A. James Clark School of Engineering

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The collections in this community comprise faculty research works, as well as graduate theses and dissertations.

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Subject: search.filters.subject.age of information

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    Using Timeliness in Tracking Infections †
    (MDPI, 2022-05-31) Bastopcu, Melih; Ulukus, Sennur
    We consider real-time timely tracking of infection status (e.g., COVID-19) of individuals in a population. In this work, a health care provider wants to detect both infected people and people who have recovered from the disease as quickly as possible. In order to measure the timeliness of the tracking process, we use the long-term average difference between the actual infection status of the people and their real-time estimate by the health care provider based on the most recent test results. We first find an analytical expression for this average difference for given test rates, infection rates and recovery rates of people. Next, we propose an alternating minimization-based algorithm to find the test rates that minimize the average difference. We observe that if the total test rate is limited, instead of testing all members of the population equally, only a portion of the population may be tested in unequal rates calculated based on their infection and recovery rates. Next, we characterize the average difference when the test measurements are erroneous (i.e., noisy). Further, we consider the case where the infection status of individuals may be dependent, which occurs when an infected person spreads the disease to another person if they are not detected and isolated by the health care provider. In addition, we consider an age of incorrect information-based error metric where the staleness metric increases linearly over time as long as the health care provider does not detect the changes in the infection status of the people. Through extensive numerical results, we observe that increasing the total test rate helps track the infection status better. In addition, an increased population size increases diversity of people with different infection and recovery rates, which may be exploited to spend testing capacity more efficiently, thereby improving the system performance. Depending on the health care provider’s preferences, test rate allocation can be adjusted to detect either the infected people or the recovered people more quickly. In order to combat any errors in the test, it may be more advantageous for the health care provider to not test everyone, and instead, apply additional tests to a selected portion of the population. In the case of people with dependent infection status, as we increase the total test rate, the health care provider detects the infected people more quickly, and thus, the average time that a person stays infected decreases. Finally, the error metric needs to be chosen carefully to meet the priorities of the health care provider, as the error metric used greatly influences who will be tested and at what test rate.
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    Age of Information in Large Networks, Distributed Computation and Learning
    (2021) Buyukates, Baturalp; Ulukus, Sennur; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    We consider timely data delivery in real-time communication networks that have gained significant importance with recent promising applications including augmented reality/virtual reality (AR/VR), autonomous vehicular networks, and smart factories. Age of information is a network performance metric introduced to guarantee access to fresh data in such systems. Considering increasing connectivity in communication networks and ever growing prominence of distributed computation and learning systems, in this dissertation, we study age of information in large networks with a particular focus on its scalability with growing network size as well as the design of distributed computation and learning systems that handle time-critical data using potentially large number of worker nodes with as little age as possible. First, we consider a multihop multicast network with a single source node sending time-sensitive updates to $n^L$ end nodes where $L$ denotes the number of hops. Updates from the source go through relay nodes in each hop to reach the end nodes. We show that by utilizing appropriate transmission stopping thresholds in each hop, the age of information at the end nodes can be made a constant independent of $n$. We then find the optimum stopping value for each hop for arbitrary shifted exponential link delays. Next, we focus on a single hop multicast network where two update streams share the same network, called type I and type II updates. We show that utilizing an earliest $k_1$ and $k_2$ transmission scheme for type I and type II updates, respectively, prevents information staleness for both update streams. We find the optimum $k_1$ and $k_2$ stopping thresholds for arbitrary shifted exponential link delays to individually and jointly minimize the average age of both update streams. Then, we consider the age scaling in a large peer-to-peer network consisting of $n$ randomly paired source-destination pairs. We first propose a three-phase transmission scheme which utilizes \emph{local cooperation} among the nodes along with \emph{mega update packets} and show that it achieves an average age scaling of $O(n^{\frac{1}{4}}\log n)$ per-user as $n$ grows. Next, we show that, under a hierarchical implementation of the proposed scheme, an average age scaling of $O(n^{\alpha(h)}\log n)$ per-user is achievable, where $h$ denotes the number of hierarchy levels and $\alpha(h) = \frac{1}{3\cdot2^h+1}$. The proposed hierarchical scheme asymptotically achieves a per-user average scaling of $O(\log n)$. Next, we consider the version age of information scaling in gossip networks, where a total of $n$ nodes are clustered into distinct communities and they are allowed to share their versions of the source information with their neighbors within each cluster. By assuming different topologies for the clusters, we show that per node average version age scalings of $O(\sqrt{n})$, $O(n^{\frac{1}{3}})$, and $O(\log n)$ are achievable in disconnected, ring, and fully connected cluster models, respectively. We then increase connectivity across clusters by implementing a hierarchical gossip mechanism to further improve the version age scaling results, and find the version age-optimum cluster sizes for various settings. Then, we consider a status updating system in which the update packets are data rich and need to be processed further to extract the embedded information. This processing is handled in a distributed manner at a computation unit which consists of a master node and $n$ worker nodes. We investigate the age performance of uncoded and coded (repetition coded, MDS coded, and multi-message MDS (MM-MDS) coded) schemes in the presence of stragglers. We show that, asymptotically, MM-MDS coded scheme outperforms the other schemes, and characterize the age-optimal codes. Next, we study age of information in federated learning and propose a novel timely communication scheme specifically designed for learning applications that use highly temporal rapidly changing client datasets such as recommendation systems and next place forecasting tasks. The proposed timely communication scheme aims to incorporate time-critical client data into the global model updates with as little age as possible by also considering the limited client availability and communication resources. We show that, in addition to ensuring timeliness, the proposed policy significantly improves the average iteration time of training without hurting the convergence performance of the algorithm. Then, we consider utilizing the age of information metric to improve convergence performance in distributed learning with coded computation and partial recovery for straggler mitigation. We propose a novel age-based encoding framework that regulates the recovery frequency of the partial computations received from the workers. We show through several experiments on a linear regression problem that the proposed age-based encoding strategy significantly improves the convergence performance compared to conventional static encoding schemes. Next, we propose a novel gradient coding (GC) scheme with dynamic clustering, called GC-DC, to improve the average iteration time of the existing static gradient coding techniques. The proposed GC-DC scheme clusters the workers and applies the GC scheme in each cluster separately. Under a time correlated straggling behavior for the workers, GC-DC dynamically forms the clusters based on the past straggling behavior to distribute straggling workers to clusters as uniformly as possible. We show through extensive simulations on both homogeneous and heterogeneous worker profiles that the GC-DC scheme significantly improves the average iteration time compared to the existing static GC schemes without any increases in the communication load. Finally, we study a status updating system with a single sampler which takes samples from an observed phenomenon and sends them to a monitor node through a single server that implements a blocking policy. We consider two scenarios with partial non-i.i.d.~components: Gilbert-Elliot service times and i.i.d.~interarrival times; and i.i.d.~service times and Gilbert-Elliot interarrival times. We characterize the average age experienced by the monitor node and determine the age-optimal state transition matrix for both of these scenarios.