In today's online environment, platforms like Facebook and Amazon rely heavily on trust and reputation management systems to ensure their integrity and security. This paper improves the EigenTrust algorithm, a not...
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ISBN:
(纸本)9783031708183;9783031708190
In today's online environment, platforms like Facebook and Amazon rely heavily on trust and reputation management systems to ensure their integrity and security. This paper improves the EigenTrust algorithm, a notable model in trust calculation, by incorporating methods from the distributedbellmanfordalgorithm and adaptive weighting to evaluate the trustworthiness of nodes in social networks. Our approach considers both direct and indirect network connections, incorporating feedback credibility. Through extensive experimental analysis, we demonstrate that our modified EigenTrust algorithm (L-level) excels in trust-based P2P systems, significantly outperforming traditional EigenTrust. It effectively reduces unauthentic downloads and maintains high success rates in environments with many malicious collectives, demonstrating robust scalability and reliability as the network expands. This research provides new insights into reputation systems in online environments and suggests directions for future progress in trust management with social networks.
We present a scalable distributed path planning algorithm for transporting a large object through an unknown environment using a group of homogeneous robots. The robots are randomly scattered across the terrain and co...
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ISBN:
(数字)9784431558798
ISBN:
(纸本)9784431558798;9784431558774
We present a scalable distributed path planning algorithm for transporting a large object through an unknown environment using a group of homogeneous robots. The robots are randomly scattered across the terrain and collectively sample the obstacles in the environment in a distributed fashion. Given this sampling and the dimensions of the bounding box of the object, the robots construct a distributed configuration space. We then use a variant of the distributed bellman-ford algorithm to construct a shortest-path tree using a custom cost function from the goal location to all other connected robots. The cost function encompasses the work required to rotate and translate the object in addition to an extra control penalty to navigate close to obstacles. Our approach sets up a framework that allows the user to balance the trade-off between the safety of the path and the mechanical work required to move the object. The path is optimal given the sampling of the robots and user input parameters. We implemented our algorithm in both simulated and real-world environments. Our approach is robust to the size and shape of the object and adapts to dynamic environments.
We consider the distributed shortest path problem in an undirected graph where the edge weights are random variables with unknown distributions. The objective is to design a distributed online learning algorithm to fi...
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ISBN:
(纸本)9781479903566
We consider the distributed shortest path problem in an undirected graph where the edge weights are random variables with unknown distributions. The objective is to design a distributed online learning algorithm to find the shortest path from a source node to a destination node where each node only knows its neighbors but not the entire network topology. The performance of the learning algorithms is measured by regret defined as the additional cost incurred over a time horizon of length T when compared to a centralized shortest path algorithm carried out under known edge weight distributions. We propose a distributed learning algorithm that achieves a regret logarithmic with the number of packets and polynomial with the network size. The same order with time and network size holds for the message complexity of the proposed algorithm. This result finds applications in cognitive radio and ad hoc networks under unknown and dynamic communication environments.
We consider the distributed shortest path problem in an undirected graph where the edge weights are random variables with unknown distributions. The objective is to design a distributed online learning algorithm to fi...
详细信息
ISBN:
(纸本)9781479903573
We consider the distributed shortest path problem in an undirected graph where the edge weights are random variables with unknown distributions. The objective is to design a distributed online learning algorithm to find the shortest path from a source node to a destination node where each node only knows its neighbors but not the entire network topology. The performance of the learning algorithms is measured by regret defined as the additional cost incurred over a time horizon of length T when compared to a centralized shortest path algorithm carried out under known edge weight distributions. We propose a distributed learning algorithm that achieves a regret logarithmic with the number of packets and polynomial with the network size. The same order with time and network size holds for the message complexity of the proposed algorithm. This result finds applications in cognitive radio and ad hoc networks under unknown and dynamic communication environments.
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