DRESS: A Distributed RMS Evaluation Simulation Software

Distributed environments consist of a huge number of entities that cooperate to achieve complex goals. When interactions occur between unknown parties, intelligent techniques for estimating agent reputations are required. Reputation management systems (RMS's) allow agents to perform such estimation in a cooperative way. In particular, distributed RMS's exploit feedbacks provided after each interaction and allow prediction of future behaviors of agents. Such systems, in contrast to centralized RMSs, are sensitive to fake information injected by malicious users; thus, predicting the performance of a distributed RMS is a very challenging task. Although many existing works have addressed some challenges concerning the design and assessment of specific RMS's, there are no simulation environments that adopt a general approach that can be applied to different application scenarios. To overcome this lack, we present DRESS, an agent-based simulation framework that aims to support researchers in the evaluation of distributed RMSs under different security attacks.

Download Full-text

A Simulation Software for the Evaluation of Vulnerabilities in Reputation Management Systems

ACM Transactions on Computer Systems ◽

10.1145/3458510 ◽

2021 ◽

Vol 37 (1-4) ◽

pp. 1-30

Author(s):

Vincenzo Agate ◽

Alessandra De Paola ◽

Giuseppe Lo Re ◽

Marco Morana

Keyword(s):

Quality Of Service ◽

Distributed Systems ◽

State Of The Art ◽

Simulation Software ◽

Reputation Management ◽

Management Systems ◽

Security Attacks ◽

Vulnerability Evaluation ◽

Multi Agent

Multi-agent distributed systems are characterized by autonomous entities that interact with each other to provide, and/or request, different kinds of services. In several contexts, especially when a reward is offered according to the quality of service, individual agents (or coordinated groups) may act in a selfish way. To prevent such behaviours, distributed Reputation Management Systems (RMSs) provide every agent with the capability of computing the reputation of the others according to direct past interactions, as well as indirect opinions reported by their neighbourhood. This last point introduces a weakness on gossiped information that makes RMSs vulnerable to malicious agents’ intent on disseminating false reputation values. Given the variety of application scenarios in which RMSs can be adopted, as well as the multitude of behaviours that agents can implement, designers need RMS evaluation tools that allow them to predict the robustness of the system to security attacks, before its actual deployment. To this aim, we present a simulation software for the vulnerability evaluation of RMSs and illustrate three case studies in which this tool was effectively used to model and assess state-of-the-art RMSs.

Download Full-text

Multi-Criteria, Co-Evolutionary Charging Behavior: An Agent-Based Simulation of Urban Electromobility

World Electric Vehicle Journal ◽

10.3390/wevj12010018 ◽

2021 ◽

Vol 12 (1) ◽

pp. 18

Author(s):

Lennart Adenaw ◽

Markus Lienkamp

Keyword(s):

Urban Areas ◽

City Planning ◽

User Behavior ◽

Transport Sector ◽

Simulation Framework ◽

Agent Based Simulation ◽

Agent Based ◽

Charging Station ◽

Spatio Temporal

In order to electrify the transport sector, scores of charging stations are needed to incentivize people to buy electric vehicles. In urban areas with a high charging demand and little space, decision-makers are in need of planning tools that enable them to efficiently allocate financial and organizational resources to the promotion of electromobility. As with many other city planning tasks, simulations foster successful decision-making. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior. The simulation framework is tested and verified by means of a case study conducted in the city of Munich. The case study shows that the presented approach realistically reproduces charging behavior and spatio-temporal charger utilization.

Download Full-text