216 research outputs found
Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications
We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches
Coalition Formation Games for Collaborative Spectrum Sensing
Collaborative Spectrum Sensing (CSS) between secondary users (SUs) in
cognitive networks exhibits an inherent tradeoff between minimizing the
probability of missing the detection of the primary user (PU) and maintaining a
reasonable false alarm probability (e.g., for maintaining a good spectrum
utilization). In this paper, we study the impact of this tradeoff on the
network structure and the cooperative incentives of the SUs that seek to
cooperate for improving their detection performance. We model the CSS problem
as a non-transferable coalitional game, and we propose distributed algorithms
for coalition formation. First, we construct a distributed coalition formation
(CF) algorithm that allows the SUs to self-organize into disjoint coalitions
while accounting for the CSS tradeoff. Then, the CF algorithm is complemented
with a coalitional voting game for enabling distributed coalition formation
with detection probability guarantees (CF-PD) when required by the PU. The
CF-PD algorithm allows the SUs to form minimal winning coalitions (MWCs), i.e.,
coalitions that achieve the target detection probability with minimal costs.
For both algorithms, we study and prove various properties pertaining to
network structure, adaptation to mobility and stability. Simulation results
show that CF reduces the average probability of miss per SU up to 88.45%
relative to the non-cooperative case, while maintaining a desired false alarm.
For CF-PD, the results show that up to 87.25% of the SUs achieve the required
detection probability through MWCComment: IEEE Transactions on Vehicular Technology, to appea
Cooperative Game Theory and Its Application in Localization Algorithms
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Chapter Cooperative Game Theory and Its Application in Localization Algorithms
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