Age of Information Optimization for Timeliness in Communication Networks

With the emergence of technologies such as autonomous vehicular systems, holographic communications, remote surgery and high frequency automated trading, timeliness of information has become more important than ever. Most traditional performance metrics, such as delay or throughput, are not sufficient to measure timeliness. For that, age of information (AoI) has been introduced recently as a new performance metric to quantify the timeliness in communication networks. In this dissertation, we consider timely update delivery problems in communication networks under various system settings. First, we introduce the concept of soft updates, where different from the existing literature, here, updates are soft and begin reducing the age immediately but drop it gradually over time. Our setting models human interactions where updates are soft, and also social media interactions where an update consists of viewing and digesting many small pieces of information posted, that are of varying importance, relevance and interest to the receiver. For given total system duration, the number of updates, and the total allowed update duration, we find the optimum start times of the soft updates and their optimum durations to minimize the overall age. Then, we consider an information updating system where not only the timeliness but also the quality of the updates is important. Here, we use distortion as a proxy for quality, and model distortion as a decreasing function of processing time spent while generating the updates. Processing longer at the transmitter results in a better quality (lower distortion) update, but it causes the update to age in the process. We determine age-optimal policies by characterizing the update request times at the receiver and the update processing times at the transmitter subject to constant or age-dependent distortion constraints on each update. Next, different from most of the existing literature on AoI where the transmission times are based on a given distribution, by assigning codeword lengths for each status update, we design transmission times through source coding schemes. In order to further improve timeliness, we propose selective encoding schemes where only the most probable updates are transmitted. For the remaining least probable updates, we consider schemes where these updates are never sent, randomly sent, or sent by an empty symbol. For all these encoding schemes, we determine the optimal number of encoded updates and their corresponding age-optimal real-valued codeword lengths to minimize the average age at the receiver. Then, we study the concept of generating partial updates which carry less information compared to the original updates, but their transmission times are shorter. Our aim is to find the age-optimal partial update generation process and the corresponding age-optimal real-valued codeword lengths for the partial updates while maintaining a desired level of fidelity between the original and partial updates. Next, we consider information freshness in a cache updating system consisting of a source, cache(s) and a user. Here, the user may receive an outdated file depending on the freshness status of the file at the cache. We characterize the binary freshness metric at the end user and propose an alternating maximization based method to optimize the overall freshness at the end user subject to total update rate constraints at the cache(s) and the user. Then, we study a caching system with a limited storage capacity for the cache. Here, the user either gets the files from the cache, but the received files can be sometimes outdated, or gets fresh files directly from the source at the expense of additional transmission times which inherently decrease the freshness. We show that when the total update rate and the storage capacity at the cache are limited, it is optimal to get the frequently changing files and files with relatively small transmission times directly from the source, and store the remaining files at the cache. Next, we focus on information freshness in structured gossip networks where in addition to the updates obtained from the source, the end nodes share their local versions of the updates via gossiping to further improve freshness. By using a stochastic hybrid systems (SHS) approach, we determine the information freshness in arbitrarily connected gossip networks. When the number of nodes gets large, we find the scaling of information freshness in disconnected, ring and fully connected network topologies. Further, we consider clustered gossip networks where multiple clusters of structured gossip networks are connected to the source through cluster heads, and find the optimal cluster sizes numerically. Then, we consider the problem of timely tracking of multiple counting random processes via exponential (Poisson) inter-sampling times, subject to a total sampling rate constraint. A specific example is how a citation index such as Google Scholar should update citation counts of individual researchers to keep the entire citation index as up-to-date as possible. We model citation arrival profile of each researcher as a counting process with a different mean, and consider the long-term average difference between the actual citation numbers and the citation numbers according to the latest updates as a measure of timeliness. We show that, in order to minimize this difference metric, Google Scholar should allocate its total update capacity to researchers proportional to the square roots of their mean citation arrival rates. Next, we consider the problem of timely tracking of multiple binary random processes via sampling rate limited Poisson sampling. As a specific example, we consider the problem of timely tracking of infection status (e.g., covid-19) of individuals in a population. Here, a health care provider wants to detect infected and recovered people as quickly as possible. We measure the timeliness of the tracking process as the long term average difference between the actual infection status of people and their real-time estimate at the health care provider which is based on the most recent test results. For given infection and recovery rates of individuals, we find the exponentially applied testing rates for individuals to minimize this difference. We observe that when the total test rate is limited, instead of applying tests to everyone, only a portion of the population should be tested. Finally, we consider a communication system with multiple information sources that generate binary status updates, which in practical application may indicate an anomaly (e.g., fire) or infection status (e.g., covid-19). Each node exhibits an anomaly or infection with probability $p$. In order to send the updates generated by these sources as timely as possible, we propose a group updating method inspired by group testing, but with the goal of minimizing the overall average age, as opposed to the average number of tests (updates). We show that when the probability $p$ is small, group updating method achieves lower average age than the sequential updating methods

Bits through Time

In any communication system, there exist two dimensions through which the information at the source becomes distorted before reaching the destination: the noisy channel and time. Messages transmitted through a noisy channel are susceptible to modification in their content, due to the action of the noise of the channel. Claude E. Shannon, in his seminal paper of 1948 "A Mathematical Theory of Communication", introduces the bit as a unit of measure of information, and he lays down the theoretical foundations needed to understand the problem of sending bits reliably through a noisy channel. The distortion measure, which he used to quantify reliability, is the error probability. In his paper, Shannon shows that any channel is characterized by a number that he calls capacity: It represents the highest transmission rate that can be used to communicate information with, through this same channel, while guaranteeing a negligible error probability. Whereas, even if the messages are sent through a perfect channel, the time they take to reach their destination causes the receiver to acquire a distorted view of the status of the source that generated these messages. For instance, take the case of a monitor interested in the status of a distant process. A sender observes this process and, to keep the monitor up-to-date, sends updates to it. However, if, at any time t, the last received update at the monitor was generated at time u(t), then the information at the receiver reflects the status of the process at time u(t), not at time t. Hence, the monitor has a distorted version of reality. In fact, it has an obsolete version with an age of t-u(t). The concept of age as a distortion measure in communication systems was first used in 2011 by Kaul et al., in order to assess the performance of a given vehicular network. The aim of the authors was to come up with a transmission scheme that would minimize an age-related metric: the average age. Since then, a growing body of works has used this metric to evaluate the performance of multiple communication systems. The drive behind this interest lies in the importance that status-update applications are gaining in today's life (in vehicular networks, warehouse and environment surveillance, news feed,etc.). In this thesis, we choose age as a distortion measure and derive expressions for the average age and the average peak-age (another age-related metric) for different communication systems. Therefore, we divide this dissertation into two parts: In the first part, we assume that the the updates are transmitted through a noiseless channel that has a random service time. In the second part, we consider a special category of noisy channels, namely the erasure channel. In the first part of this thesis, in order to compute the age-related metrics, we employ queue-theoretic concepts. We study and compare the performance of various transmission schemes under different settings.We show that the optimal transmission scheme when the monitor is interested in a single source loses its optimality when another source of higher priority shares the system. In the second part of this thesis, we introduce, in our age calculations, the distortion caused by the erasure channel on the transmitted updates. In order to combat the erasures of the channel, we first consider two flavors of the hybrid automatic repeat request (HARQ). Finally, we focus on the optimal average age that could be achieved over an erasure channel

A Bayesian Approach to Graphical Record Linkage and De-duplication

We propose an unsupervised approach for linking records across arbitrarily many files, while simultaneously detecting duplicate records within files. Our key innovation involves the representation of the pattern of links between records as a bipartite graph, in which records are directly linked to latent true individuals, and only indirectly linked to other records. This flexible representation of the linkage structure naturally allows us to estimate the attributes of the unique observable people in the population, calculate transitive linkage probabilities across records (and represent this visually), and propagate the uncertainty of record linkage into later analyses. Our method makes it particularly easy to integrate record linkage with post-processing procedures such as logistic regression, capture-recapture, etc. Our linkage structure lends itself to an efficient, linear-time, hybrid Markov chain Monte Carlo algorithm, which overcomes many obstacles encountered by previously record linkage approaches, despite the high-dimensional parameter space. We illustrate our method using longitudinal data from the National Long Term Care Survey and with data from the Italian Survey on Household and Wealth, where we assess the accuracy of our method and show it to be better in terms of error rates and empirical scalability than other approaches in the literature.Comment: 39 pages, 8 figures, 8 tables. Longer version of arXiv:1403.0211, In press, Journal of the American Statistical Association: Theory and Methods (2015

Sharing data from clinical trials: the rationale for a controlled access approach.

The move towards increased transparency around clinical trials is welcome. Much focus has been on under-reporting of trials and access to individual patient data to allow independent verification of findings. There are many other good reasons for data sharing from clinical trials. We describe some key issues in data sharing, including the challenges of open access to data. These include issues in consent and disclosure; risks in identification, including self-identification; risks in distorting data to prevent self-identification; and risks in analysis. These risks have led us to develop a controlled access policy, which safeguards the rights of patients entered in our trials, guards the intellectual property rights of the original researchers who designed the trial and collected the data, provides a barrier against unnecessary duplication, and ensures that researchers have the necessary resources and skills to analyse the data