Data networks
Optimization flow control—I: basic algorithm and convergence
IEEE/ACM Transactions on Networking (TON)
Congestion-dependent pricing of network services
IEEE/ACM Transactions on Networking (TON)
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
Stimulating cooperation in self-organizing mobile ad hoc networks
Mobile Networks and Applications
Dynamic power allocation and routing for satellite and wireless networks with time varying channels
Dynamic power allocation and routing for satellite and wireless networks with time varying channels
Efficient market mechanisms and simulation-based learning for multi-agent systems
Efficient market mechanisms and simulation-based learning for multi-agent systems
Simplification of network dynamics in large systems
IEEE/ACM Transactions on Networking (TON)
Cooperation in wireless ad hoc networks: a market-based approach
IEEE/ACM Transactions on Networking (TON)
Auction-based spectrum sharing
Mobile Networks and Applications
Resource allocation and cross-layer control in wireless networks
Foundations and Trends® in Networking
IEEE Transactions on Mobile Computing
Energy optimal control for time-varying wireless networks
IEEE Transactions on Information Theory
Dynamic power allocation and routing for time-varying wireless networks
IEEE Journal on Selected Areas in Communications
Wireless channel allocation using an auction algorithm
IEEE Journal on Selected Areas in Communications
Pricing congestible network resources
IEEE Journal on Selected Areas in Communications
IEEE/ACM Transactions on Networking (TON)
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We consider an ad-hoc wireless network operating within a free market economic model. Users send data over a choice of paths, and scheduling and routing decisions are updated dynamically based on time varying channel conditions, user mobility, and current network prices charged by intermediate nodes. Each node sets its own price for relaying services, with the goal of earning revenue that exceeds its time average reception and transmission expenses. We first develop a greedy pricing strategy that maximizes social welfare while ensuring all participants make non-negative profit. We then construct a (non-greedy) policy that balances profits more evenly by optimizing a profit fairness metric. Both algorithms operate in a distributed manner and do not require knowledge of traffic rates or channel statistics. This work demonstrates that individuals can benefit from carrying wireless devices even if they are not interested in their own personal communication.