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Maice Costa

Publications and source records attributed to Maice Costa.

6 recordsLinked to original sources

SAOITHE: Sustainable Age-of-Information-Based Timely Status Updating for Hardware-constrained Edge networks

In future large-scale deployments of 6G and beyond networks, collecting timely information, as measured by the Age of Information (AoI) metric, is becoming increasingly important. At the same time, the environmental impact, often characterized by the resulting Carbon Footprint (CF), depends on both the amount of consumed energy and the Carbon Intensity (CI), i.e., the amount of CO$_2$-equivalent emissions produced per unit of consumed energy. Since CI varies over time, minimizing energy is not equivalent to minimizing CF, as a status update with the same energy demand may result in a different carbon cost depending on when it is transmitted. This makes timely status updating a nontrivial scheduling problem. To address this challenge, we formulate carbon-aware status updating as a constrained Markov Decision Process (MDP) that minimizes a quadratic AoI penalty subject to CF budget, transmission duty-cycle, and channel-capacity constraints. We then propose Sustainable Age-of-Information-Based Timely Status Updating for Hardware-constrained Edge networks (SAOITHE), a Whittle-index-based scheduling solution that enables scalable real-time scheduling. Using real-world CI traces across low-, medium-, and high-CI regions, the results show that SAOITHE remains within the allocated CF budget while achieving lower AoI than baseline policies. Moreover, the gains are around 25% and 20% in low- and medium-CI regions, respectively, and up to 75% in high-CI settings, while preserving scalability.

cs.NI

Towards a Sustainable Age of Information Metric: Carbon Footprint of Real-Time Status Updates

The timeliness of collected information is essential for monitoring and control in data-driven intelligent infrastructures. It is typically quantified using the Age of Information (AoI) metric, which has been widely adopted to capture the freshness of information received in the form of status updates. While AoI-based metrics quantify how timely the collected information is, they largely overlook the environmental impact associated with frequent transmissions, specifically, the resulting Carbon Footprint (CF). To address this gap, we introduce a carbon-aware AoI framework. We first derive closed-form expressions for the average AoI under constrained CF budgets for the baseline $M/M/1$ and $M/M/1^*$ queuing models, assuming fixed Carbon Intensity (CI). We then extend the analysis by treating CI as a dynamic, time-varying parameter and solve the AoI minimization problem. Our results show that minimizing AoI does not inherently minimize CF, highlighting a clear trade-off between information freshness and environmental impact. CI variability further affects achievable AoI, indicating that sustainable operation requires joint optimization of CF budgets, Signal-to-noise Ratio (SNR), and transmission scheduling. This work lays the foundation for carbon-aware information freshness optimization in next-generation networks.

cs.IT

Timely NextG Communications with Decoy Assistance against Deep Learning-based Jamming

We consider the transfer of time-sensitive information in next-generation (NextG) communication systems in the presence of a deep learning based eavesdropper capable of jamming detected transmissions, subject to an average power budget. A decoy-based anti-jamming strategy is presented to confuse a jammer, causing it to waste power when disrupting decoy messages instead of real messages. We investigate the effectiveness of the anti-jamming strategy to guarantee timeliness of NextG communications in addition to reliability objectives, analyzing the Age of Information subject to jamming and channel effects. We assess the effect of power control, which determines the success of a transmission but also affects the accuracy of the adversary's detection, making it more likely for the jammer to successfully identify and jam the communication. The results demonstrate the feasibility of mitigating eavesdropping and jamming attacks in NextG communications with information freshness objectives using a decoy to guarantee timely information transfer.

cs.IT

Timeliness in NextG Spectrum Sharing under Jamming Attacks with Deep Learning

We consider the communication of time-sensitive information in NextG spectrum sharing where a deep learning-based classifier is used to identify transmission attempts. While the transmitter seeks for opportunities to use the spectrum without causing interference to an incumbent user, an adversary uses another deep learning classifier to detect and jam the signals, subject to an average power budget. We consider timeliness objectives of NextG communications and study the Age of Information (AoI) under different scenarios of spectrum sharing and jamming, analyzing the effect of transmit control, transmit probability, and channel utilization subject to wireless channel and jamming effects. The resulting signal-to-noise-plus-interference (SINR) determines the success of spectrum sharing, but also affects the accuracy of the adversary's detection, making it more likely for the jammer to successfully identify and jam the communication. Our results illustrate the benefits of spectrum sharing for anti-jamming by exemplifying how a limited-power adversary is motivated to decrease its jamming power as the channel occupancy rises in NextG spectrum sharing with timeliness objectives.

cs.IT

Robust Improvement of the Age of Information by Adaptive Packet Coding

We consider a wireless communication network with an adaptive scheme to select the number of packets to be admitted and encoded for each transmission, and characterize the information timeliness. For a network of erasure channels and discrete time, we provide closed form expressions for the Average and Peak Age of Information (AoI) as functions of admission control and adaptive coding parameters, the feedback delay, and the maximum feasible end-to-end rate that depends on channel conditions and network topology. These new results guide the system design for robust improvements of the AoI when transmitting time sensitive information in the presence of topology and channel changes. We illustrate the benefits of using adaptive packet coding to improve information timeliness by characterizing the network performance with respect to the AoI along with its relationship to throughput (rate of successfully decoded packets at the destination) and per-packet delay. We show that significant AoI performance gains can be obtained in comparison to the uncoded case, and that these gains are robust to network variations as channel conditions and network topology change.

cs.IT

On The Age Of Information In Status Update Systems With Packet Management

We consider a communication system in which status updates arrive at a source node, and should be transmitted through a network to the intended destination node. The status updates are samples of a random process under observation, transmitted as packets, which also contain the time stamp to identify when the sample was generated. The age of the information available to the destination node is the time elapsed since the last received update was generated. In this paper, we model the source-destination link using queuing theory, and we assume that the time it takes to successfully transmit a packet to the destination is an exponentially distributed service time. We analyze the age of information in the case that the source node has the capability to manage the arriving samples, possibly discarding packets in order to avoid wasting network resources with the transmission of stale information. In addition to characterizing the average age, we propose a new metric, called peak age, which provides information about the maximum value of the age, achieved immediately before receiving an update.

cs.IT