Decentralized optimization for broadcasting in Ad hoc networks

Decentralized optimization for broadcasting in Ad hoc networks

Ad hoc networks attracted extensive attention during the past decade from both, research and industry, due to their benefits. The users in an ad hoc network, unlike traditional communication networks, can communicate with each other without need of an infrastructure. Ad hoc networks not only can speed up data distribution among the nodes, but also can help mobile service providers to increase the quality of service for mobile-users, when the network experiences heavy load. One of the main applications of ad hoc networks is data broadcasting, such as file sharing or video streaming. Optimization of network performance is a major challenge in such cases. There are two main approaches for network optimization called centralized and decentralized approaches, which the latter is preferred since the nodes can organize the network without using of an external unit.

In our research we mostly use decentralized algorithms for optimization in ad hoc networks. In ad hoc networks, multi-hop relaying technique plays an important role in message dissemination. In multi-hop relaying, a message is transmitted from a source to a destination by the help of some intermediate relays. In this case, collaboration among the nodes is of great importance and has a big impact on the network performance.

Full collaboration among the users is not always possible since the users may have conflicting interests or different requirements in a network. Game theory is a powerful tool for both, modeling the behavior of the users in such networks and designing efficient mechanism for network performance optimization. A big part of our research is using game theoretical tools, or in a more general view, using decentralized algorithms to study the problems related ad hoc networks.

Some of the problems that we investigate are:

  • Minimizing the required transmit power for multi-hop broadcast.
  • cross-layer design for video streaming applications.
  • Incentive mechanisms at nodes for message forwarding in multi-hop communications.
  • Fairness among the users in terms of contribution and collaborations.
  • Increasing the spectral efficiency considering energy, time and bandwidth utilization.