Collaborative Research: CNS Core: Medium:
Information Freshness in Scalable and Energy Constrained Machine to Machine
Wireless Networks
Last
updated in January 2026.
Project
Team
·
Principal Investigators: Chih-Chun Wang (Purdue),
Bo Ji (Virginia Tech), Ashutosh Sabharwal (Rice), and Ness Shroff (OSU).
·
This is a collaborative project among
Purdue, OSU, Rice, and Virginia Tech, under the Grant Numbers CNS-2107363,
CNS-2106932, CNS- 2106993 and CNS-2106427, respectively.
Vision
The last decade has seen an unprecedented
growth in wireless devices and consumers' need to own and use them in new and
exciting ways. As these devices become more popular, Machine Type
Communications (MTC) are getting increasingly prevalent, where communications
(through timely updates) occur primarily between machines. In such MTC systems,
timely updates are needed to make decisions for various applications and
actuations, and the utility of each update crucially depends on the freshness
of information content. Furthermore, many of these IoT devices are battery powered, therefore mechanisms are needed in place to
simultaneously ensure long battery lifetimes. Taking all of
these considerations into account, the overarching goal of this project is to
develop novel tools and techniques to optimize information
freshness and energy efficiency while meeting the practical requirements of
wireless MTC systems.
The goals of this project are to develop the
analytical foundations for controlling and optimizing information freshness in
wireless MTC networks, resulting in fully distributed provably efficient
algorithms and protocols that will be extensively evaluated on a large-scale
programmable testbed at Rice University.
By addressing the
robustness, scalability, and energy efficiency in MTC networks, the
results of this project will have significant impacts on a broad class of
applications supported by these networks ranging from autonomous driving to
smart healthcare. The project will engage American higher-education students
through research engagements and outreach activities within the four
collaborating universities, including mechanisms to increase leadership and
real-world problem-solving skills.
Key Accomplishments
We conducted research on the following
aspects of Age-of-Information (AoI) optimization in
MTC networks: robustness, energy efficiency, and age-based scheduling.
Robustness
in MTC Networks
·
We consider a status update system, in which
update packets are sent to the destination via a wireless medium that allows
for multiple rates, where a higher rate also naturally corresponds to a higher
error probability. We design a low-complexity optimal scheduler that selects
between two different transmission rate and error probability pairs to be used
at each transmission epoch. To this end, we show that there exists a
threshold-type policy that is age-optimal, and that the objective function is
quasi-convex or non-decreasing in the threshold.
·
We consider a sampling problem, where the
source generates fresh samples and sends the samples through a forward channel
to a remote estimator, and acknowledgements are sent back over a feedback
channel. Due to various channel conditions, the forward channel can be
unreliable, and both the forward and feedback channels could have random
transmission delays. This problem is motivated by distributed sensing, where
the estimator can receive the signal samples from the channel and the noisy
observation from a local sensor. It is shown that the estimation error is a
non-decreasing function of the age. For general
non-decreasing functions of the age, an optimal sampling policy is designed.
The optimal policy is stationary deterministic and has a threshold structure:
If the previous transmission was successful, it waits until the age exceeds a
threshold and sends out a fresh sample. If the previous transmission failed, it sends out a fresh sample without waiting. The
threshold is a root of a fixed-point equation and can be computed with low
complexity such as bisection search. Furthermore, we generalize the work by
adding a sampling rate constraint. The optimal policy can be randomized but has
a similar threshold structure.
Energy
Efficiency in MTC Networks
·
We study transmission scheduling with imperfect
channel state information (CSI) in a system, where
updates are periodically generated and transmitted to the destination over a
time-correlated fading channel. We
consider two practical ways to obtain CSI (i.e., delayed sensing and
transmission feedback) and investigate the age minimization problem under
average energy constraints in the two different cases. We demonstrate that for
each case, the optimal policy is a randomization of two threshold-type
policies. The threshold-type policy in the case
without sensing has a threshold in the belief on good channel state while that
in the case with sensing has a threshold in the age. In addition, we propose
structure-aware scheduling algorithms for each case.
·
We consider a status update system, where an
access point collects samples from multiple sensors and transmits the collected
samples to the destination over error-prone channels. Our goal is to minimize
the long-term average age with constraints on the long-term average energy and
distortion of each update, where the distortion is determined by the number of
collected samples. Under assumption that the distortion requirement is a
non-decreasing function of the age, we show that the optimal policy is a mixture
of two stationary deterministic policies, each of which is optimal for a
parameterized average cost problem and is of a threshold-type, i.e.,
transmission is conducted when the age exceeds a threshold and the distortion
requirement is met. We derive the average cost under the threshold-type policy
as a piecewise function, and analytically characterize
its performance in the large threshold regime. With these, we devise a
low-complexity algorithm for the original problem. Moreover, we consider a
special case when the distortion requirement is independent of the age, and provide a closed-form solution for the
parameterized problem.
Age-based
Scheduling in MTC Networks
·
We consider the age-based scheduling
minimization problem, where a source can transmit the status
updates over two heterogeneous channels. This work is motivated by recent
developments in 5G mmWave technology, in which the mmWave channel is unreliable but fast, and the sub-6GHz
channel is slow but reliable. The unreliable channel is modeled as a time
correlated Markovian channel at a high data rate. The reliable channel is
deterministic with lower data rate. The scheduler decides the channel to be
used in each time slot, aiming to minimize the long-term average expected age.
It is shown that there exists a multi-dimensional threshold-based policy that
is optimal. We provide the optimal thresholds with low complexity (e.g.,
bisection search) for some portion of the values of the channel parameters.
Furthermore, we extend the study by providing for all of
the possible values of the channel parameters. To obtain the optimal solution
requires overcoming some technical challenges, because super-modularity does
not hold over some portion of the state space.
·
We focus on the canonical
update-through-queue systems (UTQS), study and quantify the impact of 2-way delay, and aim to design the corresponding optimal
age-minimization algorithms. Specifically, the UTQS model considers a typical
scenario that the source node would like to send an update packet to the
destination node. Any computation,
control, and communication delays experienced by the update packets are lumped
together as a basic first-in/first-out queue. The design goal is to adjust the
packet injection times so that the updates at the destination are the “freshest” in the long run. The
simplicity of such a UTQS model makes it very versatile, with the applications ranging from database update, remote
sensing and estimation, cyber-physical system control, cloud-to-edge computing,
etc., and is widely recognized as one of the most influential models of age
minimization. Existing results generally
fall into two categories: The open-loop setting for which the source is
oblivious of the actual packet departure time, versus the closed-loop setting
for which the decision is based on instantaneous Acknowledgement (ACK) of when
the packet has been served and left the queue. Unfortunately, neither setting
accurately reflects modern networked systems, which almost always rely on
feedback that experiences some delay. Motivated by this observation, we study
the same UTQS model but subject the ACK traffic to an independent queue so that
the closed-loop decision can only be made based on delayed feedback.
Near-optimal schedulers have been devised that successfully quantifies
the impact of 2-way delay on the age minimization problem.
·
We have focused on the study of optimizing
the tradeoff between data freshness and update cost in a discrete-time
information-update system, aiming to minimize the sum of the staleness cost and
the update cost. We first study the tradeoff between the data freshness and the
update cost by formulating an optimization problem to minimize the sum of the
staleness cost (which is a function of the age-of-information) and the update
cost. Then, we provide two useful guidelines for the design of efficient update
policies. Following these guidelines and assuming that the aggregated request
arrival process is Bernoulli, we prove that there exists a threshold-based
policy that is optimal among all online policies and thus focus on the class of
threshold-based policies. Furthermore, we derive the closed-form
formula for computing the long-term average cost under any threshold-based
policy and obtain the optimal threshold. Finally, we perform extensive
simulations using both synthetic data and real traces to verify our theoretical
results and demonstrate the superior performance of the optimal threshold-based
policy compared with several baseline policies.
Age/quality-based Scheduling for Learning
over MTC networks
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Machine learning over MTC networks have been
a driving force for both new machine learning algorithm developments and next
generation network designs, with numerous potential future applications in
autonomous driving, smart agriculture, and next-generation ML-based
communication network solutions. One challenge of distributed network learning
systems is the tradeoff between training speed (how to avoid the straggler) and
the communication and computation resources (how to distribute the tasks
efficiently). In this direction, we focus on the distributed asynchronous
gradient descent problems. We observe that if we use the “Age” as the proxy of the quality indicator, we can avoid
transmission of low-quality updates and while spending the
communication/computation resources only on the high-quality ones. By
generalizing the above observation, we have developed a new network communication
protocol that directly quantifies the quality of each asynchronous gradient
descent update, which reduces the communication overhead, improves the learning
convergence rates, stability, accuracy, and reduces the corresponding resource
consumption.
Resource-scheduling for networks with very
large degrees-of-freedom
·
We considered the resource scheduling
problem for massive MIMO systems with its optimal solution known to be NP-hard.
Massive MIMO systems have a large number of antennas,
and hence the systems have a large number of signaling
degrees-of-freedom. This research was performed in collaboration with
researchers from Nvidia.
·
Inspired by recent achievements in deep
reinforcement learning (DRL) to solve problems with large action sets, we
developed SMART, a dynamic scheduler for massive MIMO based on the
state-of-the-art Soft Actor-Critic (SAC) DRL model and the K-Nearest Neighbors
(KNN) algorithm. Through comprehensive simulations using realistic massive MIMO
channel models as well as real-world datasets from channel measurement
experiments, we demonstrate the effectiveness of our proposed model in various
channel conditions. Our results show that our proposed model performs very
close to the optimal proportionally fair (Opt-PF)
scheduler in terms of spectral efficiency and fairness with more than one order
of magnitude lower computational complexity in medium network sizes where Opt-PF is computationally feasible. Our results also show
the feasibility and high performance of our proposed scheduler in networks with
a large number of users and resource blocks.
Broader Impacts
The
PIs have continuously focused on disseminating the discovery of this project to
international research communities, see the product section below.
Wang
has continuously focused on training undergraduate students and GRAs through
research activities, group seminars, and conference presentations. Wang has
continuously supervised a Purdue-VIP undergraduate research team since fall
2019 till spring 2026, with weekly 90-min meetings dedicated to engaging
undergraduate students in graduate-level research on wireless communications.
To
further promote student engagement, the VIP teams of spring 2022 to fall 2025
continuously participated in the poster presentation of Purdue Undergraduate
Research Conference, an in-person event that have resumed on a semester-basis
after the pandemic in 2020. For each semester, there are more than 300
student-led posters participating in-person for this university-wide event.
Wang also served as one of the judges for the research talks competition
consecutively from spring 2022 to fall 2025, which awarded the top 3 in-person
research talks for each of the ten schools of Purdue University.
Ji
has served as TPC Co-Chair of AoI Workshop 2022,
co-located with IEEE INFOCOM 2022.
Shroff
co-chaired the Office of the Under Secretary of Defense Future Directions
Workshop on Wireless Communications: “XG and Beyond”.
This was a high-profile workshop involving key researchers across Networking,
Photonics, and Electronics intended to provide guidance to government agencies
about strategic investments in XG networks, which include MTC communications.
PI
Shroff and PI Ji, along with several other colleagues, co-organized the 2024 and
2025 Artificial Intelligence Modeling, Analysis, and Control of Complex Systems
(AIMACCS) Workshop, which consists of 13 keynotes, an industry roundtable panel
for students, and a student poster session, where students working on this
project presented their research results and progress with AI Edge Institute
members and other AIMACCS workshop attendees and received useful feedback.
Products
The results in the NSF Public Access
Repository will include a comprehensive listing of all journal publications
recorded to date that are associated with this award.
Journal papers:
·
G. Yao, A.M. Bedewy, and N. B. Shroff, “Age-optimal
low-power status update over time-correlated fading channel,” IEEE Transactions
on Mobile Computing, vol. 22, no. 8, August 2023, pp. 4500-4514. DOI: https://doi.org/10.1109/TMC.2022.3160050
·
J. Pan, A. M. Bedewy,
Y. Sun, and N. B. Shroff, “Age-optimal Scheduling over Hybrid Channels,” IEEE
Transactions on Mobile Computing, vol. 22, no. 12, December 2023, pp. 7027-7043.
DOI: https://doi.org/10.1109/TMC.2022.3205292
·
J. Pan, A. M. Bedewy,
Y. Sun, and N. B. Shroff, “Optimal Sampling for Data Freshness: Unreliable
Transmissions with Random Two-way Delay,” IEEE/ACM Transactions on Networking,
vol. 31, no. 1, February 2022. DOI: https://doi.org/10.1109/TNET.2022.3194417
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C. -H. Tsai and C. -C. Wang, “Unifying AoI Minimization and Remote Estimation—Optimal
Sensor/Controller Coordination With Random Two-Way
Delay,” IEEE/ACM Transactions on Networking, vol. 30, no. 1, pp. 229-242, Feb.
2022. DOI: https://doi.org/10.1109/TNET.2021.3111495
·
R. Doost-Mohammady,
L. Zhong and A. Sabharwal, “RENEW: A Software-defined Massive MIMO Wireless
Experimentation Platform,” ACM GetMobile – Mobile Computing and Computations, vol. 26, no. 2, July 2022,
pp. 12-18. DOI: https://doi.org/10.1145/3551670.3551674
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C.-H. Tsai and C.-C. Wang, “Distribution-oblivious
Online Algorithms for Age-of-Information Penalty Minimization” IEEE/ACM
Transactions on Networking, vol. 31, no. 4, pp. 1779-1794, August 2023. DOI: https://doi.org/10.1109/TNET.2022.3230009
·
Z. Liu, B. Li, Z. Zheng, T. Hou, and B. Ji,
“Towards Optimal Tradeoff Between Data Freshness and Update Cost
information-update Systems,” IEEE Internet of Things Journal (IoT-J), vol. 10,
no. 16, pp. 13988-14002, 15 August 2023.
·
S. Kang, A. Eryilmaz,
and N.B Shroff, “Remote tracking of distributed dynamic sources over a random access channel with one-bit updates,” IEEE Trans on
Network Science and Engineering, Jan. 2023.
·
M. K. C. Shisher,
B. Ji, I.-H. Hou, and Y. Sun, “Learning and Communications Co-Design for Remote
Inference Systems: Feature Length Selection and Transmission Scheduling,” IEEE
Journal on Selected Areas in Information Theory (JSAIT), vol. 4, pp. 524-538,
2023.
·
Q. An, S. Segarra, C. Dick (Nvidia), A.
Sabharwal and R. Doost-Mohammady, “A Deep
Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks,” IEEE
Transactions on Machine Learning in Communications and Networking, September
2023, pp. 242-257.
·
J. Pan, Y. Sun, and N. B. Shroff, Sampling
for Remote Estimation of the Wiener Process over an Unreliable Channel,
Performance Evaluation Review, Volume 52, Issue 1, 2023.
·
C.-C. Wang, “Optimal AoI
for Systems With Queueing Delay in Both Forward and
Backward Directions”, IEEE/ACM Transactions on Networking, vol. 32, no. 4, pp.
3173-3188, April 2024. DOI: https://doi.org/10.1109/TNET.2024.3379895
·
Z. Liu, K. Zhang, B. Li, Y. Sun, T. Hou, and
B. Ji, “Learning-augmented Online Minimization of Age of Information and
Transmission Costs,” IEEE Transactions on Network Science and Engineering
(TNSE), vol. 12, no. 5, pp. 3480-3496, September-October 2025.
Conference papers:
·
G. Yao, A.M. Bedewy, and N.B. Shroff, “Age-Optimal
Low-Power Status Update over Time-Correlated Fading Channel,” in Proceedings of
IEEE International Symposium on Information Theory, 2021.
·
G. Yao, A.M. Bedewy, and N.B. Shroff, “Battle
between Rate and Error in Minimizing Age of Information,” in Proceedings of the
Twenty-second Theory, Algorithmic Foundations, and Protocol Design for Mobile
Networks and Mobile Computing (Mobihoc), 2021, pp.
121-130.
·
J. Pan, A. M. Bedewy,
Y. Sun, and N. B. Shroff, “Minimizing Age of Information via Scheduling over
Heterogeneous Channels,” ACM MobiHoc, Shanghai,
China, July 2021.
·
J. Pan, A. M. Bedewy,
Y. Sun, and N. B. Shroff, “Optimizing Sampling for Data Freshness: Unreliable
Transmissions with Random Two-way Delay,” IEEE INFOCOM, May 2022.
·
C.-C. Wang, “How Useful is Delayed Feedback
in AoI Minimization - A Study on Systems With Queues in Both Forward and Backward Directions”, in
Proceedings of the IEEE Int'l Symp. Information Theory (ISIT), Espoo, Finland,
June 26 – July 1, 2022.
·
C.-H. Tsai and C.-C. Wang, “Jointly
Minimizing AoI Penalty and Network Cost Among
Coexisting Source-Destination Pairs,” in Proceedings of the IEEE Int'l Symp.
Information Theory (ISIT), Melbourne, Australia, USA, July 12-20, 2021.
·
Z. Liu, B. Li, Z. Zheng, Y. T. Hou, and B.
Ji, “Towards Optimal Tradeoff Between Data Freshness and Update Cost in
Information-update Systems,” Proceedings of ICCCN 2022, Virtual Event, July
2022.
·
Z. Liu, K. Zhang, B. Li, Y. Sun, T. Hou, and
B. Ji, “Learning-augmented Online Minimization of Age of Information and
Transmission Costs,” Proceedings of IEEE INFOCOM 2024, Workshop on Age and
Semantics of Information (ASoI), Vancouver, Canada,
May 2024.
·
J. Pan, Y. Sun, and N. B. Shroff, “Sampling
for Remote Estimation of the Wiener Process over an Unreliable Channel,” ACM
SIGMETRICS 2024.
·
W.J. Lee and C.-C. Wang, “AoI-optimal Scheduling for Arbitrary K-channel
Update-Through-Queue Systems,” in Proceedings of the IEEE Int'l Symp.
Information Theory (ISIT), Athens, Greece, 2024.
·
K. Zhang, Y. Sun, and B. Ji, “Multimodal
Remote Inference,” in Proceedings of IEEE MASS 2025, Chicago, IL, October 2025.
·
G. Bischoff, and C.-C. Wang, “Low-Latency
Preamble-Free Transmission of Short Messages via Quickest Change Detection,” in
Proceedings of the IEEE Int'l Symp. Information Theory (ISIT), Ann Arbor,
Michigan, USA, 2025.
Book
Chapters:
·
B. Li, B. Ji, and A. Eryilmaz,
“Age-Efficient Scheduling in Communication Networks,” in book “Age of
Information: Foundations and Applications,” Edited by Walid Saad, Harpreet
Dhillon, Nikolaos Pappas, Mohamed A. Abd-Elmagid, and Bo Zhou, Cambridge
University Press, 2023.
·
A. M. Bedewy, Y.
Sun, S. Kompella, and N. B. Shroff, “Sampling and Scheduling for Minimizing Age
of Information of Multiple Sources,” Chapter 7, in Age of Information:
Foundations and Applications, ISBN 9781108943321, Cambridge University Press,
2023.
The results in the NSF Public Access
Repository will include a comprehensive listing of all journal publications
Graduate Research Assistants &
Collaborators
Graduate
Research Assistants:
·
Zhongdong Liu;
·
Keyuan Zhang;
·
Won Jun Lee;
·
Giles Bischoff;
Other
Collaborators:
·
I-Hong Hou (Texas A&M University, USA)
·
Y. Thomas Hou (Virginia Tech, USA)
·
Bin Li (University of Rhode Island, USA)
·
Yin Sun (Auburn University, USA)
·
Zizhan Zheng (Tulane University, USA)