Workshops

Session A4E-OS: Opening Session Session A4E-KS: Keynote Session Session A4E-TS1: Technical Session I Session A4E-TS2: Technical Session II Session A4E-PS: Panel Discussion Session Session A4E-TS3: Technical Session III Session A4E-TS4: Technical Session IV Session A4E-CS: Closing Session
Session AIBESVN-S01: Application of Blockchain architecture for Internet of Vehicles Session AIBESVN-K1: Security, Privacy and Trust in Internet of Vehicles Session AIBESVN-S02: AI Assisted Autonomous Security Solutions for IoV Networks Session AIBESVN-S03: Ensuring Security and Privacy in Vehicular Networks Session AIBESVN-S04: Privacy-Preserving Resource Allocation for Vehicular Networks Session AIBESVN-K2: Automated Solutions for Smart Vehicular Networks
Session AoI-Open: Opening Session Session AoI-Key: Keynote Session AoI-CBrk: Virtual Coffee Break Session AoI-SCH: Scheduling Session AoI-SBrk: Session Break Session AoI-WN: Wireless Networks
Session BigSecurity-S2: Big Data Security 2 Session BigSecurity-S1: Big Data Security 1 Session BigSecurity-S3: Big Data Privacy 1 Session BigSecurity-S4: Big Data Privacy 2
Session CNERT-O: Opening Session Session CNERT-K: Keynote Session CNERT-P: Panel Session CNERT-S1: Session 1, From Aerial to Underwater Networks Session CNERT-S2: Session 2, Machine Learning, Time Sensitive Networking, and Software Defined Networking Session CNERT-S3: Session 3, Tools for Experimentation Session CNERT-D: Demo Session
Session ICCN-KS1: Opening and Keynote Session 1 Session ICCN-S1: Federated Learning and Data Analytics Session ICCN-S2: Cloud and Edge Computing Session ICCN-KS2: Keynote Session 2 Session ICCN-S3: Cloud and Edge Security Session ICCN-S4: Cloud Storage Session ICCN-S5: Task Scheduling and Resource Allocation
Session MobiSec-K: Keynote Session MobiSec-NS: Network Securtiy Session MobiSec-MSS: Mobile Systems Security Session MobiSec-P: Panel
Session NetSciQCom-S1: Plenary Talk Session NetSciQCom-S2: Invited Talks Session NetSciQCom-S3: Contributed Talks
Session PerAI-6G-OS: Opening Session Session PerAI-6G-KS1: Keynote Session 1 Session PerAI-6G-S1: Session 1: Distributed Learning in 6G Session Break Session: Break Session Session PerAI-6G-S2: Session 2: Resource Management in 6G Session PerAI-6G-KS2: Keynote Session 2 Session PerAI-6G-S3: Session 3: AI applications in 6G Session PerAI-6G-CS: Closing Session
Session WNA-S1: Network Measurement Algorithms Session WNA-S2: Routing and Scheduling Algorithms
Session WirelessSec-S1: Wireless Security Session WirelessSec-KS: Keynote Session WirelessSec-S2: Wireless Security and Applications

The 5th Age of Information Workshop (AoI 2022)

Session AoI-Open

Opening Session

Conference
10:00 AM — 10:15 AM EDT
Local
May 2 Mon, 10:00 AM — 10:15 AM EDT

Session AoI-Key

Keynote

Conference
10:15 AM — 11:15 AM EDT
Local
May 2 Mon, 10:15 AM — 11:15 AM EDT

Timeliness Research: The Next Decade

1
This talk does not have an abstract.
Session Chair

Sanjit K. Kaul (IIIT-Delhi, India)

Session AoI-CBrk

Virtual Coffee Break

Conference
11:15 AM — 11:30 AM EDT
Local
May 2 Mon, 11:15 AM — 11:30 AM EDT

Session AoI-SCH

Scheduling

Conference
11:30 AM — 1:00 PM EDT
Local
May 2 Mon, 11:30 AM — 1:00 PM EDT

Competitive Analyses of Online Minimum Age of Information Transmission Scheduling

Tung-Wei Kuo (National Chengchi University, Taiwan)

0
We consider a system that consists of one sender and n receivers, and the goal is to schedule message transmissions to minimize the total age of information of all receivers. Unlike most prior work, we assume that receivers arrive over time, and there are only a finite number of messages for each receiver. These natural assumptions, however, make it challenging to design and analyze scheduling algorithms. In this paper, we give the competitive ratios of some common scheduling algorithms, and prove that for every scheduling algorithm, the competitive ratio is Ā½(n^(1/5)).

Online Scheduling of Transmission and Processing for AoI Minimization with Edge Computing

Jianhang Zhu (SUN YAT-SEN University, China); Jie Gong (Sun Yat-sen University, China)

0
Age of Information (AoI), which measures the time elapsed since the generation of the last received packet at the destination, is a new metric for real-time status update tracking applications. In this paper, we consider a status-update system in which a source node samples updates and sends them to an edge server over a delay channel. The received updates are processed by the server with an infinite buffer and then delivered to a destination. The channel can send only one update at a time, and the server can process one at a time as well. The source node applies generate-at-will model according to the state of the channel, edge server, and buffer. We aim to minimize the average AoI with independent and identically distributed transmission time and processing time. We consider three online scheduling policies. The first one is the optimal long wait policy, under which the source node only transmits a new packet after the old one is delivered. Secondly, we propose a peak age threshold policy, under which the source node determines the sending time based on estimating the peak age of information (PAoI) of the packet. Finally, we improve the peak age threshold policy by considering a postponed plan to reduce the waiting time in the buffer. The AoI performance under these policies is illustrated by numerical results with different parameters.

Improving AoI via Learning-based Distributed MAC in Wireless Networks

Yash Deshpande and Onur Ayan (Technical University of Munich, Germany); Wolfgang Kellerer (Technische UniversitÅ t MÅønchen, Germany)

1
In this work, we consider a remote monitoring scenario in which multiple sensors share a wireless channel to deliver their status updates to a process monitor via an access point (AP). Moreover, we consider that the sensors randomly arrive and depart from the network as they become active and inactive. The goal of the sensors is to devise a medium access strategy to collectively minimize the long-term mean network Age of Information (AoI) of their respective processes at the remote monitor. For this purpose, we propose specific modifications to ALOHA-QT algorithm, a distributed medium access algorithm that employs a Policy Tree (PT) and Reinforcement Learning (RL) to achieve high throughput. We provide the upper bound on the mean network AoI for the proposed algorithm along with pointers for selecting its key parameter. The results reveal that the proposed algorithm reduces mean network AoI by more than 50 percent for state of the art stationary randomized policies while successfully adjusting to a changing number of active users in the network. The algorithm needs less memory and computation than ALOHA-QT while performing better in terms of AoI.

Distribution of AoI in EH-powered Multi-source Systems under Non-preemptive and Preemptive Policies

Mohamed A. Abd-Elmagid and Harpreet S Dhillon (Virginia Tech, USA)

1
In this paper, we study a multi-source updating system in which a transmitter powered by energy harvesting (EH) has multiple sources generating status updates about several physical processes. The status updates are then sent to a destination node where the freshness of each status update is measured in terms of Age of Information (AoI). The harvested energy packets and the status updates of each source are assumed to arrive at the transmitter according to independent Poisson processes, and the service time of each status update is assumed to be exponentially distributed. We focus on understanding the distributional properties of AoI under both non-preemptive and preemptive in service queueing disciplines at the transmitter. In particular, we use the stochastic hybrid systems (SHS) framework to derive closed-form expressions of the moment generating function (MGF) and average AoI under each queueing discipline. To the best of our knowledge, this paper is the first to characterize the AoI performance in EH-powered multi-source updating systems under both non-preemptive and preemptive queueing models. The generality of our results is demonstrated by recovering several existing results as special cases. Our results reveal a fundamental trade-off between obtaining a minimum sum of average AoI values associated with different sources (average sum-AoI) and achieving fairness among the average AoI values of different sources. They also reveal the impact of system design parameters on the achievable AoI performance.
Session Chair

Sennur Ulukus (University of Maryland, USA)

Session AoI-SBrk

Session Break

Conference
1:00 PM — 2:00 PM EDT
Local
May 2 Mon, 1:00 PM — 2:00 PM EDT

Session AoI-WN

Wireless Networks

Conference
2:00 PM — 3:30 PM EDT
Local
May 2 Mon, 2:00 PM — 3:30 PM EDT

Age of Loop Oriented Wireless Networked Control System: Communication and Control Co-Design in the FBL Regime

Jie Cao (Harbin Institute of Technology, Shenzhen, China); Xu Zhu (Harbin Institute of Technology, Shenzhen, China & University of Liverpool, United Kingdom (Great Britain)); Sumei Sun (Institute for Infocomm Research, Singapore); Petar Popovski (Aalborg University, Denmark); Shaohan Feng (Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore); Yufei Jiang (Harbin Institute of Technology, Shenzhen, China)

2
In this paper, age of loop (AoL) is investigated for communication and control co-design in wireless networked control systems (WNCSs), to ensure information freshness in the sensor-controller-actuator loop with two-way delay. Also, the
impact of fading channel and finite block length due to limited transmission bits are considered. A closed-form expression for Peak AoL (PAoL) is derived with respect to block length and the number of allowable transmission times in the presence of block error probability. It is shown that the average PAoL is less than the sum of the average peak Age of information (PAoI) in uplink (PAoU) and the PAoI in downlink (PAoD), due to the coupling of uplink and downlink. In addition, the average PAoL is proved to be convex with respect to block length and mono-decreasing with respect to the allowable transmission time. We then propose a low-complexity PAoL-aware block length adaptation scheme with retransmission policy and optimal control. Simulation results verify the correctness of our analytical results and demonstrate the effectiveness of the proposed PAoL-aware block length adaptation scheme in terms of information freshness and control performance.

Age of Information in the Presence of an Adversary

Subhankar Banerjee and Sennur Ulukus (University of Maryland, USA)

1
We consider a communication system where a base station serves $N$ users, one user at a time, over a wireless channel. We consider the timeliness of the communication of each user via the age of information metric. A constrained adversary can block at most a given fraction, \(\alpha\), of the time slots over a horizon of \(T\) slots, i.e., it can block at most \(\alpha T\) slots. We show that an optimum adversary blocks \(\alpha T\) consecutive time slots of a randomly selected user. The interesting consecutive property of the blocked time slots is due to the cumulative nature of the age metric.

Game Theoretic Analysis of Age of Information for Slotted ALOHA Access With Capture

Leonardo Badia (UniversitĖ† degli Studi di Padova, Italy); Andrea Zanella (University of Padova, Italy & CNIT, Italy); Michele Zorzi (UniversitĖ† degli Studi di Padova, Italy)

0
A recent line of analysis discusses the age of information as a better performance metric than throughput or delay to evaluate the performance of medium access techniques, especially when applied to remote sensing applications and more in general the Internet of Things.
Fully analytical investigations based on game theory have shown how selfish players can behave efficiently in random access systems if they are driven by AoI-based objectives.
In this paper, we expand this kind of reasoning to the case of a slotted ALOHA system with capture effect. We present a fully analytical derivation of some notable cases, based on the existing literature, and we highlight the impact of some parameters, specifically the cost coefficient and the capture threshold, towards achieving an efficient allocation that represents an equilibrium point for the network management. It is shown that, when the capture effect is relatively strong, which is easier when the number of terminals is limited, the Nash equilibrium of the system achieves near-optimal performance.

Coexistence of Age Sensitive Traffic and High Throughput Flows: Does Prioritization Help?

Tanya Shreedhar and Sanjit K Kaul (IIIT Delhi, India); Roy Yates (Rutgers University, USA)

2
We study the coexistence of high throughput traffic flows with status update flows that require timely delivery of updates. A mix of these flows share an end-to-end path that includes a WiFi access network followed by paths over the Internet to a server in the cloud. Using real-world experiments, we show that commonly used methods of prioritization (DSCP at the IP layer and EDCA at the 802.11 MAC layer) in networks are highly effective in isolating status update flows from the impact of high throughput flows in the absence of WiFi access contention, say when all flows originate from the same WiFi client. Prioritization, however, isn't as effective in the presence of contention that results from the throughput and status update flows sharing WiFi. This results in prioritized status update flows suffering from a time-average age of information at the destination server that is about the same as when all flows have the same priority.
Session Chair

Yin Sun (Auburn University, USA)

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