three heuristics for transmission scheduling in sensor networks with multiple mobile sinks
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Three heuristics for transmission scheduling in sensor networks with multiple mobile sinks. Damla Turgut and Lotzi Bölöni University of Central Florida ATSN 2008 May 13, 2008. Introduction. Traditional sensor networks - PowerPoint PPT PresentationTRANSCRIPT
Three heuristics for transmission scheduling in sensor networks with multiple mobile sinks
Damla Turgut and Lotzi BölöniUniversity of Central Florida
ATSN 2008
May 13, 2008
Introduction
Traditional sensor networks Static, low-power, forward data by hop-by-hop
routing, single or multiple sinks Energy conservation
Alternative approach Data collection by a set of mobile sinks More economical for power consumption Collect and buffer observations, transmit to them to
the closest sink Transmission scheduling problem: should I send
the data now or wait for a more favorable moment?
Contributions
Describe and compare three practically implementable heuristic algorithms H1: human-inspired simple heuristics H2: stochastic transmission H3: constant risk
Describe an optimal algorithm, based on a dynamic programming to provide a baseline for the comparisons Not practical to implement
Transmission scheduling problem
Decision of the node whether to transmit or not its currently collected set of observations to mobile sink at a given point in time Wait until mobile sink gets closer?
If wait too long, buffer may get full and loose data If wait too little, may bypass better opportunities
Send it with lower power consumption?
Assumptions
Data transmission is initiated by the node Mobile sink visits every node
All collected data may not be transmitted Data transmission between the sensor
node and the closest mobile sink Sink does not move during transmission No deadline with transmissions of data
Data buffering for an arbitrary amount of time without penalty
Objectives of the algorithms
Objectives of the nodes: Transmit all the observations Minimize the energy consumption
The scheduling strategy tries to minimize the objective function which balances these two factors Energy minimization only, no observations
may be transmitted Data loss minimization only, transmission can
occur at every opportunity
Cumulative policy penalty
Objective function: Cumulative Policy Penalty (CPP) “Cumulative” aspect is essential here Sum of the transmission energy + a penalty for lost packets We can parametrize the relative weight of the lost packets
… but it can not be lower than the transmission energy… the node will improve its score by loosing all its packets!
Transmission energy is determined by the physical factors
The model used for energy dissipation used for communication
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Related work
Routing towards mobile sink SEAD (Kim et. al.), HLETDR (Baruah et. al.)
Mobility models of the sinks Random, predictable, controlled SENMA (Tong et. al.), Chakrabarti et. al.
Mobility and routing mWSN (Chen et. al.), Luo et. al., Kansal et. al.,
Gandham et. al., Message ferrying (Zhao et. al.) Transmission scheduling
Zhao et. al, Song et. al. Combinations
Somasundara et. al., Guo et. al.
Oracle Optimal algorithm
Finds the optimal transmission schedule with the assumption that mobility patterns of the sinks is known Optimality: find a schedule which minimizes
the cumulative policy penalty for specified interval
Objective: serves the baseline for more realistic algorithms
Implementation: dynamic programming Exponential in the worst case, in practice
much faster
Three heuristics
Make their decision based on very simple calculations
Do not explore the solution space Do not plan for the future transmissions Notations used
M the current buffer content
Mfull the size of the buffer
r data collection rate
dtr transmission rate
d Current distance of the closest mobile sink
H1: Human-inspired simple heuristics
Mimic the human decision process for the transmission scheduling
Designed based on the observation of several humans play the transmission scheduling problem as
a game and then describe their strategy
Humans are not comfortable doing calculations during the game
H1: Human-inspired simple heuristics (cont’d)
Strategies developed were based on levels of the buffer and the current distance of the mobile sink
Did not adhere strictly to the stated strategy
When asked, all agreed “coin toss” is not a good strategy
H1: Human-inspired simple heuristics (cont’d)
Parameters dopt Optimal distance
ML Too low to transmit
MH Buffer emergency level
Algorithm
H2: Stochastic transmission
Transmits randomly with probability distribution affected by two factors Level of buffer
Distance of the mobile sink
Final equation
H2: Stochastic transmission
H3: Constant risk
Estimate based on historical information how much risk a decision carry
Take decisions based on a constant risk factor
Goal: prevent the algorithm from being too bold in one occasion and too cautious in others
OP[t][d]: future probability
H3: Constant risk
Parameters prisk Constant risk factor
tq Quantization of remaining time
dq Quantization of the distance to the sink
Algorithm
Experimental Study
Performed a series of experiments using the YAES simulator framework
Scenario: Mobile sinks are moving around collecting
data from sensor nodes using one-hop communication
Random waypoint mobility pattern of the sinks
Simulation parameters
Compared implementations, measurements
Four different sensor implementations Oracle Optimal (OrOpt) Human inspired (H1: HI) Stochastic (H2: STO) Constant risk (H3: CR)
Measurements collected: Total transmission energy Data loss ratio Cumulative policy penalty (CPP)
CPP w.r.t. transmission range
Consumed energy w.r.t. transmission range
Data loss ratio w.r.t transmission range
CMM vs. mobile sink count
Consumed energy w.r.t. mobile sink count
Data loss ratio w.r.t. mobile sink count
Investigated the problem of transmission scheduling
Agent approach where each node tries to maximize its utility by minimizing energy consumption and data loss
Presented an oracle optimal algorithm to provide a baseline for the comparisons
Described and compared three practically implementable heuristic algorithms H1: human-inspired simple heuristics H2: stochastic transmission H3: constant risk
Conclusions
Conclusions (cont’d)
Human intuition might lead us astray Overall, the stochastic algorithm gave the
best results, followed by constant risk The human intuition inspired algorithm
came out last As expected, the oracle optimal algorithm
provided the best results, but not by a wide margin
Thank you
Questions?