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We consider a node where packets of fixed size (inbits) are generated at arbitrary intervals. The node is required to maintain the peak age of information (AoI) at the monitor below a threshold by transmitting potentially a subset of the generated packets. At any time, depending on the packet availability and the current AoI, the node can choose which packet to transmit, and at what transmission speed (in bits per second). Power consumption is a monotonically increasing convex function of the transmission speed.

It has become increasingly common nowadays to collect observations of feature and response pairs from different environments. As a consequence, one has to apply learned predictors to data with a different distribution due to distribution shifts. One principled approach is to adopt the structural causal models to describe training and test models, following the invariance principle which says that the conditional distribution of the response given its predictors remains the same across environments.

Intelligent real-time applications, such as video surveillance, demand intensive computation to extract status information from raw sensing data. This poses a substantial challenge in orchestrating computation and communication resources to provide fresh status information. In this paper, we consider a scenario where multiple energy-constrained devices served by an edge server. To extract status information, each device can either do the computation locally or offload it to the edge server.

We study a system in which two-state Markov sources send status updates to a common receiver over a slotted ALOHA random access channel. We characterize the performance of the system in terms of state estimation entropy (SEE), which measures the uncertainty at the receiver about the sources’ state. Two channel access strategies are considered: a reactive policy that depends on the source behaviour and a random one that is independent of it.

In this paper, we study a multi-agent game between $N$ agents, which solve a consensus problem, and receive state information through a wireless network, that is controlled by a Base station (BS). Due to a hard-bandwidth constraint, the BS can concurrently connect at most $R_{d} < N$ agents over the network. This causes an intermittency in the agents’ state information, necessitating state estimation based on each agent’s information history. Under standard assumptions on the information structure, we separate each agent’s estimation and control problems.

In this paper, we analyze status update systems modeled through the Stochastic Hybrid Systems (SHSs) tool. Contrary to previous works, we allow the system’s transition dynamics to be polynomial functions of the Age of Information (AoI). This dependence allows us to encapsulate many applications and opens the door for more sophisticated systems to be studied. However, this same dependence on the AoI engenders technical and analytical difficulties that we address in this paper.

A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing (e.g., edge computing), and artificial intelligence (AI) technologies to enable many connected intelligence services. In order to handle the large amounts of network data based on digital twins (DTs), wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints by utilizing AI techniques such as causal reasoning.

In the past decade, the emergence of beyond fifth generation (B5G) wireless networks has necessitated the timely updating of system states in Internet of Things (IoT) and cyber-physical systems, where Age of Information (AoI) has been a well-concentrated metric. However, the content-agnostic nature of AoI reflects its limitation of characterizing the significance of status update messages, which induces various variants for AoI including Age of Incorrect Information (AoII).

We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series. By leveraging a recently developed stochastic monotone Variational Inequality (VI) formulation, we cast the causal discovery problem as a general convex optimization. Furthermore, we develop a non-asymptotic recovery guarantee and quantifiable uncertainty by solving a linear program to establish confidence intervals for a wide range of non-linear monotone link functions.

In this paper we examine the general problem of determining when to update information that can go out-of-date. Not updating frequently enough results in poor decision making based on stale information. Updating too often results in excessive update costs. We study the tradeoff between having stale information and the cost of updating that information. We use a general model, some versions of which match an idealized version of the Age of Information (AoI) model. We first present the assumptions, and a novel methodology for solving problems of this sort.