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Quality-of-Information Aware Transmission Policies with Time-Varying Links Ertugrul N . Ciftcioglu and Aylin Yener - PowerPoint PPT Presentation

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. . . . Motivation. QoI vs QoS: QoI : associated with a piece of information can be defined as the value provided by it to the end user/application application dependent different from the notion of Quality of Service (QoS)

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  • QoI vs QoS:

  • QoI: associated with a piece of information can be defined as the valueprovided by it to the end user/application

    • application dependent

    • different from the notion of Quality of Service (QoS)

  • QoS:defined based on traditional metrics such as delay, jitter or packet loss ratio

    • specific information content is irrelevant to determine its utility to the end user

  • QoI can be represented by a QoI-vector, which is a vector of attribute-value pairs: for example,

  • q=[type = image, timeliness = 10s, accuracy = 800 × 600 & FOV = 100mm per meter . . .],.

  • .

  • Growing interest in defining and quantifying new higher layer metrics that can capture application-specific objectives more adequately.

  • Several such metrics have been proposed:

    • accuracy

    • timeliness

    • reliability

    • precision

    • corroboration/credibility

    • freshness

    • Quality of Information (QoI) is being proposed to formally describe this class ofmetrics/attributes

Quality-of-Information Aware Transmission Policies with Time-Varying LinksErtugrul N. Ciftcioglu and Aylin Yener

Previous QoI-based networking:

Static environment

Offline algorithms and static optimization solutions sufficient for rate allocation & scheduling

What if channel states vary?

Uncertainty in delivered QoI to end users in future slots

Different solutions required

We consider two basic models with time-varying channels:

Single link

Two-hop relay network






Transmission Model

QoI Utility Maximization

Channel qualities vary within missions

Block fading model, i.e., channel gains potentially change each slot

Rate regions supported vary each slot j depending on h1(j) and h2(j)

Slotted operation


  • Why stop?

    • By continuing, more information could be sent through

    • but also extra degradation due to timeliness

      • Online QoI adaptation

  • Stopping decisions depending on QoI utility state and

  • time elapsed

  • Uncertainty: Future channel states and possible rates unknown

  • => Future QoI utility values unknown

Single Link: Optimal Solution

State (u,j), Bellman’s equation:

V(u,j)=max (u, E[V(d(j)u+g(j+1)e(j),j+1)])

Optimal to stop in (u,j) if:

u ≥E[V(d(j)u+g(j+1)e(j),j+1)] Else, it is optimal to continue.

  • Aim: Stop with maximum QoI utility (us)

Value Function

Utility ~stop

Expected Utility ~continue

Hop-by-hop scheduling (queues at relays)

Structural Result 1:

Structural Result 2:

  • In each slot, schedule either source or relay.

Optimal control policy :Threshold-type

Immediate forwarding (no queueing at relays)

  • In each slot,two phases (S->R & R->D ) scheduled jointly

  • Time division between phases within the slot optimized.

Quality of Information



Thresholds decrease withstage



QoI Utility Dynamics

Scheduling Algorithms: Methodology

Joint Stopping-Rate Allocation:

Single Link

The only decision is whether to perform stopping or not.

  • Optimal solution: Via dynamic programming

    • In the single user case, can be shown that a threshold-based policy is optimal

    • Multiple users: Suffers from curse of dimensionality

      =>Approximate solutions

      1-time lookahead approach (Biesterfeld 96):

    • Compare current QoI utility value with expected values from future decision trajectories

    • If any future trajectory offers greater expected QoI utility than current QoI utility, continue

QoI utility u(j) starts with 0 at the beginning of each mission

  • Multiple Links: Also rate allocation/link scheduling when continuing

  • Always scheduling second hop: fails due to causality requirements

  • Backpressure approaches: Suitable for long-term goals

  • Adapt Proportional Fair Queuing (PFQ)

  • Joint stopping- rate allocation algorithm:

  • If us(j)> E[us,m(j+k)], k=1…D-j, stop

    • Else, continue with

      • r1(t)=c1(t), r2(j)=0 if c1(t)/c1 > min(c2(t), x(t))/c2

      • r1(t)=0, r2(j)=c2(t) if c1(t)/c1 ≤ min(c2(t), x(t))/c2

  • At each time j since the beginning of the mission, amount of information with accuracy metric a(j) already sent in previous slots

  • Depending on rates at slot j, and scheduled link, extra information worth accuracy metric e(j) transferred

  • Depending on j, degradation g(j) due to timeliness utility function

  • At time s, 1≤s<D, the users can decide to stop,

  • resulting in the final response with utility us=u(s)

  • QoI utility dynamics (under continue decision):

    u (j+1)=d (j)u(j)+g(j+1)e(j)

    c1,c2: Mean link capacities

    x(t): Queue size at t

    1-slot degradation factor

    Simulation Results


    Consider the QoI Utility function [Ciftcioglu et al. 2011]:

    Delivery time

    Two-hop network:

    QoI-based transmission policies for time-varying physical media addressedSingle link: Optimal solution threshold-basedApproximate dynamic programming methodsfor joint stopping-link schedulingPerforms near-optimal to optimal schedulersQueues help exploiting opportunistic scheduling methods, waiting for good channel states pays off


    a: Accuracy metric corresponding to

    QoI-vector q

    td: Timeliness attribute of QoI-vector q

    l(a): # bits required to represent

    QoI-vector w/ accuracy metric a

    Define D < Tmin): Expiration time


    : Timeliness parameter



    Extra slot

    HHS outperforms IF

    Rayleigh channels

    Approximate scheduler near-optimal

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