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the page describes the 3 projects Antonios completed: Data Network Traffic Analysis, Gossiping Algorithm Development and Analysis, and ZigBee Networks

Data dissemination protocols govern interaction and exchange of data among nodes in a distributed system. An understanding of data transfer protocols provides insight into efficient middleware management. Due to their simplicity, scalability and fault-tolerance, gossip-based protocols are researched widely as an effective communication strategy. The Shuffle protocol presented in [1], is an example of a decentralized, gossip-based data transfer protocol used to spread information in a wireless network via probabilistic exchange of data. This paper presents, an asynchronous variant of the Shuffle protocol and a system model that captures variability in data transmission times. This transmission time variability is inherent in dynamic networks, where such algorithms are typically deployed. A simulation-based analysis of the protocol's performance behavior is presented. Results show the effects of transmission variability, on data replication and its coverage. Also examined is the relationship between available storage and the performance of the protocol, expressed using measures such as propagation time and work. © 2015 IEEE.

The problem of characterizing the relationship between packet size and network delay has received little attention in the field. Research in that area has been limited to either simulation studies or empirical observations that are detached from analytic traffic modeling. From a queueing viewpoint, it is simple to show that these three variables are inter-related, which necessitates a more careful study. We present a traffic model of a router fed by ON/OFF-type sources with heavy-tailed burst sizes. The traffic model considered is consistent with the evidence that Web traffic is heavy-tailed. The analysis cases that are considered establish a quantitative characterization of the complex relationship among packet payload and header sizes, traffic burstiness, and router queueing delay. © 2004 IEEE.

Monitoring of electrical distribution grids requires the joint processing of electrical measurements from different grid locations. Such type of processing is influenced by inaccuracies in measurement data originating from measurement errors, non-ideal clocks in measurement devices, and from time averaging of measurands as part of the data collection process. This paper introduces an approach to assess the impact of these three different measurement artifacts in realistic measurement scenarios of electrical distribution grids. A case study of power loss calculation in a real-life medium-voltage grid is presented, covering both technical loss obtained from current measurement and total loss obtained from power measurements. The results show that total loss in general is more robust to aggregation of power measurements over longer measurement intervals, while it is more sensitive to measurement errors and clock offsets. The results of the study are important for quantifying the trustworthiness of the obtained loss values and for the future enhancement of the measurement data collection process. © 2023 ACM.

Understanding the impact of network traffic properties on performance behavior in bottleneck links or larger networks is of primary interest to traffic analysts and network designers. Among the contributing factors, variance and correlation properties have been thoroughly studied and a large set of individual results have been obtained. However, these individual contributing factors are not sufficient to predict performance behavior. In this paper we review a unifying and versatile class of ON/OFF models through which the relationship among these parameters can be characterized and their influence on network performance be understood. The analytic performance results from the model show that there is a radically different queueing behavior when the ON period duration follows truncated power-tail distributions (even if truncated), as opposed to model variants where these distribution types are used for the OFF periods. All these models create correlation functions that only decay slowly. This motivates the development of a simple data analysis scheme to distinguish performance relevant correlation. The scheme is described both for interarrival and count processes of traffic data and its effectiveness is shown using real data traces.

Measurements of parameters in electricity grids are frequently average values over some time interval. In scenarios of distributed measurements such as in distribution grids, offsets of local clocks can result in the averaging interval being misaligned. This paper investigates the properties of the so-called time alignment error of such measurands that is caused by shifts of the averaging interval. A Markov model is derived that allows for numerically calculating the expected value and other distribution properties of this error. Actual consumption measurements of an office building are used to study the behavior of this time alignment error, and to compare the results from the trace with numerical results and simulations from a fitted Markov model. For increasing averaging interval offset, the time alignment error approaches a normal distribution, whose parameters can be calculated or approximated from the Markov model.

The operation and planning of distribution grids require the joint processing of measurements from different grid locations. Since measurement devices in low-and medium-voltage grids lack precise clock synchronization, it is important for data management platforms of distribution system operators to be able to account for the impact of nonideal clocks on measurement data. This paper formally introduces a metric termed Additive Alignment Error to capture the impact of misaligned averaging intervals of electrical measurements. A trace-driven approach for retrieval of this metric would be computationally costly for measurement devices, and therefore, it requires an online estimation procedure in the data collection platform. To overcome the need of transmission of high-resolution measurement data, this paper proposes and assesses an extension of a Markov-modulated process to model electrical traces, from which a closed-form matrix analytic formula for the Additive Alignment Error is derived. A trace-driven assessment confirms the accuracy of the model-based approach. In addition, the paper describes practical settings where the model can be utilized in data management platforms with significant reductions in computational demands on measurement devices. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

We consider the problem of propagating an update to nodes in a distributed system using two gossiping protocols. The first is an idealized algorithm with static and dynamic knowledge of the system, and the second is a simple randomized algorithm. We construct a theoretical model that allows us to derive work and completion time statistics under varying transmission delay distributions. Numerical results are obtained for both exponential and nonexponential transmission times using linear-algebraic queueing theory techniques. Additionally, we present the results of simulation experiments showing that under node churn assumptions, the randomized algorithm's performance is qualitatively different than in a fault-free system. © 2010 IEEE.

This paper reports a two-part study examining the relationship between fear of missing out (FoMO) and maladaptive behaviors in college students. This project used a cross-sectional study to examine whether college student FoMO predicts maladaptive behaviors across a range of domains (e.g., alcohol and drug use, academic misconduct, illegal behavior). Participants (N = 472) completed hard copy questionnaire packets assessing trait FoMO levels and questions pertaining to unethical and illegal behavior while in college. Part 1 utilized traditional statistical analyses (i.e., hierarchical regression modeling) to identify any relationships between FoMO, demographic variables (socioeconomic status, living situation, and gender) and the behavioral outcomes of interest. Part 2 looked to quantify the predictive power of FoMO, and demographic variables used in Part 1 through the convergent approach of supervised machine learning. Results from Part 1 indicate that college student FoMO is indeed related to many diverse maladaptive behaviors spanning the legal and illegal spectrum. Part 2, using various techniques such as recursive feature elimination (RFE) and principal component analysis (PCA) and models such as logistic regression, random forest, and Support Vector Machine (SVM), showcased the predictive power of implementing machine learning. Class membership for these behaviors (offender vs. non-offender) was predicted at rates well above baseline (e.g., 50% at baseline vs 87% accuracy for academic misconduct with just three input variables). This study demonstrated FoMO’s relationships with these behaviors as well as how machine learning can provide additional predictive insights that would not be possible through inferential statistical modeling approaches typically employed in psychology, and more broadly, the social sciences. Research in the social sciences stands to gain from regularly utilizing the more traditional statistical approaches in tandem with machine learning.

Across three online studies, we examined the relationship between the Fear of Missing Out (FoMO) and moral cognition and behavior. Study 1 (N = 283) examined whether FoMO influenced moral awareness, judgments, and recalled and predicted behavior of first-person moral violations in either higher or lower social settings. Study 2 (N = 821) examined these relationships in third-person judgments with varying agent identities in relation to the participant (agent = stranger, friend, or someone disliked). Study 3 (N = 604) examined the influence of recalling activities either engaged in or missed out on these relationships. Using the Rubin Causal Model, we created hypothetical randomized experiments from our real-world randomized experimental data with treatment conditions for lower or higher FoMO (median split), matched for relevant covariates, and compared differences in FoMO groups on moral awareness, judgments, and several other behavioral outcomes. Using a randomization-based approach, we examined these relationships with Fisher Tests and computed 95% Fisherian intervals for constant treatment effects consistent with the matched data and the hypothetical FoMO intervention. All three studies provide evidence that FoMO is robustly related to giving less severe judgments of moral violations. Moreover, those with higher FoMO were found to report a greater likelihood of committing moral violations in the past, knowing people who have committed moral violations in the past, being more likely to commit them in the future, and knowing people who are likely to commit moral violations in the future.