Robust fully distributed file caching for delay-tolerant networks: A reward-based incentive mechanism. (April 2017)

• Record Type: Journal Article
• Authors: Ezzahidi, Sidi Ahmed; Sabir, Essaid; Koulali, Sara; Bouyakhf, El-Houssine
• Is Part Of: International journal of distributed sensor networks. Volume 13:Number 4(2017)
• Publication Date: 2017-04
• Subjects: Delay-tolerant network -- content caching -- energy efficiency -- reward-penalty incentive mechanism -- Nash equilibrium -- distributed learning; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147717700149
• Languages: English
• ISSNs: 1550-1329

Abstract

This article exhibits a reward-based incentive mechanism for file caching in delay-tolerant networks. In delay-tolerant networks, nodes use relay's store-carry-forward paradigm to reach the final destination. Thereby, relay nodes may store data in their buffer and carry it till an appropriate contact opportunity with destination arises. However, the relays are not always willing to assist data forwarding due to a limited energy or a low storage capacity. Our proposal suggests a reward mechanism to uphold and to sustain cooperation among relay nodes. We model this distributed network interaction as a non-cooperative game. Namely, the source node offers to the relay nodes a positive reward if they accept to cache and to forward a given file successfully to a target destination, whereas the relay nodes may either accept or reject the source deal, depending on the reward attractiveness and on their battery status (their actual energy level). Next, full characterizations of both pure and mixed Nash equilibria are provided. Then, we propose three fully distributed algorithms to ensure convergence to the Nash equilibria (for both pure equilibrium and mixed equilibrium). Finally, we validate our proposal through extensive numerical examples and many learning simulations and draw some conclusions and insightful remarks.