We investigate a scenario where multiple sources independently and voluntarily contribute status reports, which are then aggregated through a federated process. To address the challenge of partial participation in distributed systems, we introduce the age of federated information (AoFI), a novel metric that quantifies data freshness. This metric is specifically designed to bridge the gap between classical age of information, which is unsuitable for collaborative tasks, and the often impractical age of correlated information, which requires full participation. To model distributed optimization across multiple independent sources, we adopt a game-theoretic framework. In this framework, users strategically minimize their individual penalty, computed as a global-local combination of the overall AoFI on the common receiver’s side and their individual energy expenditure. We derive the worst-case Nash equilibrium of this game and compare its efficiency with the centralized optimization optimum. Our efficiency analysis reveals a critical design tradeoff for practical industrial internet of things (IIoT) deployments: while decentralized coordination is highly efficient in high-participation regimes, performance in low-participation regimes is paradoxically optimized by actively restricting the number of sources to prevent strategic inefficiencies.

Internet of things (IoT) applications require up-todate information about the system conditions. This can often be provided from multiple alternative sources that sense the environment, but act without centralized coordination. In this paper, we consider a scenario where multiple sources can provide information for a number of tasks of the IoT application, assuming that the information content of multiple sources is generally redundant, yet one single source is generally insufficient for all tasks. In so doing, we seek for the minimization of the age of federated information (AoFI), a metric describing the age of information from multiple sources, considering that the epochs of successful updates are only those where all tasks are covered. At the same time, we would like to contain the number of active sources for cost reasons. To this end, we tackle the problem through a game-theoretic approach, where individual sources act as players minimizing a linear combination of AoFI and activation cost. We prove that this framework identifies efficient Nash equilibria very close to the optimum performance. However, the latter can only be achieved through centralized control, whereas the former allows for distributed implementation, which is key in IoT scenarios.

In the era of smart cities and Industry 4.0, Digital Twin (DT) technologies have emerged as transformative tools for optimizing urban and industrial systems. We explore the application of Green DTs (GDTs) in the context of ThirdParty Logistics (3PL) to enhance sustainability and operational efficiency. By integrating real-time data with predictive analytics, GDTs enable the optimization of delivery networks, minimizing resource consumption and carbon emissions, while addressing challenges such as traffic congestion and reverse logistics. We investigate a 3PL scenario, involving a largescale delivery network, focusing on the misalignment between environmental goals of the central operator and the profitdriven strategies of third party providers. We employ a gametheoretic approach to evaluate inefficiencies through the Price of Anarchy (PoA) and the Price of Stability (PoS). The results demonstrate the potential of GDTs to dynamically model agent behavior, optimize route planning, and enhance collaboration in decentralized supply chain networks to reduce emissions.

Mobile computing faces challenges due to the resource constraints of mobile devices, such as limited computing power, energy, and connectivity. These limitations hinder the use of high-complexity classifiers and wireless transmissions. To address this issue, we propose a novel collaboration paradigm between mobile devices and edge servers, where the edge server assists the mobile devices by dynamically retraining a low-complexity classifier to adapt to temporal changes in data distribution. We propose a novel approach called drift control protocol (DCP) which is inspired by TCP congestion control mechanism. DCP aims to strike a balance between low-complexity classifier retraining frequency and communication costs with the edge server. It adjusts the update rate of the classifier on the mobile device based on distribution drift characteristics and controls the number of input samples sent to the edge server to improve accuracy. We evaluate and study different versions of DCP using synthetic and real datasets We demonstrate that DCP keeps the error bound, while reducing the burden of the communication cost by 90% for the mobile nodes, which makes our proposal suitable for online domain adaptation.

The Metaverse is an immersive online world, accessed through headsets, seamlessly integrating virtual and augmented reality. Users navigate this digital realm through avatars, participating in real-time activities such as work, meetings, concerts. The real-time nature of the Metaverse prompts an analysis using age of information, a metric that tracks information freshness. In this environment, where users actively seek continuous stimuli, sustaining high attention is vital. We propose a game-theoretic analysis of user-server interactions for enduring cooperation, where we incorporate a discount factor to quantitatively compare present and future actions. We derive closed-form solutions for the infinite horizon game and obtain lower bounds for the discount factor chosen by the entities and upper bounds for the communication cost sustainable in order to achieve long-lasting cooperation. This enriches our understanding of temporal dynamics in ensuring information freshness, providing insight into the dynamic interplay between users and the Metaverse environment.

We consider an Internet of thing scenario, where a set of sensors collect data and exchange them with a common receiver. We analyze their interaction, considering a shared goal to minimize Age of information at the receiver's side. We argue that a fully collaborative setup, albeit generally succeeding in this task at first, often leads to resource wastage in the long run. We try to achieve a similar level of cooperation through a purely opportunistic mechanism, in which nodes are driven by selfish objectives, but still aware of the ultimate goal of maximizing information freshness. We show how our proposed approach, allowing fewer nodes to participate in the task (up to one order of magnitude), results in a better resource management, still improving the long-term average age of information. At the same time, a target number of participating nodes can be set, e.g., to a given fraction of the network, by properly tuning the individual objectives and the communication costs.

We consider a wireless jamming game played by a receiver and a malicious interferer that wants to disrupt communication. The outcome of the game depends on the characteristics of the wireless medium with distance-dependent path loss. The players can leverage this by changing their physical location. We consider both a static scenario where a position is kept forever by the players, and a dynamic one where players can change it over subsequent steps. We also include an optional feature for the receiver to catch the jammer when it is too close, which nullifies its jamming. The conclusion is that, in a mobile scenario, losing players see an improvement of their payoffs (or better, they cut their losses). As a result, we characterize the Price of Mobility, i.e., the benefit obtained thanks to the ability of changing position.