Transcript Research issues in communication systems for e

Resource Allocation for E-healthcare
Applications
Qinghua Shen
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content
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Intro: e-healthcare system
Research issues
Preliminary results
conclusion
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Intro: e-healthcare system
Randomness
of the
requests
Computing: Medical
information processing
Body
channel
Limited
sensor
energy
Wban: Remote
monitoring
Mobility
distribut
ed
Wbans: Hospital
information
collection
Emergency
traffic
support
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content
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Intro: e-healthcare system
Research issues
Preliminary results
conclusion
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Single sensor WBAN scheduling
• single sensor application
 Network model
 one PDA and one sensor with Pmax
Time is partitioned into slots with length T
a pilot of duration αT required for transmission.
Two decisions made by the sensor at each time slot
• sleep decision s(i)
•Transmission decision b(i)
 Traffic and Channel Model
 A(i): a maximum Amax and Dmax
 h(i) : pathloss in power, bounded by minimum hmin and maximum hmax
 i.i.d, stationary and ergodic
Listening
 Energy Cost Model
 Queue Update
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Transmission
Power vs. Delay trade-off
• Energy Efficient Approaches
Opportunistic Transmission
 exploiting channel dynamics
 Sleep Scheduling
 Originate from sensor networks, reduce idle listening
• Delay requirements
 Worst case delay Guarantee Dmax
 deterministic delay requirement
 Average sense delay
 little’s law
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Power vs. Delay trade-off
• Relationship between Energy and Delay single link
 Power-Rate relationship
 Shannon capacity formulation
A practical approximation --monomial function
The average power consumption
Service rate delay relationship
 Queue: service process bµ(n) and the arrival rate A(n), service process is
determined by transmission policy
Q(n)=Q(n-1)+A(n) - bµ(n)
 Queue of a system is related to the delay
• Average Delay
• Worst Case Delay Qmax doesn’t guarantee a Dmax
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Power vs. Delay trade-off
• Problem Formulation Power vs. Delay I (average sense delay [1])
 Optimization Objective
for V>0, the goal is to find the policy µ to minimize
 Define the minimal average power can be achieved as the power needed
to serve average arrival rate with no delay consideration, denoted by
, it’s the solution of the following problem with a policy Ψ(H) .
minimize EP(H, Ψ(H))
subject to: E (Ψ(H)) A
The policy for no delay consideration
doesn’t need to take current queue state
into decision making.
 lower bound is proofed[1]
and a drift policy achieves it
[1] R. Berry and R. Gallager, “Communication over
fading channels with delay constraints,” IEEE Trans.
Information Theory, vol. 48, no. 5, pp. 1135–1149, 2002.
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Power vs. Delay trade-off
• Problem Formulation Power vs. Delay II (Worst Case Delay )
 BT problem: B unit of traffic needed to transmitted by the deadline T
 Continuous case, Markov Channel, monomial power rate function [2]
formulation and solution
Transmission
• system updating equation
policy
• cost function and cost-to-go function
• solve the Hamilton–Jacobi–Bellman equation backwards to obtain the
optimal control policy
 Discrete case, i.i.d channel [3]
• monomial: optimal policy
• Shannon: no closed form
 scheduling policy characteristics
• More opportunistically when deadline is far away
• less opportunistically when queue length is large
[2] Murtaza Zafer and Eytan Modiano, Optimal Rate Control for Delay-Constrained Data Transmission over a Wireless Channel. IEEE Transactions on information
theory, Vol. 54, No. 9, Sept. 2008.
[3] J. Lee and N. Jindal, “Energy-efficient scheduling of delay constrained traffic over fading channels,” IEEE Trans. Wireless Communications, vol. 8, no. 4, pp. 1866–
1875, 2009.
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Single sensor WBAN scheduling
• Problem formulation
 1) Lyapunov optimization theory[4] adopted
 why not DP:
• Curse of dimensionality
 characteristics of Lyapunov optimization
• decomposes a time average objective into objectives for each time slot
• capture the trade-off between different system performance metrics
 2) Original Problem
 goal: average power consumption
 constraints: bounded delay, feasible rate
[4] M. Neely, “Stochastic network optimization with application to communication and
queueing systems,” Synthesis Lectures on Communication Networks, vol. 3, no. 1, pp. 1–
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211, 2010.
Single sensor WBAN scheduling
• Problem formulation
 3) Worst-Case Delay Constraint Transform[5]
 Why? No direct link between maximum delay and maximum queue
 a virtual queue Z(t) with a virtual arrival rate
 Z(t) updates:
Lemma: Suppose system is controlled so that Z(i)  Zmax, Q(i)  Qmax,
for all i, for some positive constants Zmax, Qmax. Then all data
in queue is transmitted with a maximum delay Dmax:
 Transform: from bounded delay to bounded queue length
[5] M. Neely, A. Tehrani, and A. Dimakis, “Efficient algorithms for renewable energy
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allocation to delay tolerant consumers,” in Proc.
IEEE SmartGridComm’ 10, pp. 549–554,
Single sensor WBAN scheduling
• Problem formulation
 4) Transform using Lyapunov optimization
 why? Objectives for each time slot with illustration of the trade-off
 a. quadratic form Lyapunov function
 b. one-step Laypunov drift
 c. upper bound of the drift
 d. upper bound of the drift plus a weighted cost function
 New objectives:
Weighted cost function
min
 Logic of minimization
• minimizing the upper bound of the drift controls the delay
• minimizing cost function is to minimize
the energy consumption
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Single sensor WBAN scheduling
• Problem formulation
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Final problem
Objectives: average of all possible states for each time
nonlinear
Control variables: two decision variables
one binary
one integer
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Single sensor WBAN scheduling
• Algorithm design
 two step algorithm
 sleep scheduling
Where
minimum of
, and
is the expectation of
 Opportunistic Transmission
maximal available transmission
amount given current channel
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Single sensor WBAN scheduling
• Performance Analysis
 delay performance
Algorithm designed doesn’t guarantee non-positive drift
 define two conditions
necessary for worst case delay guarantee
Theorem 1. If above conditions hold, then deterministic upper bounds exist for
actual queue and virtual queue as follows:
Recall lemma
Worst cast delay increase within
 power consumption performance
Theorem 2. Given the minimal power consumption P* that the system can achieve,
the average power consumption of our proposed algorithm Pave satisfies:
Pave  P* + C/V , where C is a constant, at the cost of a worst-case delay increases
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within O(V ).
Stationary randomize policy
Single sensor WBAN scheduling
• simulation setup

Body channel
 model suggested by IEEE 802.15 task group 6 under the frequency band
2.4GHz
 Wake up ratio: the fraction of time slots in which the sensor wakes up among
the number of total time slots
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Parameters' Value
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Single sensor WBAN scheduling
• Simulation results
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Data accumulation: for potential better channel
Flat cliff: not in a very good channel condition
Sharp cliff: in a good channel condition
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Delay growth can be bounded a linear function
of weighting factor
Larger weighting factor, poorer delay
Single sensor WBAN scheduling
• Simulation results
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The gap between power consumption of our
algorithm and the optimal one can be bounded
by a function of the inverse of weighting factor
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Smaller wakeup ratio, less power consumption
Larger virtual arrival rate, smaller delay
Larger virtual arrival rate, larger wakeup ratio
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Conclusion and Future works
• A scheduling policy for single sensor WBAN application
 Address the energy delay trade-off problem for WBAN
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limited transmission power
random traffic and channel
worst case delay guarantee
 Propose a scheduling policy for the problem
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Utilize both sleep and opportunistic transmission for
energy saving
Achieve worst case delay
Show trade-off between power consumption and delay
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