genetic Algorithm for Supporting Negotiation With Incomplete Information , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[8]  Julinda Stefa,et al.  Routing in Outer Space: Fair Traffic Load in Multihop Wireless Networks , 2009, IEEE Trans. Computers.

[9]  Levente Buttyán,et al.  Stimulating Cooperation in Self-Organizing Mobile Ad Hoc Networks , 2003, Mob. Networks Appl..

[10]  Jean-Yves Le Boudec,et al.  Performance analysis of the CONFIDANT protocol , 2002, MobiHoc '02.

[11]  Nicholas R. Jennings,et al.  Negotiation decision functions for autonomous agents , 1998, Robotics Auton. Syst..

[12]  Samir Ranjan Das,et al.  Exploiting path diversity in the link layer in wireless ad hoc networks , 2008, Ad Hoc Networks.

[13]  Y. Narahari,et al.  Auction-Based Mechanisms for Electronic Procurement , 2007, IEEE Transactions on Automation Science and Engineering.

[14]  F W Cope Derivation of the Weber-Fechner law and the Loewenstein equation as the steady-state response of an Elovich solid state biological system. , 1976, Bulletin of mathematical biology.

[15]  Michael N. Huhns,et al.  An Extended Protocol for Multiple-Issue Concurrent Negotiation , 2005, AAAI.

[16]  Jean-Yves Le Boudec,et al.  Performance analysis of the CONFIDANT protocol , 2002, MobiHoc '02.

[17]  Samir Ranjan Das,et al.  Exploiting path diversity in the link layer in wireless ad hoc networks , 2005, Sixth IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks.

[18]  Sheng Zhong,et al.  Sprite: a simple, cheat-proof, credit-based system for mobile ad-hoc networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[19]  Stanislas Dehaene,et al.  The neural basis of the Weber–Fechner law: a logarithmic mental number line , 2003, Trends in Cognitive Sciences.