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treaming codes take a string of source symbols as input and output a string of coded symbols in real time, which eliminate the queueing delay of traditional block codes and are thus especially appealing for delay sensitive applications. This work studies the asymptotics of random linear streaming codes (RLSCs) in the large finite-field-size regime under the i.i.d. symbol erasure channel models. Two important scenarios are analyzed: (i) tradeoff between decoding deadline Δ and probability of error p_e assuming infinite memory α=∞ ; and (ii) tradeoff between α and pe assuming infinite Δ=∞ . For each scenario, this work derives the corresponding asymptotic constant ρ , power β and decay rate η that satisfy p_e(x)∼ρ*x^βe^(−ηx) . The results of (i) and (ii) are then used to study an important code design problem: Under a given target deadline Δ , what is the memory length α needed for the error probability p_e to be within a factor of c>1 of the best possible p_e^* over α . Further analysis also suggests that regardless the c value being considered, the necessary memory length is approximately 3–7% of the target deadline Δ when Δ is large, the actual percentage depending on the channel model and the coding rate. Such a prediction is consistent with existing brute-force-based evaluations. 

Mobile Robots (MRs), typically equipped with single-antenna radios, face many challenges in maintaining reliable connectivity established by multiple wireless access points (APs). These challenges include the absence of direct line-of-sight (LoS), ineffective beam searching due to the time-varying channel, and interference constraints. This paper presents REMARKABLE, an online learning based adaptive beam selection strategy for robot connectivity that trains kernelized bandit model directly in real-world settings of a factory floor. REMARKABLE employs reconfigurable intelligent surfaces (RISs) with passive reflective elements to create beamforming toward target robots, eliminating the need for multiple APs. We develop a method to create a beamforming codebook, reducing the search space complexity. We also develop a reconfigurable rotational mechanism to expand RIS coverage by rotating its projection plane. To address non-stationary conditions, we adopt the bandit over bandit idea that employs adaptive restarts, allowing the system to forget outdated observations and safely relearn the optimal interference-constrained beam. We show that our approach achieves a dynamic regret and the violation bound of Õ(T^(3/4)B^(1/4)) where T is the total time, and B is the total variation budget which captures the total changes in the environment without even assuming the knowledge of B. Finally, experimental validation with custom-designed RIS hardware and mobile robots demonstrates 46.8% faster beam selection and 94.2% accuracy, outperforming classical methods across diverse mobility settings. 

We consider a real-time monitoring system where a source node (with energy limitations) aims to keep the information status at a destination node as fresh as possible by scheduling status update transmissions over a set of channels. The freshness of information at the destination node is measured in terms of the Age of Information (AoI) metric. In this setting, a natural tradeoff exists between the transmission cost (or equivalently, energy consumption) of the source and the achievable AoI performance at the destination. This tradeoff has been optimized in the existing literature under the assumption of having a complete knowledge of the channel statistics. In this work, we develop online learning-based algorithms with finite-time guarantees that optimize this tradeoff in the practical scenario where the channel statistics are unknown to the scheduler. In particular, when the channel statistics are known, the optimal scheduling policy is first proven to have a threshold-based structure with respect to the value of AoI (i.e., it is optimal to drop updates when the AoI value is below some threshold). This key insight was then utilized to develop the proposed learning algorithms that surprisingly achieve an order-optimal regret (i.e., O(1)) with respect to the time horizon length. 

The latency and control overhead of sending the preamble in synchronous communications can be excessive when transmitting short sensing/control messages. To reduce these overheads, this work proposes a preamble-free solution based on the framework of quickest change detection. Specific contributions include a joint decoding/demodulation scheme that is provably asymptotically optimal, and a more practical CuSum-like implementation. Numerical results show that the proposed scheme reduces the latency by 47%to 79% when compared to the preamble-based solutions. The scheme is also inherently robust and automatically adapts to any unknown underlying SNRs. 

In this paper, we address the challenges of asynchronous gradient descent in distributed learning environments, particularly focusing on addressing the challenges of stale gradients and the need for extensive communication resources. We develop a novel communication efficient framework that incorporates a gradient evaluation algorithm to assess and utilize delayed gradients based on their quality, ensuring efficient and effective model updates while significantly reducing communication overhead. Our proposed algorithm requires agents to only send the norm of the gradients rather than the computed gradient. The server then decides whether to accept the gradient if the ratio between the norm of the gradient and the distance between the global model parameter and the local model parameter exceeds a certain threshold. With the proper choice of the threshold, we show that the convergence rate achieves the same order as the synchronous stochastic gradient without depending on the staleness value unlike most of the existing works. Given the computational complexity of the initial algorithm, we introduce a simplified variant that prioritizes the practical applicability without compromising on the convergence rates. Our simulations demonstrate that our proposed algorithms outperform existing state-of-the-art methods, offering improved convergence rates, stability, accuracy, and resource consumption. 

The integration of the Internet of Things (IoT) and modern Artificial Intelligence (AI) has given rise to a new paradigm known as the Artificial Intelligence of Things (AIoT). In this survey, we provide a systematic and comprehensive review of AIoT research. We examine AIoT literature related to sensing, computing, and networking & communication, which form the three key components of AIoT. In addition to advancements in these areas, we review domain-specific AIoT systems that are designed for various important application domains. We have also created an accompanying GitHub repository, where we compile the papers included in this survey: https://github.com/AIoT-MLSys-Lab/AIoT-Survey. This repository will be actively maintained and updated with new research as it becomes available. As both IoT and AI become increasingly critical to our society, we believe that AIoT is emerging as an essential research field at the intersection of IoT and modern AI. It is our hope that this survey will serve as a valuable resource for those engaged in AIoT research and act as a catalyst for future explorations to bridge gaps and drive advancements in this exciting field. 

This work generalizes the Age-of-Information (AoI) minimization problem of update-through-queue systems such that in addition to deciding the waiting time, the sender also chooses over which “channel” each update packet will be served. Different channels have different costs, delays, and quality characteristics that reflect the scheduler’s selections of routing, communications, and update modes. Instead of considering only two channels with restricted parameters as in the existing works, this work studies the general K-channel problem with arbitrary parameters. The results show that both the optimal waiting time and the optimal channel-selection policies admit an elegant water-filling structure, and can be efficiently computed by the proposed low-complexity fixed-point-based numerical method. 

We consider a discrete-time system where a resource-constrained source (e.g., a small sensor) transmits its time-sensitive data to a destination over a time-varying wireless channel. Each transmission incurs a fixed transmission cost (e.g., energy cost), and no transmission results in a staleness cost represented by the Age-of-Information. The source must balance the tradeoff between transmission and staleness costs. To address this challenge, we develop a robust online algorithm to minimize the sum of transmission and staleness costs, ensuring a worst-case performance guarantee. While online algorithms are robust, they are usually overly conservative and may have a poor average performance in typical scenarios. In contrast, by leveraging historical data and prediction models, machine learning (ML) algorithms perform well in average cases. However, they typically lack worst-case performance guarantees. To achieve the best of both worlds, we design a learning-augmented online algorithm that exhibits two desired properties: (i) consistency: closely approximating the optimal offline algorithm when the ML prediction is accurate and trusted; (ii) robustness: ensuring worst case performance guarantee even ML predictions are inaccurate. Finally, we perform extensive simulations to show that our online algorithm performs well empirically and that our learning augmented algorithm achieves both consistency and robustness. 

Age-Of-Information (AoI) is a metric that focuses directly on the application-layer objectives, and a canonical AoI minimization problem is the update-through-queues models. Existing results in this direction fall into two categories: The open-loop setting for which the sender is oblivious of the packet departure time, versus the closed-loop setting for which the decision is based on instantaneous Acknowledgment (ACK). Neither setting perfectly reflects modern networked systems, which almost always rely on feedback that experiences some delay. Motivated by this observation, this work subjects the ACK traffic to a second queue so that the closed-loop decision is made based on delayed feedback. Near-optimal schedulers have been devised, which smoothly transition from the instantaneous-ACK to the open-loop schemes depending on how long the feedback delay is. The results quantify the benefits of delayed feedback for AoI minimization in the update-through-queues systems.