K. Avrachenkov

Publications (2)0 Total impact

• Source

  Available from: inria.fr

  Conference Proceeding: A local average consensus algorithm for wireless sensor networks

  K. Avrachenkov, M. El Chamie, G. Neglia
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  ABSTRACT: In many application scenarios sensors need to calculate the average of some local values, e.g. of local measurements. A possible solution is to rely on consensus algorithms. In this case each sensor maintains a local estimate of the global average, and keeps improving it by performing a weighted sum of the estimates of all its neighbors. The number of iterations needed to reach an accurate estimate depends on the weights used at each sensor. Speeding up the convergence rate is important also to reduce the number of messages exchanged among neighbors and then the energetic cost of these algorithms. While it is possible in principle to calculate the optimal weights, the known algorithm requires a single sensor to discover the topology of the whole network and perform the calculations. This may be unfeasible for large and dynamic sensor networks, because of sensor computational constraints and of the communication overhead due to the need to acquire the new topology after each change. In this paper we propose a new average consensus algorithm, where each sensor selects its own weights on the basis of some local information about its neighborhood. Our algorithm is tailored for networks having cluster structure, like it is common for wireless sensor networks. In realistic sensor network topologies, the algorithm shows faster convergence than other existing consensus protocols.
  Distributed Computing in Sensor Systems and Workshops (DCOSS), 2011 International Conference on; 07/2011
• Source

  Available from: Fabio Martignon

  Conference Proceeding: Socially-Aware Network Design Games

  J. Elias, F. Martignon, K. Avrachenkov, G. Neglia
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  ABSTRACT: In many scenarios network design is not enforced by a central authority, but arises from the interactions of several self-interested agents. This is the case of the Internet, where connectivity is due to Autonomous Systems' choices, but also of overlay networks, where each user client can decide the set of connections to establish. Recent works have used game theory, and in particular the concept of Nash Equilibrium, to characterize stable networks created by a set of selfish agents. The majority of these works assume that users are completely non-cooperative, leading, in most cases, to inefficient equilibria. To improve efficiency, in this paper we propose two novel socially-aware network design games. In the first game we incorporate a socially-aware component in the users' utility functions, while in the second game we use additionally a Stackelberg (leader-follower) approach, where a leader (e.g., the network administrator) architects the desired network buying an appropriate subset of network's links, driving in this way the users to overall efficient Nash equilibria. We provide bounds on the Price of Anarchy and other efficiency measures, and study the performance of the proposed schemes in several network scenarios, including realistic topologies where players build an overlay on top of real Internet Service Provider networks. Numerical results demonstrate that (1) introducing some incentives to make users more sociallyaware is an effective solution to achieve stable and efficient networks in a distributed way, and (2) the proposed Stackelberg approach permits to achieve dramatic performance improvements, designing almost always the socially optimal network.
  INFOCOM, 2010 Proceedings IEEE; 04/2010