Identity Deception and Game Deterrence via Signaling Games
William Casey (Carnegie Mellon University), Parisa Memarmoshrefi (University of Goettingen), Ansgar Kellner (University of Goettingen), Jose Andre Morales (Carnegie Mellon University), Bud Mishra (New York University)
Maintenance and verification of persistent identities is an important problem in the area of networking. Particularly, their critical roles in Wireless Ad-hoc networks (WANETs) have become even more prominent as they begin to be deployed in several application domains. In these contexts, Sybil attacks, making use of replicated deceptive identities, represent a major challenge for the designers of these networks. Inspired by biological models of ant colonies and their dynamics studied via information asymmetric signaling games, we propose an architecture that can withstand Sybil attacks, similar to ants, using complex chemical signaling systems and associated physical actions, naturally `authenticate' colony members. Here, we present a biomimetic authentication protocol with mechanisms similar to the physical processes of chemical di usion, and formalize approaches to tame the deceptive use of identities; we dub the resulting game an\identity management signaling game". To consider network system of nodes, pursuing non-cooperative and deceptive strategies, we develop an evolutionary game system allowing cooperative nodes to mutate deceptive strategies. We empirically study the dynamics using simulation experiments to select the parameters which a ect the overall behaviors. Through experimentation we consider how an in- centive package in the form of a shared database can impact system behavior.
Fast Redistribution of a Swarm of Heterogeneous Robots
Amanda Prorok (University of Pennsylvania), M. Ani Hsieh (Drexel University), Vijay Kumar (University of Pennsylvania)
We present a method that distributes a swarm of heterogeneous robots among a set of tasks that require specialized capabilities in order to be completed. We model the system of heterogeneous robots as a community of species, where each species (robot type) is defined by the traits (capabilities) that it owns. Our method is based on a continuous abstraction of the swarm at a macroscopic level, as we model robots switching between tasks. We formulate an optimization problem that produces an optimal set of transition rates for each species, so that the desired trait distribution among the tasks is reached as quickly as possible. Our solution is based on an analytical gradient, and is computationally efficient, even for large choices of traits and species. Finally, we show that our method is capable of producing fast convergence times when compared to state-of-the-art methods.
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Biologically-inspired adaptive routing protocol with stochastic route exploration
Tomohiro Nakao (Osaka University), Jun-nosuke Teramae (Osaka University), Naoki Wakamiya (Osaka University)
Rapid increase in amount of traffic and the number of users of information communication networks requires adaptive routing protocols that can properly respond to unexpected change of communication environment such as rapid and large fluctuation of traffic. While distributed routing protocols that use only local state of the network have been expected to suitable for adaptive routing on large scale network, the lack of global information of the network often makes it difficult to promptly respond to traffic changes of the network when it occurs at out of the local scope. In this paper, based on the biologically-inspired attractor selection model, we propose a distributed routing protocol with active and stochastic route exploration. Acquiring current state of the network beyond its local scope by utilizing stochastic nature of the protocol, the routing protocol can efficiently respond to rapid change of traffic demand on the network. In order to avoid destabilization of routings due to the exploration, we introduce a short-term memory term to the governing equation of the protocol. We also confirm that the protocol successfully balances rapid exploration with stable routing owing to the memory term by numerical simulations.