1,720,973 research outputs found
On the Anarchy of Multiple False Data Injectors for Age of Incorrect Information in Sensor Networks
No full textSensor networks, especially when deployed in a field with little supervision, are vulnerable to a broad range of attacks. In this paper, we study a scenario where multiple competitive adversaries inject false content in the sensed data with the intent of impairing network control. We use game theory to analyze the different behavior of adversaries acting independently or in a coordinated fashion. This analysis ultimately results in the evaluation of efficiency metrics for the utility of uncoordinated attackers, based on the Age of Incorrect Information (AoII), which is compared to the coordinated case. Our numerical results show that generally the lack of coordination is detrimental for the two attackers. With the exception of few edge cases, competition leads the attackers to be more concerned with prevailing over each other than actually compromising the system
Distributed and Timely Smart Microgrid Management Through Markov Games
No full textEnergy delivery within smart microgrids often requires prompt reaction to the system state. In the presence of multiple energy sources, inefficiency may arise due to their lack of coordination. In this paper, we frame the task of efficient energy management as a dynamic program, and we further expand it to the case of multiple agents. We combine this approach with game theory and we leverage the similarity between Markov games concerning information and energy exchanges in networks. This methodological motivation allows us to identify distributed control techniques for efficient energy delivery. Specifically, we highlight how a naive distributed and selfish control of individual nodes may be inefficient from a game theoretic perspective. Yet, a decentralized strategy that combines energy availability and global network cost as shared objectives can significantly improve the outcome, approaching the performance of a centralized resource allocation still in a distributed manner
Evaluating Coalition Stability in Federated Learning Under Voluntary Client Participation
No full textFederated Forests With Differential Privacy for Distributed Wearable Sensors
No full textTraining machine learning models on wearable sensor data is useful for applications like human activity recognition (HAR), but the sensitivity of such data often precludes centralized data collection. Federated learning offers a decentralized solution, enabling model training across distributed data sources. To further protect privacy, differential privacy (DP) can be integrated into the federated learning process. In this paper, we introduce a novel algorithm for training tree ensemble models (forests) within a federated learning framework under differential privacy constraints. Our approach allows each client to independently train a differentially private decision tree with randomized splits, which is sent to a central server. The server aggregates these trees into an ensemble that classifies data points via majority voting. We evaluate our DP federated forest algorithm on a HAR task, demonstrating that an effective privacy-utility tradeoff can be achieved with an appropriate selection of the privacy budget
A Bayesian Game Framework for a Semi-Supervised Allocation of the Spreading Factors in LoRa Networks
No full textLoRa networks have been gaining ground as a solution for Internet of Things because of their potential ability to handle massive number of devices. One of the most challenging problems of such networks is the need to set the Spreading Factors (SF) used by the terminals as close to a uniform distribution as possible, to guarantee reliable transmission of packets. This can be tackled through stochastic allocations based on centralized strategies, and more recently some contributions proposed fully distributed approaches based on game theory. However, these studies still consider games of complete information, where users have full knowledge on each other payoffs. In reality, it would be more appropriate to extend these approaches to Bayesian games, as we propose to do here. More precisely, we extend the game theoretic formulation to a semi-supervised allocation, where the distributed character of the allocation is retained as the nodes still act independently in choosing their SF, based on what they think it is their best preferred choice. We also utilize the central gateway as a coordinator regulating these proposals and the interaction of the nodes with the coordinator is framed as a Bayesian entry game, where nodes exploit a prior to decide whether to join the proposed allocation or not. Under this framework, nodes reach a satisfactory compromise between the assignment they receive from the network and their desired rate
Spreading Factor Allocation in LoRa Networks through a Game Theoretic Approach
Full text linkLoRa is a low-power wide-area network solution that is recently gaining popularity in the context of the Internet of Things due to its ability to handle massive number of devices. One of the main challenges faced by LoRa implementations is the allocation of Spreading Factors to the devices. While the assignment of these parameters is virtually simple to execute, scalability and complexity issues hint at its implementation through a game theoretic approach. This would offer the advantage of being readily implementable in vast networks of devices with limited hardware capabilities. Hence, we formulate the SF allocation problem as a Bayesian game, of which we compute the Bayesian Nash equilibria. We also implement the procedure in the ns- 3 network simulator and evaluate the resulting performance, showing that our approach is scalable and robust, and also offers room for improvement with respect to existing approaches
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
- …
