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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
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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 • 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
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
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 • Algorithm
H2: Stochastic transmission • Transmits randomly with probability distribution affected by two factors • Level of buffer • Distance of the mobile sink • Final equation
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 • 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
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)
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?
