More Like this

1. Private Status Updating with Erasures: A Case for Retransmission Without Resampling

   https://doi.org/10.1109/ICC45041.2023.10278957  

   Arafa, Ahmed; Banawan, Karim (May 2023, IEEE International Conference on Communications)

   A status updating system is considered in which a source updates a destination over an erasure channel. The utility of the updates is measured through a function of their age-of-information (AoI), which assesses their freshness. Correlated with the status updates is another process that needs to be kept private from the destination. Privacy is measured through a leakage function that depends on the amount and time of the status updates received: stale updates are more private than fresh ones. Different from most of the current AoI literature, a post-sampling waiting time is introduced in order to provide a privacy cover at the expense of AoI. More importantly, it is also shown that, depending on the leakage budget and the channel statistics, it can be useful to retransmit stale status updates following erasure events without resampling fresh ones. 

   more » « less
2. Learning-augmented Online Minimization of Age of Information and Transmission Costs

   Liu, Z; Zhang, K; Li, B; Sun, Y; Hou, Y; Ji, B (May 2024, IEEE)

   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. 

   more » « less
3. Jointly Minimizing AoI Penalty and Network Cost Among Coexisting Source-Destination Pairs

   https://doi.org/10.1109/ISIT45174.2021.9518170  

   Tsai, Cho-Hsin; Wang, Chih-Chun (July 2021, 2021 IEEE International Symposium on Information Theory (ISIT))

   One main objective of ultra-low-latency communications is to minimize the data staleness at the receivers, recently characterized by a metric called Age-of-Information (AoI). While the question of when to send the next update packet has been the central subject of AoI minimization, each update packet also incurs the cost of transmission that needs to be jointly considered in a practical design. With the exponential growth of interconnected devices and the increasing risk of excessive resource consumption in mind, this work derives an optimal joint cost-and-AoI minimization solution for multiple coexisting source-destination (S-D) pairs. The results admit a new AoI-market-price-based interpretation and are applicable to the setting of (a) general heterogeneous AoI penalty functions and Markov delay distributions for each S-D pair, and (b) a general network cost function of aggregate throughput of all S-D pairs. Extensive simulation is used to demonstrate the superior performance of the proposed scheme. 

   more » « less
4. Voluntary Data Preservation Mechanism in Base Station-less Sensor Networks

   Ly, Jennifer; Chen, Yutian; Tang, Bin (July 2022, 12th EAI International Conference on Game Theory for Networks (GameNets 2022).)

   We consider the problem of preserving a large amount of data generated inside base station-less sensor networks, when sensor nodes are controlled by different authorities and behave selfishly. We modify the VCG mechanism to guarantee that each node, including the source nodes with overflow data packets, will voluntarily participate in data preservation. The mechanism ensures that each node truthfully reports its private type and network achieves efficiency for all the preserved data packets. Extensive simulations are conducted to further validate our results. 

   more » « less
5. Walrasian Equilibrium-Based Pricing Mechanism for Health-Data Crowdsensing Under Information Asymmetry

   https://doi.org/10.1109/TCSS.2022.3171566  

   Guo, Xinxin; Kong, Nan; Wang, Haiyan (May 2022, IEEE Transactions on Computational Social Systems)

   While prior studies have designed incentive mechanisms to attract the public to share their collected data, they tend to ignore information asymmetry between data requesters and collectors. In reality, the sensing costs information (time cost, battery drainage, bandwidth occupation of mobile devices, and so on) is the private information of collectors, which is unknown by the data requester. In this article, we model the strategic interactions between health-data requester and collectors using a bilevel optimization model. Considering that the crowdsensing market is open and the participants are equal, we propose a Walrasian equilibrium-based pricing mechanism to coordinate the interest conflicts between health-data requesters and collectors. Specifically, based on the exchange economic theory, we transform the bilevel optimization problem into a social welfare maximization problem with the constraint condition that the balance between supply and demand, and dual decomposition is then employed to divide the social welfare maximization problem into a set of subproblems that can be solved by health-data requesters and collectors. We prove that the optimal task price is equal to the marginal utility generated by the collector's health data. To avoid obtaining the collector's private information, a distributed iterative algorithm is then designed to obtain the optimal task pricing strategy. Furthermore, we conduct computational experiments to evaluate the performance of the proposed pricing mechanism and analyze the effects of intrinsic rewards, sensing costs on optimal task prices, and collectors' health-data supplies. 

   more » « less