<p>It is predicted that there would be 41.6 billion IoT devices
by 2025, which has kindled new interests on the timing coordination between
sensors and controllers, i.e., how to use the waiting time to improve the
performance. Sun et al. showed that a <i>controller</i> can strictly improve
the data freshness, the so-called Age-of-Information (AoI), via careful
scheduling designs. The optimal waiting policy for the <i>sensor</i> side was
later characterized in the context of remote estimation. The first part of this
work develops the jointly optimal sensor/controller waiting policy. It
generalizes the above two important results in that not only do we consider
joint sensor/controller designs, but we also assume random delay in both the
forward and feedback directions. </p>
<p> </p>
<p>The second part of the work revisits and significantly
strengthens the seminal results of Sun et al on the following fronts: (i) When
designing the optimal offline schemes with full knowledge of the delay
distributions, a new <i>fixed-point-based</i> method is proposed with <i>quadratic
convergence rate</i>; (ii) When the distributional knowledge is unavailable,
two new low-complexity online algorithms are proposed, which provably attain
the optimal average AoI penalty; and (iii) the online schemes also admit a
modular architecture, which allows the designer to <i>upgrade</i> certain
components to handle additional practical challenges. Two such upgrades are
proposed: (iii.1) the AoI penalty function incurred at the destination is
unknown to the source node and must also be estimated on the fly, and (iii.2)
the unknown delay distribution is Markovian instead of i.i.d. </p>
<p> </p>
<p>With the exponential growth of interconnected IoT devices
and the increasing risk of excessive resource consumption in mind, the third
part of this work derives an optimal joint cost-and-AoI minimization solution
for multiple coexisting source-destination (S-D) pairs. The results admit a new <i>AoI-market-price</i>-based
interpretation and are applicable to the setting of (i) general heterogeneous
AoI penalty functions and Markov delay distributions for each S-D pair, and
(ii) a general network cost function of aggregate throughput of all S-D pairs. </p>
<p> </p>
<p>In each part of this work, extensive simulation is used to
demonstrate the superior performance of the proposed schemes. The discussion on
analytical as well as numerical results sheds some light on designing practical
network utility maximization protocols.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/19339076 |
| Date | 20 April 2022 |
| Creators | Cho-Hsin Tsai (12225227) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Network_Utility_Maximization_Based_on_Information_Freshness/19339076 |
Page generated in 0.0028 seconds