Mahdi Mousavi

Game-based multi-hop broadcast including power control and MRC in wireless networks

A wireless Ad Hoc network consisting of a source and multiple receiving nodes is considered. The source wants to transmit a common message throughout the whole network. The message has to be spread in a multi-hop fashion, as the transmit powers at the source and the nodes are limited. The goal of this paper is to find the multi-hop broadcast tree with a minimum energy consumption in the network. To reach this goal, a new decentralized game theoretic approach is proposed which considers the following two aspects jointly for the first time: Firstly, it optimizes the transmit powers at the source and at the individual intermediate nodes. Secondly, it employs maximum ratio combining at the receiving nodes following the fact that a node can receive several copies of the message from different sources in different time slots. The game is modeled such that the nodes are incentivized to forward the message to their neighbors. In terms of the total transmit energy, the results show that the proposed algorithm outperforms other conventional algorithms.

Energy-efficient data dissemination in Ad Hoc networks: Mechanism design with potential game

In this paper, a mechanism is designed based on game theory which aims at minimizing the transmit power in a multi-hop wireless broadcast network. There are multiple nodes in a network and among them, there is a source node which has a common message for all other nodes. For the sake of energy efficiency, the sources message should be forwarded to all nodes by a collaboration between different nodes in a multi-hop manner. Minimizing the total transmit power in the network is the goal of this paper. To this end, the nodes in the network are modeled as rational players and a mechanism is designed based on a potential game model. In this game, the action set of each node changes during the game based on the action of other players. Besides, it is proposed to exploit the weakly dominant strategy at the nodes such that the nodes change their actions even if a new action with the same cost exists. Simulation results show that the proposed decentralized mechanism significantly outperforms other conventional decentralized algorithms. Moreover, when the network is not dense, our algorithm can outperform centralized algorithms on average.

Of Strategies and Structures: Motif-Based Fingerprinting Analysis of Online Reputation Networks

Reputation networks are an important building block of distributed systems whenever reliability of nodes is an issue. However, reputation ratings can easily be undercut: colluding nodes can spread good ratings for each other while third parties are hardly able to detect the fraud. There is strong analytical evidence that reputation networks cannot be constructed in a way to guarantee security. Consequently, only statistical approaches are promising. This work pursues a statistical approach inspired by the idea that colluding nodes behavior changes the local structure of a reputation network. To measure these structural changes, we extend a graph analysis method originating from molecular biology and combine it with a machine learning approach to analyze fingerprints of nodes interactions. We evaluate our method using an adaptive Peer-to-Peer (P2P) streaming system and show that a correct classification of up to 98% is possible.

Multi-hop data dissemination with selfish nodes: Optimal decision and fair cost allocation based on the Shapley value

We consider a data dissemination scenario in a wireless network with selfish nodes. A message available at a source node has to be disseminated through the network in a multi-hop manner. In order to incentivize a node to forward the sources message to others, a forwarding cost is paid to a forwarder by its respective receiver. In the case of multicast transmission, the cost is shared among the receivers using the Shapley value (SV). Moreover, a node may exploit the maximal ratio combining (MRC) technique to receive the message from multiple transmitting nodes. In this paper, we show that in a game theoretic framework, the optimal decision of a node for receiving the message with minimum cost can be achieved by solving a linear optimization problem. In addition, we propose an algorithm by which truthfulness is a dominant strategy for the nodes and thus, fair cost allocation is guaranteed. Simulation results show that our proposed algorithm shares the cost of data dissemination among the nodes of a network in a fair manner. Compared to previous algorithms, the proposed algorithm can reduce the total cost paid by the nodes in the network for receiving messages.

Towards a framework for cross layer incentive mechanisms for multihop video dissemination

For transmitting data in scenarios showing a high user density, infrastructure based and multihop Ad hoc communication can be combined to benefit from the reliability of a stable backbone network and the increased coverage of multihop communication. Such scenarios have been investigated from a cross layer perspective in the recent years mainly focusing on pure performance optimization. However, the question of providing incentives to nodes to forward data has largely been ignored in the cross layer domain, even though providing incentives is vital for the network: each node represents a user comparing his or her satisfaction and the cost to decide on his or her participation. A likely reason for the gap in cross layer incentive research is the necessity to model users as well as the network in order to express a users utility, which requires knowledge in both fields. In order to foster future research in the area of cross layer incentive schemes, this work proposes a general cross layer simulation model combining user and network models. Moreover, an instantiation of the simulation model for the use case of live video broadcasting is presented.